ヨネザワ　マリア   YONEZAWA Maria   米澤　麻利亜    所属   医学部 医学科（東京女子医科大学病院）    職種   助教
論文種別	原著
言語種別	英語
査読の有無	査読なし
表題	Comparison of the Ability of Artificial-Intelligence-Based Computer-Aided Detection (CAD) Systems and Endoscopists to Detect Colorectal Neoplastic Lesions on Endoscopy Video
掲載誌名	正式名：Journal of clinical medicine 略　 称：J Clin Med ISSNコード：20770383/20770383
掲載区分	国外
巻・号・頁	12(14),pp.4840
著者・共著者	MISUMI Yoshitsugu, NONAKA Kouichi, TAKEUCHI Miharu, KAMITANI Yu, UECHI Yasuhiro, WATANABE Mai, KISHINO Maiko, OMORI Teppei, YONEZAWA Maria, ISOMOTO Hajime, TOKUSHIGE Katsutoshi
発行年月	2023/07
概要	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