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Quality assurance of computer-aided detection and diagnosis in colonoscopy.
Gastrointest Endosc. 2019 07; 90(1):55-63.GE

Abstract

Recent breakthroughs in artificial intelligence (AI), specifically via its emerging sub-field "deep learning," have direct implications for computer-aided detection and diagnosis (CADe and/or CADx) for colonoscopy. AI is expected to have at least 2 major roles in colonoscopy practice-polyp detection (CADe) and polyp characterization (CADx). CADe has the potential to decrease the polyp miss rate, contributing to improving adenoma detection, whereas CADx can improve the accuracy of colorectal polyp optical diagnosis, leading to reduction of unnecessary polypectomy of non-neoplastic lesions, potential implementation of a resect-and-discard paradigm, and proper application of advanced resection techniques. A growing number of medical-engineering researchers are developing both CADe and CADx systems, some of which allow real-time recognition of polyps or in vivo identification of adenomas, with over 90% accuracy. However, the quality of the developed AI systems as well as that of the study designs vary significantly, hence raising some concerns regarding the generalization of the proposed AI systems. Initial studies were conducted in an exploratory or retrospective fashion by using stored images and likely overestimating the results. These drawbacks potentially hinder smooth implementation of this novel technology into colonoscopy practice. The aim of this article is to review both contributions and limitations in recent machine-learning-based CADe and/or CADx colonoscopy studies and propose some principles that should underlie system development and clinical testing.

Authors+Show Affiliations

Showa University International Center for Endoscopy, Showa University Northern Yokohama Hospital, Yokohama, Japan; Division of Internal Medicine, University of Connecticut Health Center, Farmington, Connecticut, USA.Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.Division of Gastroenterology, University of Kansas Medical Center, Kansas City, Kansas.Center for Research in Computer Vision, University of Central Florida, Orlando, Florida.Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana.Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, Florida, USA.

Pub Type(s)

Journal Article
Review

Language

eng

PubMed ID

30926431

Citation

Vinsard, Daniela Guerrero, et al. "Quality Assurance of Computer-aided Detection and Diagnosis in Colonoscopy." Gastrointestinal Endoscopy, vol. 90, no. 1, 2019, pp. 55-63.
Vinsard DG, Mori Y, Misawa M, et al. Quality assurance of computer-aided detection and diagnosis in colonoscopy. Gastrointest Endosc. 2019;90(1):55-63.
Vinsard, D. G., Mori, Y., Misawa, M., Kudo, S. E., Rastogi, A., Bagci, U., Rex, D. K., & Wallace, M. B. (2019). Quality assurance of computer-aided detection and diagnosis in colonoscopy. Gastrointestinal Endoscopy, 90(1), 55-63. https://doi.org/10.1016/j.gie.2019.03.019
Vinsard DG, et al. Quality Assurance of Computer-aided Detection and Diagnosis in Colonoscopy. Gastrointest Endosc. 2019;90(1):55-63. PubMed PMID: 30926431.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Quality assurance of computer-aided detection and diagnosis in colonoscopy. AU - Vinsard,Daniela Guerrero, AU - Mori,Yuichi, AU - Misawa,Masashi, AU - Kudo,Shin-Ei, AU - Rastogi,Amit, AU - Bagci,Ulas, AU - Rex,Douglas K, AU - Wallace,Michael B, Y1 - 2019/03/26/ PY - 2019/01/08/received PY - 2019/03/18/accepted PY - 2019/3/31/pubmed PY - 2020/2/1/medline PY - 2019/3/31/entrez SP - 55 EP - 63 JF - Gastrointestinal endoscopy JO - Gastrointest. Endosc. VL - 90 IS - 1 N2 - Recent breakthroughs in artificial intelligence (AI), specifically via its emerging sub-field "deep learning," have direct implications for computer-aided detection and diagnosis (CADe and/or CADx) for colonoscopy. AI is expected to have at least 2 major roles in colonoscopy practice-polyp detection (CADe) and polyp characterization (CADx). CADe has the potential to decrease the polyp miss rate, contributing to improving adenoma detection, whereas CADx can improve the accuracy of colorectal polyp optical diagnosis, leading to reduction of unnecessary polypectomy of non-neoplastic lesions, potential implementation of a resect-and-discard paradigm, and proper application of advanced resection techniques. A growing number of medical-engineering researchers are developing both CADe and CADx systems, some of which allow real-time recognition of polyps or in vivo identification of adenomas, with over 90% accuracy. However, the quality of the developed AI systems as well as that of the study designs vary significantly, hence raising some concerns regarding the generalization of the proposed AI systems. Initial studies were conducted in an exploratory or retrospective fashion by using stored images and likely overestimating the results. These drawbacks potentially hinder smooth implementation of this novel technology into colonoscopy practice. The aim of this article is to review both contributions and limitations in recent machine-learning-based CADe and/or CADx colonoscopy studies and propose some principles that should underlie system development and clinical testing. SN - 1097-6779 UR - https://www.unboundmedicine.com/medline/citation/30926431/Quality_assurance_of_computer_aided_detection_and_diagnosis_in_colonoscopy_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0016-5107(19)30210-X DB - PRIME DP - Unbound Medicine ER -