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Ran J, Zhou M, Wen H. Artificial intelligence in inflammatory bowel disease. Saudi J Gastroenterol 2025:00936815-990000000-00126. [PMID: 40275746 DOI: 10.4103/sjg.sjg_46_25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2025] [Accepted: 03/28/2025] [Indexed: 04/26/2025] Open
Abstract
ABSTRACT Inflammatory bowel disease (IBD) is a complex condition influenced by various intestinal factors. Advances in next-generation sequencing, high-throughput omics, and molecular network technologies have significantly accelerated research in this field. The emergence of artificial intelligence (AI) has further enhanced the efficient utilization and interpretation of datasets, enabling the discovery of clinically actionable insights. AI is now extensively applied in gastroenterology, where it aids in endoscopic analyses, including the diagnosis of colorectal cancer, precancerous polyps, gastrointestinal inflammatory lesions, and bleeding. Additionally, AI supports clinicians in patient stratification, predicting disease progression and treatment responses, and adjusting treatment plans in a timely manner. This approach not only reduces healthcare costs but also improves patient health and safety. This review outlines the principles of AI, the current research landscape, and future directions for its applications in IBD, with the goal of advancing targeted treatment strategies.
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Affiliation(s)
- Jiaxuan Ran
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
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2
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Vezakis A, Vezakis I, Petropoulou O, Miloulis ST, Anastasiou A, Kakkos I, Matsopoulos GK. Comparative Analysis of Deep Neural Networks for Automated Ulcerative Colitis Severity Assessment. Bioengineering (Basel) 2025; 12:413. [PMID: 40281773 PMCID: PMC12024692 DOI: 10.3390/bioengineering12040413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2025] [Revised: 03/21/2025] [Accepted: 04/11/2025] [Indexed: 04/29/2025] Open
Abstract
BACKGROUND Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by continuous inflammation of the colon and rectum. Accurate disease assessment is essential for effective treatment, with endoscopic evaluation, particularly the Mayo Endoscopic Score (MES), serving as a key diagnostic tool. However, MES measurement can be subjective and inconsistent, leading to variability in treatment decisions. Deep learning approaches have shown promise in providing more objective and standardized assessments of UC severity. METHODS This study utilized publicly available endoscopic images of UC patients to analyze and compare the performance of state-of-the-art deep neural networks for automated MES classification. Several state-of-the-art architectures were tested to determine the most effective model for grading disease severity. The F1 score, accuracy, recall, and precision were calculated for all models, and statistical analysis was conducted to verify statistically significant differences between the networks. RESULTS VGG19 was found to be the best-performing network, achieving a QWK score of 0.876 and a macro-averaged F1 score of 0.7528 across all classes. However, the performance differences among the top-performing models were very small suggesting that selection should depend on specific deployment requirements. CONCLUSIONS This study demonstrates that multiple state-of-the-art deep neural network architectures could automate UC severity classification. Simpler architectures were found to achieve competitive results with larger models, challenging the assumption that larger networks necessarily provide better clinical outcomes.
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Affiliation(s)
- Andreas Vezakis
- Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (A.V.); (I.V.); (O.P.); (S.T.M.); (A.A.); (I.K.)
| | - Ioannis Vezakis
- Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (A.V.); (I.V.); (O.P.); (S.T.M.); (A.A.); (I.K.)
| | - Ourania Petropoulou
- Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (A.V.); (I.V.); (O.P.); (S.T.M.); (A.A.); (I.K.)
| | - Stavros T. Miloulis
- Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (A.V.); (I.V.); (O.P.); (S.T.M.); (A.A.); (I.K.)
| | - Athanasios Anastasiou
- Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (A.V.); (I.V.); (O.P.); (S.T.M.); (A.A.); (I.K.)
| | - Ioannis Kakkos
- Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (A.V.); (I.V.); (O.P.); (S.T.M.); (A.A.); (I.K.)
- Department of Biomedical Engineering, University of West Attica, 12243 Athens, Greece
| | - George K. Matsopoulos
- Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (A.V.); (I.V.); (O.P.); (S.T.M.); (A.A.); (I.K.)
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George AT, Rubin DT. Artificial Intelligence in Inflammatory Bowel Disease. Gastrointest Endosc Clin N Am 2025; 35:367-387. [PMID: 40021234 DOI: 10.1016/j.giec.2024.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2025]
Abstract
Artificial intelligence (AI) is being increasingly studied and implemented in gastroenterology. In inflammatory bowel disease (IBD), numerous AI models are being developed to assist with IBD diagnosis, standardization of endoscopic and radiologic disease activity, and predicting outcomes. Further prospective, multicenter studies representing diverse populations and novel applications are needed prior to routine implementation in clinical practice and expected improved outcomes for clinicians and patients.
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Affiliation(s)
- Alvin T George
- Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - David T Rubin
- Department of Medicine, Inflammatory Bowel Disease Center, The University of Chicago, Chicago, IL, USA.
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Testoni SGG, Albertini Petroni G, Annunziata ML, Dell’Anna G, Puricelli M, Delogu C, Annese V. Artificial Intelligence in Inflammatory Bowel Disease Endoscopy. Diagnostics (Basel) 2025; 15:905. [PMID: 40218255 PMCID: PMC11988936 DOI: 10.3390/diagnostics15070905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2025] [Accepted: 02/19/2025] [Indexed: 04/14/2025] Open
Abstract
Inflammatory bowel diseases (IBDs), comprising Crohn's disease (CD) and ulcerative colitis (UC), are chronic immune-mediated inflammatory diseases of the gastrointestinal (GI) tract with still-elusive etiopathogeneses and an increasing prevalence worldwide. Despite the growing availability of more advanced therapies in the last two decades, there are still a number of unmet needs. For example, the achievement of mucosal healing has been widely demonstrated as a prognostic marker for better outcomes and a reduced risk of dysplasia and cancer; however, the accuracy of endoscopy is crucial for both this aim and the precise and reproducible evaluation of endoscopic activity and the detection of dysplasia. Artificial intelligence (AI) has drastically altered the field of GI studies and is being extensively applied to medical imaging. The utilization of deep learning and pattern recognition can help the operator optimize image classification and lesion segmentation, detect early mucosal abnormalities, and eventually reveal and uncover novel biomarkers with biologic and prognostic value. The role of AI in endoscopy-and potentially also in histology and imaging in the context of IBD-is still at its initial stages but shows promising characteristics that could lead to a better understanding of the complexity and heterogeneity of IBDs, with potential improvements in patient care and outcomes. The initial experience with AI in IBDs has shown its potential value in the differentiation of UC and CD when there is no ileal involvement, reducing the significant amount of time it takes to review videos of capsule endoscopy and improving the inter- and intra-observer variability in endoscopy reports and scoring. In addition, these initial experiences revealed the ability to predict the histologic score index and the presence of dysplasia. Thus, the purpose of this review was to summarize recent advances regarding the application of AI in IBD endoscopy as there is, indeed, increasing evidence suggesting that the integration of AI-based clinical tools will play a crucial role in paving the road to precision medicine in IBDs.
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Affiliation(s)
- Sabrina Gloria Giulia Testoni
- Unit of Gastroenterology and Digestive Endoscopy, Scientific Institute for Research, Hospitalization and Healthcare Policlinico San Donato, Vita-Salute San Raffaele University, San Donato Milanese, 20097 Milan, Italy
- Unit of Gastroenterology and Digestive Endoscopy, Scientific Institute for Research, Hospitalization and Healthcare Policlinico San Donato, San Donato Milanese, 20097 Milan, Italy
| | - Guglielmo Albertini Petroni
- Unit of Gastroenterology and Digestive Endoscopy, Scientific Institute for Research, Hospitalization and Healthcare Policlinico San Donato, San Donato Milanese, 20097 Milan, Italy
| | - Maria Laura Annunziata
- Unit of Gastroenterology and Digestive Endoscopy, Scientific Institute for Research, Hospitalization and Healthcare Policlinico San Donato, San Donato Milanese, 20097 Milan, Italy
| | - Giuseppe Dell’Anna
- Unit of Gastroenterology and Digestive Endoscopy, Scientific Institute for Research, Hospitalization and Healthcare Policlinico San Donato, San Donato Milanese, 20097 Milan, Italy
| | - Michele Puricelli
- School of Specialization in Digestive System Diseases, Faculty of Medicine, University of Pavia, 27100 Pavia, Italy
| | - Claudia Delogu
- School of Specialization in Digestive System Diseases, Faculty of Medicine, University of Pavia, 27100 Pavia, Italy
| | - Vito Annese
- Unit of Gastroenterology and Digestive Endoscopy, Scientific Institute for Research, Hospitalization and Healthcare Policlinico San Donato, Vita-Salute San Raffaele University, San Donato Milanese, 20097 Milan, Italy
- Unit of Gastroenterology and Digestive Endoscopy, Scientific Institute for Research, Hospitalization and Healthcare Policlinico San Donato, San Donato Milanese, 20097 Milan, Italy
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Lee MCM, Farahvash A, Zezos P. Artificial Intelligence for Classification of Endoscopic Severity of Inflammatory Bowel Disease: A Systematic Review and Critical Appraisal. Inflamm Bowel Dis 2025:izaf050. [PMID: 40163659 DOI: 10.1093/ibd/izaf050] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2024] [Indexed: 04/02/2025]
Abstract
BACKGROUND Endoscopic scoring indices for ulcerative colitis and Crohn's disease are subject to inter-endoscopist variability. There is increasing interest in the development of deep learning models to standardize endoscopic assessment of intestinal diseases. Here, we summarize and critically appraise the literature on artificial intelligence-assisted endoscopic characterization of inflammatory bowel disease severity. METHODS A systematic search of Ovid MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and IEEE Xplore was performed to identify reports of AI systems used for endoscopic severity classification of IBD. Selected studies were critically appraised for methodological and reporting quality using APPRAISE-AI. RESULTS Thirty-one studies published between 2019 and 2024 were included. Of 31 studies, 28 studies examined endoscopic classification of ulcerative colitis and 3 examined Crohn's disease. Researchers sought to accomplish a wide range of classification tasks, including binary and multilevel classification, based on still images or full-length colonoscopy videos. Overall scores for study quality ranged from 41 (moderate quality) to 64 (high quality) out of 100, with 28 out of 31 studies within the moderate quality range. The highest-scoring domains were clinical relevance and reporting quality, while the lowest-scoring domains were robustness of results and reproducibility. CONCLUSIONS Multiple AI models have demonstrated the potential for clinical translation for ulcerative colitis. Research concerning the endoscopic severity assessment of Crohn's disease is limited and should be further explored. More rigorous external validation of AI models and increased transparency of data and codes are needed to improve the quality of AI studies.
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Affiliation(s)
- Michelle Chae Min Lee
- Division of Gastroenterology, Department of Internal Medicine, Thunder Bay Regional Health Sciences Centre, NOSM University, Thunder Bay, ON, Canada
| | - Armin Farahvash
- Division of Gastroenterology, Department of Internal Medicine, Thunder Bay Regional Health Sciences Centre, NOSM University, Thunder Bay, ON, Canada
| | - Petros Zezos
- Division of Gastroenterology, Department of Internal Medicine, Thunder Bay Regional Health Sciences Centre, NOSM University, Thunder Bay, ON, Canada
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Iacucci M, Santacroce G, Yasuharu M, Ghosh S. Artificial Intelligence-Driven Personalized Medicine: Transforming Clinical Practice in Inflammatory Bowel Disease. Gastroenterology 2025:S0016-5085(25)00494-9. [PMID: 40074186 DOI: 10.1053/j.gastro.2025.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2024] [Revised: 01/21/2025] [Accepted: 03/03/2025] [Indexed: 03/14/2025]
Abstract
Inflammatory bowel disease is marked by significant clinical heterogeneity, posing challenges for accurate diagnosis and personalized treatment strategies. Conventional approaches, such as endoscopy and histology, often fail to adequately and accurately predict medium- and long-term outcomes, leading to suboptimal patient management. Artificial intelligence is emerging as a transformative force enabling standardized, accurate, and timely disease assessment and outcome prediction, including therapeutic response. Artificial intelligence-driven intestinal barrier healing assessment provides novel insights into deep healing, facilitating the discovery of novel therapeutic targets. In addition, the automated integration of multi-omics data can enhance patient profiling and personalized management strategies. The future of inflammatory bowel disease care lies in the artificial intelligence-enabled "endo-histo-omics" integrative real-time approach, harmoniously fusing endoscopic, histologic, and molecular data. Despite challenges in its adoption, this paradigm shift has the potential to refine risk stratification, improve therapeutic precision, and enable personalized interventions, ultimately advancing the implementation of precision medicine in routine clinical practice.
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Affiliation(s)
- Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland.
| | - Giovanni Santacroce
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Maeda Yasuharu
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Subrata Ghosh
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
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Cai Y, Chen X, Chen J, Liao J, Han M, Lin D, Hong X, Hu H, Hu J. Deep learning-assisted colonoscopy images for prediction of mismatch repair deficiency in colorectal cancer. Surg Endosc 2025; 39:859-867. [PMID: 39623175 DOI: 10.1007/s00464-024-11426-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2024] [Accepted: 11/12/2024] [Indexed: 02/06/2025]
Abstract
BACKGROUND Deficient mismatch repair or microsatellite instability is a major predictive biomarker for the efficacy of immune checkpoint inhibitors of colorectal cancer. However, routine testing has not been uniformly implemented due to cost and resource constraints. METHODS We developed and validated a deep learning-based classifiers to detect mismatch repair-deficient status from routine colonoscopy images. We obtained the colonoscopy images from the imaging database at Endoscopic Center of the Sixth Affiliated Hospital, Sun Yat-sen University. Colonoscopy images from a prospective trial (Neoadjuvant PD-1 blockade by toripalimab with or without celecoxib in mismatch repair-deficient or microsatellite instability-high locally advanced colorectal cancer) were used to test the model. RESULTS A total of 5226 eligible images from 892 tumors from the consecutive patients were utilized to develop and validate the deep learning model. 2105 colorectal cancer images from 306 tumors were randomly selected to form model development dataset with a class-balanced approach. 3121 images of 488 proficient mismatch repair tumors and 98 deficient mismatch repair tumors were used to form the independent dataset. The model achieved an AUROC of 0.948 (95% CI 0.919-0.977) on the test dataset. On the independent validation dataset, the AUROC was 0.807 (0.760-0.854), and the NPV in was 94.2% (95% CI 0.918-0.967). On the prospective trial dataset, the model identified 29 tumors among the 33 deficient mismatch repair tumors (87.88%). CONCLUSIONS The model achieved a high NPV in detecting deficient mismatch repair colorectal cancers. This model might serve as an automatic screening tool.
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Affiliation(s)
- Yue Cai
- Department of Medical Oncology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, Guangdong, China
| | - Xijie Chen
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, Guangdong, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
- Department of General Surgery (Gastric Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Junguo Chen
- Department of Thoracic Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - James Liao
- Guangzhou Aptiligent Technology Co. Ltd., Guangzhou, Guangdong, China
| | - Ming Han
- Guangzhou Aptiligent Technology Co. Ltd., Guangzhou, Guangdong, China
| | - Dezheng Lin
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, Guangdong, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
- Department of General Surgery (Endoscopic Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiaoling Hong
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, Guangdong, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
- Department of General Surgery (Endoscopic Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Huabin Hu
- Department of Medical Oncology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, Guangdong, China.
| | - Jiancong Hu
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, Guangdong, China.
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
- Department of General Surgery (Endoscopic Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
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Silverman AL, Shung D, Stidham RW, Kochhar GS, Iacucci M. How Artificial Intelligence Will Transform Clinical Care, Research, and Trials for Inflammatory Bowel Disease. Clin Gastroenterol Hepatol 2025; 23:428-439.e4. [PMID: 38992406 PMCID: PMC11719376 DOI: 10.1016/j.cgh.2024.05.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 04/30/2024] [Accepted: 05/02/2024] [Indexed: 07/13/2024]
Abstract
Artificial intelligence (AI) refers to computer-based methodologies that use data to teach a computer to solve pre-defined tasks; these methods can be applied to identify patterns in large multi-modal data sources. AI applications in inflammatory bowel disease (IBD) includes predicting response to therapy, disease activity scoring of endoscopy, drug discovery, and identifying bowel damage in images. As a complex disease with entangled relationships between genomics, metabolomics, microbiome, and the environment, IBD stands to benefit greatly from methodologies that can handle this complexity. We describe current applications, critical challenges, and propose future directions of AI in IBD.
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Affiliation(s)
- Anna L Silverman
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, Scottsdale, Arizona.
| | - Dennis Shung
- Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Ryan W Stidham
- Division of Gastroenterology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, Michigan; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan; Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan
| | - Gursimran S Kochhar
- Division of Gastroenterology, Hepatology, and Nutrition, Allegheny Health Network, Pittsburgh, Pennsylvania
| | - Marietta Iacucci
- University of Birmingham, Institute of Immunology and Immunotherapy, Birmingham, United Kingdom; College of Medicine and Health, University College Cork, and APC Microbiome Ireland, Cork, Ireland
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Bazerbachi F, Murad F, Kubiliun N, Adams MA, Shahidi N, Visrodia K, Essex E, Raju G, Greenberg C, Day LW, Elmunzer BJ. Video recording in GI endoscopy. VIDEOGIE : AN OFFICIAL VIDEO JOURNAL OF THE AMERICAN SOCIETY FOR GASTROINTESTINAL ENDOSCOPY 2025; 10:67-80. [PMID: 40012896 PMCID: PMC11852952 DOI: 10.1016/j.vgie.2024.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/03/2025]
Abstract
The current approach to procedure reporting in endoscopy aims to capture essential findings and interventions but inherently sacrifices the rich detail and nuance of the entire endoscopic experience. Endoscopic video recording (EVR) provides a complete archive of the procedure, extending the utility of the encounter beyond diagnosis and intervention, and potentially adding significant value to the care of the patient and the field in general. This white paper outlines the potential of EVR in clinical care, quality improvement, education, and artificial intelligence-driven innovation, and addresses critical considerations surrounding technology, regulation, ethics, and privacy. As with other medical imaging modalities, growing adoption of EVR is inevitable, and proactive engagement of professional societies and practitioners is essential to harness the full potential of this technology toward improving clinical care, education, and research.
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Affiliation(s)
- Fateh Bazerbachi
- CentraCare, Interventional Endoscopy Program, St Cloud Hospital, St Cloud, Minnesota, USA
- Division of Gastroenterology, Hepatology and Nutrition, University of Minnesota, Minneapolis, Minnesota, USA
| | - Faris Murad
- Illinois Masonic Medical Center, Center for Advanced Care, Chicago, Illinois, USA
| | - Nisa Kubiliun
- Division of Digestive and Liver Diseases, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Megan A Adams
- Division of Gastroenterology, University of Michigan Medical School, Institute for Healthcare Policy and Innovation, Ann Arbor, Michigan, USA; Institute for Healthcare Policy and Innovation, Ann Arbor, Michigan, USA
| | - Neal Shahidi
- Division of Gastroenterology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Kavel Visrodia
- Columbia University Irving Medical Center - New York Presbyterian Hospital, New York, New York, USA
| | - Eden Essex
- American Society for GI Endoscopy, Downers Grove, Illinois, USA
| | - Gottumukkala Raju
- Division of Internal Medicine, Department of Gastroenterology Hepatology and Nutrition, MD Anderson Cancer Center, Houston, Texas, USA
| | - Caprice Greenberg
- Department of Surgery, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Lukejohn W Day
- Division of Gastroenterology, Department of Medicine, University of California San Francisco, San Francisco, California, USA
| | - B Joseph Elmunzer
- Division of Gastroenterology and Hepatology, Medical University of South Carolina, Charleston, South Carolina, USA
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Parikh M, Tejaswi S, Girotra T, Chopra S, Ramai D, Tabibian JH, Jagannath S, Ofosu A, Barakat MT, Mishra R, Girotra M. Use of Artificial Intelligence in Lower Gastrointestinal and Small Bowel Disorders: An Update Beyond Polyp Detection. J Clin Gastroenterol 2025; 59:121-128. [PMID: 39774596 DOI: 10.1097/mcg.0000000000002115] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2025]
Abstract
Machine learning and its specialized forms, such as Artificial Neural Networks and Convolutional Neural Networks, are increasingly being used for detecting and managing gastrointestinal conditions. Recent advancements involve using Artificial Neural Network models to enhance predictive accuracy for severe lower gastrointestinal (LGI) bleeding outcomes, including the need for surgery. To this end, artificial intelligence (AI)-guided predictive models have shown promise in improving management outcomes. While much literature focuses on AI in early neoplasia detection, this review highlights AI's role in managing LGI and small bowel disorders, including risk stratification for LGI bleeding, quality control, evaluation of inflammatory bowel disease, and video capsule endoscopy reading. Overall, the integration of AI into routine clinical practice is still developing, with ongoing research aimed at addressing current limitations and gaps in patient care.
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Affiliation(s)
| | - Sooraj Tejaswi
- University of California, Davis
- Sutter Health, Sacramento
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Gutierrez-Becker B, Fraessle S, Yao H, Luscher J, Girycki R, Machura B, Czornik J, Goslinsky J, Pitura M, Levitte S, Arús-Pous J, Fisher E, Bojic D, Richmond D, Bigorgne AE, Prunotto M. Ulcerative Colitis Severity Classification and Localized Extent (UC-SCALE): An Artificial Intelligence Scoring System for a Spatial Assessment of Disease Severity in Ulcerative Colitis. J Crohns Colitis 2025; 19:jjae187. [PMID: 39657580 DOI: 10.1093/ecco-jcc/jjae187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2024] [Revised: 11/04/2024] [Accepted: 12/06/2024] [Indexed: 12/12/2024]
Abstract
BACKGROUND AND AIMS Validated scoring methods such as the Mayo Clinic Endoscopic Subscore (MCES) evaluate ulcerative colitis (UC) severity at the worst colon segment, without considering disease extent. We present the Ulcerative Colitis Severity Classification and Localized Extent (UC-SCALE) algorithm, which provides a comprehensive and automated evaluation of endoscopic severity and disease extent in UC. METHODS Ulcerative Colitis Severity Classification and Localized Extent consists of 3 main elements: (1) a quality filter selecting readable images (frames) from colonoscopy videos, (2) a scoring system assigning an MCES to each readable frame, and (3) a camera localization algorithm assigning each frame to a location within the colon. Ulcerative Colitis Severity Classification and Localized Extent was trained and tested using 4326 sigmoidoscopy videos from phase III Etrolizumab clinical trials. RESULTS The high agreement between UC-SCALE and central reading at the level of the colon section (𝜅 = 0.80), and the agreement between central and local reading (𝜅 = 0.84), suggested a similar inter-rater agreement between UC-SCALE and experienced readers. Furthermore, UC-SCALE correlated with disease activity markers such calprotectin, C-reactive protein and patient-reported outcomes, Physician Global Assessment and Geboes Histologic scores (rs 0.40-0.55, ps < 0.0001). Finally, the value of using UC-SCALE was demonstrated by assessing individual endoscopic severity between baseline and induction. CONCLUSIONS Our fully automated scoring system enables accurate, objective, and localized assessment of endoscopic severity in UC patients. In addition, we provide a topological representation of the score as a marker of disease severity that correlates highly with clinical metrics. Ulcerative Colitis Severity Classification and Localized Extent reproduces central reading and holds promise to enhance disease severity evaluation in both clinical trials and everyday practice.
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Affiliation(s)
| | - Stefan Fraessle
- Roche, Pharma Research & Early Development, Data and Analytics, Basel, Switzerland
| | - Heming Yao
- Biology Research AI Development (BRAID), Genentech Research and Early Development, San Francisco, CA, USA
| | - Jerome Luscher
- Biology Research AI Development (BRAID), Genentech Research and Early Development, San Francisco, CA, USA
| | | | | | | | | | | | - Steven Levitte
- Roche, Product Development Clinical Science, Technology and Translational Research, Basel, Switzerland
| | - Josep Arús-Pous
- Roche, Pharma Research & Early Development, Data and Analytics, Basel, Switzerland
| | - Emily Fisher
- Roche, Product Development Clinical Science, Technology and Translational Research, Basel, Switzerland
| | - Daniela Bojic
- Roche, Product Development Clinical Science, Technology and Translational Research, Basel, Switzerland
| | - David Richmond
- Roche, Product Development Clinical Science, Technology and Translational Research, Basel, Switzerland
| | - Amelie E Bigorgne
- Roche, Product Development Clinical Science, Technology and Translational Research, Basel, Switzerland
| | - Marco Prunotto
- Roche, Product Development Clinical Science, Technology and Translational Research, Basel, Switzerland
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Ogata N, Maeda Y, Misawa M, Takenaka K, Takabayashi K, Iacucci M, Kuroki T, Takishima K, Sasabe K, Niimura Y, Kawashima J, Ogawa Y, Ichimasa K, Nakamura H, Matsudaira S, Sasanuma S, Hayashi T, Wakamura K, Miyachi H, Baba T, Mori Y, Ohtsuka K, Ogata H, Kudo SE. Artificial Intelligence-assisted Video Colonoscopy for Disease Monitoring of Ulcerative Colitis: A Prospective Study. J Crohns Colitis 2025; 19:jjae080. [PMID: 38828734 PMCID: PMC11725525 DOI: 10.1093/ecco-jcc/jjae080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Indexed: 06/05/2024]
Abstract
BACKGROUNDS AND AIMS The Mayo endoscopic subscore [MES] is the most popular endoscopic disease activity measure of ulcerative colitis [UC]. Artificial intelligence [AI]-assisted colonoscopy is expected to reduce diagnostic variability among endoscopists. However, no study has been conducted to ascertain whether AI-based MES assignments can help predict clinical relapse, nor has AI been verified to improve the diagnostic performance of non-specialists. METHODS This open-label, prospective cohort study enrolled 110 patients with UC in clinical remission. The AI algorithm was developed using 74 713 images from 898 patients who underwent colonoscopy at three centres. Patients were followed up after colonoscopy for 12 months, and clinical relapse was defined as a partial Mayo score > 2. A multi-video, multi-reader analysis involving 124 videos was conducted to determine whether the AI system reduced the diagnostic variability among six non-specialists. RESULTS The clinical relapse rate for patients with AI-based MES = 1 (24.5% [12/49]) was significantly higher [log-rank test, p = 0.01] than that for patients with AI-based MES = 0 (3.2% [1/31]). Relapse occurred during the 12-month follow-up period in 16.2% [13/80] of patients with AI-based MES = 0 or 1 and 50.0% [10/20] of those with AI-based MES = 2 or 3 [log-rank test, p = 0.03]. Using AI resulted in better inter- and intra-observer reproducibility than endoscopists alone. CONCLUSIONS Colonoscopy using the AI-based MES system can stratify the risk of clinical relapse in patients with UC and improve the diagnostic performance of non-specialists.
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Affiliation(s)
- Noriyuki Ogata
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Yasuharu Maeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Kento Takenaka
- Department of Gastroenterology and Hepatology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kaoru Takabayashi
- Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, Tokyo, Japan
| | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Takanori Kuroki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Kazumi Takishima
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Keisuke Sasabe
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Yu Niimura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Jiro Kawashima
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Yushi Ogawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Katsuro Ichimasa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Hiroki Nakamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Shingo Matsudaira
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Seiko Sasanuma
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Takemasa Hayashi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Kunihiko Wakamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Hideyuki Miyachi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Toshiyuki Baba
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
- Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, OsloNorway
| | - Kazuo Ohtsuka
- Department of Gastroenterology and Hepatology, Tokyo Medical and Dental University, Tokyo, Japan
- Endoscopic Unit, Tokyo Medical and Dental University, Tokyo, Japan
| | - Haruhiko Ogata
- Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, Tokyo, Japan
- Clinical Medical Research Center, International University of Health and Welfare, Narita, Japan
- Center for Diagnostic and Therapeutic Endoscopy, San-no Medical Center, Tokyo, Japan
| | - Shin-ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
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Kuroki T, Maeda Y, Kudo SE, Ogata N, Takabayashi K, Takenaka K, Kawashima J, Kawabata Y, Iwasaki S, Shiina O, Morita Y, Kouyama Y, Sakurai T, Ogawa Y, Baba T, Mori Y, Iacucci M, Ogata H, Ohtsuka K, Misawa M. Combination of white-light imaging-based and narrow-band imaging-based artificial intelligence models during colonoscopy in patients with ulcerative colitis. J Crohns Colitis 2025; 19:jjaf014. [PMID: 39888722 DOI: 10.1093/ecco-jcc/jjaf014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2024] [Indexed: 02/02/2025]
Abstract
BACKGROUND AND AIMS The long-term treat-to-target (T2T) approach in ulcerative colitis (UC) aims for endoscopic remission, but variability among endoscopists and a lack of precision in relapse prediction both limit its clinical usefulness. A recently reported white-light imaging (WLI) artificial intelligence (AI) model helps standardize diagnosis, although challenges remain. Therefore, we attempted to combine a narrow-band imaging (NBI) AI model with the WLI AI model to determine whether these challenges can be overcome. METHODS This post hoc analysis of a prospective study evaluated the efficacy of combining AI-assisted WLI and NBI models in predicting clinical relapse in patients with UC over a 12-month follow-up period. A total of 102 patients with UC in clinical remission were included, and the combined AI models were used during colonoscopy to assess relapse risk. RESULTS The study found that within the same AI-based Mayo endoscopic subscore category, patients with vascular activity were more likely to experience clinical relapse than those with vascular healing. Compared with the WLI model alone, the specificity of the combined method significantly increased from 42.2% (95% confidence interval [CI]: 32.1%-52.9%) to 61.5% (95% CI: 50.7%-71.2%) (P = .013) with its sensitivity being maintained. CONCLUSIONS The sequential use of WLI and NBI AI models can provide better stratification of relapse risk compared with using either model alone, offering a more accurate and personalized approach to treatment intensification. This dual-model AI approach aligns with the T2T approach in UC management.
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Affiliation(s)
- Takanori Kuroki
- Digestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Yasuharu Maeda
- Digestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Noriyuki Ogata
- Digestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Kaoru Takabayashi
- Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, Tokyo, Japan
| | - Kento Takenaka
- Department of Gastroenterology and Hepatology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Jiro Kawashima
- Digestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Yurie Kawabata
- Digestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Shunto Iwasaki
- Digestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Osamu Shiina
- Digestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Yuriko Morita
- Digestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Yuta Kouyama
- Digestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Tatsuya Sakurai
- Digestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Yushi Ogawa
- Digestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Toshiyuki Baba
- Digestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Yuichi Mori
- Digestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
- Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Haruhiko Ogata
- Center for Preventive Medicine, Keio University, Tokyo, Japan
- Fujita Medical Innovation Center Tokyo, Tokyo, Japan
| | - Kazuo Ohtsuka
- Department of Gastroenterology and Hepatology, Tokyo Medical and Dental University, Tokyo, Japan
- Endoscopy Unit, Tokyo Medical and Dental University, Tokyo, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
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Furlanello C, Bussola N, Merzi N, Pievani Trapletti G, Cadei M, Del Sordo R, Sidoni A, Ricci C, Lanzarotto F, Parigi TL, Villanacci V. The development of artificial intelligence in the histological diagnosis of Inflammatory Bowel Disease (IBD-AI). Dig Liver Dis 2025; 57:184-189. [PMID: 38853093 DOI: 10.1016/j.dld.2024.05.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 05/12/2024] [Accepted: 05/28/2024] [Indexed: 06/11/2024]
Abstract
BACKGROUND Inflammatory bowel disease (IBD) includes Crohn's Disease (CD) and Ulcerative Colitis (UC). Correct diagnosis requires the identification of precise morphological features such basal plasmacytosis. However, histopathological interpretation can be challenging, and it is subject to high variability. AIM The IBD-Artificial Intelligence (AI) project aims at the development of an AI-based evaluation system to support the diagnosis of IBD, semi-automatically quantifying basal plasmacytosis. METHODS A deep learning model was trained to detect and quantify plasma cells on a public dataset of 4981 annotated images. The model was then tested on an external validation cohort of 356 intestinal biopsies of CD, UC and healthy controls. AI diagnostic performance was calculated compared to human gold standard. RESULTS The system correctly found that CD and UC samples had a greater prevalence of basal plasma cells with mean number of PCs within ROIs of 38.22 (95 % CI: 31.73, 49.04) for CD, 55.16 (46.57, 65.93) for UC, and 17.25 (CI: 12.17, 27.05) for controls. Overall, OR=4.968 (CI: 1.835, 14.638) was found for IBD compared to normal mucosa (CD: +59 %; UC: +129 %). Additionally, as expected, UC samples were found to have more plasma cells in colon than CD cases. CONCLUSION Our model accurately replicated human assessment of basal plasmacytosis, underscoring the value of AI models as a potential aid IBD diagnosis.
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Affiliation(s)
| | | | | | | | - Moris Cadei
- Institute of Pathology, ASST Spedali Civili and University of Brescia, Brescia, Italy
| | - Rachele Del Sordo
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
| | - Angelo Sidoni
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
| | - Chiara Ricci
- Gastroenterology Unit, Clinical and Experimental Sciences Department, Spedali Civili Hospital, University of Brescia, Brescia, Italy
| | - Francesco Lanzarotto
- Gastroenterology Unit, Clinical and Experimental Sciences Department, Spedali Civili Hospital, University of Brescia, Brescia, Italy
| | - Tommaso Lorenzo Parigi
- Division of Immunology, Transplantation and Infectious Disease, University Vita-Salute San Raffaele, Milan, Italy
| | - Vincenzo Villanacci
- Institute of Pathology, ASST Spedali Civili and University of Brescia, Brescia, Italy.
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15
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Lee JW, Woo D, Kim KO, Kim ES, Kim SK, Lee HS, Kang B, Lee YJ, Kim J, Jang BI, Kim EY, Jo HH, Chung YJ, Ryu H, Park SK, Park DI, Yu H, Jeong S. Deep Learning Model Using Stool Pictures for Predicting Endoscopic Mucosal Inflammation in Patients With Ulcerative Colitis. Am J Gastroenterol 2025; 120:213-224. [PMID: 39051648 PMCID: PMC11676591 DOI: 10.14309/ajg.0000000000002978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 07/16/2024] [Indexed: 07/27/2024]
Abstract
INTRODUCTION Stool characteristics may change depending on the endoscopic activity of ulcerative colitis (UC). We developed a deep learning model using stool photographs of patients with UC (DLSUC) to predict endoscopic mucosal inflammation. METHODS This was a prospective multicenter study conducted in 6 tertiary referral hospitals. Patients scheduled to undergo endoscopy for mucosal inflammation monitoring were asked to take photographs of their stool using smartphones within 1 week before the day of endoscopy. DLSUC was developed using 2,161 stool pictures from 306 patients and tested on 1,047 stool images from 126 patients. The UC endoscopic index of severity was used to define endoscopic activity. The performance of DLSUC in endoscopic activity prediction was compared with that of fecal calprotectin (Fcal). RESULTS The area under the receiver operating characteristic curve (AUC) of DLSUC for predicting endoscopic activity was 0.801 (95% confidence interval [CI] 0.717-0.873), which was not statistically different from the AUC of Fcal (0.837 [95% CI, 0.767-0.899, DeLong P = 0.458]). When rectal-sparing cases (23/126, 18.2%) were excluded, the AUC of DLSUC increased to 0.849 (95% CI, 0.760-0.919). The accuracy, sensitivity, and specificity of DLSUC in predicting endoscopic activity were 0.746, 0.662, and 0.877 in all patients and 0.845, 0.745, and 0.958 in patients without rectal sparing, respectively. Active patients classified by DLSUC were more likely to experience disease relapse during a median 8-month follow-up (log-rank test, P = 0.002). DISCUSSION DLSUC demonstrated a good discriminating power similar to that of Fcal in predicting endoscopic activity with improved accuracy in patients without rectal sparing. This study implies that stool photographs are a useful monitoring tool for typical UC.
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Affiliation(s)
- Jung Won Lee
- Division of Gastroenterology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea;
| | - Dongwon Woo
- Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, Korea;
| | - Kyeong Ok Kim
- Division of Gastroenterology, Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, Korea;
| | - Eun Soo Kim
- Division of Gastroenterology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea;
| | - Sung Kook Kim
- Division of Gastroenterology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea;
| | - Hyun Seok Lee
- Division of Gastroenterology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea;
| | - Ben Kang
- Department of Pediatrics, School of Medicine, Kyungpook National University, Daegu, Korea;
| | - Yoo Jin Lee
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Korea;
| | - Jeongseok Kim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Korea;
- Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Toronto, Ontario, Canada;
| | - Byung Ik Jang
- Division of Gastroenterology, Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, Korea;
| | - Eun Young Kim
- Department of Internal Medicine, Daegu Catholic University School of Medicine, Daegu, Korea;
| | - Hyeong Ho Jo
- Department of Internal Medicine, Daegu Catholic University School of Medicine, Daegu, Korea;
| | - Yun Jin Chung
- Division of Gastroenterology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea;
| | - Hanjun Ryu
- Department of Internal Medicine, Daegu Fatima Hospital, Daegu, Korea
| | - Soo-Kyung Park
- Division of Gastroenterology, Department of Internal Medicine and Inflammatory Bowel Disease Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul
| | - Dong-Il Park
- Division of Gastroenterology, Department of Internal Medicine and Inflammatory Bowel Disease Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul
| | - Hosang Yu
- Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, Korea;
| | - Sungmoon Jeong
- Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, Korea;
- Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, Korea;
- AICU Corp., Daegu, South Korea.
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Zeng S, Dong C, Liu C, Zhen J, Pu Y, Hu J, Dong W. The global research of artificial intelligence on inflammatory bowel disease: A bibliometric analysis. Digit Health 2025; 11:20552076251326217. [PMID: 40093709 PMCID: PMC11909680 DOI: 10.1177/20552076251326217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2024] [Accepted: 02/18/2025] [Indexed: 03/19/2025] Open
Abstract
Aims This study aimed to evaluate the related research on artificial intelligence (AI) in inflammatory bowel disease (IBD) through bibliometrics analysis and identified the research basis, current hotspots, and future development. Methods The related literature was acquired from the Web of Science Core Collection (WoSCC) on 31 December 2024. Co-occurrence and cooperation relationship analysis of (cited) authors, institutions, countries, cited journals, references, and keywords in the literature were carried out through CiteSpace 6.1.R6 software and the Online Analysis platform of Literature Metrology. Meanwhile, relevant knowledge maps were drawn, and keywords clustering analysis was performed. Results According to WoSCC, 1919 authors, 790 research institutions, 184 journals, and 49 countries/regions published 176 AI-related papers in IBD during 1999-2024. The number of papers published has increased significantly since 2019, reaching a maximum by 2023. The United States had the highest number of publications and the closest collaboration with other countries. The clustering analysis showed that the earliest studies focused on "psychometric value" and then moved to "deep learning model," "intestinal ultrasound," and "new diagnostic strategies." Conclusion This study is the first bibliometric analysis to summarize the current status and to visually reveal the development trends and future research hotspots of the application of AI in IBD. The application of AI in IBD is still in its infancy, and the focus of this field will shift to improving the efficiency of diagnosis and treatment through deep learning techniques, big data-based treatment, and prognosis prediction.
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Affiliation(s)
- Suqi Zeng
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chenyu Dong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chuan Liu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Junhai Zhen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yu Pu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Jiaming Hu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Weiguo Dong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
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Maeda Y, Kudo SE, Kuroki T, Iacucci M. Automated Endoscopic Diagnosis in IBD: The Emerging Role of Artificial Intelligence. Gastrointest Endosc Clin N Am 2025; 35:213-233. [PMID: 39510689 DOI: 10.1016/j.giec.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2024]
Abstract
The emerging role of artificial intelligence (AI) in automated endoscopic diagnosis represents a significant advancement in managing inflammatory bowel disease (IBD). AI technologies are increasingly being applied to endoscopic imaging to enhance the diagnosis, prediction of severity, and progression of IBD and dysplasia-associated colitis surveillance. These AI-assisted endoscopy aim to improve diagnostic accuracy, reduce variability of endoscopy imaging interpretations, and assist clinicians in decision-making processes. By leveraging AI, healthcare providers have the potential to offer more personalized and effective treatments, ultimately improving patient outcomes in IBD care.
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Affiliation(s)
- Yasuharu Maeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki-chuo, Tsuzuki, Yokohama 224-8503, Japan; APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork T12 YT20, Ireland.
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki-chuo, Tsuzuki, Yokohama 224-8503, Japan
| | - Takanori Kuroki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki-chuo, Tsuzuki, Yokohama 224-8503, Japan
| | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork T12 YT20, Ireland
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18
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Jiang Y, Shi R, Zhou P, Lei Y, Cai Z, Sun Y, Li M. Application Value of Endoscopic Ultrasonography in Diagnosis and Treatment of Inflammatory Bowel Disease. Dig Dis Sci 2025; 70:89-99. [PMID: 39614026 DOI: 10.1007/s10620-024-08751-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Accepted: 11/09/2024] [Indexed: 12/01/2024]
Abstract
Inflammatory bowel disease refers to a group of non-specific inflammatory illnesses affecting the gastrointestinal tract. According to pathogenic characteristics, it is divided into Ulcerative colitis and Crohn's disease. The exact cause and pathogenic mechanism of these disorders are not yet fully understood. In addition, there is currently no definitive diagnostic method for inflammatory bowel disease, which mainly depends on clinical symptoms, blood testing, imaging investigations, and endoscopic examination, which includes histology. Endoscopic Ultrasonography is a digestive tract examination technique that combines endoscopy and ultrasound. Compared to conventional endoscopy, it can visualize surface and deep lesions of the gastrointestinal wall, as well as provide information on the characteristics of the surrounding layers and nearby lymph nodes. Due to these advantages, Endoscopic Ultrasonography has played a significant role in the evaluation of inflammatory bowel disease in recent years. Through this work, we aim to identify the applications of this method in the case of patients with inflammatory bowel disease.
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Affiliation(s)
- Ying Jiang
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510150, China
| | - Runjie Shi
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510150, China
| | - Peirong Zhou
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510150, China
| | - Ying Lei
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510150, China
| | - Zihong Cai
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510150, China
| | - Yan Sun
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510150, China.
| | - Mingsong Li
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510150, China
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Pessarelli T, Tontini GE, Neumann H. Advanced Endoscopic Imaging for Assessing Mucosal Healing and Histologic Remission in Inflammatory Bowel Diseases. Gastrointest Endosc Clin N Am 2025; 35:159-177. [PMID: 39510685 DOI: 10.1016/j.giec.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2024]
Abstract
Recent advances in the field of endoscopy have found fertile ground for application in inflammatory bowel diseases (IBD). Mucosal healing is a primary goal of IBD therapy, and current evidence shows that histologic remission (HR) is an additional desirable outcome. However, with the use of standard endoscopy, a considerable number of patients with histologically active disease go unrecognized. This narrative article examines the role, current or potential, of each endoscopic technique, from standard white-light endoscopy to molecular imaging, in the assessment of mucosal healing and HR in IBD.
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Affiliation(s)
- Tommaso Pessarelli
- Gastroenterology and Endoscopy Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, via Francesco Sforza 35, Milano 20122, Italy
| | - Gian Eugenio Tontini
- Gastroenterology and Endoscopy Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, via Francesco Sforza 35, Milano 20122, Italy; Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy.
| | - Helmut Neumann
- Department of Interdisciplinary Endoscopy, I. Medizinische Klinik und Poliklinik, University Hospital, Mainz, Germany; GastroZentrum LippeLange Street 55, Bad Salzuflen, Germany
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20
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Gu P, Mendonca O, Carter D, Dube S, Wang P, Huang X, Li D, Moore JH, McGovern DPB. AI-luminating Artificial Intelligence in Inflammatory Bowel Diseases: A Narrative Review on the Role of AI in Endoscopy, Histology, and Imaging for IBD. Inflamm Bowel Dis 2024; 30:2467-2485. [PMID: 38452040 DOI: 10.1093/ibd/izae030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Indexed: 03/09/2024]
Abstract
Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, and prognostication of inflammatory bowel disease (IBD). However, interpretation of these studies often relies on subjective human judgment, which can lead to delays, intra- and interobserver variability, and potential diagnostic discrepancies. With the rising incidence of IBD globally coupled with the exponential digitization of these data, there is a growing demand for innovative approaches to streamline diagnosis and elevate clinical decision-making. In this context, artificial intelligence (AI) technologies emerge as a timely solution to address the evolving challenges in IBD. Early studies using deep learning and radiomics approaches for endoscopy, histology, and imaging in IBD have demonstrated promising results for using AI to detect, diagnose, characterize, phenotype, and prognosticate IBD. Nonetheless, the available literature has inherent limitations and knowledge gaps that need to be addressed before AI can transition into a mainstream clinical tool for IBD. To better understand the potential value of integrating AI in IBD, we review the available literature to summarize our current understanding and identify gaps in knowledge to inform future investigations.
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Affiliation(s)
- Phillip Gu
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Dan Carter
- Department of Gastroenterology, Sheba Medical Center, Tel Aviv, Israel
| | - Shishir Dube
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Wang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Xiuzhen Huang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Debiao Li
- Biomedical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Dermot P B McGovern
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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21
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Gorelik MG, Gorelik AJ, Fishbein SRS, Fehlmann T, Deepak P, Bogdan R, Dantas G, Jain U. Improving Differentiation of Crohn's Disease and Ulcerative Colitis Proteomes through Protein-Wide Association Study Feature Selection in Machine Learning. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.11.13.24316854. [PMID: 39606394 PMCID: PMC11601736 DOI: 10.1101/2024.11.13.24316854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
Background and Aims Diagnostic differentiation between Crohn's disease (CD) and ulcerative colitis (UC) is crucial for timely and suitable therapeutic measures. The current gold standard for differentiating between CD and UC involves endoscopy and histology, which are invasive and costly. We aimed to identify blood plasma proteomic signatures using a Protein-Wide Association Study (PWAS) approach to differentiate CD from UC and evaluate the efficacy of these signatures as features in machine learning (ML) classifiers. Methods Among participants (n=1,106; nCD=636; nUC=470) of the Study of a Prospective Adult Research Cohort with IBD (SPARC), plasma protein (n=2,920) levels were estimated using Olink proteomics. A PWAS with Bonferroni correction for multiple testing was used to identify proteins associated with disease states after controlling for age, sex, and disease severity. ML classifiers examined the diagnostic utility of these models. Feature importance was determined via SHapley Additive exPlanations (SHAP) analysis. Results Thirteen proteins which were significantly differentially abundant in CD vs UC (all |β|s > 0.22, all adjusted p values < 8.42E-06). Random forest models of proteins differentiated between CD and UC with models trained only on PWAS identified proteins (Average ROC-AUC 0.73) outperforming models trained of the full proteome (Average ROC-AUC 0.62). SHAP analysis revealed that Granzyme B, insulin-like peptide 5 (INSL5), and interleukin-12 subunit beta (IL-12B) were the most important features. Conclusions Our findings demonstrate that PWAS-based feature selection approaches are a powerful method to identify features in complex, noisy datasets. Importantly, we have identified novel peptide based biomarkers such as INSL5, that can be potentially used to complement existing strategies to differentiate between CD and UC.
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Affiliation(s)
- Mark G Gorelik
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
| | - Aaron J Gorelik
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Skye R S Fishbein
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tara Fehlmann
- Crohn's and Colitis Foundation, New York, New York, USA
| | - Parakkal Deepak
- Division of Gastroenterology, John T. Milliken Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Gautam Dantas
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO, USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Umang Jain
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
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22
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Hashash JG, Yu Ci Ng F, Farraye FA, Wang Y, Colucci DR, Baxi S, Muneer S, Reddan M, Shingru P, Melmed GY. Inter- and Intraobserver Variability on Endoscopic Scoring Systems in Crohn's Disease and Ulcerative Colitis: A Systematic Review and Meta-Analysis. Inflamm Bowel Dis 2024; 30:2217-2226. [PMID: 38547325 DOI: 10.1093/ibd/izae051] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Indexed: 11/05/2024]
Abstract
BACKGROUND Endoscopy scoring is a key component in the diagnosis of ulcerative colitis (UC) and Crohn's disease (CD). Variability in endoscopic scoring can impact patient trial eligibility and treatment effect measurement. In this study, we examine inter- and intraobserver variability of inflammatory bowel disease endoscopic scoring systems in a systematic review and meta-analysis. METHODS We included observational studies that evaluated the inter- and intraobserver variability using UC (endoscopic Mayo Score [eMS], Ulcerative Colitis Endoscopic Index of Severity [UCEIS]) or CD (Crohn's Disease Endoscopic Index of Severity [CDEIS], Simple Endoscopic Score for Crohn's Disease [SES-CD]) systems among adults (≥18 years of age) and were published in English. The strength of agreement was categorized as fair, moderate, good, and very good. RESULTS A total of 6003 records were identified. After screening, 13 studies were included in our analysis. The overall interobserver agreement rates were 0.58 for eMS, 0.66 for UCEIS, 0.80 for CDEIS, and 0.78 for SES-CD. The overall heterogeneity (I2) for these systems ranged from 93.2% to 99.2%. A few studies assessed the intraobserver agreement rate. The overall effect sizes were 0.75 for eMS, 0.87 for UCEIS, 0.89 for CDEIS, and 0.91 for SES-CD. CONCLUSIONS The interobserver agreement rates for eMS, UCEIS, CDEIS, and SES-CD ranged from moderate to good. The intraobserver agreement rates for eMS, UCEIS, CDEIS, and SES-CD ranged from good to very good. Solutions to improve interobserver agreement could allow for more accurate patient assessment, leading to richer, more accurate clinical management and clinical trial data.
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Affiliation(s)
- Jana G Hashash
- Division of Gastroenterology and Hepatology, Inflammatory Bowel Disease Center, Mayo Clinic, Jacksonville, FL, USA
| | | | - Francis A Farraye
- Division of Gastroenterology and Hepatology, Inflammatory Bowel Disease Center, Mayo Clinic, Jacksonville, FL, USA
| | - Yeli Wang
- Iterative Health Inc., Cambridge, MA, USA
| | | | | | | | | | | | - Gil Y Melmed
- Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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23
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Nardone OM, Maeda Y, Iacucci M. AI and endoscopy/histology in UC: the rise of machine. Therap Adv Gastroenterol 2024; 17:17562848241275294. [PMID: 39435049 PMCID: PMC11491880 DOI: 10.1177/17562848241275294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 05/13/2024] [Indexed: 10/23/2024] Open
Abstract
The gap between endoscopy and histology is getting closer with the introduction of sophisticated endoscopic technologies. Furthermore, unprecedented advances in artificial intelligence (AI) have enabled objective assessment of endoscopy and digital pathology, providing accurate, consistent, and reproducible evaluations of endoscopic appearance and histologic activity. These advancements result in improved disease management by predicting treatment response and long-term outcomes. AI will also support endoscopy in raising the standard of clinical trial study design by facilitating patient recruitment and improving the validity of endoscopic readings and endoscopy quality, thus overcoming the subjective variability in scoring. Accordingly, AI will be an ideal adjunct tool for enhancing, complementing, and improving our understanding of ulcerative colitis course. This review explores promising AI applications enabled by endoscopy and histology techniques. We further discuss future directions, envisioning a bright future where AI technology extends the frontiers beyond human limits and boundaries.
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Affiliation(s)
- Olga Maria Nardone
- Division of Gastroenterology, Department of Public Health, University Federico II of Naples, Naples, Italy
| | - Yasuharu Maeda
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Marietta Iacucci
- Mercy/Cork University Hospitals, Room 1.07, Clinical Sciences Building, Cork, Ireland
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork T12YT20, Ireland
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24
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Kadota T, Hayashi H, Bise R, Tanaka K, Uchida S. Deep Bayesian active learning-to-rank with relative annotation for estimation of ulcerative colitis severity. Med Image Anal 2024; 97:103262. [PMID: 38986351 DOI: 10.1016/j.media.2024.103262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 05/04/2024] [Accepted: 06/27/2024] [Indexed: 07/12/2024]
Abstract
Automatic image-based severity estimation is an important task in computer-aided diagnosis. Severity estimation by deep learning requires a large amount of training data to achieve a high performance. In general, severity estimation uses training data annotated with discrete (i.e., quantized) severity labels. Annotating discrete labels is often difficult in images with ambiguous severity, and the annotation cost is high. In contrast, relative annotation, in which the severity between a pair of images is compared, can avoid quantizing severity and thus makes it easier. We can estimate relative disease severity using a learning-to-rank framework with relative annotations, but relative annotation has the problem of the enormous number of pairs that can be annotated. Therefore, the selection of appropriate pairs is essential for relative annotation. In this paper, we propose a deep Bayesian active learning-to-rank that automatically selects appropriate pairs for relative annotation. Our method preferentially annotates unlabeled pairs with high learning efficiency from the model uncertainty of the samples. We prove the theoretical basis for adapting Bayesian neural networks to pairwise learning-to-rank and demonstrate the efficiency of our method through experiments on endoscopic images of ulcerative colitis on both private and public datasets. We also show that our method achieves a high performance under conditions of significant class imbalance because it automatically selects samples from the minority classes.
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Affiliation(s)
- Takeaki Kadota
- Department of Advanced Information Technology, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka-shi, Fukuoka, 819-0395, Japan.
| | - Hideaki Hayashi
- Institute for Datability Science, Osaka University, 2-8, Yamadaoka, Suita-shi, Osaka, 565-0871, Japan
| | - Ryoma Bise
- Department of Advanced Information Technology, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka-shi, Fukuoka, 819-0395, Japan; Research Center for Medical Bigdata, National Institute of Informatics, 2-1-2, Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan
| | - Kiyohito Tanaka
- Department of Gastroenterology, Kyoto Second Red Cross Hospital, 355-5, Haruobicho Kamigyo-ku, Kyoto-shi, Kyoto, 602-8026, Japan
| | - Seiichi Uchida
- Department of Advanced Information Technology, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka-shi, Fukuoka, 819-0395, Japan; Research Center for Medical Bigdata, National Institute of Informatics, 2-1-2, Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan
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25
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Mota J, Almeida MJ, Martins M, Mendes F, Cardoso P, Afonso J, Ribeiro T, Ferreira J, Fonseca F, Limbert M, Lopes S, Macedo G, Castro Poças F, Mascarenhas M. Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications. J Clin Med 2024; 13:5842. [PMID: 39407902 PMCID: PMC11477032 DOI: 10.3390/jcm13195842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2024] [Revised: 09/21/2024] [Accepted: 09/22/2024] [Indexed: 10/20/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a transformative tool across several specialties, namely gastroenterology, where it has the potential to optimize both diagnosis and treatment as well as enhance patient care. Coloproctology, due to its highly prevalent pathologies and tremendous potential to cause significant mortality and morbidity, has drawn a lot of attention regarding AI applications. In fact, its application has yielded impressive outcomes in various domains, colonoscopy being one prominent example, where it aids in the detection of polyps and early signs of colorectal cancer with high accuracy and efficiency. With a less explored path but equivalent promise, AI-powered capsule endoscopy ensures accurate and time-efficient video readings, already detecting a wide spectrum of anomalies. High-resolution anoscopy is an area that has been growing in interest in recent years, with efforts being made to integrate AI. There are other areas, such as functional studies, that are currently in the early stages, but evidence is expected to emerge soon. According to the current state of research, AI is anticipated to empower gastroenterologists in the decision-making process, paving the way for a more precise approach to diagnosing and treating patients. This review aims to provide the state-of-the-art use of AI in coloproctology while also reflecting on future directions and perspectives.
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Affiliation(s)
- Joana Mota
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Maria João Almeida
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, 4200-065 Porto, Portugal;
- DigestAID—Digestive Artificial Intelligence Development, Rua Alfredo Allen n.° 455/461, 4200-135 Porto, Portugal
| | - Filipa Fonseca
- Instituto Português de Oncologia de Lisboa Francisco Gentil (IPO Lisboa), 1099-023 Lisboa, Portugal; (F.F.); (M.L.)
| | - Manuel Limbert
- Instituto Português de Oncologia de Lisboa Francisco Gentil (IPO Lisboa), 1099-023 Lisboa, Portugal; (F.F.); (M.L.)
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
| | - Susana Lopes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
| | - Fernando Castro Poças
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Department of Gastroenterology, Santo António University Hospital, 4099-001 Porto, Portugal
- Abel Salazar Biomedical Sciences Institute (ICBAS), 4050-313 Porto, Portugal
| | - Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
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26
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Kulkarni C, Liu D, Fardeen T, Dickson ER, Jang H, Sinha SR, Gubatan J. Artificial intelligence and machine learning technologies in ulcerative colitis. Therap Adv Gastroenterol 2024; 17:17562848241272001. [PMID: 39247718 PMCID: PMC11378191 DOI: 10.1177/17562848241272001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 06/17/2024] [Indexed: 09/10/2024] Open
Abstract
Interest in artificial intelligence (AI) applications for ulcerative colitis (UC) has grown tremendously in recent years. In the past 5 years, there have been over 80 studies focused on machine learning (ML) tools to address a wide range of clinical problems in UC, including diagnosis, prognosis, identification of new UC biomarkers, monitoring of disease activity, and prediction of complications. AI classifiers such as random forest, support vector machines, neural networks, and logistic regression models have been used to model UC clinical outcomes using molecular (transcriptomic) and clinical (electronic health record and laboratory) datasets with relatively high performance (accuracy, sensitivity, and specificity). Application of ML algorithms such as computer vision, guided image filtering, and convolutional neural networks have also been utilized to analyze large and high-dimensional imaging datasets such as endoscopic, histologic, and radiological images for UC diagnosis and prediction of complications (post-surgical complications, colorectal cancer). Incorporation of these ML tools to guide and optimize UC clinical practice is promising but will require large, high-quality validation studies that overcome the risk of bias as well as consider cost-effectiveness compared to standard of care.
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Affiliation(s)
- Chiraag Kulkarni
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA, USA
| | - Derek Liu
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA, USA
| | - Touran Fardeen
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA, USA
| | - Eliza Rose Dickson
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA, USA
| | - Hyunsu Jang
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA, USA
| | - Sidhartha R Sinha
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, 300 Pasteur Drive, M211, Stanford, CA 94305, USA
| | - John Gubatan
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, 300 Pasteur Drive, M211, Stanford, CA 94305, USA
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27
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Jin X, You Y, Ruan G, Zhou W, Li J, Li J. Deep mucosal healing in ulcerative colitis: how deep is better? Front Med (Lausanne) 2024; 11:1429427. [PMID: 39156693 PMCID: PMC11327023 DOI: 10.3389/fmed.2024.1429427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 07/22/2024] [Indexed: 08/20/2024] Open
Abstract
Ulcerative colitis (UC), characterized by its recurrent nature, imposes a significant disease burden and compromises the quality of life. Emerging evidence suggests that achieving clinical remission is not sufficient for long-term remission. In pursuit of a favorable prognosis, mucosal healing (MH) has been defined as the target of therapies in UC. This paradigm shift has given rise to the formulation of diverse endoscopic and histological scoring systems, providing distinct definitions for MH. Endoscopic remission (ER) has been widely employed in clinical practice, but it is susceptible to subjective factors related to endoscopists. And there's growing evidence that histological remission (HR) might be associated with a lower risk of disease flares, but the incorporation of HR as a routine therapeutic endpoint remains a debate. The integration of advanced technology has further enriched the definition of deep MH. Up to now, a universal standardized definition for deep MH in clinical practice is currently lacking. This review will focus on the definition of deep MH, from different dimensions, and analyze strengths and limitations, respectively. Subsequent multiple large-scale trials are needed to validate the concept of deep MH, offering valuable insights into potential benefits for UC patients.
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Affiliation(s)
- Xin Jin
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yan You
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Gechong Ruan
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Weixun Zhou
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Ji Li
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Jingnan Li
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
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28
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Chen YF, Liu L, Lyu B, Yang Y, Zheng SS, Huang X, Xu Y, Fan YH. Role of artificial intelligence in Crohn's disease intestinal strictures and fibrosis. J Dig Dis 2024; 25:476-483. [PMID: 39191433 DOI: 10.1111/1751-2980.13308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 07/21/2024] [Accepted: 08/07/2024] [Indexed: 08/29/2024]
Abstract
Crohn's disease (CD) is a chronic inflammatory disorder of the gastrointestinal tract. Intestinal fibrosis or stricture is one of the most prevalent complications in CD with a high recurrence rate. Manual examination of intestinal fibrosis or stricture by physicians may be biased or inefficient. A rapid development of artificial intelligence (AI) technique in recent years facilitates the detection of existing or possible intestinal fibrosis and stricture in CD through various modalities, including endoscopy, imaging examination, and serological biomarkers. We reviewed the articles on AI application in diagnosing intestinal fibrosis and stricture in CD during the past decade and categorized them into three aspects based on the detection methods, and found that AI helps accurate and expedient identification and prediction of intestinal fibrosis and stenosis in CD.
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Affiliation(s)
- Yi Fei Chen
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang Province, China
| | - Liu Liu
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang Province, China
| | - Bin Lyu
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang Province, China
| | - Ye Yang
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang Province, China
| | - Si Si Zheng
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang Province, China
| | - Xuan Huang
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang Province, China
| | - Yi Xu
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang Province, China
| | - Yi Hong Fan
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang Province, China
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Luo X, Wang J, Tan C, Dou Q, Han Z, Wang Z, Tasnim F, Wang X, Zhan Q, Li X, Zhou Q, Cheng J, Liao F, Yip HC, Jiang J, Tan RT, Liu S, Yu H. Rapid Endoscopic Diagnosis of Benign Ulcerative Colorectal Diseases With an Artificial Intelligence Contextual Framework. Gastroenterology 2024; 167:591-603.e9. [PMID: 38583724 DOI: 10.1053/j.gastro.2024.03.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 03/22/2024] [Accepted: 03/28/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND & AIMS Benign ulcerative colorectal diseases (UCDs) such as ulcerative colitis, Crohn's disease, ischemic colitis, and intestinal tuberculosis share similar phenotypes with different etiologies and treatment strategies. To accurately diagnose closely related diseases like UCDs, we hypothesize that contextual learning is critical in enhancing the ability of the artificial intelligence models to differentiate the subtle differences in lesions amidst the vastly divergent spatial contexts. METHODS White-light colonoscopy datasets of patients with confirmed UCDs and healthy controls were retrospectively collected. We developed a Multiclass Contextual Classification (MCC) model that can differentiate among the mentioned UCDs and healthy controls by incorporating the tissue object contexts surrounding the individual lesion region in a scene and spatial information from other endoscopic frames (video-level) into a unified framework. Internal and external datasets were used to validate the model's performance. RESULTS Training datasets included 762 patients, and the internal and external testing cohorts included 257 patients and 293 patients, respectively. Our MCC model provided a rapid reference diagnosis on internal test sets with a high averaged area under the receiver operating characteristic curve (image-level: 0.950 and video-level: 0.973) and balanced accuracy (image-level: 76.1% and video-level: 80.8%), which was superior to junior endoscopists (accuracy: 71.8%, P < .0001) and similar to experts (accuracy: 79.7%, P = .732). The MCC model achieved an area under the receiver operating characteristic curve of 0.988 and balanced accuracy of 85.8% using external testing datasets. CONCLUSIONS These results enable this model to fit in the routine endoscopic workflow, and the contextual framework to be adopted for diagnosing other closely related diseases.
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Affiliation(s)
- Xiaobei Luo
- Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China; Department of Gastroenterology, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, Guangdong, China.
| | - Jiahao Wang
- Mechanobiology Institute, National University of Singapore, Singapore; Institute of Bioengineering and Bioimaging, Agency for Science, Technology and Research (A∗STAR), Singapore
| | - Chuanchuan Tan
- The First Hospital of Hunan University of Chinese Medicine, Hunan, China
| | - Qi Dou
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Zelong Han
- Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Zhenjiang Wang
- Department of Gastroenterology, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, Guangdong, China
| | - Farah Tasnim
- Institute of Bioengineering and Bioimaging, Agency for Science, Technology and Research (A∗STAR), Singapore
| | - Xiyu Wang
- Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Qiang Zhan
- Department of Gastroenterology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China
| | - Xiang Li
- Digestive Department of The Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen & Longgang District People's Hospital of Shenzhen, Shenzhen, China
| | - Qunyan Zhou
- Department of Gastroenterology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China
| | - Jianbin Cheng
- Department of Gastroenterology, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, Guangdong, China
| | - Fabiao Liao
- Digestive Department of The Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen & Longgang District People's Hospital of Shenzhen, Shenzhen, China
| | - Hon Chi Yip
- Division of Upper Gastrointestinal and Metabolic Surgery, Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
| | - Jiayi Jiang
- Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Robby T Tan
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - Side Liu
- Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China; Department of Gastroenterology, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, Guangdong, China.
| | - Hanry Yu
- Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China; Mechanobiology Institute, National University of Singapore, Singapore; Institute of Bioengineering and Bioimaging, Agency for Science, Technology and Research (A∗STAR), Singapore; CAMP, Singapore-MIT Alliance for Research and Technology, Singapore; Department of Physiology, The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, Singapore.
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Iacucci M, Santacroce G, Zammarchi I, Maeda Y, Del Amor R, Meseguer P, Kolawole BB, Chaudhari U, Di Sabatino A, Danese S, Mori Y, Grisan E, Naranjo V, Ghosh S. Artificial intelligence and endo-histo-omics: new dimensions of precision endoscopy and histology in inflammatory bowel disease. Lancet Gastroenterol Hepatol 2024; 9:758-772. [PMID: 38759661 DOI: 10.1016/s2468-1253(24)00053-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/16/2024] [Accepted: 02/23/2024] [Indexed: 05/19/2024]
Abstract
Integrating artificial intelligence into inflammatory bowel disease (IBD) has the potential to revolutionise clinical practice and research. Artificial intelligence harnesses advanced algorithms to deliver accurate assessments of IBD endoscopy and histology, offering precise evaluations of disease activity, standardised scoring, and outcome prediction. Furthermore, artificial intelligence offers the potential for a holistic endo-histo-omics approach by interlacing and harmonising endoscopy, histology, and omics data towards precision medicine. The emerging applications of artificial intelligence could pave the way for personalised medicine in IBD, offering patient stratification for the most beneficial therapy with minimal risk. Although artificial intelligence holds promise, challenges remain, including data quality, standardisation, reproducibility, scarcity of randomised controlled trials, clinical implementation, ethical concerns, legal liability, and regulatory issues. The development of standardised guidelines and interdisciplinary collaboration, including policy makers and regulatory agencies, is crucial for addressing these challenges and advancing artificial intelligence in IBD clinical practice and trials.
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Affiliation(s)
- Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland.
| | - Giovanni Santacroce
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Irene Zammarchi
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Yasuharu Maeda
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Rocío Del Amor
- Instituto de Investigación e Innovación en Bioingeniería, HUMAN-tech, Universitat Politècnica de València, València, Spain
| | - Pablo Meseguer
- Instituto de Investigación e Innovación en Bioingeniería, HUMAN-tech, Universitat Politècnica de València, València, Spain; Valencian Graduate School and Research Network of Artificial Intelligence, Valencia, Spain
| | | | | | - Antonio Di Sabatino
- Department of Internal Medicine and Medical Therapeutics, University of Pavia, Pavia, Italy; First Department of Internal Medicine, San Matteo Hospital Foundation, Pavia, Italy
| | - Silvio Danese
- Gastroenterology and Endoscopy, IRCCS Ospedale San Raffaele and University Vita-Salute San Raffaele, Milan, Italy
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Enrico Grisan
- School of Engineering, London South Bank University, London, UK
| | - Valery Naranjo
- Instituto de Investigación e Innovación en Bioingeniería, HUMAN-tech, Universitat Politècnica de València, València, Spain
| | - Subrata Ghosh
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
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Rimondi A, Gottlieb K, Despott EJ, Iacucci M, Murino A, Tontini GE. Can artificial intelligence replace endoscopists when assessing mucosal healing in ulcerative colitis? A systematic review and diagnostic test accuracy meta-analysis. Dig Liver Dis 2024; 56:1164-1172. [PMID: 38057218 DOI: 10.1016/j.dld.2023.11.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/03/2023] [Accepted: 11/07/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUNDS AND AIMS Mucosal healing (MH) in inflammatory bowel diseases (IBD) is an important landmark for clinical decision making. Artificial intelligence systems (AI) that automatically deliver the grade of endoscopic inflammation may solve moderate interobserver agreement and the need of central reading in clinical trials. METHODS We performed a systematic review of EMBASE and MEDLINE databases up to 01/12/2022 following PRISMA and the Joanna Briggs Institute methodologies to answer the following question: "Can AI replace endoscopists when assessing MH in IBD?". The research was restricted to ulcerative colitis (UC), and a diagnostic odds ratio (DOR) meta-analysis was performed. Risk of bias was evaluated with QUADAS-2 tool. RESULTS A total of 21 / 739 records were selected for full text evaluation, and 12 were included in the meta-analysis. Deep learning algorithms based on convolutional neural networks architecture achieved a satisfactory performance in evaluating MH on UC, with sensitivity, specificity, DOR and SROC of respectively 0.91(CI95 %:0.86-0.95);0.89(CI95 %:0.84-0.93);92.42(CI95 %:54.22-157.53) and 0.957 when evaluating fixed images (n = 8) and 0.86(CI95 %:0.75-0.93);0.91(CI95 %:0.87-0.94);70.86(CI95 %:24.63-203.86) and 0.941 when evaluating videos (n = 6). Moderate-high levels of heterogeneity were noted, limiting the quality of the evidence. CONCLUSIONS AI systems showed high potential in detecting MH in UC with optimal diagnostic performance, although moderate-high heterogeneity of the data was noted. Standardised and shared AI training may reduce heterogeneity between systems.
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Affiliation(s)
- Alessandro Rimondi
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom.
| | | | - Edward J Despott
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom
| | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Alberto Murino
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom; Department of Gastroenterology, Cleveland Clinic London, London, United Kingdom
| | - Gian Eugenio Tontini
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Gastroenterology and Endoscopy unit, Milan, Italy
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Kuroki T, Maeda Y, Kudo SE, Ogata N, Iacucci M, Takishima K, Ide Y, Shibuya T, Semba S, Kawashima J, Kato S, Ogawa Y, Ichimasa K, Nakamura H, Hayashi T, Wakamura K, Miyachi H, Baba T, Nemoto T, Ohtsuka K, Misawa M. A novel artificial intelligence-assisted "vascular healing" diagnosis for prediction of future clinical relapse in patients with ulcerative colitis: a prospective cohort study (with video). Gastrointest Endosc 2024; 100:97-108. [PMID: 38215859 DOI: 10.1016/j.gie.2024.01.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 01/05/2024] [Accepted: 01/09/2024] [Indexed: 01/14/2024]
Abstract
BACKGROUND AND AIMS Image-enhanced endoscopy has attracted attention as a method for detecting inflammation and predicting outcomes in patients with ulcerative colitis (UC); however, the procedure requires specialist endoscopists. Artificial intelligence (AI)-assisted image-enhanced endoscopy may help nonexperts provide objective accurate predictions with the use of optical imaging. We aimed to develop a novel AI-based system using 8853 images from 167 patients with UC to diagnose "vascular-healing" and establish the role of AI-based vascular-healing for predicting the outcomes of patients with UC. METHODS This open-label prospective cohort study analyzed data for 104 patients with UC in clinical remission. Endoscopists performed colonoscopy using the AI system, which identified the target mucosa as AI-based vascular-active or vascular-healing. Mayo endoscopic subscore (MES), AI outputs, and histologic assessment were recorded for 6 colorectal segments from each patient. Patients were followed up for 12 months. Clinical relapse was defined as a partial Mayo score >2 RESULTS: The clinical relapse rate was significantly higher in the AI-based vascular-active group (23.9% [16/67]) compared with the AI-based vascular-healing group (3.0% [1/33)]; P = .01). In a subanalysis predicting clinical relapse in patients with MES ≤1, the area under the receiver operating characteristic curve for the combination of complete endoscopic remission and vascular healing (0.70) was increased compared with that for complete endoscopic remission alone (0.65). CONCLUSIONS AI-based vascular-healing diagnosis system may potentially be used to provide more confidence to physicians to accurately identify patients in remission of UC who would likely relapse rather than remain stable.
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Affiliation(s)
- Takanori Kuroki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Yasuharu Maeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan; APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Noriyuki Ogata
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Kazumi Takishima
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Yutaro Ide
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Tomoya Shibuya
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Shigenori Semba
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Jiro Kawashima
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Shun Kato
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Yushi Ogawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Katsuro Ichimasa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Hiroki Nakamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Takemasa Hayashi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Kunihiko Wakamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Hideyuki Miyachi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Toshiyuki Baba
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Tetsuo Nemoto
- Department of Diagnostic Pathology and Laboratory Medicine, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Kazuo Ohtsuka
- Department of Endoscopy, Tokyo Medical and Dental University, Medical Hospital, Tokyo, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
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Maeda Y, Kudo SE, Santacroce G, Ogata N, Misawa M, Iacucci M. Artificial intelligence-assisted colonoscopy to identify histologic remission and predict the outcomes of patients with ulcerative colitis: A systematic review. Dig Liver Dis 2024; 56:1119-1125. [PMID: 38643020 DOI: 10.1016/j.dld.2024.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 03/11/2024] [Accepted: 04/04/2024] [Indexed: 04/22/2024]
Abstract
This systematic review evaluated the current status of AI-assisted colonoscopy to identify histologic remission and predict the clinical outcomes of patients with ulcerative colitis. The use of artificial intelligence (AI) has increased substantially across several medical fields, including gastrointestinal endoscopy. Evidence suggests that it may be helpful to predict histologic remission and relapse, which would be beneficial because current histological diagnosis is limited by the inconvenience of obtaining biopsies and the high cost and time-intensiveness of pathological diagnosis. MEDLINE and the Cochrane Central Register of Controlled Trials were searched for studies published between January 1, 2000, and October 31, 2023. Nine studies fulfilled the selection criteria and were included; five evaluated the prediction of histologic remission, two assessed the prediction of clinical outcomes, and two evaluated both. Seven were prospective observational or cohort studies, while two were retrospective observational studies. No randomized controlled trials were identified. AI-assisted colonoscopy demonstrated sensitivity between 65 %-98 % and specificity values of 80 %-97 % for identifying histologic remission. Furthermore, it was able to predict future relapse in patients with ulcerative colitis. However, several challenges and barriers still exist to its routine clinical application, which should be overcome before the true potential of AI-assisted colonoscopy can be fully realized.
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Affiliation(s)
- Yasuharu Maeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki-chuo, Tsuzuki, Yokohama 224-8503, Japan; APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, T12 YT20, Ireland.
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki-chuo, Tsuzuki, Yokohama 224-8503, Japan
| | - Giovanni Santacroce
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, T12 YT20, Ireland
| | - Noriyuki Ogata
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki-chuo, Tsuzuki, Yokohama 224-8503, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki-chuo, Tsuzuki, Yokohama 224-8503, Japan
| | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, T12 YT20, Ireland
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Abe-Doi M, Murayama R, Takahashi T, Matsumoto M, Tamai N, Nakagami G, Sanada H. Effects of ultrasound with an automatic vessel detection system using artificial intelligence on the selection of puncture points among ultrasound beginner clinical nurses. J Vasc Access 2024; 25:1252-1260. [PMID: 36895159 DOI: 10.1177/11297298231156489] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND Ultrasound guidance increases the success rate of peripheral intravenous catheter placement. However, the longer time required to obtain ultrasound-guided access poses difficulties for ultrasound beginners. Notably, interpretation of ultrasonographic images is considered as one of the main reasons of difficulty in using ultrasound for catheter placement. Therefore, an automatic vessel detection system (AVDS) using artificial intelligence was developed. This study aimed to investigate the effectiveness of AVDS for ultrasound beginners in selecting puncture points and determine suitable users for this system. METHODS In this crossover experiment involving the use of ultrasound with and without AVDS, we enrolled 10 clinical nurses, including 5 with some experience in peripheral intravenous catheterization using ultrasound-aided methods (categorized as ultrasound beginners) and 5 with no experience in ultrasound and less experience in peripheral intravenous catheterization using conventional methods (categorized as inexperienced). These participants chose two puncture points (those with the largest and second largest diameter) as ideal in each forearm of a healthy volunteer. The results of this study were the time required for the selection of puncture points and the vein diameter of the selected points. RESULTS Among ultrasound beginners, the time required for puncture point selection in the right forearm second candidate vein with a small diameter (<3 mm) was significantly shorter when using ultrasound with AVDS than when using it without AVDS (mean, 87 vs 247 s). Among inexperienced nurses, no significant difference in the time required for all puncture point selections was found between the use of ultrasound with and without AVDS. In the vein diameter, significant difference was shown only in the absolute difference at left second candidate among inexperienced participants. CONCLUSION Ultrasonography beginners needed less time to select the puncture points in a small diameter vein using ultrasound with AVDS than without AVDS.
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Affiliation(s)
- Mari Abe-Doi
- Department of Gerontological Nursing/Wound Care Management, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- Former Department of Advanced Nursing Technology, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Ryoko Murayama
- Former Department of Advanced Nursing Technology, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- Research Center for Implementation Nursing Science Initiative, Research Promotion Headquarters, Fujita Health University, Aichi, Japan
| | - Toshiaki Takahashi
- Department of Gerontological Nursing/Wound Care Management, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- Former Global Nursing Research Center, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Masaru Matsumoto
- Department of Nursing, Ishikawa Prefectural Nursing University, Ishikawa, Japan
| | - Nao Tamai
- Former Department of Imaging Nursing Science, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- Department of Nursing, Graduate School of Medicine, Yokohama City University, Kanagawa, Japan
| | - Gojiro Nakagami
- Department of Gerontological Nursing/Wound Care Management, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Hiromi Sanada
- Department of Gerontological Nursing/Wound Care Management, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- Former Global Nursing Research Center, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
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Wang L, Zhang Q, Zhang P, Wu B, Chen J, Gong J, Tang K, Du S, Li S. Development of an artificial intelligent model for pre-endoscopic screening of precancerous lesions in gastric cancer. Chin Med 2024; 19:90. [PMID: 38951913 PMCID: PMC11218324 DOI: 10.1186/s13020-024-00963-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 06/18/2024] [Indexed: 07/03/2024] Open
Abstract
BACKGROUND Given the high cost of endoscopy in gastric cancer (GC) screening, there is an urgent need to explore cost-effective methods for the large-scale prediction of precancerous lesions of gastric cancer (PLGC). We aim to construct a hierarchical artificial intelligence-based multimodal non-invasive method for pre-endoscopic risk screening, to provide tailored recommendations for endoscopy. METHODS From December 2022 to December 2023, a large-scale screening study was conducted in Fujian, China. Based on traditional Chinese medicine theory, we simultaneously collected tongue images and inquiry information from 1034 participants, considering the potential of these data for PLGC screening. Then, we introduced inquiry information for the first time, forming a multimodality artificial intelligence model to integrate tongue images and inquiry information for pre-endoscopic screening. Moreover, we validated this approach in another independent external validation cohort, comprising 143 participants from the China-Japan Friendship Hospital. RESULTS A multimodality artificial intelligence-assisted pre-endoscopic screening model based on tongue images and inquiry information (AITonguequiry) was constructed, adopting a hierarchical prediction strategy, achieving tailored endoscopic recommendations. Validation analysis revealed that the area under the curve (AUC) values of AITonguequiry were 0.74 for overall PLGC (95% confidence interval (CI) 0.71-0.76, p < 0.05) and 0.82 for high-risk PLGC (95% CI 0.82-0.83, p < 0.05), which were significantly and robustly better than those of the independent use of either tongue images or inquiry information alone. In addition, AITonguequiry has superior performance compared to existing PLGC screening methodologies, with the AUC value enhancing 45% in terms of PLGC screening (0.74 vs. 0.51, p < 0.05) and 52% in terms of high-risk PLGC screening (0.82 vs. 0.54, p < 0.05). In the independent external verification, the AUC values were 0.69 for PLGC and 0.76 for high-risk PLGC. CONCLUSION Our AITonguequiry artificial intelligence model, for the first time, incorporates inquiry information and tongue images, leading to a higher precision and finer-grained pre-endoscopic screening of PLGC. This enhances patient screening efficiency and alleviates patient burden.
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Affiliation(s)
- Lan Wang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Qian Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Peng Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Bowen Wu
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Jun Chen
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Jiamin Gong
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Kaiqiang Tang
- Department of Control Science and Intelligence Engineering, Nanjing University, Nanjing, China
| | - Shiyu Du
- Department of Gastroenterology, China-Japan Friendship Hospital, Chaoyang District, Beijing, China.
| | - Shao Li
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China.
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Costa MHDM, Sassaki LY, Chebli JMF. Fecal calprotectin and endoscopic scores: The cornerstones in clinical practice for evaluating mucosal healing in inflammatory bowel disease. World J Gastroenterol 2024; 30:3022-3035. [PMID: 38983953 PMCID: PMC11230062 DOI: 10.3748/wjg.v30.i24.3022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 05/01/2024] [Accepted: 05/27/2024] [Indexed: 06/25/2024] Open
Abstract
Managing inflammatory bowel disease (IBD) is becoming increasingly complex and personalized, considering the advent of new advanced therapies with distinct mechanisms of action. Achieving mucosal healing (MH) is a pivotal therapeutic goal in IBD management and can prevent IBD progression and reduce flares, hospitalization, surgery, intestinal damage, and colorectal cancer. Employing proactive disease and therapy assessment is essential to achieve better control of intestinal inflammation, even if subclinical, to alter the natural course of IBD. Periodic monitoring of fecal calprotectin (FC) levels and interval endoscopic evaluations are cornerstones for evaluating response/remission to advanced therapies targeting IBD, assessing MH, and detecting subclinical recurrence. Here, we comment on the article by Ishida et al Moreover, this editorial aimed to review the role of FC and endoscopic scores in predicting MH in patients with IBD. Furthermore, we intend to present some evidence on the role of these markers in future targets, such as histological and transmural healing. Additional prospective multicenter studies with a stricter MH criterion, standardized endoscopic and histopathological analyses, and virtual chromoscopy, potentially including artificial intelligence and other biomarkers, are desired.
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Affiliation(s)
| | - Ligia Yukie Sassaki
- Department of Internal Medicine, Medical School, São Paulo State University (Unesp), Botucatu 18618-686, São Paulo, Brazil
| | - Júlio Maria Fonseca Chebli
- Division of Gastroenterology, Department of Medicine, University Hospital of The Federal University of Juiz de Fora, University of Juiz de Fora School of Medicine, Juiz de Fora 36036-247, Minas Gerais, Brazil
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Syed S, Boland BS, Bourke LT, Chen LA, Churchill L, Dobes A, Greene A, Heller C, Jayson C, Kostiuk B, Moss A, Najdawi F, Plung L, Rioux JD, Rosen MJ, Torres J, Zulqarnain F, Satsangi J. Challenges in IBD Research 2024: Precision Medicine. Inflamm Bowel Dis 2024; 30:S39-S54. [PMID: 38778628 DOI: 10.1093/ibd/izae084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Indexed: 05/25/2024]
Abstract
Precision medicine is part of 5 focus areas of the Challenges in IBD Research 2024 research document, which also includes preclinical human IBD mechanisms, environmental triggers, novel technologies, and pragmatic clinical research. Building on Challenges in IBD Research 2019, the current Challenges aims to provide a comprehensive overview of current gaps in inflammatory bowel diseases (IBDs) research and deliver actionable approaches to address them with a focus on how these gaps can lead to advancements in interception, remission, and restoration for these diseases. The document is the result of multidisciplinary input from scientists, clinicians, patients, and funders, and represents a valuable resource for patient-centric research prioritization. In particular, the precision medicine section is focused on the main research gaps in elucidating how to bring the best care to the individual patient in IBD. Research gaps were identified in biomarker discovery and validation for predicting disease progression and choosing the most appropriate treatment for each patient. Other gaps were identified in making the best use of existing patient biosamples and clinical data, developing new technologies to analyze large datasets, and overcoming regulatory and payer hurdles to enable clinical use of biomarkers. To address these gaps, the Workgroup suggests focusing on thoroughly validating existing candidate biomarkers, using best-in-class data generation and analysis tools, and establishing cross-disciplinary teams to tackle regulatory hurdles as early as possible. Altogether, the precision medicine group recognizes the importance of bringing basic scientific biomarker discovery and translating it into the clinic to help improve the lives of IBD patients.
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Affiliation(s)
- Sana Syed
- Department of Pediatrics, University of Virginia, Charlottesville, VA, USA
- Patient representative for Crohn's & Colitis Foundation, New York, NY, USA
| | - Brigid S Boland
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Lauren T Bourke
- Precision Medicine Drug Development, Early Respiratory and Immunology, AstraZeneca, Boston, MA, USA
| | - Lea Ann Chen
- Division of Gastroenterology, Department of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Laurie Churchill
- Leona M. and Harry B. Helmsley Charitable Trust, New York, NY, USA
| | | | - Adam Greene
- Department of Pediatrics, University of Virginia, Charlottesville, VA, USA
| | | | | | | | - Alan Moss
- Crohn's & Colitis Foundation, New York, NY, USA
| | | | - Lori Plung
- Patient representative for Crohn's & Colitis Foundation, New York, NY, USA
| | - John D Rioux
- Research Center, Montreal Heart Institute, Université de Montréal, Montréal, Québec, Canada
| | - Michael J Rosen
- Division of Pediatric Gastroenterology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Joana Torres
- Division of Gastroenterology, Hospital Beatriz Ângelo, Hospital da Luz, Lisbon, Portugal
- Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Fatima Zulqarnain
- Department of Pediatrics, University of Virginia, Charlottesville, VA, USA
| | - Jack Satsangi
- Translational Gastroenterology Unit, Experimental Medicine Division, Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
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Gomes LEM, Genaro LM, de Castro MM, Ricci RL, Pascoal LB, Silva FBC, Bonfitto PHL, Camargo MG, Corona LP, Ayrizono MDLS, de Azevedo AT, Leal RF. Infliximab monitoring in Crohn's disease: a neural network approach for evaluating disease activity and immunogenicity. Therap Adv Gastroenterol 2024; 17:17562848241251949. [PMID: 39664232 PMCID: PMC11632880 DOI: 10.1177/17562848241251949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 04/15/2024] [Indexed: 12/13/2024] Open
Abstract
Background The treatment for Crohn's disease (CD) has increasingly required the use of biological agents. Safe and affordable tests have led to the active implementation of therapeutic drug monitoring (TDM) in clinical practice, which, although not yet widely available across all health services, has been proven effective. Objective To analyze serum infliximab (IFX) and antidrug antibody (ADA) levels in CD patients, compare two tests, as well as construct a prediction of neural network using a combination of clinical, epidemiological, and laboratory variables. Design Cross-sectional observational study. Method A cross-sectional observational study was conducted on 75 CD patients in the maintenance phase of IFX treatment. The participants were allocated into two groups: CD in activity (CDA) and in remission (CDR). Disease activity was defined by endoscopic or radiological criteria. Serum IFX levels were measured by enzyme-linked immunosorbent assay (ELISA) and rapid lateral flow assay; ADA levels were measured by ELISA. A nonparametric test was used for statistical analysis; p value of ⩽0.05 was considered significant. Differences between ELISA and rapid lateral flow results within the measurement range were assessed by the Wilcoxon test, Passing-Bablok regression, and Bland-Altman method. Prediction models were created using four neural network sets. Neural networks and performance receiver operating characteristic curves were created using the Keras package in Python software. Results Most participants exhibited supratherapeutic IFX levels (>7 mg/mL). Both tests showed no difference in IFX levels between the CDA and CDR groups (p > 0.05). The use of immunosuppressive therapy did not affect IFX levels (p > 0.05). Only 14.66% of patients had ADA levels >5 AU/mL, and all ADA-positive participants exhibited subtherapeutic IFX levels in both tests. The median results of both tests showed significant differences and moderate agreement (r = -0.6758, p < 0.001). Of the four neural networks developed, two showed excellent performance, with area under the curve (AUCs) of 82-92% and 100%. Conclusion Most participants exhibited supratherapeutic IFX levels, with no significant serum level difference between the groups. There was moderate agreement between tests. Two neural network sets showed disease activity and the presence of ADA, noninvasively determined in patients using IFX by presenting an AUC of >80%.
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Affiliation(s)
- Luis Eduardo Miani Gomes
- Inflammatory Bowel Disease Research Laboratory (LabDII), Gastrocenter, Colorectal Surgery Unit, Surgery Department, School of Medical Sciences, University of Campinas, Campinas, São Paulo, Brazil
| | - Livia Moreira Genaro
- Inflammatory Bowel Disease Research Laboratory (LabDII), Gastrocenter, Colorectal Surgery Unit, Surgery Department, School of Medical Sciences, University of Campinas, Campinas, São Paulo, Brazil
| | - Marina Moreira de Castro
- Inflammatory Bowel Disease Research Laboratory (LabDII), Gastrocenter, Colorectal Surgery Unit, Surgery Department, School of Medical Sciences, University of Campinas, Campinas, São Paulo, Brazil
| | - Renato Lazarin Ricci
- Inflammatory Bowel Disease Research Laboratory (LabDII), Gastrocenter, Colorectal Surgery Unit, Surgery Department, School of Medical Sciences, University of Campinas, Campinas, São Paulo, Brazil
| | - Livia Bitencourt Pascoal
- Inflammatory Bowel Disease Research Laboratory (LabDII), Gastrocenter, Colorectal Surgery Unit, Surgery Department, School of Medical Sciences, University of Campinas, Campinas, São Paulo, Brazil
| | - Filipe Botto Crispim Silva
- Inflammatory Bowel Disease Research Laboratory (LabDII), Gastrocenter, Colorectal Surgery Unit, Surgery Department, School of Medical Sciences, University of Campinas, Campinas, São Paulo, Brazil
| | - Pedro Henrique Leite Bonfitto
- Inflammatory Bowel Disease Research Laboratory (LabDII), Gastrocenter, Colorectal Surgery Unit, Surgery Department, School of Medical Sciences, University of Campinas, Campinas, São Paulo, Brazil
| | - Michel Gardere Camargo
- Inflammatory Bowel Disease Research Laboratory (LabDII), Gastrocenter, Colorectal Surgery Unit, Surgery Department, School of Medical Sciences, University of Campinas, Campinas, São Paulo, Brazil
| | - Ligiana Pires Corona
- Nutritional Epidemiology Laboratory, School of Applied Sciences, University of Campinas, Limeira, São Paulo, Brazil
| | - Maria de Lourdes Setsuko Ayrizono
- Inflammatory Bowel Disease Research Laboratory (LabDII), Gastrocenter, Colorectal Surgery Unit, Surgery Department, School of Medical Sciences, University of Campinas, Campinas, São Paulo, Brazil
| | - Anibal Tavares de Azevedo
- Simulation Laboratory, School of Applied Sciences, University of Campinas, Limeira, São Paulo, Brazil
| | - Raquel Franco Leal
- Inflammatory Bowel Disease Research Laboratory (LabDII), Gastrocenter, Colorectal Surgery Unit, Surgery Department, School of Medical Sciences, University of Campinas, Carlos Chagas Street, 420, Cidade Universitária Zeferino Vaz, Campinas 13083-878, São Paulo, Brazil
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Takabayashi K, Kobayashi T, Matsuoka K, Levesque BG, Kawamura T, Tanaka K, Kadota T, Bise R, Uchida S, Kanai T, Ogata H. Artificial intelligence quantifying endoscopic severity of ulcerative colitis in gradation scale. Dig Endosc 2024; 36:582-590. [PMID: 37690125 DOI: 10.1111/den.14677] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 09/05/2023] [Indexed: 09/12/2023]
Abstract
OBJECTIVES Existing endoscopic scores for ulcerative colitis (UC) objectively categorize disease severity based on the presence or absence of endoscopic findings; therefore, it may not reflect the range of clinical severity within each category. However, inflammatory bowel disease (IBD) expert endoscopists categorize the severity and diagnose the overall impression of the degree of inflammation. This study aimed to develop an artificial intelligence (AI) system that can accurately represent the assessment of the endoscopic severity of UC by IBD expert endoscopists. METHODS A ranking-convolutional neural network (ranking-CNN) was trained using comparative information on the UC severity of 13,826 pairs of endoscopic images created by IBD expert endoscopists. Using the trained ranking-CNN, the UC Endoscopic Gradation Scale (UCEGS) was used to express severity. Correlation coefficients were calculated to ensure that there were no inconsistencies in assessments of severity made using UCEGS diagnosed by the AI and the Mayo Endoscopic Subscore, and the correlation coefficients of the mean for test images assessed using UCEGS by four IBD expert endoscopists and the AI. RESULTS Spearman's correlation coefficient between the UCEGS diagnosed by AI and Mayo Endoscopic Subscore was approximately 0.89. The correlation coefficients between IBD expert endoscopists and the AI of the evaluation results were all higher than 0.95 (P < 0.01). CONCLUSIONS The AI developed here can diagnose UC severity endoscopically similar to IBD expert endoscopists.
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Affiliation(s)
- Kaoru Takabayashi
- Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, Tokyo, Japan
| | - Taku Kobayashi
- Center for Advanced IBD Research and Treatment, Kitasato University Kitasato Institute Hospital, Tokyo, Japan
| | - Katsuyoshi Matsuoka
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Toho University Sakura Medical Center, Chiba, Japan
| | - Barrett G Levesque
- Division of Gastroenterology, Los Angeles County/University of Southern California Medical Center, Los Angeles, USA
| | - Takuji Kawamura
- Department of Gastroenterology, Kyoto Second Red Cross Hospital, Kyoto, Japan
| | - Kiyohito Tanaka
- Department of Gastroenterology, Kyoto Second Red Cross Hospital, Kyoto, Japan
| | - Takeaki Kadota
- Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan
| | - Ryoma Bise
- Research Center for Medical Bigdata, National Institute of Informatics, Tokyo, Japan
- Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan
| | - Seiichi Uchida
- Research Center for Medical Bigdata, National Institute of Informatics, Tokyo, Japan
- Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan
| | - Takanori Kanai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Haruhiko Ogata
- Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, Tokyo, Japan
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40
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Guo F, Meng H. Application of artificial intelligence in gastrointestinal endoscopy. Arab J Gastroenterol 2024; 25:93-96. [PMID: 38228443 DOI: 10.1016/j.ajg.2023.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 09/06/2023] [Accepted: 12/30/2023] [Indexed: 01/18/2024]
Abstract
Endoscopy is an important method for diagnosing gastrointestinal (GI) diseases. In this study, we provide an overview of the advances in artificial intelligence (AI) technology in the field of GI endoscopy over recent years, including esophagus, stomach, large intestine, and capsule endoscopy (small intestine). AI-assisted endoscopy shows high accuracy, sensitivity, and specificity in the detection and diagnosis of GI diseases at all levels. Hence, AI will make a breakthrough in the field of GI endoscopy in the near future. However, AI technology currently has some limitations and is still in the preclinical stages.
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Affiliation(s)
- Fujia Guo
- The first Affiliated Hospital, Dalian Medical University, Dalian 116044, China
| | - Hua Meng
- The first Affiliated Hospital, Dalian Medical University, Dalian 116044, China.
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41
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Rymarczyk D, Schultz W, Borowa A, Friedman JR, Danel T, Branigan P, Chałupczak M, Bracha A, Krawiec T, Warchoł M, Li K, De Hertogh G, Zieliński B, Ghanem LR, Stojmirovic A. Deep Learning Models Capture Histological Disease Activity in Crohn's Disease and Ulcerative Colitis with High Fidelity. J Crohns Colitis 2024; 18:604-614. [PMID: 37814351 PMCID: PMC11037111 DOI: 10.1093/ecco-jcc/jjad171] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Indexed: 10/11/2023]
Abstract
BACKGROUND AND AIMS Histological disease activity in inflammatory bowel disease [IBD] is associated with clinical outcomes and is an important endpoint in drug development. We developed deep learning models for automating histological assessments in IBD. METHODS Histology images of intestinal mucosa from phase 2 and phase 3 clinical trials in Crohn's disease [CD] and ulcerative colitis [UC] were used to train artificial intelligence [AI] models to predict the Global Histology Activity Score [GHAS] for CD and Geboes histopathology score for UC. Three AI methods were compared. AI models were evaluated on held-back testing sets, and model predictions were compared against an expert central reader and five independent pathologists. RESULTS The model based on multiple instance learning and the attention mechanism [SA-AbMILP] demonstrated the best performance among competing models. AI-modelled GHAS and Geboes subgrades matched central readings with moderate to substantial agreement, with accuracies ranging from 65% to 89%. Furthermore, the model was able to distinguish the presence and absence of pathology across four selected histological features, with accuracies for colon in both CD and UC ranging from 87% to 94% and for CD ileum ranging from 76% to 83%. For both CD and UC and across anatomical compartments [ileum and colon] in CD, comparable accuracies against central readings were found between the model-assigned scores and scores by an independent set of pathologists. CONCLUSIONS Deep learning models based upon GHAS and Geboes scoring systems were effective at distinguishing between the presence and absence of IBD microscopic disease activity.
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Affiliation(s)
- Dawid Rymarczyk
- AI Lab, Ardigen SA, Kraków, Poland
- Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
| | - Weiwei Schultz
- Data Science & Digital Health, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | - Adriana Borowa
- AI Lab, Ardigen SA, Kraków, Poland
- Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
| | - Joshua R Friedman
- Data Science & Digital Health, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | - Tomasz Danel
- AI Lab, Ardigen SA, Kraków, Poland
- Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
| | - Patrick Branigan
- Immunology TA, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | | | | | | | | | - Katherine Li
- Immunology TA, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | - Gert De Hertogh
- Department of Pathology, University Hospitals KU Leuven, Belgium
| | - Bartosz Zieliński
- AI Lab, Ardigen SA, Kraków, Poland
- Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
| | - Louis R Ghanem
- Immunology TA, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | - Aleksandar Stojmirovic
- Data Science & Digital Health, Janssen Research & Development, LLC, Spring House, Pennsylvania
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42
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Zhao SQ, Liu WT. Progress in artificial intelligence assisted digestive endoscopy diagnosis of digestive system diseases. WORLD CHINESE JOURNAL OF DIGESTOLOGY 2024; 32:171-181. [DOI: 10.11569/wcjd.v32.i3.171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2024]
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Hou M, Wang J, Liu T, Li Z, Hounye AH, Liu X, Wang K, Chen S. A graph-optimized deep learning framework for recognition of Barrett’s esophagus and reflux esophagitis. MULTIMEDIA TOOLS AND APPLICATIONS 2024; 83:83747-83767. [DOI: 10.1007/s11042-024-18910-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 01/12/2024] [Accepted: 03/11/2024] [Indexed: 01/03/2025]
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44
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Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform 2024; 12:e52073. [PMID: 38506918 PMCID: PMC10993141 DOI: 10.2196/52073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/12/2023] [Accepted: 01/30/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. OBJECTIVE This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. METHODS We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. RESULTS Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. CONCLUSIONS GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.
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Affiliation(s)
- Dobin Yim
- Loyola University, Maryland, MD, United States
| | - Jiban Khuntia
- University of Colorado Denver, Denver, CO, United States
| | | | - Arlen Meyers
- University of Colorado Denver, Denver, CO, United States
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Liu X, Reigle J, Prasath VBS, Dhaliwal J. Artificial intelligence image-based prediction models in IBD exhibit high risk of bias: A systematic review. Comput Biol Med 2024; 171:108093. [PMID: 38354499 DOI: 10.1016/j.compbiomed.2024.108093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 01/04/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024]
Abstract
BACKGROUND There has been an increase in the development of both machine learning (ML) and deep learning (DL) prediction models in Inflammatory Bowel Disease. We aim in this systematic review to assess the methodological quality and risk of bias of ML and DL IBD image-based prediction studies. METHODS We searched three databases, PubMed, Scopus and Embase, to identify ML and DL diagnostic or prognostic predictive models using imaging data in IBD, to Dec 31, 2022. We restricted our search to include studies that primarily used conventional imaging data, were undertaken in human participants, and published in English. Two reviewers independently reviewed the abstracts. The methodological quality of the studies was determined, and risk of bias evaluated using the prediction risk of bias assessment tool (PROBAST). RESULTS Forty studies were included, thirty-nine developed diagnostic models. Seven studies utilized ML approaches, six were retrospective and none used multicenter data for model development. Thirty-three studies utilized DL approaches, ten were prospective, and twelve multicenter studies. Overall, all studies demonstrated high risk of bias. ML studies were evaluated in 4 domains all rated as high risk of bias: participants (6/7), predictors (1/7), outcome (3/7), and analysis (7/7), and DL studies evaluated in 3 domains: participants (24/33), outcome (10/33), and analysis (18/33). The majority of image-based studies used colonoscopy images. CONCLUSION The risk of bias was high in AI IBD image-based prediction models, owing to insufficient sample size, unreported missingness and lack of an external validation cohort. Models with a high risk of bias are unlikely to be generalizable and suitable for clinical implementation.
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Affiliation(s)
- Xiaoxuan Liu
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
| | - James Reigle
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA
| | - V B Surya Prasath
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA
| | - Jasbir Dhaliwal
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA.
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Santacroce G, Zammarchi I, Tan CK, Coppola G, Varley R, Ghosh S, Iacucci M. Present and future of endoscopy precision for inflammatory bowel disease. Dig Endosc 2024; 36:292-304. [PMID: 37643635 DOI: 10.1111/den.14672] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 08/28/2023] [Indexed: 08/31/2023]
Abstract
Several advanced imaging techniques are now available for endoscopists managing inflammatory bowel disease (IBD) patients. These tools, including dye-based and virtual chromoendoscopy, probe-based confocal laser endomicroscopy and endocytoscopy, are increasingly innovative applications in clinical practice. They allow for a more in-depth and refined evaluation of the mucosal and vascular bowel surface, getting closer to histology. They have demonstrated a remarkable ability in assessing intestinal inflammation, histologic remission, and predicting relapse and favorable long-term outcomes. In addition, the future application of molecular endoscopy to predict biological drug responses has yielded preliminary but encouraging results. Furthermore, these techniques are crucial in detecting and characterizing IBD-related dysplasia, assisting endoscopic mucosal resection and submucosal dissection towards a surgery-sparing approach. Artificial intelligence (AI) holds great potential in this promising landscape, as it can provide an objective and reproducible assessment of inflammation and dysplasia. Moreover, it can improve the prediction of outcomes and aid in subsequent therapeutic decision-making. This review aims to summarize the promising role of state-of-the-art advanced endoscopic techniques and related AI-enabled models for managing IBD, paving the way for precision medicine.
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Affiliation(s)
- Giovanni Santacroce
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Irene Zammarchi
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Chin Kimg Tan
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
- Gastroenterology and Hepatology, Changi General Hospital, Singapore City, Singapore
| | - Gaetano Coppola
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
- Internal Medicine and Gastroenterology - Hepatology Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Rachel Varley
- Department of Gastroenterology, Mercy University Hospital, Cork, Ireland
| | - Subrata Ghosh
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
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Pal P, Pooja K, Nabi Z, Gupta R, Tandan M, Rao GV, Reddy N. Artificial intelligence in endoscopy related to inflammatory bowel disease: A systematic review. Indian J Gastroenterol 2024; 43:172-187. [PMID: 38418774 DOI: 10.1007/s12664-024-01531-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/08/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND AND OBJECTIVES In spite of rapid growth of artificial intelligence (AI) in digestive endoscopy in lesion detection and characterization, the role of AI in inflammatory bowel disease (IBD) endoscopy is not clearly defined. We aimed at systematically reviewing the role of AI in IBD endoscopy and identifying future research areas. METHODS We searched the PubMed and Embase database using keywords ("artificial intelligence" OR "machine learning" OR "computer-aided" OR "convolutional neural network") AND ("inflammatory bowel disease" OR "ulcerative colitis" OR "Crohn's") AND ("endoscopy" or "colonoscopy" or "capsule endoscopy" or "device assisted enteroscopy") between 1975 and September 2023 and identified 62 original articles for detailed review. Review articles, consensus guidelines, case reports/series, editorials, letter to the editor, non-peer-reviewed pre-prints and conference abstracts were excluded. The quality of the included studies was assessed using the MI-CLAIM checklist. RESULTS The accuracy of AI models (25 studies) to assess ulcerative colitis (UC) endoscopic activity ranged between 86.54% and 94.5%. AI-assisted capsule endoscopy reading (12 studies) substantially reduced analyzable images and reading time with excellent accuracy (90.5% to 99.9%). AI-assisted analysis of colonoscopic images can help differentiate IBD from non-IBD, UC from non-UC and UC from Crohn's disease (CD) (three studies) with 72.1%, 98.3% and > 90% accuracy, respectively. AI models based on non-invasive clinical and radiologic parameters could predict endoscopic activity (three studies). AI-assisted virtual chromoendoscopy (four studies) could predict histologic remission and long-term outcomes. Computer-assisted detection (CADe) of dysplasia (two studies) is feasible along with AI-based differentiation of high from low-grade IBD neoplasia (79% accuracy). AI is effective in linking electronic medical record data (two studies) with colonoscopic videos to facilitate widespread machine learning. CONCLUSION AI-assisted IBD endoscopy has the potential to impact clinical management by automated detection and characterization of endoscopic lesions. Large, multi-center, prospective studies and commercially available IBD-specific endoscopic AI algorithms are warranted.
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Affiliation(s)
- Partha Pal
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India.
| | - Kanapuram Pooja
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
| | - Zaheer Nabi
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
| | - Rajesh Gupta
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
| | - Manu Tandan
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
| | - Guduru Venkat Rao
- Surgical Gastroenterology, Asian Institute of Gastroenterology, Hyderabad 500 082, India
| | - Nageshwar Reddy
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
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Danieli MG, Brunetto S, Gammeri L, Palmeri D, Claudi I, Shoenfeld Y, Gangemi S. Machine learning application in autoimmune diseases: State of art and future prospectives. Autoimmun Rev 2024; 23:103496. [PMID: 38081493 DOI: 10.1016/j.autrev.2023.103496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 11/29/2023] [Indexed: 04/30/2024]
Abstract
Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.
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Affiliation(s)
- Maria Giovanna Danieli
- SOS Immunologia delle Malattie Rare e dei Trapianti. AOU delle Marche & Dipartimento di Scienze Cliniche e Molecolari, Università Politecnica delle Marche, via Tronto 10/A, 60126 Torrette di Ancona, Italy; Postgraduate School of Allergy and Clinical Immunology, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy.
| | - Silvia Brunetto
- Operative Unit of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
| | - Luca Gammeri
- Operative Unit of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
| | - Davide Palmeri
- Postgraduate School of Allergy and Clinical Immunology, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy
| | - Ilaria Claudi
- Postgraduate School of Allergy and Clinical Immunology, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy
| | - Yehuda Shoenfeld
- Zabludowicz Center for Autoimmune Diseases, Sheba Medical Center, and Reichman University Herzliya, Israel.
| | - Sebastiano Gangemi
- Operative Unit of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy.
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Omori T, Yamamoto T, Murasugi S, Koroku M, Yonezawa M, Nonaka K, Nagashima Y, Nakamura S, Tokushige K. Comparison of Endoscopic and Artificial Intelligence Diagnoses for Predicting the Histological Healing of Ulcerative Colitis in a Real-World Clinical Setting. CROHN'S & COLITIS 360 2024; 6:otae005. [PMID: 38419859 PMCID: PMC10901431 DOI: 10.1093/crocol/otae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Indexed: 03/02/2024] Open
Abstract
Background Artificial intelligence (AI)-assisted colonoscopy systems with contact microscopy capabilities have been reported previously; however, no studies regarding the clinical use of a commercially available system in patients with ulcerative colitis (UC) have been reported. In this study, the diagnostic performance of an AI-assisted ultra-magnifying colonoscopy system for histological healing was compared with that of conventional light non-magnifying endoscopic evaluation in patients with UC. Methods The data of 52 patients with UC were retrospectively analyzed. The Mayo endoscopic score (MES) was determined by 3 endoscopists. Using the AI system, healing of the same spot assessed via MES was defined as a predicted Geboes score (GS) < 3.1. The GS was then determined using pathology specimens from the same site. Results A total of 191 sites were evaluated, including 159 with a GS < 3.1. The MES diagnosis identified 130 sites as MES0. A total of 120 sites were determined to have healed based on AI. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of MES0 for the diagnosis of GS < 3.1 were 79.2%, 90.6%, 97.7%, 46.8%, and 81.2%, respectively. The AI system performed similarly to MES for the diagnosis of GS < 3.1: sensitivity, 74.2%; specificity: 93.8%; PPV: 98.3%; NPV: 42.3%; and accuracy: 77.5%. The AI system also significantly identified a GS of < 3.1 in the setting of MES1 (P = .0169). Conclusions The histological diagnostic yield the MES- and AI-assisted diagnoses was comparable. Healing decisions using AI may avoid the need for histological examinations.
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Affiliation(s)
- Teppei Omori
- Institute of Gastroenterology, Tokyo Women's Medical University, Tokyo, Japan
| | - Tomoko Yamamoto
- Department of Surgical Pathology, Tokyo Women's Medical University, Tokyo, Japan
| | - Shun Murasugi
- Institute of Gastroenterology, Tokyo Women's Medical University, Tokyo, Japan
| | - Miki Koroku
- Institute of Gastroenterology, Tokyo Women's Medical University, Tokyo, Japan
| | - Maria Yonezawa
- Institute of Gastroenterology, Tokyo Women's Medical University, Tokyo, Japan
| | - Kouichi Nonaka
- Department of Digestive Endoscopy, Tokyo Women's Medical University Hospital, Tokyo, Japan
| | - Yoji Nagashima
- Department of Surgical Pathology, Tokyo Women's Medical University, Tokyo, Japan
| | - Shinichi Nakamura
- Institute of Gastroenterology, Tokyo Women's Medical University, Tokyo, Japan
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Ahn JC, Shah VH. Artificial intelligence in gastroenterology and hepatology. ARTIFICIAL INTELLIGENCE IN CLINICAL PRACTICE 2024:443-464. [DOI: 10.1016/b978-0-443-15688-5.00016-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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