NONAKA Kouichi
   Department   School of Medicine(Tokyo Women's Medical University Hospital), School of Medicine
   Position   Professor
Article types Original article
Language English
Peer review Non peer reviewed
Title Comparison of the Ability of Artificial-Intelligence-Based Computer-Aided Detection (CAD) Systems and Endoscopists to Detect Colorectal Neoplastic Lesions on Endoscopy Video
Journal Formal name：Journal of clinical medicine
Abbreviation：J Clin Med
ISSN code：20770383/20770383
Domestic / ForeginForegin
Volume, Issue, Page 12(14),pp.4840
Author and coauthor MISUMI Yoshitsugu, NONAKA Kouichi, TAKEUCHI Miharu, KAMITANI Yu, UECHI Yasuhiro, WATANABE Mai, KISHINO Maiko, OMORI Teppei, YONEZAWA Maria, ISOMOTO Hajime, TOKUSHIGE Katsutoshi
Authorship 2nd author
Publication date 2023/07
Summary Artificial-intelligence-based computer-aided diagnosis (CAD) systems have developed remarkably in recent years. These systems can help increase the adenoma detection rate (ADR), an important quality indicator in colonoscopies. While there have been many still-image-based studies on the usefulness of CAD, few have reported on its usefulness using actual clinical videos. However, no studies have compared the CAD group and control groups using the exact same case videos. This study aimed to determine whether CAD or endoscopists were superior in identifying colorectal neoplastic lesions in videos. In this study, we examined 34 lesions from 21 cases. CAD performed better than four of the six endoscopists (three experts and three beginners), including all the beginners. The time to lesion detection with beginners and experts was 2.147 ± 1.118 s and 1.394 ± 0.805 s, respectively, with significant differences between beginners and experts (p < 0.001) and between beginners and CAD (both p < 0.001). The time to lesion detection was significantly shorter for experts and CAD than for beginners. No significant difference was found between experts and CAD (p = 1.000). CAD could be useful as a diagnostic support tool for beginners to bridge the experience gap with experts.
DOI 10.3390/jcm12144840
PMID 37510955