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Gigola F, Amato T, Del Riccio M, Raffaele A, Morabito A, Coletta R. Artificial intelligence in clinical practice: a cross-sectional survey of paediatric surgery residents' perspectives. BMJ Health Care Inform 2025; 32:e101456. [PMID: 40398897 PMCID: PMC12097045 DOI: 10.1136/bmjhci-2025-101456] [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] [Subscribe] [Scholar Register] [Received: 02/03/2025] [Accepted: 05/07/2025] [Indexed: 05/23/2025] Open
Abstract
OBJECTIVES The aim of this study was to compare the performances of residents and ChatGPT in answering validated questions and assess paediatric surgery residents' acceptance, perceptions and readiness to integrate artificial intelligence (AI) into clinical practice. METHODS We conducted a cross-sectional study using randomly selected questions and clinical cases on paediatric surgery topics. We examined residents' acceptance of AI before and after comparing their results to ChatGPT's results using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. Data analysis was performed using Jamovi V.2.4.12.0. RESULTS 30 residents participated. ChatGPT-4.0's median score was 13.75, while ChatGPT-3.5's was 8.75. The median score among residents was 8.13. Differences appeared statistically significant. ChatGPT outperformed residents specifically in definition questions (ChatGPT-4.0 vs residents, p<0.0001; ChatGPT-3.5 vs residents, p=0.03). In the UTAUT2 Questionnaire, respondents expressed a more positive evaluation of ChatGPT with higher mean values for each construct and lower fear of technology after learning about test scores. DISCUSSION ChatGPT performed better than residents in knowledge-based questions and simple clinical cases. The accuracy of ChatGPT declined when confronted with more complex questions. The UTAUT questionnaire results showed that learning about the potential of ChatGPT could lead to a shift in perception, resulting in a more positive attitude towards AI. CONCLUSION Our study reveals residents' positive receptivity towards AI, especially after being confronted with its efficacy. These results highlight the importance of integrating AI-related topics into medical curricula and residency to help future physicians and surgeons better understand the advantages and limitations of AI.
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Affiliation(s)
- Francesca Gigola
- School of Pediatric Surgery, University of Florence, Florence, Italy
| | - Tommaso Amato
- School of Pediatric Surgery, University of Florence, Florence, Italy
| | - Marco Del Riccio
- Department of Health Sciences, University of Florence, Florence, Italy
| | - Alessandro Raffaele
- Department of Maternal and Child Health, IRCCS Policlinico San Matteo Pavia, Pavia, Italy
- Department of Clinical-Surgical, Diagnostic and Pediatric Science, University of Pavia, Pavia, Italy
| | - Antonino Morabito
- Department of Neurosciences Psychology, University of Florence, Florence, Italy
- Department of Paediatric Surgery, Meyer Children's Hospital IRCCS, Florence, Italy
| | - Riccardo Coletta
- Department of Neurosciences Psychology, University of Florence, Florence, Italy
- Department of Paediatric Surgery, Meyer Children's Hospital IRCCS, Florence, Italy
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Dhali A, Kipkorir V, Maity R, Srichawla B, Biswas J, Rathna R, Bharadwaj H, Ongidi I, Chaudhry T, Morara G, Waithaka M, Rugut C, Lemashon M, Cheruiyot I, Ojuka D, Ray S, Dhali G. Artificial Intelligence-Assisted Capsule Endoscopy Versus Conventional Capsule Endoscopy for Detection of Small Bowel Lesions: A Systematic Review and Meta-Analysis. J Gastroenterol Hepatol 2025; 40:1105-1118. [PMID: 40083189 PMCID: PMC12062924 DOI: 10.1111/jgh.16931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Revised: 01/04/2025] [Accepted: 03/01/2025] [Indexed: 03/16/2025]
Abstract
BACKGROUND Capsule endoscopy (CE) is a valuable tool used in the diagnosis of small intestinal lesions. The study aims to systematically review the literature and provide a meta-analysis of the diagnostic accuracy, specificity, sensitivity, and negative and positive predictive values of AI-assisted CE in the diagnosis of small bowel lesions in comparison to CE. METHODS Literature searches were performed through PubMed, SCOPUS, and EMBASE to identify studies eligible for inclusion. All publications up to 24 November 2024 were included. Original articles (including observational studies and randomized control trials), systematic reviews, meta-analyses, and case series reporting outcomes on AI-assisted CE in the diagnosis of small bowel lesions were included. The extracted data were pooled, and a meta-analysis was performed for the appropriate variables, considering the clinical and methodological heterogeneity among the included studies. Comprehensive Meta-Analysis v4.0 (Biostat Inc.) was used for the analysis of the data. RESULTS A total of 14 studies were included in the present study. The mean age of participants across the studies was 54.3 years (SD 17.7), with 55.4% men and 44.6% women. The pooled accuracy for conventional CE was 0.966 (95% CI: 0.925-0.988), whereas for AI-assisted CE, it was 0.9185 (95% CI: 0.9138-0.9233). Conventional CE exhibited a pooled sensitivity of 0.860 (95% CI: 0.786-0.934) compared with AI-assisted CE at 0.9239 (95% CI: 0.8648-0.9870). The positive predictive value for conventional CE was 0.982 (95% CI: 0.976-0.987), whereas AI-assisted CE had a PPV of 0.8928 (95% CI: 0.7554-0.999). The pooled specificity for conventional CE was 0.998 (95% CI: 0.996-0.999) compared with 0.5367 (95% CI: 0.5244-0.5492) for AI-assisted CE. Negative predictive values were higher in AI-assisted CE at 0.9425 (95% CI: 0.9389-0.9462) versus 0.760 (95% CI: 0.577-0.943) for conventional CE. CONCLUSION AI-assisted CE displays superior diagnostic accuracy, sensitivity, and positive predictive values albeit the lower pooled specificity in comparison with conventional CE. Its use would ensure accurate detection of small bowel lesions and further enhance their management.
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Affiliation(s)
- Arkadeep Dhali
- Academic Unit of GastroenterologySheffield Teaching Hospitals NHS Foundation TrustSheffieldUK
- School of Medicine and Population HealthUniversity of SheffieldSheffieldUK
| | | | - Rick Maity
- Institute of Post Graduate Medical Education and ResearchKolkataIndia
| | | | | | | | | | - Ibsen Ongidi
- Faculty of Health SciencesUniversity of NairobiNairobiKenya
| | - Talha Chaudhry
- Faculty of Health SciencesUniversity of NairobiNairobiKenya
| | - Gisore Morara
- Faculty of Health SciencesUniversity of NairobiNairobiKenya
| | | | - Clinton Rugut
- Faculty of Health SciencesUniversity of NairobiNairobiKenya
| | | | | | - Daniel Ojuka
- Faculty of Health SciencesUniversity of NairobiNairobiKenya
| | - Sukanta Ray
- Institute of Post Graduate Medical Education and ResearchKolkataIndia
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Al-Bayati K, Stone JK, Berzin TM. The Use of Artificial Intelligence for Endoscopic Evaluation of the Small Bowel. Gastrointest Endosc Clin N Am 2025; 35:355-366. [PMID: 40021233 DOI: 10.1016/j.giec.2024.12.001] [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
There remains great potential for widespread implementation of artificial intelligence (AI) in managing small bowel disorders. Studies have shown excellent accuracy in diagnosing various diseases and lesions throughout the small bowel, with most showing equivalency or superiority to expert endoscopists or capsule readers. AI has significant potential to improve efficiency in small bowel investigation and, depending on the relative costs of these new technologies, could reduce health care expenditure. AI algorithms for capsule endoscopy will offer many clinical and efficiency benefits but we also must remain alert to new risks, particularly with regard to automation bias and unique cybersecurity considerations.
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Affiliation(s)
- Karam Al-Bayati
- Department of Internal Medicine, PGY-1 in Internal Medicine, Max Rady College of Medicine, University of Manitoba, John Buhler Research Center, 805C-715 McDermot Avenue, Winnipeg, MB R3E0V9, Canada
| | - James K Stone
- Department of Internal Medicine, Section of Gastroenterology, Max Rady College of Medicine, University of Manitoba, John Buhler Research Center, 805C-715 McDermot Avenue, Winnipeg, MB R3E0V9, Canada
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA.
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Hirata I, Tsuboi A, Matsubara Y, Sumioka A, Takasago T, Tanaka H, Yamashita K, Takigawa H, Urabe Y, Oka S. Clinical usefulness and acceptability of small-bowel capsule endoscopy with panoramic imaging compared with axial imaging in Japanese patients. DEN OPEN 2025; 5:e389. [PMID: 38845631 PMCID: PMC11154819 DOI: 10.1002/deo2.389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 04/25/2024] [Accepted: 05/05/2024] [Indexed: 06/09/2024]
Abstract
Objectives We aimed to evaluate the usefulness and acceptability of CapsoCam Plus (CapsoCam) in Japanese patients. Methods This retrospective single-center study enrolled 930 patients with suspected small-bowel bleeding (SSBB) who underwent capsule endoscopy. Thirty-three patients using CapsoCam and PillCam SB3 (SB3) were matched using propensity score matching. The diagnostic yield and the acceptability of CapsoCam were evaluated. Results There was no SSBB case where capsule endoscopy was performed within 48 h of bleeding. CapsoCam had a significantly higher observation rate of the entire small bowel (97% vs. 73%, p = 0.006) and Vater's papilla (82% vs. 15%, p < 0.001) than SB3. The reading time of CapsoCam was significantly longer than that of SB3 (30 vs. 25 min, p < 0.001), and CapsoCam's time from the capsule endoscopy swallowing to read completion was longer than that of SB3 (37 vs. 12 h, p < 0.001). The two groups showed no difference in the capsule endoscopy findings according to the P classification. Notably, 85% of the patients using CapsoCam reported examination distress as "not at all" or "almost not," and 94% reported swallowing difficulty as "very easy" or "easy." Conclusions CapsoCam took time to read; however, it is a well-tolerated examination with a high observation rate of Vater's papilla and entire small-bowel mucosa. Detectability of bleeding sources was comparable in both modalities for cases of occult SSBB and overt SSBB more than 48 h after bleeding. CapsoCam is a useful modality for patients with SSBB.
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Affiliation(s)
- Issei Hirata
- Department of GastroenterologyGraduate School of Biomedical and Health SciencesHiroshima UniversityHiroshimaJapan
| | - Akiyoshi Tsuboi
- Department of GastroenterologyGraduate School of Biomedical and Health SciencesHiroshima UniversityHiroshimaJapan
| | - Yuka Matsubara
- Department of GastroenterologyGraduate School of Biomedical and Health SciencesHiroshima UniversityHiroshimaJapan
| | - Akihiko Sumioka
- Department of GastroenterologyGraduate School of Biomedical and Health SciencesHiroshima UniversityHiroshimaJapan
| | - Takeshi Takasago
- Department of GastroenterologyGraduate School of Biomedical and Health SciencesHiroshima UniversityHiroshimaJapan
| | - Hidenori Tanaka
- Department of GastroenterologyGraduate School of Biomedical and Health SciencesHiroshima UniversityHiroshimaJapan
| | - Ken Yamashita
- Department of GastroenterologyGraduate School of Biomedical and Health SciencesHiroshima UniversityHiroshimaJapan
| | - Hidehiko Takigawa
- Department of GastroenterologyGraduate School of Biomedical and Health SciencesHiroshima UniversityHiroshimaJapan
| | - Yuji Urabe
- Department of GastroenterologyGraduate School of Biomedical and Health SciencesHiroshima UniversityHiroshimaJapan
| | - Shiro Oka
- Department of GastroenterologyGraduate School of Biomedical and Health SciencesHiroshima UniversityHiroshimaJapan
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Kang AJ, Rodrigues T, Patel RV, Keswani RN. Impact of Artificial Intelligence on Gastroenterology Trainee Education. Gastrointest Endosc Clin N Am 2025; 35:457-467. [PMID: 40021241 DOI: 10.1016/j.giec.2024.12.008] [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: 03/03/2025]
Abstract
Artificial intelligence (AI) is transforming gastroenterology, particularly in endoscopy, which has a direct impact on trainees and their education. AI can serve as a valuable resource, providing real-time feedback and aiding in tasks like polyp detection and lesion differentiation, which are challenging for trainees. However, its implementation raises concerns about cognitive overload, overreliance, and even access disparities, which could affect training outcomes. Beyond endoscopy, AI shows promise in clinical management and interpreting diagnostic studies such as motility testing. Thoughtful adoption of AI can optimize training and prepare future trainees for the modern healthcare landscape.
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Affiliation(s)
- Anthony J Kang
- Division of Gastroenterology & Hepatology, Northwestern Feinberg School of Medicine, Chicago, IL, USA
| | - Terrance Rodrigues
- Division of Gastroenterology & Hepatology, Northwestern Feinberg School of Medicine, Chicago, IL, USA
| | - Ronak V Patel
- Division of Gastroenterology & Hepatology, Northwestern Feinberg School of Medicine, Chicago, IL, USA
| | - Rajesh N Keswani
- Division of Gastroenterology & Hepatology, Northwestern Feinberg School of Medicine, Chicago, IL, USA.
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Sathiaseelan M, Grammatikopoulos T. Utility of endoscopy in paediatric gastroenterology and hepatology-Review and updates. Dig Liver Dis 2025:S1590-8658(25)00211-7. [PMID: 40024816 DOI: 10.1016/j.dld.2025.01.199] [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: 05/05/2024] [Revised: 01/16/2025] [Accepted: 01/27/2025] [Indexed: 03/04/2025]
Abstract
Paediatric endoscopy has been an integral part of the diagnostic evaluation and management of gastroenterology and hepatology diseases in children. This area of clinical medicine has made meteoric advancements since it was first introduced conserving it's traditional roles of gastroscopy and colonoscopy but broadening significantly it's clinical utility and diagnostic accuracy with new and emerging technology. This article aims to explore and review the current utility and emerging applications of diagnostic and therapeutic endoscopy for the practicing paediatric gastroenterologist and hepatologist.
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Affiliation(s)
- Mohana Sathiaseelan
- Paediatric Liver, GI & Nutrition Centre and MowatLabs, King's College Hospital, London, United Kingdom.
| | - Tassos Grammatikopoulos
- Paediatric Liver, GI & Nutrition Centre and MowatLabs, King's College Hospital, London, United Kingdom; Institute of Liver Studies, School of Immunology & Microbial Sciences, King's College London, London, United Kingdom
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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: 0] [Impact Index Per Article: 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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Xu X, Liu J, Qiu J, Fan B, He T, Feng S, Sun J, Ge Z. The Application Value of an Artificial Intelligence-Driven Intestinal Image Recognition Model to Evaluate Intestinal Preparation before Colonoscopy. Br J Hosp Med (Lond) 2025; 86:1-11. [PMID: 39862028 DOI: 10.12968/hmed.2024.0577] [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: 01/27/2025]
Abstract
Aims/Background Artificial intelligence (AI), with advantages such as automatic feature extraction and high data processing capacity and being unaffected by fatigue, can accurately analyze images obtained from colonoscopy, assess the quality of bowel preparation, and reduce the subjectivity of the operating physician, which may help to achieve standardization and normalization of colonoscopy. In this study, we aimed to explore the value of using an AI-driven intestinal image recognition model to evaluate intestinal preparation before colonoscopy. Methods In this retrospective analysis, we analyzed the clinical data of 98 patients who underwent colonoscopy in Nantong First People's Hospital from May 2023 to October 2023. Among them, 47 cases were evaluated based on the intestinal preparation map and the last fecal characteristics (Regular group), and 51 cases were evaluated using an AI-driven intestinal image recognition model (AI group). The duration of colonoscopy examination, intestinal cleanliness, incidence of adverse reactions, and satisfaction with intestinal preparation of the two groups were analyzed. Results The time for colonoscopy in the AI group was shorter than that in the Regular group, and the intestinal cleanliness score in the AI group was higher than that in the Regular group (p < 0.05). The incidence of adverse reactions in the AI group (3.92%) was lower than that in the Regular group (10.64%), but the difference was not statistically significant (p > 0.05). The satisfaction rate of intestinal preparation in the AI group (96.08%) was comparable to that of the Regular group (82.98%) (p > 0.05). Conclusion Compared with the assessment based solely on the intestinal preparation map and the last fecal characteristics, the application of AI intestinal image recognition model in intestinal preparation before colonoscopy can shorten the time of colonoscopy and improve intestinal cleanliness, but with comparable patient satisfaction and safety.
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Affiliation(s)
- Xirong Xu
- Digestive Endoscopy Center, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China
| | - Jiahao Liu
- School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Jianwei Qiu
- Department of Gastroenterology, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China
| | - Benfang Fan
- Department of Gastroenterology, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China
| | - Tao He
- Party and Government Office, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China
| | - Shichun Feng
- Department of Gastrointestinal Surgery, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China
| | - Jinjie Sun
- Department of Gastrointestinal Surgery, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China
| | - Zhenming Ge
- Department of Gastroenterology, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China
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Labaki C, Uche-Anya EN, Berzin TM. Artificial Intelligence in Gastrointestinal Endoscopy. Gastroenterol Clin North Am 2024; 53:773-786. [PMID: 39489586 DOI: 10.1016/j.gtc.2024.08.005] [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: 11/05/2024]
Abstract
Recent advancements in artificial intelligence (AI) have significantly impacted the field of gastrointestinal (GI) endoscopy, with applications spanning a wide range of clinical indications. The central goals for AI in GI endoscopy are to improve endoscopic procedural performance and quality assessment, optimize patient outcomes, and reduce administrative burden. Despite early progress, such as Food and Drug Administration approval of the first computer-aided polyp detection system in 2021, there are numerous important challenges to be faced on the path toward broader adoption of AI algorithms in clinical endoscopic practice.
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Affiliation(s)
- Chris Labaki
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 300 Brookline Avenue, Boston, MA, USA
| | - Eugenia N Uche-Anya
- Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, USA
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, USA.
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Lingam G, Shakir T, Kader R, Chand M. Role of artificial intelligence in colorectal cancer. Artif Intell Gastrointest Endosc 2024; 5:90723. [DOI: 10.37126/aige.v5.i2.90723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 04/10/2024] [Accepted: 04/19/2024] [Indexed: 05/11/2024] Open
Abstract
The sphere of artificial intelligence (AI) is ever expanding. Applications for clinical practice have been emerging over recent years. Although its uptake has been most prominent in endoscopy, this represents only one aspect of holistic patient care. There are a multitude of other potential avenues in which gastrointestinal care may be involved. We aim to review the role of AI in colorectal cancer as a whole. We performed broad scoping and focused searches of the applications of AI in the field of colorectal cancer. All trials including qualitative research were included from the year 2000 onwards. Studies were grouped into pre-operative, intra-operative and post-operative aspects. Pre-operatively, the major use is with endoscopic recognition. Colonoscopy has embraced the use for human derived classifications such as Narrow-band Imaging International Colorectal Endoscopic, Japan Narrow-band Imaging Expert Team, Paris and Kudo. However, novel detection and diagnostic methods have arisen from advances in AI classification. Intra-operatively, adjuncts such as image enhanced identification of structures and assessment of perfusion have led to improvements in clinical outcomes. Post-operatively, monitoring and surveillance have taken strides with potential socioeconomic and environmental savings. The uses of AI within the umbrella of colorectal surgery are multiple. We have identified existing technologies which are already augmenting cancer care. The future applications are exciting and could at least match, if not surpass human standards.
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Affiliation(s)
- Gita Lingam
- Department of General Surgery, Princess Alexandra Hospital, Harlow CM20 1QX, United Kingdom
| | - Taner Shakir
- Department of Colorectal Surgery, University College London, London W1W 7TY, United Kingdom
| | - Rawen Kader
- Department of Gastroenterology, University College London, University College London Hospitals Nhs Foundation Trust, London W1B, United Kingdom
| | - Manish Chand
- Gastroenterological Intervention Centre, University College London, London W1W 7TS, United Kingdom
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Takahashi H, Ohno E, Furukawa T, Yamao K, Ishikawa T, Mizutani Y, Iida T, Shiratori Y, Oyama S, Koyama J, Mori K, Hayashi Y, Oda M, Suzuki T, Kawashima H. Artificial intelligence in a prediction model for postendoscopic retrograde cholangiopancreatography pancreatitis. Dig Endosc 2024; 36:463-472. [PMID: 37448120 DOI: 10.1111/den.14622] [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: 04/11/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
OBJECTIVES In this study we aimed to develop an artificial intelligence-based model for predicting postendoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP). METHODS We retrospectively reviewed ERCP patients at Nagoya University Hospital (NUH) and Toyota Memorial Hospital (TMH). We constructed two prediction models, a random forest (RF), one of the machine-learning algorithms, and a logistic regression (LR) model. First, we selected features of each model from 40 possible features. Then the models were trained and validated using three fold cross-validation in the NUH cohort and tested in the TMH cohort. The area under the receiver operating characteristic curve (AUROC) was used to assess model performance. Finally, using the output parameters of the RF model, we classified the patients into low-, medium-, and high-risk groups. RESULTS A total of 615 patients at NUH and 544 patients at TMH were enrolled. Ten features were selected for the RF model, including albumin, creatinine, biliary tract cancer, pancreatic cancer, bile duct stone, total procedure time, pancreatic duct injection, pancreatic guidewire-assisted technique without a pancreatic stent, intraductal ultrasonography, and bile duct biopsy. In the three fold cross-validation, the RF model showed better predictive ability than the LR model (AUROC 0.821 vs. 0.660). In the test, the RF model also showed better performance (AUROC 0.770 vs. 0.663, P = 0.002). Based on the RF model, we classified the patients according to the incidence of PEP (2.9%, 10.0%, and 23.9%). CONCLUSION We developed an RF model. Machine-learning algorithms could be powerful tools to develop accurate prediction models.
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Affiliation(s)
- Hidekazu Takahashi
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Eizaburo Ohno
- Department of Gastroenterology and Hepatology, Fujita Health University Graduate School of Medicine, Aichi, Japan
| | - Taiki Furukawa
- Department of Medical IT, Nagoya University Hospital, Aichi, Japan
| | - Kentaro Yamao
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Takuya Ishikawa
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Yasuyuki Mizutani
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Tadashi Iida
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | | | - Shintaro Oyama
- Department of Medical IT, Nagoya University Hospital, Aichi, Japan
| | - Junji Koyama
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Kensaku Mori
- Department of Intelligent Systems, Nagoya University Graduate School of Informatics, Aichi, Japan
| | - Yuichiro Hayashi
- Department of Intelligent Systems, Nagoya University Graduate School of Informatics, Aichi, Japan
| | - Masahiro Oda
- Information Strategy Office, Information and Communications, Nagoya University, Aichi, Japan
| | - Takahisa Suzuki
- Department of Gastroenterology, Toyota Memorial Hospital, Aichi, Japan
| | - Hiroki Kawashima
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
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Yokote A, Umeno J, Kawasaki K, Fujioka S, Fuyuno Y, Matsuno Y, Yoshida Y, Imazu N, Miyazono S, Moriyama T, Kitazono T, Torisu T. Small bowel capsule endoscopy examination and open access database with artificial intelligence: The SEE-artificial intelligence project. DEN OPEN 2024; 4:e258. [PMID: 37359150 PMCID: PMC10288072 DOI: 10.1002/deo2.258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/28/2023]
Abstract
OBJECTIVES Artificial intelligence (AI) may be practical for image classification of small bowel capsule endoscopy (CE). However, creating a functional AI model is challenging. We attempted to create a dataset and an object detection CE AI model to explore modeling problems to assist in reading small bowel CE. METHODS We extracted 18,481 images from 523 small bowel CE procedures performed at Kyushu University Hospital from September 2014 to June 2021. We annotated 12,320 images with 23,033 disease lesions, combined them with 6161 normal images as the dataset, and examined the characteristics. Based on the dataset, we created an object detection AI model using YOLO v5 and we tested validation. RESULTS We annotated the dataset with 12 types of annotations, and multiple annotation types were observed in the same image. We test validated our AI model with 1396 images, and sensitivity for all 12 types of annotations was about 91%, with 1375 true positives, 659 false positives, and 120 false negatives detected. The highest sensitivity for individual annotations was 97%, and the highest area under the receiver operating characteristic curve was 0.98, but the quality of detection varied depending on the specific annotation. CONCLUSIONS Object detection AI model in small bowel CE using YOLO v5 may provide effective and easy-to-understand reading assistance. In this SEE-AI project, we open our dataset, the weights of the AI model, and a demonstration to experience our AI. We look forward to further improving the AI model in the future.
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Affiliation(s)
- Akihito Yokote
- Department of Medicine and Clinical Science Graduate School of Medical Science Kyushu University Fukuoka Japan
| | - Junji Umeno
- Department of Medicine and Clinical Science Graduate School of Medical Science Kyushu University Fukuoka Japan
| | - Keisuke Kawasaki
- Department of Medicine and Clinical Science Graduate School of Medical Science Kyushu University Fukuoka Japan
| | - Shin Fujioka
- Department of Endoscopic Diagnostics and Therapeutics Kyushu University Hospital Fukuoka Japan
| | - Yuta Fuyuno
- Department of Medicine and Clinical Science Graduate School of Medical Science Kyushu University Fukuoka Japan
| | - Yuichi Matsuno
- Department of Medicine and Clinical Science Graduate School of Medical Science Kyushu University Fukuoka Japan
| | - Yuichiro Yoshida
- Department of Medicine and Clinical Science Graduate School of Medical Science Kyushu University Fukuoka Japan
| | - Noriyuki Imazu
- Department of Medicine and Clinical Science Graduate School of Medical Science Kyushu University Fukuoka Japan
| | - Satoshi Miyazono
- Department of Medicine and Clinical Science Graduate School of Medical Science Kyushu University Fukuoka Japan
| | - Tomohiko Moriyama
- International Medical Department Kyushu University Hospital Fukuoka Japan
| | - Takanari Kitazono
- Department of Medicine and Clinical Science Graduate School of Medical Science Kyushu University Fukuoka Japan
| | - Takehiro Torisu
- Department of Medicine and Clinical Science Graduate School of Medical Science Kyushu University Fukuoka Japan
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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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14
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Tettey F, Parupelli SK, Desai S. A Review of Biomedical Devices: Classification, Regulatory Guidelines, Human Factors, Software as a Medical Device, and Cybersecurity. BIOMEDICAL MATERIALS & DEVICES 2024; 2:316-341. [DOI: 10.1007/s44174-023-00113-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 06/29/2023] [Indexed: 01/05/2025]
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15
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Mota J, Almeida MJ, Mendes F, Martins M, Ribeiro T, Afonso J, Cardoso P, Cardoso H, Andrade P, Ferreira J, Mascarenhas M, Macedo G. From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy? Diagnostics (Basel) 2024; 14:291. [PMID: 38337807 PMCID: PMC10855436 DOI: 10.3390/diagnostics14030291] [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: 11/13/2023] [Revised: 01/09/2024] [Accepted: 01/16/2024] [Indexed: 02/12/2024] Open
Abstract
The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.
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Affiliation(s)
- Joana Mota
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Maria João Almeida
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Helder Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Patrícia Andrade
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal;
- Digestive Artificial Intelligence Development, R. Alfredo Allen 455-461, 4200-135 Porto, Portugal
| | - Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- ManopH Gastroenterology Clinic, R. de Sá da Bandeira 752, 4000-432 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
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Aoki T, Yamada A, Oka S, Tsuboi M, Kurokawa K, Togo D, Tanino F, Teshima H, Saito H, Suzuki R, Arai J, Abe S, Kondo R, Yamashita A, Tsuboi A, Nakada A, Niikura R, Tsuji Y, Hayakawa Y, Matsuda T, Nakahori M, Tanaka S, Kato Y, Tada T, Fujishiro M. Comparison of clinical utility of deep learning-based systems for small-bowel capsule endoscopy reading. J Gastroenterol Hepatol 2024; 39:157-164. [PMID: 37830487 DOI: 10.1111/jgh.16369] [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: 03/16/2023] [Revised: 09/01/2023] [Accepted: 09/17/2023] [Indexed: 10/14/2023]
Abstract
BACKGROUND AND AIM Convolutional neural network (CNN) systems that automatically detect abnormalities from small-bowel capsule endoscopy (SBCE) images are still experimental, and no studies have directly compared the clinical usefulness of different systems. We compared endoscopist readings using an existing and a novel CNN system in a real-world SBCE setting. METHODS Thirty-six complete SBCE videos, including 43 abnormal lesions (18 mucosal breaks, 8 angioectasia, and 17 protruding lesions), were retrospectively prepared. Three reading processes were compared: (A) endoscopist readings without CNN screening, (B) endoscopist readings after an existing CNN screening, and (C) endoscopist readings after a novel CNN screening. RESULTS The mean number of small-bowel images was 14 747 per patient. Among these images, existing and novel CNN systems automatically captured 24.3% and 9.4% of the images, respectively. In this process, both systems extracted all 43 abnormal lesions. Next, we focused on the clinical usefulness. The detection rates of abnormalities by trainee endoscopists were not significantly different across the three processes: A, 77%; B, 67%; and C, 79%. The mean reading time of the trainees was the shortest during process C (10.1 min per patient), followed by processes B (23.1 min per patient) and A (33.6 min per patient). The mean psychological stress score while reading videos (scale, 1-5) was the lowest in process C (1.8) but was not significantly different between processes B (2.8) and A (3.2). CONCLUSIONS Our novel CNN system significantly reduced endoscopist reading time and psychological stress while maintaining the detectability of abnormalities. CNN performance directly affects clinical utility and should be carefully assessed.
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Affiliation(s)
- Tomonori Aoki
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Division of Next-Generation Endoscopic Computer Vision, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Atsuo Yamada
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Shiro Oka
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Mayo Tsuboi
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Ken Kurokawa
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Daichi Togo
- Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan
| | - Fumiaki Tanino
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Hajime Teshima
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Hiroaki Saito
- Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan
| | - Ryuta Suzuki
- Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan
| | - Junya Arai
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Sohei Abe
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Ryo Kondo
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Aya Yamashita
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Akiyoshi Tsuboi
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Ayako Nakada
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Ryota Niikura
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Department of Gastroenterological Endoscopy, Tokyo Medical University, Tokyo, Japan
| | - Yosuke Tsuji
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Division of Next-Generation Endoscopic Computer Vision, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Yoku Hayakawa
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Tomoki Matsuda
- Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan
| | - Masato Nakahori
- Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan
| | - Shinji Tanaka
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | | | - Tomohiro Tada
- AI Medical Service Inc, Tokyo, Japan
- Department of Surgical Oncology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
| | - Mitsuhiro Fujishiro
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
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Mascarenhas M, Ribeiro T, Afonso J, Mendes F, Cardoso P, Martins M, Ferreira J, Macedo G. Smart Endoscopy Is Greener Endoscopy: Leveraging Artificial Intelligence and Blockchain Technologies to Drive Sustainability in Digestive Health Care. Diagnostics (Basel) 2023; 13:3625. [PMID: 38132209 PMCID: PMC10743290 DOI: 10.3390/diagnostics13243625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/14/2023] [Accepted: 11/25/2023] [Indexed: 12/23/2023] Open
Abstract
The surge in the implementation of artificial intelligence (AI) in recent years has permeated many aspects of our life, and health care is no exception. Whereas this technology can offer clear benefits, some of the problems associated with its use have also been recognised and brought into question, for example, its environmental impact. In a similar fashion, health care also has a significant environmental impact, and it requires a considerable source of greenhouse gases. Whereas efforts are being made to reduce the footprint of AI tools, here, we were specifically interested in how employing AI tools in gastroenterology departments, and in particular in conjunction with capsule endoscopy, can reduce the carbon footprint associated with digestive health care while offering improvements, particularly in terms of diagnostic accuracy. We address the different ways that leveraging AI applications can reduce the carbon footprint associated with all types of capsule endoscopy examinations. Moreover, we contemplate how the incorporation of other technologies, such as blockchain technology, into digestive health care can help ensure the sustainability of this clinical speciality and by extension, health care in general.
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Affiliation(s)
- Miguel Mascarenhas
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal; (T.R.); (J.A.); (P.C.); (M.M.)
- WGO Training Center, 4200-437 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal; (T.R.); (J.A.); (P.C.); (M.M.)
- WGO Training Center, 4200-437 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal; (T.R.); (J.A.); (P.C.); (M.M.)
- WGO Training Center, 4200-437 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal; (T.R.); (J.A.); (P.C.); (M.M.)
- WGO Training Center, 4200-437 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal; (T.R.); (J.A.); (P.C.); (M.M.)
- WGO Training Center, 4200-437 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal; (T.R.); (J.A.); (P.C.); (M.M.)
- WGO Training Center, 4200-437 Porto, Portugal
| | - João Ferreira
- Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal;
| | - Guilherme Macedo
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal; (T.R.); (J.A.); (P.C.); (M.M.)
- WGO Training Center, 4200-437 Porto, Portugal
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18
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Sumioka A, Tsuboi A, Oka S, Kato Y, Matsubara Y, Hirata I, Takigawa H, Yuge R, Shimamoto F, Tada T, Tanaka S. Disease surveillance evaluation of primary small-bowel follicular lymphoma using capsule endoscopy images based on a deep convolutional neural network (with video). Gastrointest Endosc 2023; 98:968-976.e3. [PMID: 37482106 DOI: 10.1016/j.gie.2023.07.024] [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: 01/31/2023] [Revised: 07/01/2023] [Accepted: 07/09/2023] [Indexed: 07/25/2023]
Abstract
BACKGROUND AND AIMS Capsule endoscopy (CE) is useful in evaluating disease surveillance for primary small-bowel follicular lymphoma (FL), but some cases are difficult to evaluate objectively. This study evaluated the usefulness of a deep convolutional neural network (CNN) system using CE images for disease surveillance of primary small-bowel FL. METHODS We enrolled 26 consecutive patients with primary small-bowel FL diagnosed between January 2011 and January 2021 who underwent CE before and after a watch-and-wait strategy or chemotherapy. Disease surveillance by the CNN system was evaluated by the percentage of FL-detected images among all CE images of the small-bowel mucosa. RESULTS Eighteen cases (69%) were managed with a watch-and-wait approach, and 8 cases (31%) were treated with chemotherapy. Among the 18 cases managed with the watch-and-wait approach, the outcome of lesion evaluation by the CNN system was almost the same in 13 cases (72%), aggravation in 4 (22%), and improvement in 1 (6%). Among the 8 cases treated with chemotherapy, the outcome of lesion evaluation by the CNN system was improvement in 5 cases (63%), almost the same in 2 (25%), and aggravation in 1 (12%). The physician and CNN system reported similar results regarding disease surveillance evaluation in 23 of 26 cases (88%), whereas a discrepancy between the 2 was found in the remaining 3 cases (12%), attributed to poor small-bowel cleansing level. CONCLUSIONS Disease surveillance evaluation of primary small-bowel FL using CE images by the developed CNN system was useful under the condition of excellent small-bowel cleansing level.
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Affiliation(s)
- Akihiko Sumioka
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
| | - Akiyoshi Tsuboi
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Shiro Oka
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
| | | | - Yuka Matsubara
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
| | - Issei Hirata
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
| | - Hidehiko Takigawa
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Ryo Yuge
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Fumio Shimamoto
- Faculty of Health Sciences, Hiroshima Shudo University, Hiroshima, Japan
| | - Tomohiro Tada
- AI Medical Service Inc, Tokyo, Japan; Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
| | - Shinji Tanaka
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
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Singeap AM, Sfarti C, Minea H, Chiriac S, Cuciureanu T, Nastasa R, Stanciu C, Trifan A. Small Bowel Capsule Endoscopy and Enteroscopy: A Shoulder-to-Shoulder Race. J Clin Med 2023; 12:7328. [PMID: 38068379 PMCID: PMC10707315 DOI: 10.3390/jcm12237328] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/17/2023] [Accepted: 11/24/2023] [Indexed: 01/11/2025] Open
Abstract
Traditional methods have their limitations when it comes to unraveling the mysteries of the small bowel, an area historically seen as the "black box" of the gastrointestinal tract. This is where capsule endoscopy and enteroscopy have stepped in, offering a remarkable synergy that transcends the sum of their individual capabilities. From their introduction, small bowel capsule endoscopy and device-assisted enteroscopy have consistently evolved and improved, both on their own and interdependently. Each technique's history may be told as a success story, and their interaction has revolutionized the approach to the small bowel. Both have advantages that could be ideally combined into a perfect technique: safe, non-invasive, and capable of examining the entire small bowel, taking biopsies, and applying therapeutical interventions. Until the realization of this perfect tool becomes a reality, the key for an optimal approach lies in the right selection of exploration method. In this article, we embark on a journey through the intertwined development of capsule endoscopy and enteroscopy, exploring the origins, technological advancements, clinical applications, and evolving inquiries that have continually reshaped the landscape of small bowel imaging.
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Affiliation(s)
- Ana-Maria Singeap
- Department of Gastroenterology, Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (A.-M.S.); (C.S.); (S.C.); (T.C.); (R.N.); (C.S.); (A.T.)
- Institute of Gastroenterology and Hepatology, “St. Spiridon” University Hospital, 700111 Iasi, Romania
| | - Catalin Sfarti
- Department of Gastroenterology, Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (A.-M.S.); (C.S.); (S.C.); (T.C.); (R.N.); (C.S.); (A.T.)
- Institute of Gastroenterology and Hepatology, “St. Spiridon” University Hospital, 700111 Iasi, Romania
| | - Horia Minea
- Department of Gastroenterology, Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (A.-M.S.); (C.S.); (S.C.); (T.C.); (R.N.); (C.S.); (A.T.)
- Institute of Gastroenterology and Hepatology, “St. Spiridon” University Hospital, 700111 Iasi, Romania
| | - Stefan Chiriac
- Department of Gastroenterology, Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (A.-M.S.); (C.S.); (S.C.); (T.C.); (R.N.); (C.S.); (A.T.)
- Institute of Gastroenterology and Hepatology, “St. Spiridon” University Hospital, 700111 Iasi, Romania
| | - Tudor Cuciureanu
- Department of Gastroenterology, Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (A.-M.S.); (C.S.); (S.C.); (T.C.); (R.N.); (C.S.); (A.T.)
- Institute of Gastroenterology and Hepatology, “St. Spiridon” University Hospital, 700111 Iasi, Romania
| | - Robert Nastasa
- Department of Gastroenterology, Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (A.-M.S.); (C.S.); (S.C.); (T.C.); (R.N.); (C.S.); (A.T.)
- Institute of Gastroenterology and Hepatology, “St. Spiridon” University Hospital, 700111 Iasi, Romania
| | - Carol Stanciu
- Department of Gastroenterology, Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (A.-M.S.); (C.S.); (S.C.); (T.C.); (R.N.); (C.S.); (A.T.)
- Institute of Gastroenterology and Hepatology, “St. Spiridon” University Hospital, 700111 Iasi, Romania
| | - Anca Trifan
- Department of Gastroenterology, Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (A.-M.S.); (C.S.); (S.C.); (T.C.); (R.N.); (C.S.); (A.T.)
- Institute of Gastroenterology and Hepatology, “St. Spiridon” University Hospital, 700111 Iasi, Romania
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Kim MJ, Kim SH, Kim SM, Nam JH, Hwang YB, Lim YJ. The Advent of Domain Adaptation into Artificial Intelligence for Gastrointestinal Endoscopy and Medical Imaging. Diagnostics (Basel) 2023; 13:3023. [PMID: 37835766 PMCID: PMC10572560 DOI: 10.3390/diagnostics13193023] [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: 08/03/2023] [Revised: 09/01/2023] [Accepted: 09/12/2023] [Indexed: 10/15/2023] Open
Abstract
Artificial intelligence (AI) is a subfield of computer science that aims to implement computer systems that perform tasks that generally require human learning, reasoning, and perceptual abilities. AI is widely used in the medical field. The interpretation of medical images requires considerable effort, time, and skill. AI-aided interpretations, such as automated abnormal lesion detection and image classification, are promising areas of AI. However, when images with different characteristics are extracted, depending on the manufacturer and imaging environment, a so-called domain shift problem occurs in which the developed AI has a poor versatility. Domain adaptation is used to address this problem. Domain adaptation is a tool that generates a newly converted image which is suitable for other domains. It has also shown promise in reducing the differences in appearance among the images collected from different devices. Domain adaptation is expected to improve the reading accuracy of AI for heterogeneous image distributions in gastrointestinal (GI) endoscopy and medical image analyses. In this paper, we review the history and basic characteristics of domain shift and domain adaptation. We also address their use in gastrointestinal endoscopy and the medical field more generally through published examples, perspectives, and future directions.
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Affiliation(s)
- Min Ji Kim
- Division of Gastroenterology, Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Republic of Korea; (M.J.K.); (S.H.K.); (J.H.N.)
| | - Sang Hoon Kim
- Division of Gastroenterology, Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Republic of Korea; (M.J.K.); (S.H.K.); (J.H.N.)
| | - Suk Min Kim
- Department of Intelligent Systems and Robotics, College of Electrical & Computer Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea; (S.M.K.); (Y.B.H.)
| | - Ji Hyung Nam
- Division of Gastroenterology, Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Republic of Korea; (M.J.K.); (S.H.K.); (J.H.N.)
| | - Young Bae Hwang
- Department of Intelligent Systems and Robotics, College of Electrical & Computer Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea; (S.M.K.); (Y.B.H.)
| | - Yun Jeong Lim
- Division of Gastroenterology, Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Republic of Korea; (M.J.K.); (S.H.K.); (J.H.N.)
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21
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Lei II, Tompkins K, White E, Watson A, Parsons N, Noufaily A, Segui S, Wenzek H, Badreldin R, Conlin A, Arasaradnam RP. Study of capsule endoscopy delivery at scale through enhanced artificial intelligence-enabled analysis (the CESCAIL study). Colorectal Dis 2023. [PMID: 37272471 DOI: 10.1111/codi.16575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 03/05/2023] [Accepted: 03/21/2023] [Indexed: 06/06/2023]
Abstract
AIM Lower gastrointestinal (GI) diagnostics have been facing relentless capacity constraints for many years, even before the COVID-19 era. Restrictions from the COVID pandemic have resulted in a significant backlog in lower GI diagnostics. Given recent developments in deep neural networks (DNNs) and the application of artificial intelligence (AI) in endoscopy, automating capsule video analysis is now within reach. Comparable to the efficiency and accuracy of AI applications in small bowel capsule endoscopy, AI in colon capsule analysis will also improve the efficiency of video reading and address the relentless demand on lower GI services. The aim of the CESCAIL study is to determine the feasibility, accuracy and productivity of AI-enabled analysis tools (AiSPEED) for polyp detection compared with the 'gold standard': a conventional care pathway with clinician analysis. METHOD This multi-centre, diagnostic accuracy study aims to recruit 674 participants retrospectively and prospectively from centres conducting colon capsule endoscopy (CCE) as part of their standard care pathway. After the study participants have undergone CCE, the colon capsule videos will be uploaded onto two different pathways: AI-enabled video analysis and the gold standard conventional clinician analysis pathway. The reports generated from both pathways will be compared for accuracy (sensitivity and specificity). The reading time can only be compared in the prospective cohort. In addition to validating the AI tool, this study will also provide observational data concerning its use to assess the pathway execution in real-world performance. RESULTS The study is currently recruiting participants at multiple centres within the United Kingdom and is at the stage of collecting data. CONCLUSION This standard diagnostic accuracy study carries no additional risk to patients as it does not affect the standard care pathway, and hence patient care remains unaffected.
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Affiliation(s)
- Ian Io Lei
- Department of Gastroenterology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - Katie Tompkins
- Department of Gastroenterology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | | | - Angus Watson
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | | | | | - Santi Segui
- Department of Maths and Computer Science, University of Barcelona, Barcelona, Spain
| | - Hagen Wenzek
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Rawya Badreldin
- Department of Gastroenterology, James Paget University Hospitals NHS Foundation Trust, Lowestoft, UK
| | - Abby Conlin
- Department of Gastroenterology, Northern Care Alliance NHS Foundation Trust, Salford, UK
| | - Ramesh P Arasaradnam
- Department of Gastroenterology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Leicester Cancer Centre, University of Leicester, Leicester, UK
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22
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Ribeiro T, Mascarenhas Saraiva MJ, Afonso J, Cardoso P, Mendes F, Martins M, Andrade AP, Cardoso H, Mascarenhas Saraiva M, Ferreira J, Macedo G. Design of a Convolutional Neural Network as a Deep Learning Tool for the Automatic Classification of Small-Bowel Cleansing in Capsule Endoscopy. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59040810. [PMID: 37109768 PMCID: PMC10145655 DOI: 10.3390/medicina59040810] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/03/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023]
Abstract
Background and objectives: Capsule endoscopy (CE) is a non-invasive method to inspect the small bowel that, like other enteroscopy methods, requires adequate small-bowel cleansing to obtain conclusive results. Artificial intelligence (AI) algorithms have been seen to offer important benefits in the field of medical imaging over recent years, particularly through the adaptation of convolutional neural networks (CNNs) to achieve more efficient image analysis. Here, we aimed to develop a deep learning model that uses a CNN to automatically classify the quality of intestinal preparation in CE. Methods: A CNN was designed based on 12,950 CE images obtained at two clinical centers in Porto (Portugal). The quality of the intestinal preparation was classified for each image as: excellent, ≥90% of the image surface with visible mucosa; satisfactory, 50-90% of the mucosa visible; and unsatisfactory, <50% of the mucosa visible. The total set of images was divided in an 80:20 ratio to establish training and validation datasets, respectively. The CNN prediction was compared with the classification established by consensus of a group of three experts in CE, currently considered the gold standard to evaluate cleanliness. Subsequently, how the CNN performed in diagnostic terms was evaluated using an independent validation dataset. Results: Among the images obtained, 3633 were designated as unsatisfactory preparation, 6005 satisfactory preparation, and 3312 with excellent preparation. When differentiating the classes of small-bowel preparation, the algorithm developed here achieved an overall accuracy of 92.1%, with a sensitivity of 88.4%, a specificity of 93.6%, a positive predictive value of 88.5%, and a negative predictive value of 93.4%. The area under the curve for the detection of excellent, satisfactory, and unsatisfactory classes was 0.98, 0.95, and 0.99, respectively. Conclusions: A CNN-based tool was developed to automatically classify small-bowel preparation for CE, and it was seen to accurately classify intestinal preparation for CE. The development of such a system could enhance the reproducibility of the scales used for such purposes.
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Affiliation(s)
- Tiago Ribeiro
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Miguel José Mascarenhas Saraiva
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - João Afonso
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Pedro Cardoso
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Francisco Mendes
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Miguel Martins
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Ana Patrícia Andrade
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - Hélder Cardoso
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | | | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, 4200-465 Porto, Portugal
| | - Guilherme Macedo
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
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23
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Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple. Diagnostics (Basel) 2023; 13:diagnostics13061038. [PMID: 36980347 PMCID: PMC10047552 DOI: 10.3390/diagnostics13061038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/07/2023] [Accepted: 02/21/2023] [Indexed: 03/12/2023] Open
Abstract
Artificial intelligence (AI) applications have become widely popular across the healthcare ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project following the recent COVID pandemic’s impact. It demonstrated its capability to relieve the national backlog in endoscopy. As a result, AI-assisted colon capsule video analysis has become gastroenterology’s most active research area. However, with rapid AI advances, mastering these complex machine learning concepts remains challenging for healthcare professionals. This forms a barrier for clinicians to take on this new technology and embrace the new era of big data. This paper aims to bridge the knowledge gap between the current CCE system and the future, fully integrated AI system. The primary focus is on simplifying the technical terms and concepts in machine learning. This will hopefully address the general “fear of the unknown in AI” by helping healthcare professionals understand the basic principle of machine learning in capsule endoscopy and apply this knowledge in their future interactions and adaptation to AI technology. It also summarises the evidence of AI in CCE and its impact on diagnostic pathways. Finally, it discusses the unintended consequences of using AI, ethical challenges, potential flaws, and bias within clinical settings.
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Diaconu C, State M, Birligea M, Ifrim M, Bajdechi G, Georgescu T, Mateescu B, Voiosu T. The Role of Artificial Intelligence in Monitoring Inflammatory Bowel Disease-The Future Is Now. Diagnostics (Basel) 2023; 13:735. [PMID: 36832222 PMCID: PMC9954871 DOI: 10.3390/diagnostics13040735] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/02/2023] [Accepted: 02/02/2023] [Indexed: 02/17/2023] Open
Abstract
Crohn's disease and ulcerative colitis remain debilitating disorders, characterized by progressive bowel damage and possible lethal complications. The growing number of applications for artificial intelligence in gastrointestinal endoscopy has already shown great potential, especially in the field of neoplastic and pre-neoplastic lesion detection and characterization, and is currently under evaluation in the field of inflammatory bowel disease management. The application of artificial intelligence in inflammatory bowel diseases can range from genomic dataset analysis and risk prediction model construction to the disease grading severity and assessment of the response to treatment using machine learning. We aimed to assess the current and future role of artificial intelligence in assessing the key outcomes in inflammatory bowel disease patients: endoscopic activity, mucosal healing, response to treatment, and neoplasia surveillance.
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Affiliation(s)
- Claudia Diaconu
- Gastroenterology Department, Colentina Clinical Hospital, 020125 Bucharest, Romania
| | - Monica State
- Gastroenterology Department, Colentina Clinical Hospital, 020125 Bucharest, Romania
- Internal Medicine Department, Carol Davila University of Medicine and Pharmacy, 50474 Bucharest, Romania
| | - Mihaela Birligea
- Gastroenterology Department, Colentina Clinical Hospital, 020125 Bucharest, Romania
| | - Madalina Ifrim
- Gastroenterology Department, Colentina Clinical Hospital, 020125 Bucharest, Romania
| | - Georgiana Bajdechi
- Gastroenterology Department, Colentina Clinical Hospital, 020125 Bucharest, Romania
| | - Teodora Georgescu
- Gastroenterology Department, Colentina Clinical Hospital, 020125 Bucharest, Romania
| | - Bogdan Mateescu
- Gastroenterology Department, Colentina Clinical Hospital, 020125 Bucharest, Romania
- Internal Medicine Department, Carol Davila University of Medicine and Pharmacy, 50474 Bucharest, Romania
| | - Theodor Voiosu
- Gastroenterology Department, Colentina Clinical Hospital, 020125 Bucharest, Romania
- Internal Medicine Department, Carol Davila University of Medicine and Pharmacy, 50474 Bucharest, Romania
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25
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Da Rio L, Spadaccini M, Parigi TL, Gabbiadini R, Dal Buono A, Busacca A, Maselli R, Fugazza A, Colombo M, Carrara S, Franchellucci G, Alfarone L, Facciorusso A, Hassan C, Repici A, Armuzzi A. Artificial intelligence and inflammatory bowel disease: Where are we going? World J Gastroenterol 2023; 29:508-520. [PMID: 36688019 PMCID: PMC9850939 DOI: 10.3748/wjg.v29.i3.508] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/05/2022] [Accepted: 12/27/2022] [Indexed: 01/12/2023] Open
Abstract
Inflammatory bowel diseases, namely ulcerative colitis and Crohn's disease, are chronic and relapsing conditions that pose a growing burden on healthcare systems worldwide. Because of their complex and partly unknown etiology and pathogenesis, the management of ulcerative colitis and Crohn's disease can prove challenging not only from a clinical point of view but also for resource optimization. Artificial intelligence, an umbrella term that encompasses any cognitive function developed by machines for learning or problem solving, and its subsets machine learning and deep learning are becoming ever more essential tools with a plethora of applications in most medical specialties. In this regard gastroenterology is no exception, and due to the importance of endoscopy and imaging numerous clinical studies have been gradually highlighting the relevant role that artificial intelligence has in inflammatory bowel diseases as well. The aim of this review was to summarize the most recent evidence on the use of artificial intelligence in inflammatory bowel diseases in various contexts such as diagnosis, follow-up, treatment, prognosis, cancer surveillance, data collection, and analysis. Moreover, insights into the potential further developments in this field and their effects on future clinical practice were discussed.
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Affiliation(s)
- Leonardo Da Rio
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Tommaso Lorenzo Parigi
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Roberto Gabbiadini
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Arianna Dal Buono
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Anita Busacca
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Alessandro Fugazza
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Matteo Colombo
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Silvia Carrara
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Gianluca Franchellucci
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Ludovico Alfarone
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Medical Sciences, University of Foggia, Foggia 71122, Foggia, Italy
| | - Cesare Hassan
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Alessandro Armuzzi
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
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26
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Ribeiro T, Mascarenhas M, Afonso J, Cardoso H, Andrade P, Lopes S, Ferreira J, Mascarenhas Saraiva M, Macedo G. Artificial intelligence and colon capsule endoscopy: Automatic detection of ulcers and erosions using a convolutional neural network. J Gastroenterol Hepatol 2022; 37:2282-2288. [PMID: 36181257 DOI: 10.1111/jgh.16011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/12/2022] [Accepted: 09/25/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND AND AIM Colon capsule endoscopy (CCE) has become a minimally invasive alternative for conventional colonoscopy. Nevertheless, each CCE exam produces between 50 000 and 100 000 frames, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNNs) are a type of artificial intelligence (AI) architecture with high performance in image analysis. This study aims to develop a CNN model for the identification of colonic ulcers and erosions in CCE images. METHODS A CNN model was designed using a database of CCE images. A total of 124 CCE exams performed between 2010 and 2020 in two centers were reviewed. For CNN development, a total of 37 319 images were extracted, 33 749 showing normal colonic mucosa and 3570 showing colonic ulcers and erosions. Datasets for CNN training, validation, and testing were created. The performance of the algorithm was evaluated regarding its sensitivity, specificity, positive and negative predictive values, accuracy, and area under the curve. RESULTS The network had a sensitivity of 96.9% and a specificity of 99.9% specific for the detection of colonic ulcers and erosions. The algorithm had an overall accuracy of 99.6%. The area under the curve was 1.00. The CNN had an image processing capacity of 90 frames per second. CONCLUSIONS The developed algorithm is the first CNN-based model to accurately detect ulcers and erosions in CCE images, also providing a good image processing performance. The development of these AI systems may contribute to improve both the diagnostic and time efficiency of CCE exams, facilitating CCE adoption to routine clinical practice.
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Affiliation(s)
- Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Porto, Portugal.,World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Miguel Mascarenhas
- Department of Gastroenterology, São João University Hospital, Porto, Portugal.,World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, Porto, Portugal.,Faculty of Medicine of the University of Porto, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal.,World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Hélder Cardoso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal.,World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, Porto, Portugal.,Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Patrícia Andrade
- Department of Gastroenterology, São João University Hospital, Porto, Portugal.,World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, Porto, Portugal.,Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Susana Lopes
- Department of Gastroenterology, São João University Hospital, Porto, Portugal.,World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, Porto, Portugal.,Faculty of Medicine of the University of Porto, Porto, Portugal
| | - João Ferreira
- Faculty of Engineering of the University of Porto, Porto, Portugal.,INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | | | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Porto, Portugal.,World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, Porto, Portugal.,Faculty of Medicine of the University of Porto, Porto, Portugal
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27
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Alfarone L, Parigi TL, Gabbiadini R, Dal Buono A, Spinelli A, Hassan C, Iacucci M, Repici A, Armuzzi A. Technological advances in inflammatory bowel disease endoscopy and histology. Front Med (Lausanne) 2022; 9:1058875. [PMID: 36438050 PMCID: PMC9691880 DOI: 10.3389/fmed.2022.1058875] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 10/26/2022] [Indexed: 09/29/2023] Open
Abstract
Accurate disease characterization is the pillar of modern treatment of inflammatory bowel disease (IBD) and endoscopy is the mainstay of disease assessment and colorectal cancer surveillance. Recent technological progress has enhanced and expanded the use of endoscopy in IBD. In particular, numerous artificial intelligence (AI)-powered systems have shown to support human endoscopists' evaluations, improving accuracy and consistency while saving time. Moreover, advanced optical technologies such as endocytoscopy (EC), allowing high magnification in vivo, can bridge endoscopy with histology. Furthermore, molecular imaging, through probe based confocal laser endomicroscopy allows the real-time detection of specific biomarkers on gastrointestinal surface, and could be used to predict therapeutic response, paving the way to precision medicine. In parallel, as the applications of AI spread, computers are positioned to resolve some of the limitations of human histopathology evaluation, such as interobserver variability and inconsistencies in assessment. The aim of this review is to summarize the most promising advances in endoscopic and histologic assessment of IBD.
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Affiliation(s)
- Ludovico Alfarone
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IBD Center, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Tommaso Lorenzo Parigi
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IBD Center, IRCCS Humanitas Research Hospital, Milan, Italy
| | | | | | - Antonino Spinelli
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Colon and Rectal Surgery Division, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Endoscopy Unit, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Marietta Iacucci
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, United Kingdom
- Department of Gastroenterology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Endoscopy Unit, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Alessandro Armuzzi
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IBD Center, IRCCS Humanitas Research Hospital, Milan, Italy
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28
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Leenhardt R, Koulaouzidis A, Histace A, Baatrup G, Beg S, Bourreille A, de Lange T, Eliakim R, Iakovidis D, Dam Jensen M, Keuchel M, Margalit Yehuda R, McNamara D, Mascarenhas M, Spada C, Segui S, Smedsrud P, Toth E, Tontini GE, Klang E, Dray X, Kopylov U. Key research questions for implementation of artificial intelligence in capsule endoscopy. Therap Adv Gastroenterol 2022; 15:17562848221132683. [PMID: 36338789 PMCID: PMC9629556 DOI: 10.1177/17562848221132683] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 09/27/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) is rapidly infiltrating multiple areas in medicine, with gastrointestinal endoscopy paving the way in both research and clinical applications. Multiple challenges associated with the incorporation of AI in endoscopy are being addressed in recent consensus documents. OBJECTIVES In the current paper, we aimed to map future challenges and areas of research for the incorporation of AI in capsule endoscopy (CE) practice. DESIGN Modified three-round Delphi consensus online survey. METHODS The study design was based on a modified three-round Delphi consensus online survey distributed to a group of CE and AI experts. Round one aimed to map out key research statements and challenges for the implementation of AI in CE. All queries addressing the same questions were merged into a single issue. The second round aimed to rank all generated questions during round one and to identify the top-ranked statements with the highest total score. Finally, the third round aimed to redistribute and rescore the top-ranked statements. RESULTS Twenty-one (16 gastroenterologists and 5 data scientists) experts participated in the survey. In the first round, 48 statements divided into seven themes were generated. After scoring all statements and rescoring the top 12, the question of AI use for identification and grading of small bowel pathologies was scored the highest (mean score 9.15), correlation of AI and human expert reading-second (9.05), and real-life feasibility-third (9.0). CONCLUSION In summary, our current study points out a roadmap for future challenges and research areas on our way to fully incorporating AI in CE reading.
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Affiliation(s)
- Romain Leenhardt
- Centre of Digestive Endoscopy, Sorbonne Université, Hôpital Saint-Antoine, 184 rue du Faubourg Saint Antoine, AP-HP, Paris 75012, France
- ETIS UMR 8051 (CY Paris Cergy University, ENSEA, CNRS), Cergy, France
| | - Anastasios Koulaouzidis
- Department of Social Medicine and Public Health, Pomeranian Medical University, Szczecin, Poland
- Department of Surgery, Odense University Hospital, Odense, Denmark
- Department of Clinical research, University of Southern Denmark, Odense, Denmark
| | - Aymeric Histace
- ETIS UMR 8051 (CY Paris Cergy University, ENSEA, CNRS), Cergy, France
| | - Gunnar Baatrup
- Department of Surgery, Odense University Hospital, Odense, Denmark
- Department of Clinical research, University of Southern Denmark, Odense, Denmark
| | - Sabina Beg
- Department of Gastroenterology, Imperial College NHS Healthcare Trust, London, UK
| | - Arnaud Bourreille
- Nantes Université, CHU Nantes, Institut des maladies de l’appareil digestif (IMAD), Hépato-gastroentérologie, Nantes, France
| | - Thomas de Lange
- Department of Medicine and emergencies-Mölndal, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Molecular and Clinical and Medicine, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden
| | - Rami Eliakim
- Department of Gastroenterology, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Dimitris Iakovidis
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Michael Dam Jensen
- Department of Internal Medicine, Section of Gastroenterology, Lillebaelt Hospital, Vejle, Denmark
| | - Martin Keuchel
- Clinic for Internal Medicine, Agaplesion Bethesda Krankenhaus Bergedorf, Hamburg, Germany
| | - Reuma Margalit Yehuda
- Department of Gastroenterology, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Deirdre McNamara
- Trinity Academic Gastroenterology Group, Department of Clinical Medicine, Tallaght Hospital, Trinity College Dublin, Dublin, Ireland
| | - Miguel Mascarenhas
- Department of Gastroenterology, Centro Hospitalar São João, Porto, Portugal
| | - Cristiano Spada
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy
- Digestive Endoscopy Unit, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Santi Segui
- Department of Mathematics and Computer Science, Universitat de Barcelona, Barcelona, Spain
| | - Pia Smedsrud
- Simula Metropolitan Centre for Digital Engineering, University of Oslo, Augere Medical AS, Oslo, Norway
| | - Ervin Toth
- Department of Gastroenterology, Skåne University Hospital, Lund University, Malmö, Sweden
| | - Gian Eugenio Tontini
- Department of Pathophysiology and Transplantation, University of Milan and Gastroenterology and Endoscopy Unit, Fondazione IRCCS Ca’Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Eyal Klang
- Sheba ARC, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Xavier Dray
- Sorbonne Université, Centre of Digestive Endoscopy, Hôpital Saint-Antoine, AP-HP, Paris, France
- ETIS UMR 8051 (CY Paris Cergy University, ENSEA, CNRS), Cergy, France
| | - Uri Kopylov
- Department of Gastroenterology, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
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Gilabert P, Vitrià J, Laiz P, Malagelada C, Watson A, Wenzek H, Segui S. Artificial intelligence to improve polyp detection and screening time in colon capsule endoscopy. Front Med (Lausanne) 2022; 9:1000726. [PMCID: PMC9606587 DOI: 10.3389/fmed.2022.1000726] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 09/23/2022] [Indexed: 11/13/2022] Open
Abstract
Colon Capsule Endoscopy (CCE) is a minimally invasive procedure which is increasingly being used as an alternative to conventional colonoscopy. Videos recorded by the capsule cameras are long and require one or more experts' time to review and identify polyps or other potential intestinal problems that can lead to major health issues. We developed and tested a multi-platform web application, AI-Tool, which embeds a Convolution Neural Network (CNN) to help CCE reviewers. With the help of artificial intelligence, AI-Tool is able to detect images with high probability of containing a polyp and prioritize them during the reviewing process. With the collaboration of 3 experts that reviewed 18 videos, we compared the classical linear review method using RAPID Reader Software v9.0 and the new software we present. Applying the new strategy, reviewing time was reduced by a factor of 6 and polyp detection sensitivity was increased from 81.08 to 87.80%.
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Affiliation(s)
- Pere Gilabert
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain,*Correspondence: Pere Gilabert
| | - Jordi Vitrià
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Pablo Laiz
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Carolina Malagelada
- Digestive System Research Unit, University Hospital Vall d'Hebron, Barcelona, Spain,Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Angus Watson
- Department of Colorectal Surgery, Raigmore Hospital, NHS Highland, Inverness, United Kingdom
| | - Hagen Wenzek
- CorporateHealth International ApS, Odense, Denmark
| | - Santi Segui
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
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Automatic detection of crohn disease in wireless capsule endoscopic images using a deep convolutional neural network. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04146-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
AbstractThe diagnosis of Crohn’s disease (CD) in the small bowel is generally performed by observing a very large number of images captured by capsule endoscopy (CE). This diagnostic technique entails a heavy workload for the specialists in terms of time spent reviewing the images. This paper presents a convolutional neural network capable of classifying the CE images to identify those ones affected by lesions indicative of the disease. The architecture of the proposed network was custom designed to solve this image classification problem. This allowed different design decisions to be made with the aim of improving its performance in terms of accuracy and processing speed compared to other state-of-the-art deep-learning-based reference architectures. The experimentation was carried out on a set of 15,972 images extracted from 31 CE videos of patients affected by CD, 7,986 of which showed lesions associated with the disease. The training, validation/selection and evaluation of the network was performed on 70%, 10% and 20% of the total images, respectively. The ROC curve obtained on the test image set has an area greater than 0.997, with points in a 95-99% sensitivity range associated with specificities of 99-96%. These figures are higher than those achieved by EfficientNet-B5, VGG-16, Xception or ResNet networks which also require an average processing time per image significantly higher than the one needed in the proposed architecture. Therefore, the network outlined in this paper is proving to be sufficiently promising to be considered for integration into tools used by specialists in their diagnosis of CD. In the sample of images analysed, the network was able to detect 99% of the images with lesions, filtering out for specialist review 96% of those with no signs of disease.
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Odeyinka O, Alhashimi R, Thoota S, Ashok T, Palyam V, Azam AT, Sange I. The Role of Capsule Endoscopy in Crohn's Disease: A Review. Cureus 2022; 14:e27242. [PMID: 36039259 PMCID: PMC9401636 DOI: 10.7759/cureus.27242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/25/2022] [Indexed: 12/09/2022] Open
Abstract
Crohn’s disease (CD) is a chronic inflammatory disorder with a predilection for the small bowel. Although awareness of this disorder has increased over the years, it remains a diagnostic challenge for many physicians. This is exacerbated by the rising incidence and high recurrence rate following therapy in certain individuals. It is currently agreed that a multimodality approach is the best one, but with the advent of new modalities, that could be changing. Furthermore, given its impact on the mental health of patients and the cost of treatment, it is pertinent that we arrive at not only convenient but accurate modalities in its diagnosis and management. Among these investigative modalities is the relatively novel capsule endoscopy (CE) that not only provides a more patient-friendly alternative but avoids the need for invasiveness. Asides from its diagnostic capability, its influence on therapy and monitoring of known CD patients following treatment has been shown. This article has reviewed the current literature comparing the relevance of CE with other available modalities in diagnosing CD patients. We explored its therapeutic impact and how it influences monitoring post-treatment in CD. This article also discusses the complications of CE and the possible solutions to these complications in the future.
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Alemanni LV, Fabbri S, Rondonotti E, Mussetto A. Recent developments in small bowel endoscopy: the "black box" is now open! Clin Endosc 2022; 55:473-479. [PMID: 35831981 PMCID: PMC9329645 DOI: 10.5946/ce.2022.113] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/28/2022] [Accepted: 05/11/2022] [Indexed: 12/09/2022] Open
Abstract
Over the last few years, capsule endoscopy has been established as a fundamental device in the practicing gastroenterologist's toolbox. Its utilization in diagnostic algorithms for suspected small bowel bleeding, Crohn's disease, and small bowel tumors has been approved by several guidelines. The advent of double-balloon enteroscopy has significantly increased the therapeutic possibilities and release of multiple devices (single-balloon enteroscopy and spiral enteroscopy) aimed at improving the performance of small bowel enteroscopy. Recently, some important innovations have appeared in the small bowel endoscopy scene, providing further improvement to its evolution. Artificial intelligence in capsule endoscopy should increase diagnostic accuracy and reading efficiency, and the introduction of motorized spiral enteroscopy into clinical practice could also improve the therapeutic yield. This review focuses on the most recent studies on artificial-intelligence-assisted capsule endoscopy and motorized spiral enteroscopy.
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Affiliation(s)
- Luigina Vanessa Alemanni
- Gastroenterology Unit, Santa Maria delle Croci Hospital, Ravenna, Italy
- Department of Medical and Surgical Sciences, S. Orsola-Malpighi Hospital, Bologna, Italy
| | - Stefano Fabbri
- Gastroenterology Unit, Santa Maria delle Croci Hospital, Ravenna, Italy
- Department of Medical and Surgical Sciences, S. Orsola-Malpighi Hospital, Bologna, Italy
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Hosoe N, Horie T, Tojo A, Sakurai H, Hayashi Y, Limpias Kamiya KJL, Sujino T, Takabayashi K, Ogata H, Kanai T. Development of a Deep-Learning Algorithm for Small Bowel-Lesion Detection and a Study of the Improvement in the False-Positive Rate. J Clin Med 2022; 11:3682. [PMID: 35806969 PMCID: PMC9267395 DOI: 10.3390/jcm11133682] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 02/04/2023] Open
Abstract
Deep learning has recently been gaining attention as a promising technology to improve the identification of lesions, and deep-learning algorithms for lesion detection have been actively developed in small-bowel capsule endoscopy (SBCE). We developed a detection algorithm for abnormal findings by deep learning (convolutional neural network) the SBCE imaging data of 30 cases with abnormal findings. To enable the detection of a wide variety of abnormal findings, the training data were balanced to include all major findings identified in SBCE (bleeding, angiodysplasia, ulceration, and neoplastic lesions). To reduce the false-positive rate, "findings that may be responsible for hemorrhage" and "findings that may require therapeutic intervention" were extracted from the images of abnormal findings and added to the training dataset. For the performance evaluation, the sensitivity and the specificity were calculated using 271 detectable findings in 35 cases. The sensitivity was calculated using 68,494 images of non-abnormal findings. The sensitivity and specificity were 93.4% and 97.8%, respectively. The average number of images detected by the algorithm as having abnormal findings was 7514. We developed an image-reading support system using deep learning for SBCE and obtained a good detection performance.
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Affiliation(s)
- Naoki Hosoe
- Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo 160-8582, Japan; (T.S.); (K.T.); (H.O.)
| | - Tomofumi Horie
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo 160-8582, Japan; (T.H.); (A.T.); (H.S.); (Y.H.); (K.J.-L.L.K.); (T.K.)
| | - Anna Tojo
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo 160-8582, Japan; (T.H.); (A.T.); (H.S.); (Y.H.); (K.J.-L.L.K.); (T.K.)
| | - Hinako Sakurai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo 160-8582, Japan; (T.H.); (A.T.); (H.S.); (Y.H.); (K.J.-L.L.K.); (T.K.)
| | - Yukie Hayashi
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo 160-8582, Japan; (T.H.); (A.T.); (H.S.); (Y.H.); (K.J.-L.L.K.); (T.K.)
| | - Kenji Jose-Luis Limpias Kamiya
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo 160-8582, Japan; (T.H.); (A.T.); (H.S.); (Y.H.); (K.J.-L.L.K.); (T.K.)
| | - Tomohisa Sujino
- Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo 160-8582, Japan; (T.S.); (K.T.); (H.O.)
| | - Kaoru Takabayashi
- Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo 160-8582, Japan; (T.S.); (K.T.); (H.O.)
| | - Haruhiko Ogata
- Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo 160-8582, Japan; (T.S.); (K.T.); (H.O.)
| | - Takanori Kanai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo 160-8582, Japan; (T.H.); (A.T.); (H.S.); (Y.H.); (K.J.-L.L.K.); (T.K.)
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Penrice DD, Rattan P, Simonetto DA. Artificial Intelligence and the Future of Gastroenterology and Hepatology. GASTRO HEP ADVANCES 2022; 1:581-595. [PMID: 39132066 PMCID: PMC11307848 DOI: 10.1016/j.gastha.2022.02.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 02/22/2022] [Indexed: 08/13/2024]
Abstract
The integration of artificial intelligence (AI) into gastroenterology and hepatology (GI) will inevitably transform the practice of GI in the coming decade. While the application of AI in health care is not new, advancements are occurring rapidly, and the future landscape of AI is beginning to come into focus. From endoscopic assistance via computer vision technology to the predictive capabilities of the vast information contained in the electronic health records, AI promises to optimize and expedite clinical and procedural practice and research in GI. The extensive body of literature already available on AI applications in gastroenterology may seem daunting at first; however, this review aims to provide a breakdown of the key studies conducted thus far and demonstrate the many potential ways this technology may impact the field. This review will also take a look into the future and imagine how GI can be transformed over the coming years, as well as potential limitations and pitfalls that must be overcome to realize this future.
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Affiliation(s)
- Daniel D. Penrice
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Puru Rattan
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
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Chetcuti Zammit S, Sidhu R. Artificial intelligence within the small bowel: are we lagging behind? Curr Opin Gastroenterol 2022; 38:307-317. [PMID: 35645023 DOI: 10.1097/mog.0000000000000827] [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] [Indexed: 12/10/2022]
Abstract
PURPOSE OF REVIEW The use of artificial intelligence in small bowel capsule endoscopy is expanding. This review focusses on the use of artificial intelligence for small bowel pathology compared with human data and developments to date. RECENT FINDINGS The diagnosis and management of small bowel disease has been revolutionized with the advent of capsule endoscopy. Reading of capsule endoscopy videos however is time consuming with an average reading time of 40 min. Furthermore, the fatigued human eye may miss subtle lesions including indiscreet mucosal bulges. In recent years, artificial intelligence has made significant progress in the field of medicine including gastroenterology. Machine learning has enabled feature extraction and in combination with deep neural networks, image classification has now materialized for routine endoscopy for the clinician. SUMMARY Artificial intelligence is in built within the Navicam-Ankon capsule endoscopy reading system. This development will no doubt expand to other capsule endoscopy platforms and capsule endoscopies that are used to visualize other parts of the gastrointestinal tract as a standard. This wireless and patient friendly technique combined with rapid reading platforms with the help of artificial intelligence will become an attractive and viable choice to alter how patients are investigated in the future.
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Affiliation(s)
| | - Reena Sidhu
- Academic Department of Gastroenterology, Royal Hallamshire Hospital
- Academic Unit of Gastroenterology, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
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Abstract
Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.
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Affiliation(s)
- Yutaka Okagawa
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Department of Gastroenterology, Tonan Hospital, Sapporo, Japan
| | - Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Ichiro Oda
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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Afonso J, Mascarenhas M, Ribeiro T, Cardoso H, Andrade P, Ferreira JP, Saraiva MM, Macedo G. Deep Learning for Automatic Identification and Characterization of the Bleeding Potential of Enteric Protruding Lesions in Capsule Endoscopy. GASTRO HEP ADVANCES 2022; 1:835-843. [PMID: 39131843 PMCID: PMC11307543 DOI: 10.1016/j.gastha.2022.04.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 04/12/2022] [Indexed: 08/13/2024]
Abstract
Background and Aims Capsule endoscopy (CE) revolutionized the study of the small intestine, overcoming the limitations of conventional endoscopy. Nevertheless, reviewing CE images is time-consuming. Convolutional Neural Networks (CNNs) are an artificial intelligence architecture with high performance levels for image analysis. Protruding lesions of the small intestine exhibit enormous morphologic diversity in CE images. We aimed to develop a CNN-based algorithm for automatic detection of varied small-bowel protruding lesions. Methods A CNN was developed using a pool of CE images containing protruding lesions or normal mucosa/other findings. A total of 2565 patients were included. These images were inserted into a CNN model with transfer learning. We evaluated the performance of the network by calculating its sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Results A CNN was developed based on a total of 21,320 CE images. Training and validation data sets comprising 80% and 20% of the total pool of images, respectively, were constructed for development and testing of the network. The algorithm automatically detected small-bowel protruding lesions with an accuracy of 97.1%. Our CNN had a sensitivity, specificity, positive, and negative predictive values of 95.9%, 97.1%, 83.0%, and 95.7%, respectively. The CNN operated at a rate of approximately 355 frames per second. Conclusion We developed an accurate CNN for automatic detection of enteric protruding lesions with a wide range of morphologies. The development of these tools may enhance the diagnostic efficiency of CE.
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Affiliation(s)
- João Afonso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Miguel Mascarenhas
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Hélder Cardoso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Patrícia Andrade
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - João P.S. Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
| | | | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
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Lu B. Image Aided Recognition of Wireless Capsule Endoscope Based on the Neural Network. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:3880356. [PMID: 35432820 PMCID: PMC9010152 DOI: 10.1155/2022/3880356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 03/22/2022] [Indexed: 11/18/2022]
Abstract
Wireless capsule endoscopy is an important method for diagnosing small bowel diseases, but it will collect thousands of endoscopy images that need to be diagnosed. The analysis of these images requires a huge workload and may cause manual reading errors. This article attempts to use neural networks instead of artificial endoscopic image analysis to assist doctors in diagnosing and treating endoscopic images. First, in image preprocessing, the image is converted from RGB color mode to lab color mode, texture features are extracted for network training, and finally, the accuracy of the algorithm is verified. After inputting the retained endoscopic image verification set into the neural network algorithm, the conclusion is that the accuracy of the neural network model constructed in this study is 97.69%, which can effectively distinguish normal, benign lesions, and malignant tumors. Experimental studies have proved that the neural network algorithm can effectively assist the endoscopist's diagnosis and improve the diagnosis efficiency. This research hopes to provide a reference for the application of neural network algorithms in the field of endoscopic images.
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Affiliation(s)
- Bin Lu
- Department of Gastrointestinal Surgery, Affiliated Hospital of Shaoxing University (The Shaoxing Municipal Hospital), Shaoxing 312000, China
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Mascarenhas M, Ribeiro T, Afonso J, Ferreira JP, Cardoso H, Andrade P, Parente MP, Jorge RN, Mascarenhas Saraiva M, Macedo G. Deep learning and colon capsule endoscopy: automatic detection of blood and colonic mucosal lesions using a convolutional neural network. Endosc Int Open 2022; 10:E171-E177. [PMID: 35186665 PMCID: PMC8850002 DOI: 10.1055/a-1675-1941] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 09/21/2021] [Indexed: 10/31/2022] Open
Abstract
Background and study aims Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. However, CCE produces long videos, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence (AI) algorithms with high performance levels in image analysis. We aimed to develop a deep learning model for automatic identification and differentiation of significant colonic mucosal lesions and blood in CCE images. Patients and methods A retrospective multicenter study including 124 CCE examinations was conducted for development of a CNN model, using a database of CCE images including anonymized images of patients with normal colon mucosa, several mucosal lesions (erosions, ulcers, vascular lesions and protruding lesions) and luminal blood. For CNN development, 9005 images (3,075 normal mucosa, 3,115 blood and 2,815 mucosal lesions) were ultimately extracted. Two image datasets were created and used for CNN training and validation. Results The mean (standard deviation) sensitivity and specificity of the CNN were 96.3 % (3.9 %) and 98.2 % (1.8 %) Mucosal lesions were detected with a sensitivity of 92.0 % and a specificity of 98.5 %. Blood was detected with a sensitivity and specificity of 97.2 % and 99.9 %, respectively. The algorithm was 99.2 % sensitive and 99.6 % specific in distinguishing blood from mucosal lesions. The CNN processed 65 frames per second. Conclusions This is the first CNN-based algorithm to accurately detect and distinguish colonic mucosal lesions and luminal blood in CCE images. AI may improve diagnostic and time efficiency of CCE exams, thus facilitating CCE adoption to routine clinical practice.
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Affiliation(s)
- Miguel Mascarenhas
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal,Faculty of Medicine of the University of Porto Porto, Portugal
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João P.S. Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal,INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal.
| | - Hélder Cardoso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal,Faculty of Medicine of the University of Porto Porto, Portugal
| | - Patrícia Andrade
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal,Faculty of Medicine of the University of Porto Porto, Portugal
| | - Marco P.L. Parente
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal,INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal.
| | - Renato N. Jorge
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal,INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal.
| | | | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Porto, Portugal,WGO Gastroenterology and Hepatology Training Center, Porto, Portugal,Faculty of Medicine of the University of Porto Porto, Portugal
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Parigi TL, Mastrorocco E, Da Rio L, Allocca M, D’Amico F, Zilli A, Fiorino G, Danese S, Furfaro F. Evolution and New Horizons of Endoscopy in Inflammatory Bowel Diseases. J Clin Med 2022; 11:jcm11030872. [PMID: 35160322 PMCID: PMC8837111 DOI: 10.3390/jcm11030872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 01/30/2022] [Accepted: 02/01/2022] [Indexed: 12/15/2022] Open
Abstract
Endoscopy is the mainstay of inflammatory bowel disease (IBD) evaluation and the pillar of colorectal cancer surveillance. Endoscopic equipment, both hardware and software, are advancing at an incredible pace. Virtual chromoendoscopy is now widely available, allowing the detection of subtle inflammatory changes, thus reducing the gap between endoscopic and histologic assessment. The progress in the field of artificial intelligence (AI) has been remarkable, and numerous applications are now in an advanced stage of development. Computer-aided diagnosis (CAD) systems are likely to reshape most of the evaluations that are now prerogative of human endoscopists. Furthermore, sophisticated tools such as endocytoscopy and probe-based confocal laser endomicroscopy (pCLE) are enhancing our assessment of inflammation and dysplasia. Finally, pCLE combined with molecular labeling could pave the way to a new paradigm of personalized medicine. This review aims to summarize the main changes that occurred in the field of IBD endoscopy and to explore the most promising novelties.
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Affiliation(s)
- Tommaso Lorenzo Parigi
- Department of Biomedical Sciences, Humanitas University, 20090 Milan, Italy; (T.L.P.); (E.M.); (L.D.R.)
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham B15 2TT, UK
| | - Elisabetta Mastrorocco
- Department of Biomedical Sciences, Humanitas University, 20090 Milan, Italy; (T.L.P.); (E.M.); (L.D.R.)
| | - Leonardo Da Rio
- Department of Biomedical Sciences, Humanitas University, 20090 Milan, Italy; (T.L.P.); (E.M.); (L.D.R.)
| | - Mariangela Allocca
- Gastroenterology and Endoscopy, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.A.); (F.D.); (A.Z.); (G.F.); (S.D.)
| | - Ferdinando D’Amico
- Gastroenterology and Endoscopy, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.A.); (F.D.); (A.Z.); (G.F.); (S.D.)
| | - Alessandra Zilli
- Gastroenterology and Endoscopy, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.A.); (F.D.); (A.Z.); (G.F.); (S.D.)
| | - Gionata Fiorino
- Gastroenterology and Endoscopy, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.A.); (F.D.); (A.Z.); (G.F.); (S.D.)
| | - Silvio Danese
- Gastroenterology and Endoscopy, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.A.); (F.D.); (A.Z.); (G.F.); (S.D.)
| | - Federica Furfaro
- IBD Center, Humanitas Research Hospital, 20089 Milan, Italy
- Correspondence: ; Tel.: +39-0282245555
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Zhuang H, Bao A, Tan Y, Wang H, Xie Q, Qiu M, Xiong W, Liao F. Application and prospect of artificial intelligence in digestive endoscopy. Expert Rev Gastroenterol Hepatol 2022; 16:21-31. [PMID: 34937459 DOI: 10.1080/17474124.2022.2020646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
INTRODUCTION With the progress of science and technology, artificial intelligence represented by deep learning has gradually begun to be applied in the medical field. Artificial intelligence has been applied to benign gastrointestinal lesions, tumors, early cancer, inflammatory bowel disease, gallbladder, pancreas, and other diseases. This review summarizes the latest research results on artificial intelligence in digestive endoscopy and discusses the prospect of artificial intelligence in digestive system diseases. AREAS COVERED We retrieved relevant documents on artificial intelligence in digestive tract diseases from PubMed and Medline. This review elaborates on the knowledge of computer-aided diagnosis in digestive endoscopy. EXPERT OPINION Artificial intelligence significantly improves diagnostic accuracy, reduces physicians' workload, and provides a shred of evidence for clinical diagnosis and treatment. Shortly, artificial intelligence will have high application value in the field of medicine.
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Affiliation(s)
- Huangming Zhuang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Anyu Bao
- Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yulin Tan
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Hanyu Wang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Qingfang Xie
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Meiqi Qiu
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Wanli Xiong
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Fei Liao
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
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Ribeiro T, Saraiva MM, Ferreira JPS, Cardoso H, Afonso J, Andrade P, Parente M, Jorge RN, Macedo G. Artificial intelligence and capsule endoscopy: automatic detection of vascular lesions using a convolutional neural network. Ann Gastroenterol 2021; 34:820-828. [PMID: 34815648 PMCID: PMC8596215 DOI: 10.20524/aog.2021.0653] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 04/07/2021] [Indexed: 12/09/2022] Open
Abstract
Background Capsule endoscopy (CE) is the first line for evaluation of patients with obscure gastrointestinal bleeding. A wide range of small intestinal vascular lesions with different hemorrhagic potential are frequently found in these patients. Nevertheless, reading CE exams is time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence tools with high performance levels in image analysis. This study aimed to develop a CNN-based model for identification and differentiation of vascular lesions with distinct hemorrhagic potential in CE images. Methods The development of the CNN was based on a database of CE images. This database included images of normal small intestinal mucosa, red spots, and angiectasia/varices. The hemorrhagic risk was assessed by Saurin's classification. For CNN development, 11,588 images (9525 normal mucosa, 1026 red spots, and 1037 angiectasia/varices) were ultimately extracted. Two image datasets were created for CNN training and testing. Results The network was 91.8% sensitive and 95.9% specific for detection of vascular lesions, providing accurate predictions in 94.4% of cases. In particular, the CNN had a sensitivity and specificity of 97.1% and 95.3%, respectively, for detection of red spots. Detection of angiectasia/varices occurred with a sensitivity of 94.1% and a specificity of 95.1%. The CNN had a frame reading rate of 145 frames/sec. Conclusions The developed algorithm is the first CNN-based model to accurately detect and distinguish enteric vascular lesions with different hemorrhagic risk. CNN-assisted CE reading may improve the diagnosis of these lesions and overall CE efficiency.
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Affiliation(s)
- Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,WGO Gastroenterology and Hepatology Training Center (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo)
| | - Miguel Mascarenhas Saraiva
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,WGO Gastroenterology and Hepatology Training Center (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro (Miguel Mascarenhas Saraiva, Hélder Cardoso, Patrícia Andrade, Guilherme Macedo)
| | - João P S Ferreira
- Faculty of Engineering of the University of Porto (João P.S. Ferreira, Marco Parente, Renato Natal Jorge).,INEGI - Institute of Science and Innovation in Mechanical and Industrial Engineering (João P.S. Ferreira, Marco Parente, Renato Natal Jorge), Porto, Portugal
| | - Hélder Cardoso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,WGO Gastroenterology and Hepatology Training Center (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro (Miguel Mascarenhas Saraiva, Hélder Cardoso, Patrícia Andrade, Guilherme Macedo)
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,WGO Gastroenterology and Hepatology Training Center (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo)
| | - Patrícia Andrade
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,WGO Gastroenterology and Hepatology Training Center (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro (Miguel Mascarenhas Saraiva, Hélder Cardoso, Patrícia Andrade, Guilherme Macedo)
| | - Marco Parente
- Faculty of Engineering of the University of Porto (João P.S. Ferreira, Marco Parente, Renato Natal Jorge).,INEGI - Institute of Science and Innovation in Mechanical and Industrial Engineering (João P.S. Ferreira, Marco Parente, Renato Natal Jorge), Porto, Portugal
| | - Renato Natal Jorge
- Faculty of Engineering of the University of Porto (João P.S. Ferreira, Marco Parente, Renato Natal Jorge).,INEGI - Institute of Science and Innovation in Mechanical and Industrial Engineering (João P.S. Ferreira, Marco Parente, Renato Natal Jorge), Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,WGO Gastroenterology and Hepatology Training Center (Tiago Ribeiro, Miguel Mascarenhas Saraiva, Hélder Cardoso, João Afonso, Patrícia Andrade, Guilherme Macedo).,Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro (Miguel Mascarenhas Saraiva, Hélder Cardoso, Patrícia Andrade, Guilherme Macedo)
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Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
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Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
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Chen D, Fulmer C, Gordon IO, Syed S, Stidham RW, Vande Casteele N, Qin Y, Falloon K, Cohen BL, Wyllie R, Rieder F. Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease - What the Clinician Needs to Know. J Crohns Colitis 2021; 16:460-471. [PMID: 34558619 PMCID: PMC8919817 DOI: 10.1093/ecco-jcc/jjab169] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Artificial intelligence [AI] techniques are quickly spreading across medicine as an analytical method to tackle challenging clinical questions. What were previously thought of as highly complex data sources, such as images or free text, are now becoming manageable. Novel analytical methods merge the latest developments in information technology infrastructure with advances in computer science. Once primarily associated with Silicon Valley, AI techniques are now making their way into medicine, including in the field of inflammatory bowel diseases [IBD]. Understanding potential applications and limitations of these techniques can be difficult, in particular for busy clinicians. In this article, we explain the basic terminologies and provide a particular focus on the foundations behind state-of-the-art AI methodologies in both imaging and text. We explore the growing applications of AI in medicine, with a specific focus on IBD to inform the practising gastroenterologist and IBD specialist. Finally, we outline possible future uses of these technologies in daily clinical practice.
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Affiliation(s)
- David Chen
- Medical Operations, Cleveland Clinic Foundation, Cleveland, OH, USA,Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Clifton Fulmer
- Department of Pathology, Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Ilyssa O Gordon
- Department of Pathology, Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Sana Syed
- Division of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, VA, USA,School of Data Science, University of Virginia, Charlottesville, VA, USA
| | - Ryan W Stidham
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, MI, USA,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | | | - Yi Qin
- Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Katherine Falloon
- Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Benjamin L Cohen
- Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Robert Wyllie
- Medical Operations, Cleveland Clinic Foundation, Cleveland, OH, USA,Department of Pediatric Gastroenterology, Hepatology, and Nutrition, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Florian Rieder
- Corresponding author: Florian Rieder, MD, Department of Inflammation and Immunity, and Department of Gastroenterology, Hepatology, & Nutrition, Cleveland Clinic Foundation, 9500 Euclid Ave., Cleveland, OH 44195, USA. Tel: (216) 445-5631; Fax: (216) 636-0104; E-mail:
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Artificial Intelligence in Capsule Endoscopy: A Practical Guide to Its Past and Future Challenges. Diagnostics (Basel) 2021; 11:diagnostics11091722. [PMID: 34574063 PMCID: PMC8469774 DOI: 10.3390/diagnostics11091722] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/15/2021] [Accepted: 09/17/2021] [Indexed: 12/20/2022] Open
Abstract
Artificial intelligence (AI) has revolutionized the medical diagnostic process of various diseases. Since the manual reading of capsule endoscopy videos is a time-intensive, error-prone process, computerized algorithms have been introduced to automate this process. Over the past decade, the evolution of convolutional neural network (CNN) enabled AI to detect multiple lesions simultaneously with increasing accuracy and sensitivity. Difficulty in validating CNN performance and unique characteristics of capsule endoscopy images make computer-aided reading systems in capsule endoscopy still on a preclinical level. Although AI technology can be used as an auxiliary second observer in capsule endoscopy, it is expected that in the near future, it will effectively reduce the reading time and ultimately become an independent, integrated reading system.
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Oka A, Ishimura N, Ishihara S. A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics (Basel) 2021; 11:1719. [PMID: 34574060 PMCID: PMC8468082 DOI: 10.3390/diagnostics11091719] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/17/2021] [Accepted: 09/17/2021] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is rapidly becoming an essential tool in the medical field as well as in daily life. Recent developments in deep learning, a subfield of AI, have brought remarkable advances in image recognition, which facilitates improvement in the early detection of cancer by endoscopy, ultrasonography, and computed tomography. In addition, AI-assisted big data analysis represents a great step forward for precision medicine. This review provides an overview of AI technology, particularly for gastroenterology, hepatology, and pancreatology, to help clinicians utilize AI in the near future.
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Affiliation(s)
- Akihiko Oka
- Department of Internal Medicine II, Faculty of Medicine, Shimane University, Izumo 693-8501, Shimane, Japan; (N.I.); (S.I.)
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Skamnelos A, Lazaridis N, Vlachou E, Koukias N, Apostolopoulos P, Murino A, Christodoulou D, Despott EJ. The role of small-bowel endoscopy in inflammatory bowel disease: an updated review on the state-of-the-art in 2021. Ann Gastroenterol 2021; 34:599-611. [PMID: 34475730 PMCID: PMC8375652 DOI: 10.20524/aog.2021.0652] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 04/14/2021] [Indexed: 12/11/2022] Open
Abstract
The impact of small-bowel (SB) capsule endoscopy and device-assisted enteroscopy on clinical practice, since their introduction 2 decades ago, has been remarkable. These disruptive technologies have transformed the investigation and management of SB pathology and now have a firmly established place in guidelines and clinical algorithms. Furthermore, recent years have witnessed innovations, driven by the demand of new goals in the management of inflammatory bowel disease (IBD), such as mucosal healing and evolving strategies based on tight monitoring and accelerated escalation of care. These developments in SB endoscopy have also been paralleled by refinement in dedicated radiological SB imaging technologies. This updated review highlights the current state of the art and more recent innovations with a focus on their role in IBD.
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Affiliation(s)
- Alexandros Skamnelos
- Royal Free Unit for Endoscopy, The Royal Free Hospital and UCL Institute for Liver and Digestive Health, Hampstead, London, United Kingdom (Alexandros Skamnelos, Nikolaos Lazaridis, Erasmia Vlachou, Nikolaos Koukias, Alberto Murino, Edward J. Despott).,Division of Gastroenterology, University Hospital and Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece (Alexandros Skamnelos, Dimitrios Christodoulou)
| | - Nikolaos Lazaridis
- Royal Free Unit for Endoscopy, The Royal Free Hospital and UCL Institute for Liver and Digestive Health, Hampstead, London, United Kingdom (Alexandros Skamnelos, Nikolaos Lazaridis, Erasmia Vlachou, Nikolaos Koukias, Alberto Murino, Edward J. Despott).,Genesis Hospital of Thessaloniki, Thessaloniki, Greece (Nikolaos Lazaridis)
| | - Erasmia Vlachou
- Royal Free Unit for Endoscopy, The Royal Free Hospital and UCL Institute for Liver and Digestive Health, Hampstead, London, United Kingdom (Alexandros Skamnelos, Nikolaos Lazaridis, Erasmia Vlachou, Nikolaos Koukias, Alberto Murino, Edward J. Despott).,Army Share Funds Hospital (NIMTS), Athens, Greece (Erasmia Vlachou, Periklis Apostolopoulos)
| | - Nikolaos Koukias
- Royal Free Unit for Endoscopy, The Royal Free Hospital and UCL Institute for Liver and Digestive Health, Hampstead, London, United Kingdom (Alexandros Skamnelos, Nikolaos Lazaridis, Erasmia Vlachou, Nikolaos Koukias, Alberto Murino, Edward J. Despott).,Department of Gastroenterology, University Hospital of Patras, Patras, Greece (Nikolaos Koukias)
| | - Periklis Apostolopoulos
- Army Share Funds Hospital (NIMTS), Athens, Greece (Erasmia Vlachou, Periklis Apostolopoulos)
| | - Alberto Murino
- Royal Free Unit for Endoscopy, The Royal Free Hospital and UCL Institute for Liver and Digestive Health, Hampstead, London, United Kingdom (Alexandros Skamnelos, Nikolaos Lazaridis, Erasmia Vlachou, Nikolaos Koukias, Alberto Murino, Edward J. Despott)
| | - Dimitrios Christodoulou
- Division of Gastroenterology, University Hospital and Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece (Alexandros Skamnelos, Dimitrios Christodoulou)
| | - Edward J Despott
- Royal Free Unit for Endoscopy, The Royal Free Hospital and UCL Institute for Liver and Digestive Health, Hampstead, London, United Kingdom (Alexandros Skamnelos, Nikolaos Lazaridis, Erasmia Vlachou, Nikolaos Koukias, Alberto Murino, Edward J. Despott)
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Yang Y, Li YX, Yao RQ, Du XH, Ren C. Artificial intelligence in small intestinal diseases: Application and prospects. World J Gastroenterol 2021; 27:3734-3747. [PMID: 34321840 PMCID: PMC8291013 DOI: 10.3748/wjg.v27.i25.3734] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/09/2021] [Accepted: 05/08/2021] [Indexed: 02/06/2023] Open
Abstract
The small intestine is located in the middle of the gastrointestinal tract, so small intestinal diseases are more difficult to diagnose than other gastrointestinal diseases. However, with the extensive application of artificial intelligence in the field of small intestinal diseases, with its efficient learning capacities and computational power, artificial intelligence plays an important role in the auxiliary diagnosis and prognosis prediction based on the capsule endoscopy and other examination methods, which improves the accuracy of diagnosis and prediction and reduces the workload of doctors. In this review, a comprehensive retrieval was performed on articles published up to October 2020 from PubMed and other databases. Thereby the application status of artificial intelligence in small intestinal diseases was systematically introduced, and the challenges and prospects in this field were also analyzed.
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Affiliation(s)
- Yu Yang
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Yu-Xuan Li
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Ren-Qi Yao
- Trauma Research Center, The Fourth Medical Center and Medical Innovation Research Division of the Chinese People‘s Liberation Army General Hospital, Beijing 100048, China
- Department of Burn Surgery, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Xiao-Hui Du
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Chao Ren
- Trauma Research Center, The Fourth Medical Center and Medical Innovation Research Division of the Chinese People‘s Liberation Army General Hospital, Beijing 100048, China
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Tziortziotis I, Laskaratos FM, Coda S. Role of Artificial Intelligence in Video Capsule Endoscopy. Diagnostics (Basel) 2021; 11:1192. [PMID: 34209029 PMCID: PMC8303156 DOI: 10.3390/diagnostics11071192] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 06/28/2021] [Indexed: 02/06/2023] Open
Abstract
Capsule endoscopy (CE) has been increasingly utilised in recent years as a minimally invasive tool to investigate the whole gastrointestinal (GI) tract and a range of capsules are currently available for evaluation of upper GI, small bowel, and lower GI pathology. Although CE is undoubtedly an invaluable test for the investigation of small bowel pathology, it presents considerable challenges and limitations, such as long and laborious reading times, risk of missing lesions, lack of bowel cleansing score and lack of locomotion. Artificial intelligence (AI) seems to be a promising tool that may help improve the performance metrics of CE, and consequently translate to better patient care. In the last decade, significant progress has been made to apply AI in the field of endoscopy, including CE. Although it is certain that AI will find soon its place in day-to-day endoscopy clinical practice, there are still some open questions and barriers limiting its widespread application. In this review, we provide some general information about AI, and outline recent advances in AI and CE, issues around implementation of AI in medical practice and potential future applications of AI-aided CE.
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Affiliation(s)
- Ioannis Tziortziotis
- Endoscopy Unit, Digestive Diseases Centre, Queen’s Hospital, Barking Havering and Redbridge University Hospitals NHS Trust, Rom Valley Way, Romford, London RM7 0AG, UK; (I.T.); (S.C.)
| | - Faidon-Marios Laskaratos
- Endoscopy Unit, Digestive Diseases Centre, Queen’s Hospital, Barking Havering and Redbridge University Hospitals NHS Trust, Rom Valley Way, Romford, London RM7 0AG, UK; (I.T.); (S.C.)
| | - Sergio Coda
- Endoscopy Unit, Digestive Diseases Centre, Queen’s Hospital, Barking Havering and Redbridge University Hospitals NHS Trust, Rom Valley Way, Romford, London RM7 0AG, UK; (I.T.); (S.C.)
- Photonics Group-Department of Physics, Imperial College London, Exhibition Rd, South Kensington, London SW7 2BX, UK
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A Current and Newly Proposed Artificial Intelligence Algorithm for Reading Small Bowel Capsule Endoscopy. Diagnostics (Basel) 2021; 11:diagnostics11071183. [PMID: 34209948 PMCID: PMC8306692 DOI: 10.3390/diagnostics11071183] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 06/26/2021] [Accepted: 06/28/2021] [Indexed: 12/09/2022] Open
Abstract
Small bowel capsule endoscopy (SBCE) is one of the most useful methods for diagnosing small bowel mucosal lesions. However, it takes a long time to interpret the capsule images. To solve this problem, artificial intelligence (AI) algorithms for SBCE readings are being actively studied. In this article, we analyzed several studies that applied AI algorithms to SBCE readings, such as automatic lesion detection, automatic classification of bowel cleanliness, and automatic compartmentalization of small bowels. In addition to automatic lesion detection using AI algorithms, a new direction of AI algorithms related to shorter reading times and improved lesion detection accuracy should be considered. Therefore, it is necessary to develop an integrated AI algorithm composed of algorithms with various functions in order to be used in clinical practice.
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