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Zhou X, Wu J, Li H, Zeng X, Luo HL. Efficacy of a full management model in daytime surgery for gastrointestinal polyps based on WeChat: A study protocol for randomized controlled trials. J Eval Clin Pract 2024. [PMID: 38798179 DOI: 10.1111/jep.14022] [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: 03/15/2024] [Accepted: 05/08/2024] [Indexed: 05/29/2024]
Abstract
OBJECTIVE The objective of this study is to improve the efficiency of daytime surgery for gastrointestinal polyp and ensure the safety of patients. We tried an information management method based on WeChat platform in patients undergoing daytime gastrointestinal polypectomy and to explore the feasibility and effectiveness of a full management model. METHODS Five hundred and ninety-three patients were randomly divided into two groups: the control group was treated with traditional management methods and the experimental group was treated with the whole-process management mode based on the WeChat platform. The WeChat platform-based full management model included establishing a day surgery management WeChat group, developing multidisciplinary, full-management protocols and processes for day surgery, establishing an information-based surgical scheduling system and adopting diverse forms of day surgery education and continuity of care. This feature included illustrated brochures, vivid verbal presentations, WeChat public numbers and Internet management platforms. The treatment time, hospitalization cost and patient satisfaction of the two groups were counted. RESULTS In the experimental group, 408 patients were enrolled. The preoperative waiting time and patients' length of stay were 3 days and 1 day, respectively. The medical and nursing intake time was 7 min. The procedure cancellation rate and postoperative complications rate was 0.07% and 0.02%. In the control group, 185 patients were enrolled in the study, The preoperative waiting time and patients' length of stay was 7 days and 3 days. The medical and nursing intake time was 28 min. The procedure cancellation rate and postoperative complications rate were 0.13% and 0.05%, respectively. The hospitalization costs were reduced by an average of $140/person and the satisfaction scores were higher than the control group. In summary, the preoperative waiting time, medical reception time, surgical cancellation rate, length of hospital stay and hospitalization cost in the observation group were less than those in the control group (p < 0.05). Patient satisfaction scores were significantly higher than those in the control group (p < 0.05). CONCLUSION Through the full management model based on WeChat, the preoperative waiting time, medical reception time, surgical cancellation rate, length of hospital stay and hospitalization cost in the experimental group were less than those in the control group. Patient satisfaction scores were significantly higher than those in the control group and the difference was statistically significant.
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Affiliation(s)
- Xin Zhou
- Department of Gastroenterology, Mianyang Central Hospital, Mianyang, China
| | - Jiao Wu
- Department of Gastroenterology, Mianyang Central Hospital, Mianyang, China
| | - Hong Li
- Department of Gastroenterology, Mianyang Central Hospital, Mianyang, China
| | - Xin Zeng
- Department of Gastroenterology, Mianyang Central Hospital, Mianyang, China
| | - Huai-Li Luo
- Department of Cardiology, Mianyang Central Hospital, Mianyang, China
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Lee JY, Park J, Lee HJ, Park H, Jin EH, Park K, Baek JE, Yang DH, Hong SW, Kim N, Byeon JS. Automatic assessment of bowel preparation by an artificial intelligence model and its clinical applicability. J Gastroenterol Hepatol 2024. [PMID: 38766682 DOI: 10.1111/jgh.16618] [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: 01/03/2024] [Revised: 04/06/2024] [Accepted: 05/02/2024] [Indexed: 05/22/2024]
Abstract
BACKGROUND AND AIM Reliable bowel preparation assessment is important in colonoscopy. However, current scoring systems are limited by laborious and time-consuming tasks and interobserver variability. We aimed to develop an artificial intelligence (AI) model to assess bowel cleanliness and evaluate its clinical applicability. METHODS A still image-driven AI model to assess the Boston Bowel Preparation Scale (BBPS) was developed and validated using 2361 colonoscopy images. For evaluating real-world applicability, the model was validated using 113 10-s colonoscopy video clips and 30 full colonoscopy videos to identify "adequate (BBPS 2-3)" or "inadequate (BBPS 0-1)" preparation. The model was tested with an external dataset of 29 colonoscopy videos. The clinical applicability of the model was evaluated using 225 consecutive colonoscopies. Inter-rater variability was analyzed between the AI model and endoscopists. RESULTS The AI model achieved an accuracy of 94.0% and an area under the receiver operating characteristic curve of 0.939 with the still images. Model testing with an external dataset showed an accuracy of 95.3%, an area under the receiver operating characteristic curve of 0.976, and a sensitivity of 100% for the detection of inadequate preparations. The clinical applicability study showed an overall agreement rate of 85.3% between endoscopists and the AI model, with Fleiss' kappa of 0.686. The agreement rate was lower for the right colon compared with the transverse and left colon, with Fleiss' kappa of 0.563, 0.575, and 0.789, respectively. CONCLUSIONS The AI model demonstrated accurate bowel preparation assessment and substantial agreement with endoscopists. Further refinement of the AI model is warranted for effective monitoring of qualified colonoscopy in large-scale screening programs.
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Affiliation(s)
- Ji Young Lee
- Health Screening Promotion Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jooyoung Park
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hyo Jeong Lee
- Health Screening Promotion Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hana Park
- Health Screening Promotion Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Eun Hyo Jin
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Kanggil Park
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Ji Eun Baek
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Dong-Hoon Yang
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seung Wook Hong
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jeong-Sik Byeon
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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Parasa S, Berzin T, Leggett C, Gross S, Repici A, Ahmad OF, Chiang A, Coelho-Prabhu N, Cohen J, Dekker E, Keswani RN, Kahn CE, Hassan C, Petrick N, Mountney P, Ng J, Riegler M, Mori Y, Saito Y, Thakkar S, Waxman I, Wallace MB, Sharma P. Consensus statements on the current landscape of artificial intelligence applications in endoscopy, addressing roadblocks, and advancing artificial intelligence in gastroenterology. Gastrointest Endosc 2024:S0016-5107(23)03139-5. [PMID: 38639679 DOI: 10.1016/j.gie.2023.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 12/02/2023] [Indexed: 04/20/2024]
Abstract
BACKGROUND AND AIMS The American Society for Gastrointestinal Endoscopy (ASGE) AI Task Force along with experts in endoscopy, technology space, regulatory authorities, and other medical subspecialties initiated a consensus process that analyzed the current literature, highlighted potential areas, and outlined the necessary research in artificial intelligence (AI) to allow a clearer understanding of AI as it pertains to endoscopy currently. METHODS A modified Delphi process was used to develop these consensus statements. RESULTS Statement 1: Current advances in AI allow for the development of AI-based algorithms that can be applied to endoscopy to augment endoscopist performance in detection and characterization of endoscopic lesions. Statement 2: Computer vision-based algorithms provide opportunities to redefine quality metrics in endoscopy using AI, which can be standardized and can reduce subjectivity in reporting quality metrics. Natural language processing-based algorithms can help with the data abstraction needed for reporting current quality metrics in GI endoscopy effortlessly. Statement 3: AI technologies can support smart endoscopy suites, which may help optimize workflows in the endoscopy suite, including automated documentation. Statement 4: Using AI and machine learning helps in predictive modeling, diagnosis, and prognostication. High-quality data with multidimensionality are needed for risk prediction, prognostication of specific clinical conditions, and their outcomes when using machine learning methods. Statement 5: Big data and cloud-based tools can help advance clinical research in gastroenterology. Multimodal data are key to understanding the maximal extent of the disease state and unlocking treatment options. Statement 6: Understanding how to evaluate AI algorithms in the gastroenterology literature and clinical trials is important for gastroenterologists, trainees, and researchers, and hence education efforts by GI societies are needed. Statement 7: Several challenges regarding integrating AI solutions into the clinical practice of endoscopy exist, including understanding the role of human-AI interaction. Transparency, interpretability, and explainability of AI algorithms play a key role in their clinical adoption in GI endoscopy. Developing appropriate AI governance, data procurement, and tools needed for the AI lifecycle are critical for the successful implementation of AI into clinical practice. Statement 8: For payment of AI in endoscopy, a thorough evaluation of the potential value proposition for AI systems may help guide purchasing decisions in endoscopy. Reliable cost-effectiveness studies to guide reimbursement are needed. Statement 9: Relevant clinical outcomes and performance metrics for AI in gastroenterology are currently not well defined. To improve the quality and interpretability of research in the field, steps need to be taken to define these evidence standards. Statement 10: A balanced view of AI technologies and active collaboration between the medical technology industry, computer scientists, gastroenterologists, and researchers are critical for the meaningful advancement of AI in gastroenterology. CONCLUSIONS The consensus process led by the ASGE AI Task Force and experts from various disciplines has shed light on the potential of AI in endoscopy and gastroenterology. AI-based algorithms have shown promise in augmenting endoscopist performance, redefining quality metrics, optimizing workflows, and aiding in predictive modeling and diagnosis. However, challenges remain in evaluating AI algorithms, ensuring transparency and interpretability, addressing governance and data procurement, determining payment models, defining relevant clinical outcomes, and fostering collaboration between stakeholders. Addressing these challenges while maintaining a balanced perspective is crucial for the meaningful advancement of AI in gastroenterology.
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Affiliation(s)
| | | | | | - Seth Gross
- NYU Langone Health, New York, New York, USA
| | | | | | - Austin Chiang
- Medtronic Gastrointestinal, Santa Clara, California, USA
| | | | | | | | | | - Charles E Kahn
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Nicholas Petrick
- Center for Devices and Radiological Health, U.S. Food and Drug Administration
| | | | - Jonathan Ng
- Iterative Health, Boston, Massachusetts, USA
| | | | | | | | - Shyam Thakkar
- West Virginia University Medicine, Morgantown, West Virginia, USA
| | - Irving Waxman
- Rush University Medical Center, Chicago, Illinois, USA
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Spadaccini M, Troya J, Khalaf K, Facciorusso A, Maselli R, Hann A, Repici A. Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going? Dig Liver Dis 2024:S1590-8658(24)00249-4. [PMID: 38458884 DOI: 10.1016/j.dld.2024.01.203] [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: 10/12/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 03/10/2024]
Abstract
Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. However, there are limitations and variations in the performance of endoscopists, leading to missed lesions and suboptimal outcomes. The emergence of artificial intelligence (AI) in endoscopy offers promising opportunities to improve the quality and efficacy of screening colonoscopies. In particular, AI applications, including computer-aided detection (CADe) and computer-aided characterization (CADx), have demonstrated the potential to enhance adenoma detection and optical diagnosis accuracy. Additionally, AI-assisted quality control systems aim to standardize the endoscopic examination process. This narrative review provides an overview of AI principles and discusses the current knowledge on AI-assisted endoscopy in the context of screening colonoscopies. It highlights the significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy. However, further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.
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Affiliation(s)
- Marco Spadaccini
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy.
| | - Joel Troya
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Kareem Khalaf
- Division of Gastroenterology, St. Michael's Hospital, University of Toronto, Toronto, Canada
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Surgical and Medical Sciences, University of Foggia, Foggia, Italy
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
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5
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Tan CH, Goh WWB, So JBY, Sung JJY. Clinical use cases in artificial intelligence: current trends and future opportunities. Singapore Med J 2024; 65:183-185. [PMID: 38527304 PMCID: PMC11060646 DOI: 10.4103/singaporemedj.smj-2023-193] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/19/2024] [Indexed: 03/27/2024]
Affiliation(s)
- Cher Heng Tan
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, Singapore
| | - Jimmy Bok Yan So
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- University Surgical Cluster, National University Hospital, Singapore
- Division of Surgical Oncology, National University Cancer Institute, Singapore
| | - Joseph J Y Sung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
- Department of Gastroenterology and Hepatology, Tan Tock Seng Hospital, Singapore
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Soo JMP, Koh FHX. Detection of sessile serrated adenoma using artificial intelligence-enhanced endoscopy: an Asian perspective. ANZ J Surg 2024; 94:362-365. [PMID: 38149749 DOI: 10.1111/ans.18785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 11/04/2023] [Indexed: 12/28/2023]
Abstract
BACKGROUND As the serrated pathway has gained prominence as an alternative colorectal carcinogenesis pathway, sessile serrated adenomas or polyps (SSA/P) have been highlighted as lesions to rule out during colonoscopy. These lesions are however morphologically difficult to detect on endoscopy and can be mistaken for hyperplastic polyps due to similar endoscopic features. With the underlying nature of rapid progression and malignant transformation, interval cancer is a likely consequence of undetected or overlooked SSA/P. Real-time artificial intelligence (AI)-assisted colonoscopy via the computer-assisted detection system (CADe) is an increasingly useful tool in improving adenoma detection rate by providing a second eye during the procedure. In this article, we describe a guide through a video to illustrate the detection of SSA/P during AI-assisted colonoscopy. METHODS Consultant-grade endoscopists utilized real-time AI-assisted colonoscopy device, as part of a larger prospective study, to detect suspicious lesions which were later histopathologically confirmed to be SSA/P. RESULTS All lesions were picked up by the CADe where a real-time green box highlighted suspicious polyps to the clinician. Three SSA/P of varying morphology are described with reference to classical SSA/P features and with comparison to the features of the hyperplastic polyp found in our study. All three SSA/P observed are in keeping with the JNET Classification (Type 1). CONCLUSION In conclusion, CADe is a most useful aid to clinicians during endoscopy in the detection of SSA/P but must be complemented with factors such as good endoscopy skill and bowel prep for effective detection, and biopsy coupled with subsequent accurate histological diagnosis.
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Affiliation(s)
- Joycelyn Mun-Peng Soo
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Frederick Hong-Xiang Koh
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore, Singapore
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7
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Zhu Y, Lyu X, Tao X, Wu L, Yin A, Liao F, Hu S, Wang Y, Zhang M, Huang L, Wang J, Zhang C, Gong D, Jiang X, Zhao L, Yu H. A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination. BMC Gastroenterol 2024; 24:10. [PMID: 38166722 PMCID: PMC10759410 DOI: 10.1186/s12876-023-03067-w] [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: 04/08/2023] [Accepted: 11/28/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Double-balloon enteroscopy (DBE) is a standard method for diagnosing and treating small bowel disease. However, DBE may yield false-negative results due to oversight or inexperience. We aim to develop a computer-aided diagnostic (CAD) system for the automatic detection and classification of small bowel abnormalities in DBE. DESIGN AND METHODS A total of 5201 images were collected from Renmin Hospital of Wuhan University to construct a detection model for localizing lesions during DBE, and 3021 images were collected to construct a classification model for classifying lesions into four classes, protruding lesion, diverticulum, erosion & ulcer and angioectasia. The performance of the two models was evaluated using 1318 normal images and 915 abnormal images and 65 videos from independent patients and then compared with that of 8 endoscopists. The standard answer was the expert consensus. RESULTS For the image test set, the detection model achieved a sensitivity of 92% (843/915) and an area under the curve (AUC) of 0.947, and the classification model achieved an accuracy of 86%. For the video test set, the accuracy of the system was significantly better than that of the endoscopists (85% vs. 77 ± 6%, p < 0.01). For the video test set, the proposed system was superior to novices and comparable to experts. CONCLUSIONS We established a real-time CAD system for detecting and classifying small bowel lesions in DBE with favourable performance. ENDOANGEL-DBE has the potential to help endoscopists, especially novices, in clinical practice and may reduce the miss rate of small bowel lesions.
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Affiliation(s)
- Yijie Zhu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiaoguang Lyu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiao Tao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Anning Yin
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Fei Liao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Shan Hu
- School of Computer Science, Wuhan University, Wuhan, China
| | - Yang Wang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mengjiao Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Li Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Junxiao Wang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chenxia Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Dexin Gong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiaoda Jiang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Liang Zhao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China.
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China.
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8
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Feng L, Xu J, Ji X, Chen L, Xing S, Liu B, Han J, Zhao K, Li J, Xia S, Guan J, Yan C, Tong Q, Long H, Zhang J, Chen R, Tian D, Luo X, Xiao F, Liao J. Development and validation of a three-dimensional deep learning-based system for assessing bowel preparation on colonoscopy video. Front Med (Lausanne) 2023; 10:1296249. [PMID: 38164219 PMCID: PMC10757977 DOI: 10.3389/fmed.2023.1296249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024] Open
Abstract
Background The performance of existing image-based training models in evaluating bowel preparation on colonoscopy videos was relatively low, and only a few models used external data to prove their generalization. Therefore, this study attempted to develop a more precise and stable AI system for assessing bowel preparation of colonoscopy video. Methods We proposed a system named ViENDO to assess the bowel preparation quality, including two CNNs. First, Information-Net was used to identify and filter out colonoscopy video frames unsuitable for Boston bowel preparation scale (BBPS) scoring. Second, BBPS-Net was trained and tested with 5,566 suitable short video clips through three-dimensional (3D) convolutional neural network (CNN) technology to detect BBPS-based insufficient bowel preparation. Then, ViENDO was applied to complete withdrawal colonoscopy videos from multiple centers to predict BBPS segment scores in clinical settings. We also conducted a human-machine contest to compare its performance with endoscopists. Results In video clips, BBPS-Net for determining inadequate bowel preparation generated an area under the curve of up to 0.98 and accuracy of 95.2%. When applied to full-length withdrawal colonoscopy videos, ViENDO assessed bowel cleanliness with an accuracy of 93.8% in the internal test set and 91.7% in the external dataset. The human-machine contest demonstrated that the accuracy of ViENDO was slightly superior compared to most endoscopists, though no statistical significance was found. Conclusion The 3D-CNN-based AI model showed good performance in evaluating full-length bowel preparation on colonoscopy video. It has the potential as a substitute for endoscopists to provide BBPS-based assessments during daily clinical practice.
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Affiliation(s)
- Lina Feng
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiaxin Xu
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xuantao Ji
- Wuhan United Imaging Healthcare Surgical Technology Co., Ltd., Wuhan, China
| | - Liping Chen
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuai Xing
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bo Liu
- Wuhan United Imaging Healthcare Surgical Technology Co., Ltd., Wuhan, China
| | - Jian Han
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kai Zhao
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Junqi Li
- Changzhou United Imaging Healthcare Surgical Technology Co., Ltd., Changzhou, China
| | - Suhong Xia
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jialun Guan
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chenyu Yan
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Qiaoyun Tong
- Department of Gastroenterology, Yichang Central People’s Hospital, China Three Gorges University, Yichang, China
| | - Hui Long
- Department of Gastroenterology, Tianyou Hospital, Wuhan University of Science and Technology, Wuhan, China
| | - Juanli Zhang
- Department of Gastroenterology, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
- Department of Gastroenterology, Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, China
| | - Ruihong Chen
- Department of Gastroenterology, Xiantao First People’s Hospital Affiliated to Yangtze University, Wuhan, China
| | - Dean Tian
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoping Luo
- Department of Pediatrics, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fang Xiao
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiazhi Liao
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Liu P, Wu J, He C, Wang W. ENDOANGEL versus water exchange for the detection of colorectal adenomas. Therap Adv Gastroenterol 2023; 16:17562848231218570. [PMID: 38116388 PMCID: PMC10729641 DOI: 10.1177/17562848231218570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 11/10/2023] [Indexed: 12/21/2023] Open
Abstract
Background Recently, the ENDOANGEL (EN) system, a computer-assisted detection technique, and water exchange (WE) assisted colonoscopy have both been shown to increase the colorectal adenoma detection rate (ADR). Objectives The aim of this study was to compare the ADR between EN- and WE-assisted colonoscopy. Design This was a retrospective study. Methods Data from patients who underwent either EN- or WE-assisted colonoscopy between October 2021 and August 2022 were analysed consecutively. The primary outcome measure was the ADR. Results The ADR was found to be similar between the EN and WE groups, with 80 out of 199 (40.2%) patients in the EN group compared to 78 out of 174 (44.8%) patients in the WE group [1.21; 95% confidence interval (CI), 0.80-1.83]. In the analysis using stabilized inverse probability treatment weighting after adjustment for confounding factors, both colonoscopy methods had similar performance in terms of ADR (1.41; 95% CI, 0.88-2.27). Conclusion EN was found to be comparable to WE in terms of ADR during colonoscopy, and both methods may be effectively used in clinical practice.
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Affiliation(s)
- Pengwei Liu
- Department of Gastroenterology, Yijishan Hospital of Wannan Medical College, Wuhu, China
| | - Jie Wu
- Department of Gastroenterology, Yijishan Hospital of Wannan Medical College, Wuhu, China
| | - Chiyi He
- Department of Gastroenterology, Yijishan Hospital of Wannan Medical College, No. 2 Zheshan West Road, Wuhu 241001, China
| | - Wei Wang
- Department of Gastroenterology, Yijishan Hospital of Wannan Medical College, No. 2 Zheshan West Road, Wuhu 241001, China
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10
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Guillen-Grima F, Guillen-Aguinaga S, Guillen-Aguinaga L, Alas-Brun R, Onambele L, Ortega W, Montejo R, Aguinaga-Ontoso E, Barach P, Aguinaga-Ontoso I. Evaluating the Efficacy of ChatGPT in Navigating the Spanish Medical Residency Entrance Examination (MIR): Promising Horizons for AI in Clinical Medicine. Clin Pract 2023; 13:1460-1487. [PMID: 37987431 PMCID: PMC10660543 DOI: 10.3390/clinpract13060130] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/15/2023] [Accepted: 11/16/2023] [Indexed: 11/22/2023] Open
Abstract
The rapid progress in artificial intelligence, machine learning, and natural language processing has led to increasingly sophisticated large language models (LLMs) for use in healthcare. This study assesses the performance of two LLMs, the GPT-3.5 and GPT-4 models, in passing the MIR medical examination for access to medical specialist training in Spain. Our objectives included gauging the model's overall performance, analyzing discrepancies across different medical specialties, discerning between theoretical and practical questions, estimating error proportions, and assessing the hypothetical severity of errors committed by a physician. MATERIAL AND METHODS We studied the 2022 Spanish MIR examination results after excluding those questions requiring image evaluations or having acknowledged errors. The remaining 182 questions were presented to the LLM GPT-4 and GPT-3.5 in Spanish and English. Logistic regression models analyzed the relationships between question length, sequence, and performance. We also analyzed the 23 questions with images, using GPT-4's new image analysis capability. RESULTS GPT-4 outperformed GPT-3.5, scoring 86.81% in Spanish (p < 0.001). English translations had a slightly enhanced performance. GPT-4 scored 26.1% of the questions with images in English. The results were worse when the questions were in Spanish, 13.0%, although the differences were not statistically significant (p = 0.250). Among medical specialties, GPT-4 achieved a 100% correct response rate in several areas, and the Pharmacology, Critical Care, and Infectious Diseases specialties showed lower performance. The error analysis revealed that while a 13.2% error rate existed, the gravest categories, such as "error requiring intervention to sustain life" and "error resulting in death", had a 0% rate. CONCLUSIONS GPT-4 performs robustly on the Spanish MIR examination, with varying capabilities to discriminate knowledge across specialties. While the model's high success rate is commendable, understanding the error severity is critical, especially when considering AI's potential role in real-world medical practice and its implications for patient safety.
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Affiliation(s)
- Francisco Guillen-Grima
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
- Healthcare Research Institute of Navarra (IdiSNA), 31008 Pamplona, Spain
- Department of Preventive Medicine, Clinica Universidad de Navarra, 31008 Pamplona, Spain
- CIBER in Epidemiology and Public Health (CIBERESP), Institute of Health Carlos III, 46980 Madrid, Spain
| | - Sara Guillen-Aguinaga
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
| | - Laura Guillen-Aguinaga
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
- Department of Nursing, Kystad Helse-og Velferdssenter, 7026 Trondheim, Norway
| | - Rosa Alas-Brun
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
| | - Luc Onambele
- School of Health Sciences, Catholic University of Central Africa, Yaoundé 1100, Cameroon;
| | - Wilfrido Ortega
- Department of Surgery, Medical and Social Sciences, University of Alcala de Henares, 28871 Alcalá de Henares, Spain;
| | - Rocio Montejo
- Department of Obstetrics and Gynecology, Institute of Clinical Sciences, University of Gothenburg, 413 46 Gothenburg, Sweden;
- Department of Obstetrics and Gynecology, Sahlgrenska University Hospital, 413 46 Gothenburg, Sweden
| | | | - Paul Barach
- Jefferson College of Population Health, Philadelphia, PA 19107, USA;
- School of Medicine, Thomas Jefferson University, Philadelphia, PA 19107, USA
- Interdisciplinary Research Institute for Health Law and Science, Sigmund Freud University, 1020 Vienna, Austria
- Department of Surgery, Imperial College, London SW7 2AZ, UK
| | - Ines Aguinaga-Ontoso
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
- Healthcare Research Institute of Navarra (IdiSNA), 31008 Pamplona, Spain
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11
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Vulpoi RA, Luca M, Ciobanu A, Olteanu A, Bărboi O, Iov DE, Nichita L, Ciortescu I, Cijevschi Prelipcean C, Ștefănescu G, Mihai C, Drug VL. The Potential Use of Artificial Intelligence in Irritable Bowel Syndrome Management. Diagnostics (Basel) 2023; 13:3336. [PMID: 37958232 PMCID: PMC10648815 DOI: 10.3390/diagnostics13213336] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/24/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023] Open
Abstract
Irritable bowel syndrome (IBS) has a global prevalence of around 4.1% and is associated with a low quality of life and increased healthcare costs. Current guidelines recommend that IBS is diagnosed using the symptom-based Rome IV criteria. Despite this, when patients seek medical attention, they are usually over-investigated. This issue might be resolved by novel technologies in medicine, such as the use of Artificial Intelligence (AI). In this context, this paper aims to review AI applications in IBS. AI in colonoscopy proved to be useful in organic lesion detection and diagnosis and in objectively assessing the quality of the procedure. Only a recently published study talked about the potential of AI-colonoscopy in IBS. AI was also used to study biofilm characteristics in the large bowel and establish a potential relationship with IBS. Moreover, an AI algorithm was developed in order to correlate specific bowel sounds with IBS. In addition to that, AI-based smartphone applications have been developed to facilitate the monitoring of IBS symptoms. From a therapeutic standpoint, an AI system was created to recommend specific diets based on an individual's microbiota. In conclusion, future IBS diagnosis and treatment may benefit from AI.
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Affiliation(s)
- Radu Alexandru Vulpoi
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Mihaela Luca
- Institute of Computer Science, Romanian Academy-Iasi Branch, 700481 Iasi, Romania; (M.L.); (A.C.)
| | - Adrian Ciobanu
- Institute of Computer Science, Romanian Academy-Iasi Branch, 700481 Iasi, Romania; (M.L.); (A.C.)
| | - Andrei Olteanu
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Oana Bărboi
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Diana-Elena Iov
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Loredana Nichita
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Irina Ciortescu
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Cristina Cijevschi Prelipcean
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Gabriela Ștefănescu
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Cătălina Mihai
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Vasile Liviu Drug
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
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Gimeno-García AZ, Benítez-Zafra F, Nicolás-Pérez D, Hernández-Guerra M. Colon Bowel Preparation in the Era of Artificial Intelligence: Is There Potential for Enhancing Colon Bowel Cleansing? MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1834. [PMID: 37893552 PMCID: PMC10608636 DOI: 10.3390/medicina59101834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/10/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023]
Abstract
BACKGROUND AND OBJECTIVES Proper bowel preparation is of paramount importance for enhancing adenoma detection rates and reducing postcolonoscopic colorectal cancer risk. Despite recommendations from gastroenterology societies regarding the optimal rates of successful bowel preparation, these guidelines are frequently unmet. Various approaches have been employed to enhance the rates of successful bowel preparation, yet the quality of cleansing remains suboptimal. Intensive bowel preparation techniques, supplementary administration of bowel solutions, and educational interventions aimed at improving patient adherence to instructions have been commonly utilized, particularly among patients at a high risk of inadequate bowel preparation. Expedited strategies conducted on the same day as the procedure have also been endorsed by scientific organizations. More recently, the utilization of artificial intelligence (AI) has emerged for the preprocedural detection of inadequate bowel preparation, holding the potential to guide the preparation process immediately preceding colonoscopy. This manuscript comprehensively reviews the current strategies employed to optimize bowel cleansing, with a specific focus on patients with elevated risks for inadequate bowel preparation. Additionally, the prospective role of AI in this context is thoroughly examined. CONCLUSIONS While a majority of outpatients may achieve cleanliness with standard cleansing protocols, dealing with hard-to-prepare patients remains a challenge. Rescue strategies based on AI are promising, but such evidence remains limited. To ensure proper bowel cleansing, a combination of strategies should be performed.
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Hassan C, Repici A, Sharma P. Incorporating Artificial Intelligence Into Gastroenterology Practices. Clin Gastroenterol Hepatol 2023; 21:1687-1689. [PMID: 36841445 DOI: 10.1016/j.cgh.2023.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
Affiliation(s)
- Cesare Hassan
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center - IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Alessandro Repici
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center - IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Prateek Sharma
- Division of Gastroenterology and Hepatology, VA Medical Center and University of Kansas School of Medicine, Kansas City, Kansas.
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14
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Maida M, Marasco G, Facciorusso A, Shahini E, Sinagra E, Pallio S, Ramai D, Murino A. Effectiveness and application of artificial intelligence for endoscopic screening of colorectal cancer: the future is now. Expert Rev Anticancer Ther 2023; 23:719-729. [PMID: 37194308 DOI: 10.1080/14737140.2023.2215436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 05/15/2023] [Indexed: 05/18/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) in gastrointestinal endoscopy includes systems designed to interpret medical images and increase sensitivity during examination. This may be a promising solution to human biases and may provide support during diagnostic endoscopy. AREAS COVERED This review aims to summarize and evaluate data supporting AI technologies in lower endoscopy, addressing their effectiveness, limitations, and future perspectives. EXPERT OPINION Computer-aided detection (CADe) systems have been studied with promising results, allowing for an increase in adenoma detection rate (ADR), adenoma per colonoscopy (APC), and a reduction in adenoma miss rate (AMR). This may lead to an increase in the sensitivity of endoscopic examinations and a reduction in the risk of interval-colorectal cancer. In addition, computer-aided characterization (CADx) has also been implemented, aiming to distinguish adenomatous and non-adenomatous lesions through real-time assessment using advanced endoscopic imaging techniques. Moreover, computer-aided quality (CADq) systems have been developed with the aim of standardizing quality measures in colonoscopy (e.g. withdrawal time and adequacy of bowel cleansing) both to improve the quality of examinations and set a reference standard for randomized controlled trials.
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Affiliation(s)
- Marcello Maida
- Gastroenterology and Endoscopy Unit, S. Elia-Raimondi Hospital, Caltanissetta, Italy
| | - Giovanni Marasco
- IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Antonio Facciorusso
- Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
| | - Endrit Shahini
- Gastroenterology Unit, National Institute of Gastroenterology-IRCCS "Saverio de Bellis", Castellana Grotte, Bari, Italy
| | - Emanuele Sinagra
- Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, Cefalu, Italy
| | - Socrate Pallio
- Digestive Diseases Endoscopy Unit, Policlinico G. Martino Hospital, University of Messina, Messina, Italy
| | - Daryl Ramai
- Gastroenterology & Hepatology, University of Utah Health, Salt Lake City, UT, USA
| | - Alberto Murino
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, UK
- Department of Gastroenterology, Cleveland Clinic London, London, UK
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15
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Ren Y, Zou D, Xu W, Zhao X, Lu W, He X. Bimodal segmentation and classification of endoscopic ultrasonography images for solid pancreatic tumor. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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16
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Gimeno-García AZ, Hernández-Pérez A, Nicolás-Pérez D, Hernández-Guerra M. Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward? Cancers (Basel) 2023; 15:cancers15082193. [PMID: 37190122 DOI: 10.3390/cancers15082193] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/04/2023] [Accepted: 04/05/2023] [Indexed: 05/17/2023] Open
Abstract
Growing evidence indicates that artificial intelligence (AI) applied to medicine is here to stay. In gastroenterology, AI computer vision applications have been stated as a research priority. The two main AI system categories are computer-aided polyp detection (CADe) and computer-assisted diagnosis (CADx). However, other fields of expansion are those related to colonoscopy quality, such as methods to objectively assess colon cleansing during the colonoscopy, as well as devices to automatically predict and improve bowel cleansing before the examination, predict deep submucosal invasion, obtain a reliable measurement of colorectal polyps and accurately locate colorectal lesions in the colon. Although growing evidence indicates that AI systems could improve some of these quality metrics, there are concerns regarding cost-effectiveness, and large and multicentric randomized studies with strong outcomes, such as post-colonoscopy colorectal cancer incidence and mortality, are lacking. The integration of all these tasks into one quality-improvement device could facilitate the incorporation of AI systems in clinical practice. In this manuscript, the current status of the role of AI in colonoscopy is reviewed, as well as its current applications, drawbacks and areas for improvement.
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Affiliation(s)
- Antonio Z Gimeno-García
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - Anjara Hernández-Pérez
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - David Nicolás-Pérez
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - Manuel Hernández-Guerra
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
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Dhaliwal J, Walsh CM. Artificial Intelligence in Pediatric Endoscopy: Current Status and Future Applications. Gastrointest Endosc Clin N Am 2023; 33:291-308. [PMID: 36948747 DOI: 10.1016/j.giec.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
The application of artificial intelligence (AI) has great promise for improving pediatric endoscopy. The majority of preclinical studies have been undertaken in adults, with the greatest progress being made in the context of colorectal cancer screening and surveillance. This development has only been possible with advances in deep learning, like the convolutional neural network model, which has enabled real-time detection of pathology. Comparatively, the majority of deep learning systems developed in inflammatory bowel disease have focused on predicting disease severity and were developed using still images rather than videos. The application of AI to pediatric endoscopy is in its infancy, thus providing an opportunity to develop clinically meaningful and fair systems that do not perpetuate societal biases. In this review, we provide an overview of AI, summarize the advances of AI in endoscopy, and describe its potential application to pediatric endoscopic practice and education.
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Affiliation(s)
- Jasbir Dhaliwal
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Cincinnati Children's Hospital Medictal Center, University of Cincinnati, OH, USA.
| | - Catharine M Walsh
- Division of Gastroenterology, Hepatology, and Nutrition, and the SickKids Research and Learning Institutes, The Hospital for Sick Children, Toronto, ON, Canada; Department of Paediatrics and The Wilson Centre, University of Toronto, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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18
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Gravina AG, Pellegrino R, Romeo M, Palladino G, Cipullo M, Iadanza G, Olivieri S, Zagaria G, De Gennaro N, Santonastaso A, Romano M, Federico A. Quality of bowel preparation in patients with inflammatory bowel disease undergoing colonoscopy: What factors to consider? World J Gastrointest Endosc 2023; 15:133-145. [PMID: 37034970 PMCID: PMC10080552 DOI: 10.4253/wjge.v15.i3.133] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/07/2022] [Accepted: 02/15/2023] [Indexed: 03/16/2023] Open
Abstract
An adequate bowel preparation in patients with inflammatory bowel disease (IBD) is a prerequisite for successful colonoscopy for screening, diagnosis, and surveillance. Several bowel preparation formulations are available, both high- and low-volume based on polyethylene glycol. Generally, low-volume formulations are also based on several compounds such as magnesium citrate preparations with sodium picosulphate, oral sulphate solution, and oral sodium phosphate-based solutions. Targeted studies on the quality of bowel preparation prior to colonoscopy in the IBD population are still required, with current evidence from existing studies being inconclusive. New frontiers are also moving towards the use of alternatives to anterograde ones, using preparations based on retrograde colonic lavage.
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Affiliation(s)
| | - Raffaele Pellegrino
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples 80138, Italy
| | - Mario Romeo
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples 80138, Italy
| | - Giovanna Palladino
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples 80138, Italy
| | - Marina Cipullo
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples 80138, Italy
| | - Giorgia Iadanza
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples 80138, Italy
| | - Simone Olivieri
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples 80138, Italy
| | - Giuseppe Zagaria
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples 80138, Italy
| | - Nicola De Gennaro
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples 80138, Italy
| | - Antonio Santonastaso
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples 80138, Italy
| | - Marco Romano
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples 80138, Italy
| | - Alessandro Federico
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Naples 80138, Italy
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Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening. Diagnostics (Basel) 2023; 13:diagnostics13061102. [PMID: 36980409 PMCID: PMC10047293 DOI: 10.3390/diagnostics13061102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/19/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention.
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20
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Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022; 3:117-141. [DOI: 10.35712/aig.v3.i5.117] [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: 10/09/2022] [Revised: 11/21/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Medicine, NYU Langone Health, New York, NY 10016, United States
| | - Robert J Duve
- Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, United States
| | - Matthew O'Mara
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
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21
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Wang J, Wang Z, Chen M, Xiao Y, Chen S, Wu L, Yao L, Jiang X, Li J, Xu M, Lin M, Zhu Y, Luo R, Zhang C, Li X, Yu H. An interpretable artificial intelligence system for detecting risk factors of gastroesophageal variceal bleeding. NPJ Digit Med 2022; 5:183. [PMID: 36536039 PMCID: PMC9763258 DOI: 10.1038/s41746-022-00729-z] [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: 06/04/2022] [Accepted: 11/29/2022] [Indexed: 12/23/2022] Open
Abstract
Bleeding risk factors for gastroesophageal varices (GEV) detected by endoscopy in cirrhotic patients determine the prophylactical treatment patients will undergo in the following 2 years. We propose a methodology for measuring the risk factors. We create an artificial intelligence system (ENDOANGEL-GEV) containing six models to segment GEV and to classify the grades (grades 1-3) and red color signs (RC, RC0-RC3) of varices. It also summarizes changes in the above results with region in real time. ENDOANGEL-GEV is trained using 6034 images from 1156 cirrhotic patients across three hospitals (dataset 1) and validated on multicenter datasets with 11009 images from 141 videos (dataset 2) and in a prospective study recruiting 161 cirrhotic patients from Renmin Hospital of Wuhan University (dataset 3). In dataset 1, ENDOANGEL-GEV achieves intersection over union values of 0.8087 for segmenting esophageal varices and 0.8141 for gastric varices. In dataset 2, the system maintains fairly accuracy across images from three hospitals. In dataset 3, ENDOANGEL-GEV surpasses attended endoscopists in detecting RC of GEV and classifying grades (p < 0.001). When ranking the risk of patients combined with the Child‒Pugh score, ENDOANGEL-GEV outperforms endoscopists for esophageal varices (p < 0.001) and shows comparable performance for gastric varices (p = 0.152). Compared with endoscopists, ENDOANGEL-GEV may help 12.31% (16/130) more patients receive the right intervention. We establish an interpretable system for the endoscopic diagnosis and risk stratification of GEV. It will assist in detecting the first bleeding risk factors accurately and expanding the scope of quantitative measurement of diseases.
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Affiliation(s)
- Jing Wang
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhengqiang Wang
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mingkai Chen
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yong Xiao
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Shi Chen
- Department of Gastroenterology, Wuhan Puren Hospital, Wuhan, China
| | - Lianlian Wu
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Liwen Yao
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiaoda Jiang
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jiao Li
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ming Xu
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mengjuan Lin
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yijie Zhu
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Renquan Luo
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chenxia Zhang
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xun Li
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Honggang Yu
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
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Low DJ, Hong Z, Jugnundan S, Mukherjee A, Grover SC. Automated Detection of Bowel Preparation Scoring and Adequacy With Deep Convolutional Neural Networks. J Can Assoc Gastroenterol 2022; 5:256-260. [PMID: 36467599 PMCID: PMC9713630 DOI: 10.1093/jcag/gwac013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/10/2023] Open
Abstract
INTRODUCTION Adequate bowel preparation is integral to effective colonoscopy. Inadequate bowel preparation has been associated with reduced adenoma detection rate and increased post-colonoscopy colorectal cancer (PCCRC). As a result, the USMSTF recommends early interval reevaluation for colonoscopies with inadequate bowel preparation. However, bowel preparation documentation is highly variable with subjective interpretation. In this study, we developed deep convolutional neural networks (DCNN) to objectively ascertain bowel preparation. METHODS Bowel preparation scores were assigned using the Boston Bowel Preparation Scale (BBPS). Bowel preparation adequacy and inadequacy were defined as BBPS ≥2 and BBPS <2, respectively. A total of 38523 images were extracted from 28 colonoscopy videos and split into 26966 images for training, 7704 for validation, and 3853 for testing. Two DCNNs were created using a Densenet-169 backbone in PyTorch library evaluating BBPS score and bowel preparation adequacy. We used Adam optimiser with an initial learning rate of 3 × 10-4 and a scheduler to decay the learning rate of each parameter group by 0.1 every 7 epochs along with focal loss as our criterion for both classifiers. RESULTS The overall accuracy for BBPS subclassification and determination of adequacy was 91% and 98%, respectively. The accuracy for BBPS 0, BBPS 1, BBPS 2, and BBPS 3 was 84%, 91%, 85%, and 96%, respectively. CONCLUSION We developed DCCNs capable of assessing bowel preparation adequacy and scoring with a high degree of accuracy. However, this algorithm will require further research to assess its efficacy in real-time colonoscopy.
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Affiliation(s)
- Daniel J Low
- St. Michael’s Hospital, Toronto, ON M5B 1W8, Canada
| | - Zhuoqiao Hong
- Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | | | - Samir C Grover
- Correspondence: Samir Grover, MD, MEd, St. Michael’s Hospital, 30 Bond Street, Toronto, ON M5B 1W8, Canada, e-mail:
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23
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Messmann H, Bisschops R, Antonelli G, Libânio D, Sinonquel P, Abdelrahim M, Ahmad OF, Areia M, Bergman JJGHM, Bhandari P, Boskoski I, Dekker E, Domagk D, Ebigbo A, Eelbode T, Eliakim R, Häfner M, Haidry RJ, Jover R, Kaminski MF, Kuvaev R, Mori Y, Palazzo M, Repici A, Rondonotti E, Rutter MD, Saito Y, Sharma P, Spada C, Spadaccini M, Veitch A, Gralnek IM, Hassan C, Dinis-Ribeiro M. Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2022; 54:1211-1231. [PMID: 36270318 DOI: 10.1055/a-1950-5694] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
This ESGE Position Statement defines the expected value of artificial intelligence (AI) for the diagnosis and management of gastrointestinal neoplasia within the framework of the performance measures already defined by ESGE. This is based on the clinical relevance of the expected task and the preliminary evidence regarding artificial intelligence in artificial or clinical settings. MAIN RECOMMENDATIONS:: (1) For acceptance of AI in assessment of completeness of upper GI endoscopy, the adequate level of mucosal inspection with AI should be comparable to that assessed by experienced endoscopists. (2) For acceptance of AI in assessment of completeness of upper GI endoscopy, automated recognition and photodocumentation of relevant anatomical landmarks should be obtained in ≥90% of the procedures. (3) For acceptance of AI in the detection of Barrett's high grade intraepithelial neoplasia or cancer, the AI-assisted detection rate for suspicious lesions for targeted biopsies should be comparable to that of experienced endoscopists with or without advanced imaging techniques. (4) For acceptance of AI in the management of Barrett's neoplasia, AI-assisted selection of lesions amenable to endoscopic resection should be comparable to that of experienced endoscopists. (5) For acceptance of AI in the diagnosis of gastric precancerous conditions, AI-assisted diagnosis of atrophy and intestinal metaplasia should be comparable to that provided by the established biopsy protocol, including the estimation of extent, and consequent allocation to the correct endoscopic surveillance interval. (6) For acceptance of artificial intelligence for automated lesion detection in small-bowel capsule endoscopy (SBCE), the performance of AI-assisted reading should be comparable to that of experienced endoscopists for lesion detection, without increasing but possibly reducing the reading time of the operator. (7) For acceptance of AI in the detection of colorectal polyps, the AI-assisted adenoma detection rate should be comparable to that of experienced endoscopists. (8) For acceptance of AI optical diagnosis (computer-aided diagnosis [CADx]) of diminutive polyps (≤5 mm), AI-assisted characterization should match performance standards for implementing resect-and-discard and diagnose-and-leave strategies. (9) For acceptance of AI in the management of polyps ≥ 6 mm, AI-assisted characterization should be comparable to that of experienced endoscopists in selecting lesions amenable to endoscopic resection.
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Affiliation(s)
- Helmut Messmann
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, Catholic University of Leuven (KUL), TARGID, University Hospital Leuven, Leuven, Belgium
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli Hospital, Ariccia, Rome, Italy
- Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Sapienza University of Rome, Italy
| | - Diogo Libânio
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto, Portugal
- MEDCIDS, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Pieter Sinonquel
- Department of Gastroenterology and Hepatology, Catholic University of Leuven (KUL), TARGID, University Hospital Leuven, Leuven, Belgium
| | - Mohamed Abdelrahim
- Endoscopy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Omer F Ahmad
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London Hospital, London, UK
- Division of Surgery and Interventional Sciences, University College London Hospital, London, UK
- Gastrointestinal Services, University College London Hospital, London, UK
| | - Miguel Areia
- Gastroenterology Department, Portuguese Oncology Institute of Coimbra, Coimbra, Portugal
| | | | - Pradeep Bhandari
- Endoscopy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Ivo Boskoski
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Dirk Domagk
- Department of Medicine I, Josephs-Hospital Warendorf, Academic Teaching Hospital, University of Muenster, Warendorf, Germany
| | - Alanna Ebigbo
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Tom Eelbode
- Department of Electrical Engineering (ESAT/PSI), Medical Imaging Research Center, KU Leuven, Leuven, Belgium
| | - Rami Eliakim
- Department of Gastroenterology, Sheba Medical Center Tel Hashomer & Sackler School of Medicine, Tel-Aviv University, Ramat Gan, Israel
| | - Michael Häfner
- 2nd Medical Department, Barmherzige Schwestern Krankenhaus, Vienna, Austria
| | - Rehan J Haidry
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London Hospital, London, UK
- Division of Surgery and Interventional Sciences, University College London Hospital, London, UK
| | - Rodrigo Jover
- Servicio de Gastroenterología, Hospital General Universitario Dr. Balmis, Instituto de Investigación Biomédica de Alicante ISABIAL, Departamento de Medicina Clínica, Universidad Miguel Hernández, Alicante, Spain
| | - Michal F Kaminski
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Department of Gastroenterology, Hepatology and Clinical Oncology, Centre of Postgraduate Medical Education, Warsaw, Poland
- Department of Oncological Gastroenterology and Department of Cancer Prevention, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Roman Kuvaev
- Endoscopy Department, Yaroslavl Regional Cancer Hospital, Yaroslavl, Russian Federation
- Department of Gastroenterology, Faculty of Additional Professional Education, N.A. Pirogov Russian National Research Medical University, Moscow, Russian Federation
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | | | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | | | - Matthew D Rutter
- North Tees and Hartlepool NHS Foundation Trust, Stockton-on-Tees, UK
- Population Health Sciences Institute, Newcastle University, Newcastle, UK
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Prateek Sharma
- Gastroenterology and Hepatology Division, University of Kansas School of Medicine, Kansas, USA
- Kansas City VA Medical Center, Kansas City, USA
| | - Cristiano Spada
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Digestive Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Andrew Veitch
- Department of Gastroenterology, Royal Wolverhampton Hospitals NHS Trust, Wolverhampton, UK
| | - Ian M Gralnek
- Ellen and Pinchas Mamber Institute of Gastroenterology and Hepatology, Emek Medical Center, Afula, Israel
- Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Mario Dinis-Ribeiro
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto, Portugal
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Gan PL, Huang S, Pan X, Xia HF, Lü MH, Zhou X, Tang XW. The scientific progress and prospects of artificial intelligence in digestive endoscopy: A comprehensive bibliometric analysis. Medicine (Baltimore) 2022; 101:e31931. [PMID: 36451438 PMCID: PMC9704924 DOI: 10.1097/md.0000000000031931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Artificial intelligence (AI) has been used for diagnosis and outcome prediction in clinical practice. Furthermore, AI in digestive endoscopy has attracted much attention and shown promising and stimulating results. This study aimed to determine the development trends and research hotspots of AI in digestive endoscopy by visualizing articles. Publications on AI in digestive endoscopy research were retrieved from the Web of Science Core Collection on April 25, 2022. VOSviewer and CiteSpace were used to assess and plot the research outputs. This analytical research was based on original articles and reviews. A total of 524 records of AI research in digestive endoscopy, published between 2005 and 2022, were retrieved. The number of articles has increased 27-fold from 2017 to 2021. Fifty-one countries and 994 institutions contributed to all publications. Asian countries had the highest number of publications. China, the USA, and Japan were consistently the leading driving forces and mainly contributed (26%, 21%, and 14.31%, respectively). With a solid academic reputation in this area, Japan has the highest number of citations per article. Tada Tomohiro published the most articles and received the most citations.. Gastrointestinal endoscopy published the largest number of publications, and 4 of the top 10 cited papers were published in this journal. "The Classification," "ulcerative colitis," "capsule endoscopy," "polyp detection," and "early gastric cancer" were the leading research hotspots. Our study provides systematic elaboration for researchers to better understand the development of AI in gastrointestinal endoscopy.
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Affiliation(s)
- Pei-Ling Gan
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Shu Huang
- Department of Gastroenterology, the People’s Hospital of Lianshui, Huaian, China
| | - Xiao Pan
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Hui-Fang Xia
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Mu-Han Lü
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xian Zhou
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
- * Correspondence: Xiao-Wei Tang and Xian Zhou, Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province 646099, China (e-mail: and )
| | - Xiao-Wei Tang
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
- * Correspondence: Xiao-Wei Tang and Xian Zhou, Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province 646099, China (e-mail: and )
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25
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Liu W, Wu Y, Yuan X, Zhang J, Zhou Y, Zhang W, Zhu P, Tao Z, He L, Hu B, Yi Z. Artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination. Endoscopy 2022; 54:972-979. [PMID: 35391493 PMCID: PMC9500011 DOI: 10.1055/a-1799-8297] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND This study aimed to develop an artificial intelligence (AI)-based system for measuring fold examination quality (FEQ) of colonoscopic withdrawal technique. We also examined the relationship between the system's evaluation of FEQ and FEQ scores from experts, and adenoma detection rate (ADR) and withdrawal time of colonoscopists, and evaluated the system's ability to improve FEQ during colonoscopy. METHODS First, we developed an AI-based system for measuring FEQ. Next, 103 consecutive colonoscopies performed by 11 colonoscopists were collected for evaluation. Three experts graded FEQ of each colonoscopy, after which the recorded colonoscopies were evaluated by the system. We further assessed the system by correlating its evaluation of FEQ against expert scoring, historical ADR, and withdrawal time of each colonoscopist. We also conducted a prospective observational study to evaluate the system's performance in enhancing fold examination. RESULTS The system's evaluations of FEQ of each endoscopist were significantly correlated with experts' scores (r = 0.871, P < 0.001), historical ADR (r = 0.852, P = 0.001), and withdrawal time (r = 0.727, P = 0.01). For colonoscopies performed by colonoscopists with previously low ADRs (< 25 %), AI assistance significantly improved the FEQ, evaluated by both the AI system (0.29 [interquartile range (IQR) 0.27-0.30] vs. 0.23 [0.17-0.26]) and experts (14.00 [14.00-15.00] vs. 11.67 [10.00-13.33]) (both P < 0.001). CONCLUSION The system's evaluation of FEQ was strongly correlated with FEQ scores from experts, historical ADR, and withdrawal time of each colonoscopist. The system has the potential to enhance FEQ.
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Affiliation(s)
- Wei Liu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yu Wu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Xianglei Yuan
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jingyu Zhang
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, Sichuan, China
| | - Yao Zhou
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Wanhong Zhang
- Department of Gastroenterology, Cangxi Peopleʼs Hospital, Guangyuan, Sichuan, China
| | - Peipei Zhu
- Department of Gastroenterology, Dazhou Integrated Traditional Chinese and Western Medicine Hosptial, Dazhou, Sichuan, China
| | - Zhang Tao
- Department of Gastroenterology, Nanchong Central Hospital, Nanchong, Sichuan, China
| | - Long He
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhang Yi
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China
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26
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Chang YY, Li PC, Chang RF, Chang YY, Huang SP, Chen YY, Chang WY, Yen HH. Development and validation of a deep learning-based algorithm for colonoscopy quality assessment. Surg Endosc 2022; 36:6446-6455. [PMID: 35132449 DOI: 10.1007/s00464-021-08993-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 12/31/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Quality indicators should be assessed and monitored to improve colonoscopy quality in clinical practice. Endoscopists must enter relevant information in the endoscopy reporting system to facilitate data collection, which may be inaccurate. The current study aimed to develop a full deep learning-based algorithm to identify and analyze intra-procedural colonoscopy quality indicators based on endoscopy images obtained during the procedure. METHODS A deep learning system for classifying colonoscopy images for quality assurance purposes was developed and its performance was assessed with an independent dataset. The system was utilized to analyze captured images and results were compared with those of real-world reports. RESULTS In total, 10,417 images from the hospital endoscopy database and 3157 from Hyper-Kvasir open dataset were utilized to develop the quality assurance algorithm. The overall accuracy of the algorithm was 96.72% and that of the independent test dataset was 94.71%. Moreover, 761 real-world reports and colonoscopy images were analyzed. The accuracy of electronic reports about cecal intubation rate was 99.34% and that of the algorithm was 98.95%. The agreement rate for the assessment of polypectomy rates using the electronic reports and the algorithm was 0.87 (95% confidence interval 0.83-0.90). A good correlation was found between the withdrawal time calculated using the algorithm and that entered by the physician (correlation coefficient r = 0.959, p < 0.0001). CONCLUSION We proposed a novel deep learning-based algorithm that used colonoscopy images for quality assurance purposes. This model can be used to automatically assess intra-procedural colonoscopy quality indicators in clinical practice.
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Affiliation(s)
- Yuan-Yen Chang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Pai-Chi Li
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Ruey-Feng Chang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
- Artificial Intelligence Development Center, Changhua Christian Hospital, Changhua, Taiwan
| | - Yu-Yao Chang
- Department of Colorectal Surgery, Changhua Christian Hospital, Changhua, Taiwan
| | - Siou-Ping Huang
- Division of Gastroenterology, Changhua Christian Hospital, Changhua, Taiwan
| | - Yang-Yuan Chen
- Division of Gastroenterology, Changhua Christian Hospital, Changhua, Taiwan
| | - Wen-Yen Chang
- Department of Medical Education, National Taiwan University Hospital, Taipei, Taiwan
| | - Hsu-Heng Yen
- Artificial Intelligence Development Center, Changhua Christian Hospital, Changhua, Taiwan.
- Department of Colorectal Surgery, Changhua Christian Hospital, Changhua, Taiwan.
- Division of Gastroenterology, Changhua Christian Hospital, Changhua, Taiwan.
- Department of Electrical Engineering, Chung Yuan University, Taoyuan, Taiwan.
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
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27
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Auriemma F, Sferrazza S, Bianchetti M, Savarese MF, Lamonaca L, Paduano D, Piazza N, Giuffrida E, Mete LS, Tucci A, Milluzzo SM, Iannelli C, Repici A, Mangiavillano B. From advanced diagnosis to advanced resection in early neoplastic colorectal lesions: Never-ending and trending topics in the 2020s. World J Gastrointest Surg 2022; 14:632-655. [PMID: 36158280 PMCID: PMC9353749 DOI: 10.4240/wjgs.v14.i7.632] [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: 02/07/2021] [Revised: 05/02/2021] [Accepted: 06/20/2022] [Indexed: 02/06/2023] Open
Abstract
Colonoscopy represents the most widespread and effective tool for the prevention and treatment of early stage preneoplastic and neoplastic lesions in the panorama of cancer screening. In the world there are different approaches to the topic of colorectal cancer prevention and screening: different starting ages (45-50 years); different initial screening tools such as fecal occult blood with immunohistochemical or immune-enzymatic tests; recto-sigmoidoscopy; and colonoscopy. The key aspects of this scenario are composed of a proper bowel preparation that ensures a valid diagnostic examination, experienced endoscopist in detection of preneoplastic and early neoplastic lesions and open-minded to upcoming artificial intelligence-aided examination, knowledge in the field of resection of these lesions (from cold-snaring, through endoscopic mucosal resection and endoscopic submucosal dissection, up to advanced tools), and management of complications.
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Affiliation(s)
- Francesco Auriemma
- Gastrointestinal Endoscopy Unit, Humanitas Mater Domini, Castellanza 21053, Italy
| | - Sandro Sferrazza
- Gastroenterology and Endoscopy Unit, Santa Chiara Hospital, Trento 38014, Italy
| | - Mario Bianchetti
- Digestive Endoscopy Unit, San Giuseppe Hospital - Multimedica, Milan 20123, Italy
| | - Maria Flavia Savarese
- Department of Gastroenterology and Gastrointestinal Endoscopy, General Hospital, Sanremo 18038, Italy
| | - Laura Lamonaca
- Gastrointestinal Endoscopy Unit, Humanitas Mater Domini, Castellanza 21053, Italy
| | - Danilo Paduano
- Gastrointestinal Endoscopy Unit, Humanitas Mater Domini, Castellanza 21053, Italy
| | - Nicole Piazza
- Gastroenterology Unit, IRCCS Policlinico San Donato, San Donato Milanese; Department of Biomedical Sciences for Health, University of Milan, Milan 20122, Italy
| | - Enrica Giuffrida
- Gastroenterology and Hepatology Unit, A.O.U. Policlinico “G. Giaccone", Palermo 90127, Italy
| | - Lupe Sanchez Mete
- Department of Gastroenterology and Digestive Endoscopy, IRCCS Regina Elena National Cancer Institute, Rome 00144, Italy
| | - Alessandra Tucci
- Department of Gastroenterology, Molinette Hospital, Città della salute e della Scienza di Torino, Turin 10126, Italy
| | | | - Chiara Iannelli
- Department of Health Sciences, Magna Graecia University, Catanzaro 88100, Italy
| | - Alessandro Repici
- Digestive Endoscopy Unit and Gastroenterology, Humanitas Clinical and Research Center and Humanitas University, Rozzano 20089, Italy
| | - Benedetto Mangiavillano
- Biomedical Science, Hunimed, Pieve Emanuele 20090, Italy
- Gastrointestinal Endoscopy Unit, Humanitas Mater Domini, Castellanza, Varese 21053, Italy
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28
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Meining A, Hann A, Fuchs KH. Innovations in GI-endoscopy. Arab J Gastroenterol 2022; 23:139-143. [PMID: 35738990 DOI: 10.1016/j.ajg.2022.06.003] [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] [Indexed: 11/29/2022]
Abstract
Gastrointestinal endoscopy covers both diagnosis and therapy. Due to its diagnostic accuracy and minimal invasiveness, several innovations have been made within the last years including artificial intelligence and endoscopic tumor resection. The present review highlights some of these innovation. In addition, a special focus is set on the experience made by our own research group trying to combine the expertise of endoscopists/ physicians as well as engineers and computer scientists.
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Affiliation(s)
- Alexander Meining
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany.
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Karl Hermann Fuchs
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
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29
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Low DJ, Hong Z, Lee JH. Artificial intelligence implementation in pancreaticobiliary endoscopy. Expert Rev Gastroenterol Hepatol 2022; 16:493-498. [PMID: 35639864 DOI: 10.1080/17474124.2022.2083604] [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] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Artificial intelligence has been rapidly deployed in gastroenterology and endoscopy. The acceleration of deep convolutional neural networks along with hardware development has allowed implementation of artificial intelligence algorithms into real-time endoscopy, particularly colonoscopy. However, artificial intelligence implementation in pancreaticobiliary endoscopy is nascent. AREAS COVERED Initial studies have been conducted in endoscopic retrograde pancreatography (ERCP), endoscopic ultrasound (EUS), and digital single operator cholangioscopy (DSOC). Machine learning has been implemented in identifying significant landmarks, including the ampulla on ERCP, and the bile duct, pancreas, and portal confluence on EUS. Moreover, artificial intelligence algorithms have been deployed in differentiating pathology including pancreas cancer, autoimmune pancreatitis, pancreatic cystic lesions, and biliary strictures. EXPERT OPINION There have been relatively few studies with limited sample sizes in developing these machine learning algorithms. Despite the early successful demonstration of artificial intelligence in pancreaticobiliary endoscopy, additional research needs to be conducted with larger data sets to improve generalizability and assessed in real-time endoscopy before clinical implementation. However, pancreaticobiliary endoscopy remains a promising avenue of artificial intelligence application with the potential to improve clinical practice and outcomes.
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Affiliation(s)
- Daniel J Low
- Department of Gastroenterology Hepatology and Nutrition, Division of Internal Medicine, MD Anderson Cancer Center, Houston, TX, USA.,Division of Gastroenterology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Zhuoqiao Hong
- System Design & Management, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jeffrey H Lee
- Department of Gastroenterology Hepatology and Nutrition, Division of Internal Medicine, MD Anderson Cancer Center, Houston, TX, USA
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30
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Kiwan W. A Better Prep School: Does an Interactive Instruction Video Improve Colon Cleanliness? Dig Dis Sci 2022; 67:1920-1921. [PMID: 34435268 DOI: 10.1007/s10620-021-07222-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/03/2021] [Indexed: 12/09/2022]
Affiliation(s)
- Wissam Kiwan
- University of Southern California, Los Angeles, USA.
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31
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Busnatu Ș, Niculescu AG, Bolocan A, Petrescu GED, Păduraru DN, Năstasă I, Lupușoru M, Geantă M, Andronic O, Grumezescu AM, Martins H. Clinical Applications of Artificial Intelligence-An Updated Overview. J Clin Med 2022; 11:jcm11082265. [PMID: 35456357 PMCID: PMC9031863 DOI: 10.3390/jcm11082265] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.
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Affiliation(s)
- Ștefan Busnatu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Adelina-Gabriela Niculescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
| | - Alexandra Bolocan
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - George E. D. Petrescu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Dan Nicolae Păduraru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Iulian Năstasă
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Mircea Lupușoru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Marius Geantă
- Centre for Innovation in Medicine, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania;
| | - Octavian Andronic
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Alexandru Mihai Grumezescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
- Research Institute of the University of Bucharest—ICUB, University of Bucharest, 050657 Bucharest, Romania
- Academy of Romanian Scientists, Ilfov No. 3, 50044 Bucharest, Romania
- Correspondence:
| | - Henrique Martins
- Faculty of Health Sciences, Universidade da Beira Interior, 6200-506 Covilha, Portugal;
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32
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Tavanapong W, Oh J, Riegler MA, Khaleel M, Mittal B, de Groen PC. Artificial Intelligence for Colonoscopy: Past, Present, and Future. IEEE J Biomed Health Inform 2022; 26:3950-3965. [PMID: 35316197 PMCID: PMC9478992 DOI: 10.1109/jbhi.2022.3160098] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
During the past decades, many automated image analysis methods have been developed for colonoscopy. Real-time implementation of the most promising methods during colonoscopy has been tested in clinical trials, including several recent multi-center studies. All trials have shown results that may contribute to prevention of colorectal cancer. We summarize the past and present development of colonoscopy video analysis methods, focusing on two categories of artificial intelligence (AI) technologies used in clinical trials. These are (1) analysis and feedback for improving colonoscopy quality and (2) detection of abnormalities. Our survey includes methods that use traditional machine learning algorithms on carefully designed hand-crafted features as well as recent deep-learning methods. Lastly, we present the gap between current state-of-the-art technology and desirable clinical features and conclude with future directions of endoscopic AI technology development that will bridge the current gap.
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33
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Schmitz R, Werner R, Repici A, Bisschops R, Meining A, Zornow M, Messmann H, Hassan C, Sharma P, Rösch T. Artificial intelligence in GI endoscopy: stumbling blocks, gold standards and the role of endoscopy societies. Gut 2022; 71:451-454. [PMID: 33479051 DOI: 10.1136/gutjnl-2020-323115] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 01/04/2021] [Accepted: 01/05/2021] [Indexed: 02/06/2023]
Affiliation(s)
- Rüdiger Schmitz
- Interdisciplinary Endoscopy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Rene Werner
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alessandro Repici
- Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy.,Humanitas University, Department of Biomedical Sciences, Milan, Italy
| | - Raf Bisschops
- Gastroenterology, University Hospital Gasthuisberg, Leuven, Belgium
| | - Alexander Meining
- Department of Gastroenterology, University of Würzburg, Würzburg, Germany
| | - Michael Zornow
- Chair for Public and European Law, University of Göttingen, Göttingen, Germany
| | - Helmut Messmann
- Department of Gastroenterology, Universitätsklinikum Augsburg, Augsburg, Germany
| | - Cesare Hassan
- Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy
| | - Prateek Sharma
- Division of Gastroenterology and Hepatology, Veterans Affairs Medical Center and University of Kansas, Lawrence, Kansas, USA
| | - Thomas Rösch
- Interdisciplinary Endoscopy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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34
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Wang YP, Jheng YC, Sung KY, Lin HE, Hsin IF, Chen PH, Chu YC, Lu D, Wang YJ, Hou MC, Lee FY, Lu CL. Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy. Diagnostics (Basel) 2022; 12:diagnostics12030613. [PMID: 35328166 PMCID: PMC8947406 DOI: 10.3390/diagnostics12030613] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 02/22/2022] [Accepted: 02/24/2022] [Indexed: 12/29/2022] Open
Abstract
Background: Adequate bowel cleansing is important for colonoscopy performance evaluation. Current bowel cleansing evaluation scales are subjective, with a wide variation in consistency among physicians and low reported rates of accuracy. We aim to use machine learning to develop a fully automatic segmentation method for the objective evaluation of the adequacy of colon preparation. Methods: Colonoscopy videos were retrieved from a video data cohort and transferred to qualified images, which were randomly divided into training, validation, and verification datasets. The fecal residue was manually segmented. A deep learning model based on the U-Net convolutional network architecture was developed to perform automatic segmentation. The performance of the automatic segmentation was evaluated on the overlap area with the manual segmentation. Results: A total of 10,118 qualified images from 119 videos were obtained. The model averaged 0.3634 s to segmentate one image automatically. The models produced a strong high-overlap area with manual segmentation, with 94.7% ± 0.67% of that area predicted by our AI model, which correlated well with the area measured manually (r = 0.915, p < 0.001). The AI system can be applied in real-time qualitatively and quantitatively. Conclusions: We established a fully automatic segmentation method to rapidly and accurately mark the fecal residue-coated mucosa for the objective evaluation of colon preparation.
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Affiliation(s)
- Yen-Po Wang
- Endoscopy Center for Diagnosis and Treatment, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (Y.-P.W.); (Y.-C.J.); (K.-Y.S.); (H.-E.L.); (I.-F.H.); (P.-H.C.); (D.L.); (M.-C.H.)
- Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan;
- Institute of Brain Science, National Yang Ming Chiao Tung University School of Medicine, Taipei 112, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei 112, Taiwan;
| | - Ying-Chun Jheng
- Endoscopy Center for Diagnosis and Treatment, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (Y.-P.W.); (Y.-C.J.); (K.-Y.S.); (H.-E.L.); (I.-F.H.); (P.-H.C.); (D.L.); (M.-C.H.)
- Faculty of Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei 112, Taiwan;
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Kuang-Yi Sung
- Endoscopy Center for Diagnosis and Treatment, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (Y.-P.W.); (Y.-C.J.); (K.-Y.S.); (H.-E.L.); (I.-F.H.); (P.-H.C.); (D.L.); (M.-C.H.)
- Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan;
- Faculty of Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei 112, Taiwan;
| | - Hung-En Lin
- Endoscopy Center for Diagnosis and Treatment, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (Y.-P.W.); (Y.-C.J.); (K.-Y.S.); (H.-E.L.); (I.-F.H.); (P.-H.C.); (D.L.); (M.-C.H.)
- Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan;
- Faculty of Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei 112, Taiwan;
| | - I-Fang Hsin
- Endoscopy Center for Diagnosis and Treatment, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (Y.-P.W.); (Y.-C.J.); (K.-Y.S.); (H.-E.L.); (I.-F.H.); (P.-H.C.); (D.L.); (M.-C.H.)
- Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan;
- Faculty of Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei 112, Taiwan;
| | - Ping-Hsien Chen
- Endoscopy Center for Diagnosis and Treatment, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (Y.-P.W.); (Y.-C.J.); (K.-Y.S.); (H.-E.L.); (I.-F.H.); (P.-H.C.); (D.L.); (M.-C.H.)
- Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan;
- Faculty of Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei 112, Taiwan;
| | - Yuan-Chia Chu
- Information Management Office, Taipei Veterans General Hospital, Taipei 112, Taiwan;
- Big Data Center, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei 112, Taiwan
| | - David Lu
- Endoscopy Center for Diagnosis and Treatment, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (Y.-P.W.); (Y.-C.J.); (K.-Y.S.); (H.-E.L.); (I.-F.H.); (P.-H.C.); (D.L.); (M.-C.H.)
| | - Yuan-Jen Wang
- Faculty of Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei 112, Taiwan;
- Healthcare and Management Center, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Ming-Chih Hou
- Endoscopy Center for Diagnosis and Treatment, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (Y.-P.W.); (Y.-C.J.); (K.-Y.S.); (H.-E.L.); (I.-F.H.); (P.-H.C.); (D.L.); (M.-C.H.)
- Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan;
- Faculty of Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei 112, Taiwan;
| | - Fa-Yauh Lee
- Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan;
- Faculty of Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei 112, Taiwan;
| | - Ching-Liang Lu
- Endoscopy Center for Diagnosis and Treatment, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (Y.-P.W.); (Y.-C.J.); (K.-Y.S.); (H.-E.L.); (I.-F.H.); (P.-H.C.); (D.L.); (M.-C.H.)
- Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan;
- Institute of Brain Science, National Yang Ming Chiao Tung University School of Medicine, Taipei 112, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei 112, Taiwan;
- Correspondence: ; Tel.: +886-2-2875-7272
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Li JW, Wang LM, Ang TL. Artificial intelligence-assisted colonoscopy: a narrative review of current data and clinical applications. Singapore Med J 2022; 63:118-124. [PMID: 35509251 PMCID: PMC9251247 DOI: 10.11622/smedj.2022044] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
Colonoscopy is the reference standard procedure for the prevention and diagnosis of colorectal cancer, which is a leading cause of cancer-related deaths in Singapore. Artificial intelligence systems are automated, objective and reproducible. Artificial intelligence-assisted colonoscopy has recently been introduced into clinical practice as a clinical decision support tool. This review article provides a summary of the current published data and discusses ongoing research and current clinical applications of artificial intelligence-assisted colonoscopy.
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Affiliation(s)
- James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- SingHealth Duke-NUS Medicine Academic Clinical Programme, Singapore
| | - Lai Mun Wang
- Pathology Section, Department of Laboratory Medicine, Changi General Hospital, Singapore
- SingHealth Duke-NUS Pathology Academic Clinical Programme, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- SingHealth Duke-NUS Medicine Academic Clinical Programme, Singapore
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36
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Lee JY, Calderwood AH, Karnes W, Requa J, Jacobson BC, Wallace MB. Artificial intelligence for the assessment of bowel preparation. Gastrointest Endosc 2022; 95:512-518.e1. [PMID: 34896100 DOI: 10.1016/j.gie.2021.11.041] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 11/22/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS A reliable assessment of bowel preparation is important to ensure high-quality colonoscopy. Current bowel preparation scoring systems are limited by interobserver variability. This study aimed to demonstrate objective assessment of bowel preparation adequacy using an artificial intelligence (AI)/convolutional neural network (CNN) algorithm developed from colonoscopy videos. METHODS Two CNNs were developed using a training set of 73,304 images from 200 colonoscopies. First, a binary CNN was developed and trained to distinguish video frames that were appropriate versus inappropriate for scoring with the Boston Bowel Preparation Scale (BBPS). A second multiclass CNN was developed and trained on 26,950 appropriate frames that were expertly annotated with BBPS segment scores (0-3). We validated the algorithm using 252 10-second video clips that were assigned BBPS segment scores by 2 experts. The algorithm provided mean BBPS scores based on the algorithm (AI-BBPS) by calculating mean BBPS based on each frame's scoring. We maximized the algorithm's performance by choosing a dichotomized AI-BBPS score that closely matched dichotomized BBPS scores (ie, adequate vs inadequate). We tested the mean BBPS score based on the algorithm AI-BBPS against human rating using 30 independent 10-second video clips (test set 1) and 10 full withdrawal colonoscopy videos (test set 2). RESULTS In the validation set, the algorithm demonstrated an area under the curve of .918 and accuracy of 85.3% for detection of inadequate bowel cleanliness. In test set 1, sensitivity for inadequate bowel preparation was 100% and agreement between raters and AI was 76.7% to 83.3%. In test set 2, sensitivity for inadequate bowel preparation for each segment was 100% and agreement between raters and AI was 68.9% to 89.7%. Agreement between raters alone versus raters and AI were similar (κ = .694 and .649, respectively). CONCLUSIONS The algorithm assessment of bowel cleanliness as measured with the BBPS showed good performance and agreement with experts including full withdrawal colonoscopies.
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Affiliation(s)
- Ji Young Lee
- Health Screening and Promotion Center, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, South Korea
| | - Audrey H Calderwood
- Department of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA; The Geisel School of Medicine at Dartmouth and the Dartmouth Institute of Health Policy and Clinical Practice, Hanover, New Hampshire, USA
| | - William Karnes
- Department of Gastroenterology and Department of Internal Medicine, University of California Irvine Medical Center, Orange, California, USA; Docbot, Irvine, California
| | | | - Brian C Jacobson
- Department of Medicine, Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic Jacksonville, Florida, USA; Center of Research in Computer Vision, University of Central Florida, Orlando, Florida, USA
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Jo HE, Lee JE, Kim SH, Hong SJ, Choi SY, Lee MH, Lim S, Lee S, Hwang JA, Moon JE. Correlation of timed barium esophagography with Eckardt score in primary achalasia patients treated with peroral endoscopic myotomy. Abdom Radiol (NY) 2022; 47:538-546. [PMID: 34919159 DOI: 10.1007/s00261-021-03379-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 12/02/2021] [Accepted: 12/03/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE The purpose of our study was to evaluate the role of timed barium esophagogram (TBE) in quantitative measurement of improved esophageal emptying in primary achalasia patients treated with POEM. Also, we investigated the correlation of TBE with improvement of clinical symptoms as measured by Eckardt score. METHODS This retrospective study included 30 patients who underwent POEM due to primary achalasia. As a baseline study, all patients underwent TBE and were evaluated for clinical status by Eckardt score based on presence and frequency of dysphagia, regurgitation, substernal pain, and weight loss. Follow-up evaluation was performed within 3 months after POEM. Pre- and post-POEM TBE results were compared using a calculated value based on summation of height of the barium columns on 1-, 2- and 5-min delayed images. Also, the correlation of TBE with improvement of Eckardt score was evaluated using Pearson's correlation test. RESULTS There was a significant decrease in the calculated value of height between pre- and post-POEM TBE studies (40.5 ± 15.8-17.0 ± 11.6, p < 0.01). Also, the Eckardt score decreased significantly after POEM (6.7 ± 2.0-0.8 ± 1.0, p < 0.01). Pearson's correlation test revealed a positive correlation between improvement of TBE results and Eckardt score (correlation coefficient = 0.56, p < 0.01). CONCLUSION TBE is an objective method for quantitative measurement of improved esophageal emptying in primary achalasia patients treated with POEM and shows positive correlation with clinical symptoms evaluated by Eckardt score.
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Affiliation(s)
- Ha Eun Jo
- Department of Radiology, Soonchunhyang University College of Medicine, Bucheon Hospital, 170 Jomaru-ro, Bucheon-Si, Gyeonggi-do, 14584, Republic of Korea
| | - Ji Eun Lee
- Department of Radiology, Soonchunhyang University College of Medicine, Bucheon Hospital, 170 Jomaru-ro, Bucheon-Si, Gyeonggi-do, 14584, Republic of Korea.
| | - Shin Hee Kim
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Bucheon Hospital, 170 Jomaru-ro, Bucheon-Si, Gyeonggi-do, 14584, Republic of Korea
| | - Su Jin Hong
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Bucheon Hospital, 170 Jomaru-ro, Bucheon-Si, Gyeonggi-do, 14584, Republic of Korea
| | - Seo-Youn Choi
- Department of Radiology, Soonchunhyang University College of Medicine, Bucheon Hospital, 170 Jomaru-ro, Bucheon-Si, Gyeonggi-do, 14584, Republic of Korea
| | - Min Hee Lee
- Department of Radiology, Soonchunhyang University College of Medicine, Bucheon Hospital, 170 Jomaru-ro, Bucheon-Si, Gyeonggi-do, 14584, Republic of Korea
| | - Sanghyeok Lim
- Department of Radiology, Soonchunhyang University College of Medicine, Bucheon Hospital, 170 Jomaru-ro, Bucheon-Si, Gyeonggi-do, 14584, Republic of Korea
| | - Sunyoung Lee
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Jeong Ah Hwang
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Ilwon-Ro, Gangnam-gu, Seoul, 06351, Korea
| | - Ji Eun Moon
- Department of Biostatistics, Clinical Trial Center, Soonchunhyang University College of Medicine, Bucheon Hospital, 170 Jomaru-ro, Bucheon-Si, Gyeonggi-do, 14584, Republic of Korea
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38
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Hann A, Meining A. Artificial Intelligence in Endoscopy. Visc Med 2022; 37:471-475. [PMID: 35083312 DOI: 10.1159/000519407] [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: 05/21/2021] [Accepted: 08/30/2021] [Indexed: 12/23/2022] Open
Abstract
Background Owing to their rapid development, artificial intelligence (AI) technologies offer a great promise for gastroenterology practice and research. At present, AI-guided image interpretation has already been used with success for endoscopic detection of early malignant lesions. Nonetheless, there are complex challenges and possible shortcomings that must be considered before full implementation can be realized. Summary In this review, the current status of AI in endoscopy is summarized. Future perspectives and open questions for further studies are stressed. Key Messages The usage of AI algorithms for polyp detection in screening colonoscopy results in a significant increase in the adenoma detection rate, mainly attributed to the identification of diminutive polyps. Computer-aided characterization of colorectal polyps accompanies the detection, but further studies are needed to evaluate the clinical benefit. In contrast to colonoscopy, usage of AI in gastroscopy is currently rather limited. Regarding other fields of endoscopic imaging, capsule endoscopy is the ideal imaging platform for AI, due to the potential of saving time in the video analysis.
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Affiliation(s)
- Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, Gastroenterology, University Hospital Würzburg, Würzburg, Germany
| | - Alexander Meining
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, Gastroenterology, University Hospital Würzburg, Würzburg, Germany
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39
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Liang F, Wang S, Zhang K, Liu TJ, Li JN. Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer. World J Gastrointest Oncol 2022; 14:124-152. [PMID: 35116107 PMCID: PMC8790413 DOI: 10.4251/wjgo.v14.i1.124] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/19/2021] [Accepted: 11/15/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) technology has made leaps and bounds since its invention. AI technology can be subdivided into many technologies such as machine learning and deep learning. The application scope and prospect of different technologies are also totally different. Currently, AI technologies play a pivotal role in the highly complex and wide-ranging medical field, such as medical image recognition, biotechnology, auxiliary diagnosis, drug research and development, and nutrition. Colorectal cancer (CRC) is a common gastrointestinal cancer that has a high mortality, posing a serious threat to human health. Many CRCs are caused by the malignant transformation of colorectal polyps. Therefore, early diagnosis and treatment are crucial to CRC prognosis. The methods of diagnosing CRC are divided into imaging diagnosis, endoscopy, and pathology diagnosis. Treatment methods are divided into endoscopic treatment, surgical treatment, and drug treatment. AI technology is in the weak era and does not have communication capabilities. Therefore, the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients. This article reviews the application of AI in the diagnosis, treatment, and prognosis of CRC and provides the prospects for the broader application of AI in CRC.
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Affiliation(s)
- Feng Liang
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Shu Wang
- Department of Radiotherapy, Jilin University Second Hospital, Changchun 130041, Jilin Province, China
| | - Kai Zhang
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Tong-Jun Liu
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Jian-Nan Li
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
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40
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Poddar U, Vadlapudi SS. What Is the Best for Colon Preparation: Single-Dose, Split-Dose or Add-ons to Polyethylene Glycol? Indian Pediatr 2022. [DOI: 10.1007/s13312-021-2389-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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41
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Taveira F, Hassan C, Kaminski MF, Ponchon T, Benamouzig R, Bugajski M, de Castelbajac F, Cesaro P, Chergui H, Goran L, Minelli Grazioli L, Janičko M, Januszewicz W, Lamonaca L, Lenz J, Negreanu L, Repici A, Spada C, Spadaccini M, State M, Szlak J, Veseliny E, Dinis-Ribeiro M, Areia M. The Colon Endoscopic Bubble Scale (CEBuS): a two-phase evaluation study. Endoscopy 2022; 54:45-51. [PMID: 33285583 DOI: 10.1055/a-1331-4325] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND To date, no scale has been validated to assess bubbles associated with bowel preparation. This study aimed to develop and assess the reliability of a novel scale - the Colon Endoscopic Bubble Scale (CEBuS). METHODS This was a multicenter, prospective, observational study with two online evaluation phases of 45 randomly distributed still colonoscopy images (15 per scale grade). Observers assessed images twice, 2 weeks apart, using CEBuS (CEBuS-0 - no or minimal bubbles, covering < 5 % of the surface; CEBuS-1 - bubbles covering 5 %-50 %; CEBuS-2 - bubbles covering > 50 %) and reporting the clinical action (do nothing; wash with water; wash with simethicone). RESULTS CEBuS provided high levels of agreement both in evaluation Phase 1 (4 experts) and Phase 2 (6 experts and 13 non-experts), with almost perfect intraobserver reliability: kappa 0.82 (95 % confidence interval 0.75-0.88) and 0.86 (0.85-0.88); interobserver agreement - intraclass correlation coefficient (ICC) 0.83 (0.73-0.89) and 0.90 (0.86-0.94). Previous endoscopic experience had no influence on agreement among experts vs. non-experts: kappa 0.86 (0.80-0.91) vs. 0.87 (0.84-0.89) and ICC 0.91 (0.87-0.94) vs. 0.90 (0.86-0.94), respectively. Interobserver agreement on clinical action was ICC 0.63 (0.43-0.78) in Phase 1 and 0.77 (0.68-0.84) in Phase 2. Absolute agreement on clinical action per scale grade was 85 % (82-88) for CEBuS-0, 21 % (16-26) for CEBuS-1, and 74 % (70-78) for CEBuS-2. CONCLUSION CEBuS proved to be a reliable instrument to standardize the evaluation of colonic bubbles during colonoscopy. Assessment in daily practice is warranted.
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Affiliation(s)
- Filipe Taveira
- Department of Gastroenterology, Portuguese Oncology Institute of Coimbra, Coimbra, Portugal
| | - Cesare Hassan
- Gastroenterology and Digestive Endoscopy Unit, Nuovo Regina Margherita Hospital, Rome, Italy
| | - Michal F Kaminski
- Department of Gastroenterological Oncology, The Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland.,Department of Gastroenterology, Hepatology and Clinical Oncology, Medical Center for Postgraduate Education, Warsaw, Poland.,Department of Cancer Prevention, The Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland.,Department of Health Management and Health Economics, University of Oslo, Oslo, Norway
| | - Thierry Ponchon
- Department of Hepatogastroenterology, Hôpital Edouard Herriot, Lyon, France
| | - Robert Benamouzig
- Service de Gastroentérologie, Hôpital Avicenne (APHP), Bobigny, France
| | - Marek Bugajski
- Department of Gastroenterological Oncology, The Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland.,Department of Gastroenterology, Hepatology and Clinical Oncology, Medical Center for Postgraduate Education, Warsaw, Poland
| | | | - Paola Cesaro
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy
| | - Hasnae Chergui
- Service de Gastroentérologie, Hôpital Avicenne (APHP), Bobigny, France
| | - Loredana Goran
- Department of Gastroenterology, University Hospital, 'Carol Davila' University Bucharest, Romania
| | | | - Martin Janičko
- 2nd Department of Internal Medicine, Pavol Jozef Šafárik University, Košice, Slovakia
| | - Wladyslaw Januszewicz
- Department of Gastroenterological Oncology, The Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland.,Department of Gastroenterology, Hepatology and Clinical Oncology, Medical Center for Postgraduate Education, Warsaw, Poland
| | - Laura Lamonaca
- Endoscopy Unit, Humanitas Clinical and Research Center - IRCCS -, Rozzano, Lombardia, Italy
| | - Jamila Lenz
- Department of Hepatogastroenterology, Hôpital Edouard Herriot, Lyon, France
| | - Lucian Negreanu
- Department of Gastroenterology, University Hospital, 'Carol Davila' University Bucharest, Romania
| | - Alessandro Repici
- Endoscopy Unit, Humanitas Clinical and Research Center - IRCCS -, Rozzano, Lombardia, Italy
| | - Cristiano Spada
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy.,Digestive Endoscopy Unit, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Marco Spadaccini
- Endoscopy Unit, Humanitas Clinical and Research Center - IRCCS -, Rozzano, Lombardia, Italy
| | - Monica State
- Department of Gastroenterology, University Hospital, 'Carol Davila' University Bucharest, Romania
| | - Jakub Szlak
- Department of Gastroenterological Oncology, The Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Eduard Veseliny
- 2nd Department of Internal Medicine, Pavol Jozef Šafárik University, Košice, Slovakia
| | - Mário Dinis-Ribeiro
- Center for Research in Health Technologies and Information Systems (CINTESIS), Faculty of Medicine, Porto, Portugal.,Gastroenterology Department, Portuguese Oncology Institute of Porto, Portugal
| | - Miguel Areia
- Department of Gastroenterology, Portuguese Oncology Institute of Coimbra, Coimbra, Portugal.,Center for Research in Health Technologies and Information Systems (CINTESIS), Faculty of Medicine, Porto, Portugal
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Glissen Brown JR, Waljee AK, Mori Y, Sharma P, Berzin TM. Charting a path forward for clinical research in artificial intelligence and gastroenterology. Dig Endosc 2022; 34:4-12. [PMID: 33715244 DOI: 10.1111/den.13974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 03/02/2021] [Accepted: 03/11/2021] [Indexed: 12/12/2022]
Abstract
Gastroenterology has been an early leader in bridging the gap between artificial intelligence (AI) model development and clinical trial validation, and in recent years we have seen the publication of several randomized clinical trials examining the role of AI in gastroenterology. As AI applications for clinical medicine advance rapidly, there is a clear need for guidance surrounding AI-specific study design, evaluation, comparison, analysis and reporting of results. Several initiatives are in the publication or pre-publication phase including AI-specific amendments to minimum reporting guidelines for clinical trials, society task force initiatives aimed at priority use cases and research priorities, and minimum reporting guidelines that guide the reporting of clinical prediction models. In this paper, we examine applications of AI in clinical trials and discuss elements of newly published AI-specific extensions to the Consolidated Standards of Reporting Trials and Standard Protocol Items: Recommendations for Interventional Trials statements that guide clinical trial reporting and development. We then review AI applications at the pre-trial level in both endoscopy and other subfields of gastroenterology and explore areas where further guidance is needed to supplement the current guidance available at the pre-trial level.
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Affiliation(s)
- Jeremy R Glissen Brown
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA
| | - Akbar K Waljee
- Division of Gastroenterology, University of Michigan Health System, University of Michigan, Ann Arbor, USA
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan.,Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, University of Kansas Medical Center, Kansas City, KS, USA.,Department of Gastroenterology, Kansas City VA Medical Center, Kansas City, USA
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA
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Di Leo M, Iannone A, Arena M, Losurdo G, Palamara MA, Iabichino G, Consolo P, Rendina M, Luigiano C, Di Leo A. Novel frontiers of agents for bowel cleansing for colonoscopy. World J Gastroenterol 2021; 27:7748-7770. [PMID: 34963739 PMCID: PMC8661374 DOI: 10.3748/wjg.v27.i45.7748] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/23/2021] [Accepted: 11/25/2021] [Indexed: 02/06/2023] Open
Abstract
The incidence of colorectal cancer (CRC) is characterized by rapid declines in the wake of widespread screening. Colonoscopy is the gold standard for CRC screening, but its accuracy is related to high quality of bowel preparation (BP). In this review, we aimed to summarized the current strategy to increase bowel cleansing before colonoscopy. Newly bowel cleansing agents were developed with the same efficacy of previous agent but requiring less amount of liquid to improve patients’ acceptability. The role of the diet before colonoscopy was also changed, as well the contribution of educational intervention and the use of adjunctive drugs to improve patients’ tolerance and/or quality of BP. The review also described BP in special situations, as lower gastrointestinal bleeding, elderly people, patients with chronic kidney disease, patients with inflammatory bowel disease, patients with congestive heart failure, inpatient, patient with previous bowel resection, pregnant/lactating patients. The review underlined the quality of BP should be described using a validate scale in colonoscopy report and it explored the available scales. Finally, the review explored the possible contribution of bowel cleansing in post-colonoscopy syndrome that can be related by a transient alteration of gut microbiota. Moreover, the study underlined several points needed to further investigations.
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Affiliation(s)
- Milena Di Leo
- Unit of Digestive Endoscopy, San Paolo Hospital, Milan 20090, Italy
| | - Andrea Iannone
- Section of Gastroenterology, Department of Emergency and Organ Transplantation, University of Bari, Bari 70124, Italy
| | - Monica Arena
- Unit of Digestive Endoscopy, San Paolo Hospital, Milan 20090, Italy
| | - Giuseppe Losurdo
- Section of Gastroenterology, Department of Emergency and Organ Transplantation, University of Bari, Bari 70124, Italy
| | | | | | - Pierluigi Consolo
- Unit of Digestive Endoscopy, University of Messina, Hospital "G. Martino", Messina 98121, Italy
| | - Maria Rendina
- Section of Gastroenterology, Department of Emergency and Organ Transplantation, University of Bari, Bari 70124, Italy
| | - Carmelo Luigiano
- Unit of Digestive Endoscopy, San Paolo Hospital, Milan 20090, Italy
| | - Alfredo Di Leo
- Section of Gastroenterology, Department of Emergency and Organ Transplantation, University of Bari, Bari 70124, Italy
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Li JW, Chia T, Fock KM, Chong KDW, Wong YJ, Ang TL. Artificial intelligence and polyp detection in colonoscopy: Use of a single neural network to achieve rapid polyp localization for clinical use. J Gastroenterol Hepatol 2021; 36:3298-3307. [PMID: 34327729 DOI: 10.1111/jgh.15642] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 05/11/2021] [Accepted: 07/22/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND AIM Artificial intelligence has been extensively studied to assist clinicians in polyp detection, but such systems usually require expansive processing power, making them prohibitively expensive and hindering wide adaption. The current study used a fast object detection algorithm, known as the YOLOv3 algorithm, to achieve real-time polyp detection on a laptop. In addition, we evaluated and classified the causes of false detections to further improve accuracy. METHODS The YOLOv3 algorithm was trained and validated with 6038 and 2571 polyp images, respectively. Videos from live colonoscopies in a tertiary center and those obtained from public databases were used for the training and validation sets. The algorithm was tested on 10 unseen videos from the CVC-Video ClinicDB dataset. Only bounding boxes with an intersection over union area of > 0.3 were considered positive predictions. RESULTS Polyp detection rate in our study was 100%, with the algorithm able to detect every polyp in each video. Sensitivity, specificity, and F1 score were 74.1%, 85.1%, and 83.3, respectively. The algorithm achieved a speed of 61.2 frames per second (fps) on a desktop RTX2070 GPU and 27.2 fps on a laptop GTX2060 GPU. Nearly a quarter of false negatives happened when the polyps were at the corner of an image. Image blurriness accounted for approximately 3% and 9% of false positive and false negative detections, respectively. CONCLUSION The YOLOv3 algorithm can achieve real-time poly detection with high accuracy and speed on a desktop GPU, making it low cost and accessible to most endoscopy centers worldwide.
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Affiliation(s)
- James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Medicine Academic Clinical Programme, SingHealth Duke-NUS, Singapore
| | | | - Kwong Ming Fock
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Medicine Academic Clinical Programme, SingHealth Duke-NUS, Singapore
| | | | - Yu Jun Wong
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore.,Medicine Academic Clinical Programme, SingHealth Duke-NUS, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Medicine Academic Clinical Programme, SingHealth Duke-NUS, Singapore
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Low DJ, Hong Z, Khan R, Bansal R, Gimpaya N, Grover SC. Automated detection of cecal intubation with variable bowel preparation using a deep convolutional neural network. Endosc Int Open 2021; 9:E1778-E1784. [PMID: 34790545 PMCID: PMC8589561 DOI: 10.1055/a-1546-8266] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 06/04/2021] [Indexed: 12/15/2022] Open
Abstract
Background and study aims Colonoscopy completion reduces post-colonoscopy colorectal cancer. As a result, there have been attempts at implementing artificial intelligence to automate the detection of the appendiceal orifice (AO) for quality assurance. However, the utilization of these algorithms has not been demonstrated in suboptimal conditions, including variable bowel preparation. We present an automated computer-assisted method using a deep convolutional neural network to detect the AO irrespective of bowel preparation. Methods A total of 13,222 images (6,663 AO and 1,322 non-AO) were extracted from 35 colonoscopy videos recorded between 2015 and 2018. The images were labelled with Boston Bowel Preparation Scale scores. A total of 11,900 images were used for training/validation and 1,322 for testing. We developed a convolutional neural network (CNN) with a DenseNet architecture pre-trained on ImageNet as a feature extractor on our data and trained a classifier uniquely tailored for identification of AO and non-AO images using binary cross entropy loss. Results The deep convolutional neural network was able to correctly classify the AO and non-AO images with an accuracy of 94 %. The area under the receiver operating curve of this neural network was 0.98. The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were 0.96, 0.92, 0.92 and 0.96, respectively. AO detection was > 95 % regardless of BBPS scores, while non-AO detection improved from BBPS 1 score (83.95 %) to BBPS 3 score (98.28 %). Conclusions A deep convolutional neural network was created demonstrating excellent discrimination between AO from non-AO images despite variable bowel preparation. This algorithm will require further testing to ascertain its effectiveness in real-time colonoscopy.
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Affiliation(s)
| | | | - Rishad Khan
- St. Michael’s Hospital, University of Toronto
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Zhu XW, Yan J, He YL, Liu G, Li X. Application of deep learning based artificial intelligence technology in identification of colorectal polyps. Shijie Huaren Xiaohua Zazhi 2021; 29:1201-1206. [DOI: 10.11569/wcjd.v29.i20.1201] [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] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer is a cancer type that is most suitable for screening since subjects at risk of this malignancy can clearly benefit from colonoscopy screening. In 2017, there were about 431951 new cases of colorectal cancer in China, with an increase of 203.5% in 28 years. Early detection and early removal of adenomatous polyps and other precancerous lesions during colonoscopy can prevent the occurrence of colorectal cancer. However, various factors lead to missed diagnosis of polyps during colonoscopy, which increases the risk of colorectal cancer. In recent years, with the rapid development of artificial intelligence technology in the medical field, colonoscopy assisted by artificial intelligence can increase the detection rate of polyps and improve the quality of colonoscopy. This paper mainly reviews the quality control, bowel preparation, diagnosis and classification of colorectal polyps, and the future opportunities and challenges faced by convolutional neural network based artificial intelligence technology in the field of colonoscopy, hoping to provide some reference for clinical work.
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Affiliation(s)
- Xing-Wang Zhu
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Jun Yan
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, Gansu Province, China,Gansu Province Key Laboratory of Biological Therapy and Regenerative Medicine, Lanzhou 730000, Gansu Province, China,Cancer Prevention and Treatment Center of Lanzhou University School of Medicine, Lanzhou 730000, Gansu Province, China,Gansu Provincial Institute of Hepatobiliary and Pancreatic Surgery, Lanzhou 730000, Gansu Province, China,Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Ying-Li He
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Gang Liu
- Lanzhou University School of Information Science & Engineering, Lanzhou 730000, Gansu Province, China
| | - Xun Li
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, Gansu Province, China,Gansu Province Key Laboratory of Biological Therapy and Regenerative Medicine, Lanzhou 730000, Gansu Province, China,Cancer Prevention and Treatment Center of Lanzhou University School of Medicine, Lanzhou 730000, Gansu Province, China,Gansu Provincial Institute of Hepatobiliary and Pancreatic Surgery, Lanzhou 730000, Gansu Province, China,Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
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Hann A, Troya J, Fitting D. Current status and limitations of artificial intelligence in colonoscopy. United European Gastroenterol J 2021; 9:527-533. [PMID: 34617420 PMCID: PMC8259277 DOI: 10.1002/ueg2.12108] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 02/28/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) using deep learning methods for polyp detection (CADe) and characterization (CADx) is on the verge of clinical application. CADe already implied its potential use in randomized controlled trials. Further efforts are needed to take CADx to the next level of development. AIM This work aims to give an overview of the current status of AI in colonoscopy, without going into too much technical detail. METHODS A literature search to identify important studies exploring the use of AI in colonoscopy was performed. RESULTS This review focuses on AI performance in screening colonoscopy summarizing the first prospective trials for CADe, the state of research in CADx as well as current limitations of those systems and legal issues.
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Affiliation(s)
- Alexander Hann
- Department of Internal Medicine II, Interventional and Experimental Endoscopy (InExEn), University Hospital Wuerzburg, Würzburg, Germany
| | - Joel Troya
- Department of Internal Medicine II, Interventional and Experimental Endoscopy (InExEn), University Hospital Wuerzburg, Würzburg, Germany
| | - Daniel Fitting
- Department of Internal Medicine II, Interventional and Experimental Endoscopy (InExEn), University Hospital Wuerzburg, Würzburg, Germany
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Glissen Brown JR, Berzin TM. Adoption of New Technologies: Artificial Intelligence. Gastrointest Endosc Clin N Am 2021; 31:743-758. [PMID: 34538413 DOI: 10.1016/j.giec.2021.05.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Over the past decade, artificial intelligence (AI) has been broadly applied to many aspects of human life, with recent groundbreaking successes in facial recognition, natural language processing, autonomous driving, and medical imaging. Gastroenterology has applied AI to a vast array of clinical problems, and some of the earliest prospective trials examining AI in medicine have been in computer vision applied to endoscopy. Evidence is mounting for 2 broad areas of AI as applied to gastroenterology: computer-aided detection and computer-aided diagnosis.
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Affiliation(s)
- Jeremy R Glissen Brown
- Center for Advanced Endoscopy, Division of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02130, USA.
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Division of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02130, USA
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Zhou W, Yao L, Wu H, Zheng B, Hu S, Zhang L, Li X, He C, Wang Z, Li Y, Huang C, Guo M, Zhang X, Zhu Q, Wu L, Deng Y, Zhang J, Tan W, Li C, Zhang C, Gong R, Du H, Zhou J, Sharma P, Yu H. Multi-step validation of a deep learning-based system for the quantification of bowel preparation: a prospective, observational study. LANCET DIGITAL HEALTH 2021; 3:e697-e706. [PMID: 34538736 DOI: 10.1016/s2589-7500(21)00109-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 05/30/2021] [Accepted: 06/03/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Inadequate bowel preparation is associated with a decrease in adenoma detection rate (ADR). A deep learning-based bowel preparation assessment system based on the Boston bowel preparation scale (BBPS) has been previously established to calculate the automatic BBPS (e-BBPS) score (ranging 0-20). The aims of this study were to investigate whether there was a statistically inverse relationship between the e-BBPS score and the ADR, and to determine the threshold of e-BBPS score for adequate bowel preparation in colonoscopy screening. METHODS In this prospective, observational study, we trained and internally validated the e-BBPS system using retrospective colonoscopy images and videos from the Endoscopy Center of Wuhan University, annotated by endoscopists. We externally validated the system using colonoscopy images and videos from the First People's Hospital of Yichang and the Third Hospital of Wuhan. To prospectively validate the system, we recruited consecutive patients at Renmin Hospital of Wuhan University aged between 18 and 75 years undergoing colonoscopy. The exclusion criteria included: contraindication to colonoscopy, family polyposis syndrome, inflammatory bowel disease, history of surgery for colorectal or colorectal cancer, known or suspected bowel obstruction or perforation, patients who were pregnant or lactating, inability to receive caecal intubation, and lumen obstruction. We did colonoscopy procedures and collected withdrawal videos, which were reviewed and the e-BBPS system was applied to all colon segments. The primary outcome of this study was ADR, defined as the proportion of patients with one or more conventional adenomas detected during colonoscopy. We calculated the ADR of each e-BBPS score and did a correlation analysis using Spearman analysis. FINDINGS From May 11 to Aug 10, 2020, 616 patients underwent screening colonoscopies, which evaluated. There was a significant inverse correlation between the e-BBPS score and ADR (Spearman's rank -0·976, p<0·010). The ADR for the e-BBPS scores 1-8 was 28·57%, 28·68%, 26·79%, 19·19%, 17·57%, 17·07%, 14·81%, and 0%, respectively. According to the 25% ADR standard for screening colonoscopy, an e-BBPS score of 3 was set as a threshold to guarantee an ADR of more than 25%, and so high-quality endoscopy. Patients with scores of more than 3 had a significantly lower ADR than those with a score of 3 or less (ADR 15·93% vs 28·03%, p<0·001, 95% CI 0·28-0·66, odds ratio 0·43). INTERPRETATION The e-BBPS system has potential to provide a more objective and refined threshold for the quantification of adequate bowel preparation. FUNDING Project of Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision and Hubei Province Major Science and Technology Innovation Project.
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Affiliation(s)
- Wei Zhou
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Liwen Yao
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Huiling Wu
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Biqing Zheng
- Wuhan ENDOANGEL Medical Technology Company, Wuhan, China
| | - Shan Hu
- Wuhan ENDOANGEL Medical Technology Company, Wuhan, China
| | - Lihui Zhang
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xun Li
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chunping He
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhengqiang Wang
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yanxia Li
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chao Huang
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mingwen Guo
- Department of Gastroenterology, First People's Hospital of Yichang, Yichang, China
| | - Xiaoqing Zhang
- Department of Gastroenterology, First People's Hospital of Yichang, Yichang, China
| | - Qingxi Zhu
- Department of Gastroenterology, Third Hospital of Wuhan, Wuhan, China
| | - Lianlian Wu
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yunchao Deng
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Zhang
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wei Tan
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chao Li
- Wuhan ENDOANGEL Medical Technology Company, Wuhan, China
| | - Chenxia Zhang
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Rongrong Gong
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hongliu Du
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jie Zhou
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Prateek Sharma
- Department of Gastroenterology, University of Kansas School of Medicine, Kansas City, MO, USA; Veterans Affairs Medical Center, Kansas City, MO, USA
| | - Honggang Yu
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
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Xu M, Zhou W, Wu L, Zhang J, Wang J, Mu G, Huang X, Li Y, Yuan J, Zeng Z, Wang Y, Huang L, Liu J, Yu H. Artificial intelligence in the diagnosis of gastric precancerous conditions by image-enhanced endoscopy: a multicenter, diagnostic study (with video). Gastrointest Endosc 2021; 94:540-548.e4. [PMID: 33722576 DOI: 10.1016/j.gie.2021.03.013] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 03/06/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS Gastric precancerous conditions, including gastric atrophy (GA) and intestinal metaplasia (IM), play an important role in the development of gastric cancer. Image-enhanced endoscopy (IEE) shows great potential in diagnosing gastric precancerous conditions and adenocarcinoma. In this study, a deep convolutional neural network system, named ENDOANGEL, was constructed to detect gastric precancerous conditions by IEE. METHODS Endoscopic images were retrospectively obtained from 5 hospitals in China for the development, validation, and internal and external test of the system. Prospective consecutive patients receiving IEE were enrolled from January 13, 2020 to October 29, 2020 in Renmin Hospital of Wuhan University to assess in real time the applicability of the proposed computer-aided detection (CADe) system in clinical practice, and the performance of CADe was compared with that of endoscopists. RESULTS Six thousand two hundred fifty endoscopic images from 760 patients and 98 video clips from 77 individuals undergoing IEE were enrolled in this study. The diagnostic accuracy of GA was .901 (95% confidence interval [CI], .883-.917) in the internal test set, .864 (95% CI, .842-.884) in the multicenter external test set, and .878 (95% CI, .796-.935) in the prospective video test set. The diagnostic accuracy of IM was .908 (95% CI, .889-.924) in the internal test set, .859 (95% CI, .837-.880) in the multicenter external test set, and .898 (95% CI, .820-.950) in the prospective video test set. CADe achieved similar diagnostic accuracy to that of the experts for detecting GA (.869 [95% CI, .790-.927] vs .846 [95% CI, .808-.879], P = .396) and IM (.888 [95% CI, .812-.941] vs .820 [95% CI, .780-.855], P = .117) and was superior to that of nonexperts for GA (.750 [95% CI, .711-.786], P = .008) and IM (.736 [95% CI, .697-.773], P = .028). CONCLUSIONS CADe achieved high diagnostic accuracy in gastric precancerous conditions, which was similar to that of experts and superior to that of nonexperts. Thus, CADe provides possibilities for a wide application in assisting in the diagnosis of gastric precancerous conditions.
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Affiliation(s)
- Ming Xu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wei Zhou
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jing Wang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ganggang Mu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xu Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yanxia Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jingping Yuan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhi Zeng
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yonggui Wang
- School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
| | - Li Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Liu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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