Quality assurance of computer-aided detection and diagnosis in colonoscopy

Daniela Guerrero Vinsard, Yuichi Mori, Masashi Misawa, Shin ei Kudo, Amit Rastogi, Ulas Bagci, Douglas Rex, Michael B. Wallace

Research output: Contribution to journalReview article

1 Citation (Scopus)

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.

Original languageEnglish (US)
JournalGastrointestinal endoscopy
DOIs
StatePublished - Jan 1 2019

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Colonoscopy
Artificial Intelligence
Polyps
Adenoma
Research Personnel
Learning
Technology

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Gastroenterology

Cite this

Vinsard, D. G., Mori, Y., Misawa, M., Kudo, S. E., Rastogi, A., Bagci, U., ... Wallace, M. B. (2019). Quality assurance of computer-aided detection and diagnosis in colonoscopy. Gastrointestinal endoscopy. https://doi.org/10.1016/j.gie.2019.03.019

Quality assurance of computer-aided detection and diagnosis in colonoscopy. / Vinsard, Daniela Guerrero; Mori, Yuichi; Misawa, Masashi; Kudo, Shin ei; Rastogi, Amit; Bagci, Ulas; Rex, Douglas; Wallace, Michael B.

In: Gastrointestinal endoscopy, 01.01.2019.

Research output: Contribution to journalReview article

Vinsard, DG, Mori, Y, Misawa, M, Kudo, SE, Rastogi, A, Bagci, U, Rex, D & Wallace, MB 2019, 'Quality assurance of computer-aided detection and diagnosis in colonoscopy', Gastrointestinal endoscopy. https://doi.org/10.1016/j.gie.2019.03.019
Vinsard, Daniela Guerrero ; Mori, Yuichi ; Misawa, Masashi ; Kudo, Shin ei ; Rastogi, Amit ; Bagci, Ulas ; Rex, Douglas ; Wallace, Michael B. / Quality assurance of computer-aided detection and diagnosis in colonoscopy. In: Gastrointestinal endoscopy. 2019.
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