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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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Zeng S, Dong C, Liu C, Zhen J, Pu Y, Hu J, Dong W. The global research of artificial intelligence on inflammatory bowel disease: A bibliometric analysis. Digit Health 2025; 11:20552076251326217. [PMID: 40093709 PMCID: PMC11909680 DOI: 10.1177/20552076251326217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2024] [Accepted: 02/18/2025] [Indexed: 03/19/2025] Open
Abstract
Aims This study aimed to evaluate the related research on artificial intelligence (AI) in inflammatory bowel disease (IBD) through bibliometrics analysis and identified the research basis, current hotspots, and future development. Methods The related literature was acquired from the Web of Science Core Collection (WoSCC) on 31 December 2024. Co-occurrence and cooperation relationship analysis of (cited) authors, institutions, countries, cited journals, references, and keywords in the literature were carried out through CiteSpace 6.1.R6 software and the Online Analysis platform of Literature Metrology. Meanwhile, relevant knowledge maps were drawn, and keywords clustering analysis was performed. Results According to WoSCC, 1919 authors, 790 research institutions, 184 journals, and 49 countries/regions published 176 AI-related papers in IBD during 1999-2024. The number of papers published has increased significantly since 2019, reaching a maximum by 2023. The United States had the highest number of publications and the closest collaboration with other countries. The clustering analysis showed that the earliest studies focused on "psychometric value" and then moved to "deep learning model," "intestinal ultrasound," and "new diagnostic strategies." Conclusion This study is the first bibliometric analysis to summarize the current status and to visually reveal the development trends and future research hotspots of the application of AI in IBD. The application of AI in IBD is still in its infancy, and the focus of this field will shift to improving the efficiency of diagnosis and treatment through deep learning techniques, big data-based treatment, and prognosis prediction.
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Affiliation(s)
- Suqi Zeng
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chenyu Dong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chuan Liu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Junhai Zhen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yu Pu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Jiaming Hu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Weiguo Dong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
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Mascarenhas M, Martins M, Ribeiro T, Afonso J, Cardoso P, Mendes F, Cardoso H, Almeida R, Ferreira J, Fonseca J, Macedo G. Software as a Medical Device (SaMD) in Digestive Healthcare: Regulatory Challenges and Ethical Implications. Diagnostics (Basel) 2024; 14:2100. [PMID: 39335779 PMCID: PMC11431531 DOI: 10.3390/diagnostics14182100] [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: 07/24/2024] [Revised: 08/29/2024] [Accepted: 09/05/2024] [Indexed: 09/30/2024] Open
Abstract
The growing integration of software in healthcare, particularly the rise of standalone software as a medical device (SaMD), is transforming digestive medicine, a field heavily reliant on medical imaging for both diagnosis and therapeutic interventions. This narrative review aims to explore the impact of SaMD on digestive healthcare, focusing on the evolution of these tools and their regulatory and ethical challenges. Our analysis highlights the exponential growth of SaMD in digestive healthcare, driven by the need for precise diagnostic tools and personalized treatment strategies. This rapid advancement, however, necessitates the parallel development of a robust regulatory framework to ensure SaMDs are transparent and deliver universal clinical benefits without the introduction of bias or harm. In addition, the discussion highlights the importance of adherence to the FAIR principles for data management-findability, accessibility, interoperability, and reusability. However, enhanced accessibility and interoperability require rigorous protocols to ensure compliance with data protection guidelines and adequate data security, both of which are crucial for effective integration of SaMDs into clinical workflows. In conclusion, while SaMDs hold significant promise for improving patients' outcomes in digestive medicine, their successful integration into clinical workflow depends on rigorous data protection protocols and clinical validation. Future directions include the need for adequate clinical and real-world studies to demonstrate that these devices are safe and well-suited to healthcare settings.
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Affiliation(s)
- Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine of University of Porto, 4200 427 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
| | - Hélder Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - Rute Almeida
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine of University of Porto, 4200 427 Porto, Portugal
| | - João Ferreira
- Department of Mechanic Engineering, Faculty of Engineering of University of Porto, 4200 427 Porto, Portugal
- DigestAID-Digestive Artificial Intelligence Development, 4200 427 Porto, Portugal
| | - João Fonseca
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine of University of Porto, 4200 427 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
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Nam SJ, Moon G, Park JH, Kim Y, Lim YJ, Choi HS. Deep Learning-Based Real-Time Organ Localization and Transit Time Estimation in Wireless Capsule Endoscopy. Biomedicines 2024; 12:1704. [PMID: 39200169 PMCID: PMC11351118 DOI: 10.3390/biomedicines12081704] [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: 05/29/2024] [Revised: 07/16/2024] [Accepted: 07/22/2024] [Indexed: 09/02/2024] Open
Abstract
BACKGROUND Wireless capsule endoscopy (WCE) has significantly advanced the diagnosis of gastrointestinal (GI) diseases by allowing for the non-invasive visualization of the entire small intestine. However, machine learning-based methods for organ classification in WCE often rely on color information, leading to decreased performance when obstacles such as food debris are present. This study proposes a novel model that integrates convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to analyze multiple frames and incorporate temporal information, ensuring that it performs well even when visual information is limited. METHODS We collected data from 126 patients using PillCam™ SB3 (Medtronic, Minneapolis, MN, USA), which comprised 2,395,932 images. Our deep learning model was trained to identify organs (stomach, small intestine, and colon) using data from 44 training and 10 validation cases. We applied calibration using a Gaussian filter to enhance the accuracy of detecting organ boundaries. Additionally, we estimated the transit time of the capsule in the gastric and small intestine regions using a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) designed to be aware of the sequence information of continuous videos. Finally, we evaluated the model's performance using WCE videos from 72 patients. RESULTS Our model demonstrated high performance in organ classification, achieving an accuracy, sensitivity, and specificity of over 95% for each organ (stomach, small intestine, and colon), with an overall accuracy and F1-score of 97.1%. The Matthews Correlation Coefficient (MCC) and Geometric Mean (G-mean) were used to evaluate the model's performance on imbalanced datasets, achieving MCC values of 0.93 for the stomach, 0.91 for the small intestine, and 0.94 for the colon, and G-mean values of 0.96 for the stomach, 0.95 for the small intestine, and 0.97 for the colon. Regarding the estimation of gastric and small intestine transit times, the mean time differences between the model predictions and ground truth were 4.3 ± 9.7 min for the stomach and 24.7 ± 33.8 min for the small intestine. Notably, the model's predictions for gastric transit times were within 15 min of the ground truth for 95.8% of the test dataset (69 out of 72 cases). The proposed model shows overall superior performance compared to a model using only CNN. CONCLUSIONS The combination of CNN and LSTM proves to be both accurate and clinically effective for organ classification and transit time estimation in WCE. Our model's ability to integrate temporal information allows it to maintain high performance even in challenging conditions where color information alone is insufficient. Including MCC and G-mean metrics further validates the robustness of our approach in handling imbalanced datasets. These findings suggest that the proposed method can significantly improve the diagnostic accuracy and efficiency of WCE, making it a valuable tool in clinical practice for diagnosing and managing GI diseases.
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Affiliation(s)
- Seung-Joo Nam
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea
| | - Gwiseong Moon
- Ziovision Co., Ltd., Chuncheon 24341, Republic of Korea
| | | | - Yoon Kim
- Ziovision Co., Ltd., Chuncheon 24341, Republic of Korea
- Department of Computer Science and Engineering, College of IT, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - Yun Jeong Lim
- Division of Gastroenterology, Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, 27 Dongguk-ro, Ilsandong-gu, Goyang 10326, Republic of Korea
| | - Hyun-Soo Choi
- Ziovision Co., Ltd., Chuncheon 24341, Republic of Korea
- Department of Computer Science and Engineering, Seoul National University of Science and Technology, 232, Gongneung-ro, Nowon-gu, Seoul 01811, Republic of Korea
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Zhang RY, Qiang PP, Cai LJ, Li T, Qin Y, Zhang Y, Zhao YQ, Wang JP. Automatic detection of small bowel lesions with different bleeding risks based on deep learning models. World J Gastroenterol 2024; 30:170-183. [PMID: 38312122 PMCID: PMC10835517 DOI: 10.3748/wjg.v30.i2.170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/15/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Deep learning provides an efficient automatic image recognition method for small bowel (SB) capsule endoscopy (CE) that can assist physicians in diagnosis. However, the existing deep learning models present some unresolved challenges. AIM To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks, and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups. METHODS The proposed model represents a two-stage method that combined image classification with object detection. First, we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images, normal SB mucosa images, and invalid images. Then, the improved YOLO-V5 detection model was utilized to detect the type of lesion and its risk of bleeding, and the location of the lesion was marked. We constructed training and testing sets and compared model-assisted reading with physician reading. RESULTS The accuracy of the model constructed in this study reached 98.96%, which was higher than the accuracy of other systems using only a single module. The sensitivity, specificity, and accuracy of the model-assisted reading detection of all images were 99.17%, 99.92%, and 99.86%, which were significantly higher than those of the endoscopists' diagnoses. The image processing time of the model was 48 ms/image, and the image processing time of the physicians was 0.40 ± 0.24 s/image (P < 0.001). CONCLUSION The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images, which enhances the diagnostic efficiency of physicians and improves the ability of physicians to identify high-risk bleeding groups.
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Affiliation(s)
- Rui-Ya Zhang
- Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, Shanxi Province, China
| | - Peng-Peng Qiang
- School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi Province, China
| | - Ling-Jun Cai
- Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, Shanxi Province, China
| | - Tao Li
- School of Life Sciences and Technology, Mudanjiang Normal University, Mudanjiang 157011, Heilongjiang Province, China
| | - Yan Qin
- Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, Shanxi Province, China
| | - Yu Zhang
- Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, Shanxi Province, China
| | - Yi-Qing Zhao
- Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, Shanxi Province, China
| | - Jun-Ping Wang
- Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, Shanxi Province, China
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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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Wang X, Hu X, Xu Y, Yong J, Li X, Zhang K, Gan T, Yang J, Rao N. A systematic review on diagnosis and treatment of gastrointestinal diseases by magnetically controlled capsule endoscopy and artificial intelligence. Therap Adv Gastroenterol 2023; 16:17562848231206991. [PMID: 37900007 PMCID: PMC10612444 DOI: 10.1177/17562848231206991] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/21/2023] [Indexed: 10/31/2023] Open
Abstract
Background Magnetically controlled capsule endoscopy (MCCE) is a non-invasive, painless, comfortable, and safe equipment to diagnose gastrointestinal diseases (GID), partially overcoming the shortcomings of conventional endoscopy and wireless capsule endoscopy (WCE). With advancements in technology, the main technical parameters of MCCE have continuously been improved, and MCCE has become more intelligent. Objectives The aim of this systematic review was to summarize the research progress of MCCE and artificial intelligence (AI) in the diagnosis and treatment of GID. Data Sources and Methods We conducted a systematic search of PubMed and EMBASE for published studies on GID detection of MCCE, physical factors related to MCCE imaging quality, the application of AI in aiding MCCE, and its additional functions. We synergistically reviewed the included studies, extracted relevant data, and made comparisons. Results MCCE was confirmed to have the same performance as conventional gastroscopy and WCE in detecting common GID, while it lacks research in detecting early gastric cancer (EGC). The body position and cleanliness of the gastrointestinal tract are the main factors affecting imaging quality. The applications of AI in screening intestinal diseases have been comprehensive, while in the detection of common gastric diseases such as ulcers, it has been developed. MCCE can perform some additional functions, such as observations of drug behavior in the stomach and drug damage to the gastric mucosa. Furthermore, it can be improved to perform a biopsy. Conclusion This comprehensive review showed that the MCCE technology has made great progress, but studies on GID detection and treatment by MCCE are in the primary stage. Further studies are required to confirm the performance of MCCE.
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Affiliation(s)
- Xiaotong Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoming Hu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yongxue Xu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiahao Yong
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiang Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Kaixuan Zhang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Tao Gan
- Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu, China
| | - Jinlin Yang
- Digestive Endoscopic Center of West China Hospital, Sichuan University, No.37 Guoxue Alley, Wuhou District, Chengdu City, Chengdu, Sichuan Province 610017, China
| | - Nini Rao
- School of Life Science and Technology, University of Electronic Science and Technology of China, No. 4, Section Two, Jianshe North Road, Chengdu 610054, China
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Oh S, Oh D, Kim D, Song W, Hwang Y, Cho N, Lim YJ. Video Analysis of Small Bowel Capsule Endoscopy Using a Transformer Network. Diagnostics (Basel) 2023; 13:3133. [PMID: 37835876 PMCID: PMC10572266 DOI: 10.3390/diagnostics13193133] [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: 08/01/2023] [Revised: 09/19/2023] [Accepted: 09/30/2023] [Indexed: 10/15/2023] Open
Abstract
Although wireless capsule endoscopy (WCE) detects small bowel diseases effectively, it has some limitations. For example, the reading process can be time consuming due to the numerous images generated per case and the lesion detection accuracy may rely on the operators' skills and experiences. Hence, many researchers have recently developed deep-learning-based methods to address these limitations. However, they tend to select only a portion of the images from a given WCE video and analyze each image individually. In this study, we note that more information can be extracted from the unused frames and the temporal relations of sequential frames. Specifically, to increase the accuracy of lesion detection without depending on experts' frame selection skills, we suggest using whole video frames as the input to the deep learning system. Thus, we propose a new Transformer-architecture-based neural encoder that takes the entire video as the input, exploiting the power of the Transformer architecture to extract long-term global correlation within and between the input frames. Subsequently, we can capture the temporal context of the input frames and the attentional features within a frame. Tests on benchmark datasets of four WCE videos showed 95.1% sensitivity and 83.4% specificity. These results may significantly advance automated lesion detection techniques for WCE images.
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Affiliation(s)
- SangYup Oh
- School of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Kwanak-gu, Seoul 08826, Republic of Korea; (S.O.); (W.S.)
| | - DongJun Oh
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Republic of Korea;
| | - Dongmin Kim
- JLK TOWER, Gangnam-gu, Seoul 06141, Republic of Korea;
| | - Woohyuk Song
- School of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Kwanak-gu, Seoul 08826, Republic of Korea; (S.O.); (W.S.)
| | - Youngbae Hwang
- Department of Electronics Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea;
| | - Namik Cho
- School of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Kwanak-gu, Seoul 08826, Republic of Korea; (S.O.); (W.S.)
| | - Yun Jeong Lim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Republic of Korea;
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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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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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Mascarenhas M, Afonso J, Ribeiro T, Andrade P, Cardoso H, Macedo G. The Promise of Artificial Intelligence in Digestive Healthcare and the Bioethics Challenges It Presents. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59040790. [PMID: 37109748 PMCID: PMC10145124 DOI: 10.3390/medicina59040790] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/27/2023] [Accepted: 04/02/2023] [Indexed: 04/29/2023]
Abstract
With modern society well entrenched in the digital area, the use of Artificial Intelligence (AI) to extract useful information from big data has become more commonplace in our daily lives than we perhaps realize. Medical specialties that rely heavily on imaging techniques have become a strong focus for the incorporation of AI tools to aid disease diagnosis and monitoring, yet AI-based tools that can be employed in the clinic are only now beginning to become a reality. However, the potential introduction of these applications raises a number of ethical issues that must be addressed before they can be implemented, among the most important of which are issues related to privacy, data protection, data bias, explainability and responsibility. In this short review, we aim to highlight some of the most important bioethical issues that will have to be addressed if AI solutions are to be successfully incorporated into healthcare protocols, and ideally, before they are put in place. In particular, we contemplate the use of these aids in the field of gastroenterology, focusing particularly on capsule endoscopy and highlighting efforts aimed at resolving the issues associated with their use when available.
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Affiliation(s)
- Miguel Mascarenhas
- Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - Patrícia Andrade
- Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - Hélder Cardoso
- Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - Guilherme Macedo
- Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
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Chung J, Oh DJ, Park J, Kim SH, Lim YJ. Automatic Classification of GI Organs in Wireless Capsule Endoscopy Using a No-Code Platform-Based Deep Learning Model. Diagnostics (Basel) 2023; 13:diagnostics13081389. [PMID: 37189489 DOI: 10.3390/diagnostics13081389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/03/2023] [Accepted: 04/10/2023] [Indexed: 05/17/2023] Open
Abstract
The first step in reading a capsule endoscopy (CE) is determining the gastrointestinal (GI) organ. Because CE produces too many inappropriate and repetitive images, automatic organ classification cannot be directly applied to CE videos. In this study, we developed a deep learning algorithm to classify GI organs (the esophagus, stomach, small bowel, and colon) using a no-code platform, applied it to CE videos, and proposed a novel method to visualize the transitional area of each GI organ. We used training data (37,307 images from 24 CE videos) and test data (39,781 images from 30 CE videos) for model development. This model was validated using 100 CE videos that included "normal", "blood", "inflamed", "vascular", and "polypoid" lesions. Our model achieved an overall accuracy of 0.98, precision of 0.89, recall of 0.97, and F1 score of 0.92. When we validated this model relative to the 100 CE videos, it produced average accuracies for the esophagus, stomach, small bowel, and colon of 0.98, 0.96, 0.87, and 0.87, respectively. Increasing the AI score's cut-off improved most performance metrics in each organ (p < 0.05). To locate a transitional area, we visualized the predicted results over time, and setting the cut-off of the AI score to 99.9% resulted in a better intuitive presentation than the baseline. In conclusion, the GI organ classification AI model demonstrated high accuracy on CE videos. The transitional area could be more easily located by adjusting the cut-off of the AI score and visualization of its result over time.
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Affiliation(s)
- Joowon Chung
- Department of Internal Medicine, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul 01830, Republic of Korea
| | - Dong Jun Oh
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Republic of Korea
| | - Junseok Park
- Department of Internal Medicine, Digestive Disease Center, Institute for Digestive Research, Soonchunhyang University College of Medicine, Seoul 04401, Republic of Korea
| | - Su Hwan Kim
- Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul 07061, Republic of Korea
| | - Yun Jeong Lim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Republic of Korea
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Athanasiou SA, Sergaki ES, Polydorou AA, Polydorou AA, Stavrakakis GS, Afentakis NM, Vardiambasis IO, Zervakis ME. Revealing the Boundaries of Selected Gastro-Intestinal (GI) Organs by Implementing CNNs in Endoscopic Capsule Images. Diagnostics (Basel) 2023; 13:865. [PMID: 36900009 PMCID: PMC10000441 DOI: 10.3390/diagnostics13050865] [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: 09/29/2022] [Revised: 02/03/2023] [Accepted: 02/22/2023] [Indexed: 03/06/2023] Open
Abstract
PURPOSE The detection of where an organ starts and where it ends is achievable and, since this information can be delivered in real time, it could be quite important for several reasons. For one, by having the practical knowledge of the Wireless Endoscopic Capsule (WEC) transition through an organ's domain, we are able to align and control the endoscopic operation with any other possible protocol, i.e., delivering some form of treatment on the spot. Another is having greater anatomical topography information per session, therefore treating the individual in detail (not "in general"). Even the fact that by gathering more accurate information for a patient by merely implementing clever software procedures is a task worth exploiting, since the problems we have to overcome in real-time processing of the capsule findings (i.e., wireless transfer of images to another unit that will apply the necessary real time computations) are still challenging. This study proposes a computer-aided detection (CAD) tool, a CNN algorithm deployed to run on field programmable gate array (FPGA), able to automatically track the capsule transitions through the entrance (gate) of esophagus, stomach, small intestine and colon, in real time. The input data are the wireless transmitted image shots of the capsule's camera (while the endoscopy capsule is operating). METHODS We developed and evaluated three distinct multiclass classification CNNs, trained on the same dataset of total 5520 images extracted by 99 capsule videos (total 1380 frames from each organ of interest). The proposed CNNs differ in size and number of convolution filters. The confusion matrix is obtained by training each classifier and evaluating the trained model on an independent test dataset comprising 496 images extracted by 39 capsule videos, 124 from each GI organ. The test dataset was further evaluated by one endoscopist, and his findings were compared with CNN-based results. The statistically significant of predictions between the four classes of each model and the comparison between the three distinct models is evaluated by calculating the p-values and chi-square test for multi class. The comparison between the three models is carried out by calculating the macro average F1 score and Mattheus correlation coefficient (MCC). The quality of the best CNN model is estimated by calculations of sensitivity and specificity. RESULTS Our experimental results of independent validation demonstrate that the best of our developed models addressed this topological problem by exhibiting an overall sensitivity (96.55%) and specificity of (94.73%) in the esophagus, (81.08% sensitivity and 96.55% specificity) in the stomach, (89.65% sensitivity and 97.89% specificity) in the small intestine and (100% sensitivity and 98.94% specificity) in the colon. The average macro accuracy is 95.56%, the average macro sensitivity is 91.82%.
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Affiliation(s)
- Sofia A. Athanasiou
- School of Electrical and Computer Engineering, Technical University of Crete, 73100 Chania, Greece
- Department of Electronic Engineering, Hellenic Mediterranean University, 73133 Chania, Greece
| | - Eleftheria S. Sergaki
- School of Electrical and Computer Engineering, Technical University of Crete, 73100 Chania, Greece
| | - Andreas A. Polydorou
- Aretaeio Hospital, 2nd University Department of Surgery, Medical School, National and Kapodistrian University of Athens, 11528 Athens, Greece
| | - Alexios A. Polydorou
- Medical School, National and Kapodistrian University of Athens, 11528 Athens, Greece
| | - George S. Stavrakakis
- School of Electrical and Computer Engineering, Technical University of Crete, 73100 Chania, Greece
| | - Nikolaos M. Afentakis
- Department of Electronic Engineering, Hellenic Mediterranean University, 73133 Chania, Greece
| | - Ioannis O. Vardiambasis
- Department of Electronic Engineering, Hellenic Mediterranean University, 73133 Chania, Greece
| | - Michail E. Zervakis
- School of Electrical and Computer Engineering, Technical University of Crete, 73100 Chania, Greece
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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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He C, Wang Q, Jiang X, Jiang B, Qian YY, Pan J, Liao Z. Magnetic capsule endoscopy: concept and application of artificial intelligence. ARTIFICIAL INTELLIGENCE IN CAPSULE ENDOSCOPY 2023:217-241. [DOI: 10.1016/b978-0-323-99647-1.00009-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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Son G, Eo T, An J, Oh DJ, Shin Y, Rha H, Kim YJ, Lim YJ, Hwang D. Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering. Diagnostics (Basel) 2022; 12:diagnostics12081858. [PMID: 36010210 PMCID: PMC9406835 DOI: 10.3390/diagnostics12081858] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/28/2022] [Accepted: 07/28/2022] [Indexed: 12/22/2022] Open
Abstract
By automatically classifying the stomach, small bowel, and colon, the reading time of the wireless capsule endoscopy (WCE) can be reduced. In addition, it is an essential first preprocessing step to localize the small bowel in order to apply automated small bowel lesion detection algorithms based on deep learning. The purpose of the study was to develop an automated small bowel detection method from long untrimmed videos captured from WCE. Through this, the stomach and colon can also be distinguished. The proposed method is based on a convolutional neural network (CNN) with a temporal filtering on the predicted probabilities from the CNN. For CNN, we use a ResNet50 model to classify three organs including stomach, small bowel, and colon. The hybrid temporal filter consisting of a Savitzky–Golay filter and a median filter is applied to the temporal probabilities for the “small bowel” class. After filtering, the small bowel and the other two organs are differentiated with thresholding. The study was conducted on dataset of 200 patients (100 normal and 100 abnormal WCE cases), which was divided into a training set of 140 cases, a validation set of 20 cases, and a test set of 40 cases. For the test set of 40 patients (20 normal and 20 abnormal WCE cases), the proposed method showed accuracy of 99.8% in binary classification for the small bowel. Transition time errors for gastrointestinal tracts were only 38.8 ± 25.8 seconds for the transition between stomach and small bowel and 32.0 ± 19.1 seconds for the transition between small bowel and colon, compared to the ground truth organ transition points marked by two experienced gastroenterologists.
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Affiliation(s)
- Geonhui Son
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Taejoon Eo
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Jiwoong An
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Dong Jun Oh
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Korea;
| | - Yejee Shin
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Hyenogseop Rha
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - You Jin Kim
- IntroMedic, Capsule Endoscopy Medical Device Manufacturer, Seoul 08375, Korea;
| | - Yun Jeong Lim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Korea;
- Correspondence: (Y.J.L.); (D.H.)
| | - Dosik Hwang
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
- Center for Healthcare Robotics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Korea
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul 03722, Korea
- Department of Radiology and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul 03722, Korea
- Correspondence: (Y.J.L.); (D.H.)
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Patel A, Vedantam D, Poman DS, Motwani L, Asif N. Obscure Gastrointestinal Bleeding and Capsule Endoscopy: A Win-Win Situation or Not? Cureus 2022; 14:e27137. [PMID: 36017285 PMCID: PMC9392966 DOI: 10.7759/cureus.27137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/22/2022] [Indexed: 11/05/2022] Open
Abstract
Obscure gastrointestinal bleeding (OGIB) refers to bleeding of uncertain origin that persists or recurs after negative workup using any of the radiologic evaluation modalities. It can be divided into two types based on whether clinically evident bleeding is present, namely, obscure overt and obscure occult bleeding. As the visualization of the bowel mucosa is challenging, capsule endoscopy (CE) is the ideal go-to procedure as the process is wireless, ingestible, small, disposable, and, most importantly, non-invasive. This review article has compiled various studies to shed light on the guidelines for using CE, its structure and procedure, patient preferences, diagnostic yield, cost-effectiveness, and the future. The goal of this review is to show the influence of CE on OGIB on the aspects mentioned earlier.
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