Performance and attitudes toward real-time computer-aided polyp detection during colonoscopy in a large tertiary referral center in the United States - 16/06/23
Abstract |
Background and Aims |
Computer-aided detection (CADe) has been shown to improve polyp detection in clinical trials. Limited data exist on the impact, utilization, and attitudes toward artificial intelligence (AI)-assisted colonoscopy in daily clinical practice. We aimed to evaluate the effectiveness of the first U.S. Food and Drug Administration–approved CADe device for polyp detection in the United States and the attitudes toward its implementation.
Methods |
We performed a retrospective analysis of a prospectively maintained database of patients undergoing colonoscopy at a tertiary center in the United States before and after a real-time CADe system was made available. The decision to activate the CADe system was at the discretion of the endoscopist. An anonymous survey was circulated among endoscopy physicians and staff at the beginning and conclusion of the study period regarding their attitudes toward AI-assisted colonoscopy.
Results |
CADe was activated in 52.1% of cases. Compared with historical control subjects, there was no statistically significant difference in adenomas detected per colonoscopy (1.08 vs 1.04, P = .65), even after excluding diagnostic and therapeutic indications and cases where CADe was not activated (1.27 vs 1.17, P = .45). In addition, there was no statistically significant difference in adenoma detection rate (ADR), median procedure, and withdrawal times. Survey results demonstrated mixed attitudes toward AI-assisted colonoscopy, of which main concerns were high number of false-positive signals (82.4%), high level of distraction (58.8%), and impression it prolonged procedure time (47.1%).
Conclusions |
CADe did not improve adenoma detection in daily practice among endoscopists with high baseline ADRs. Despite its availability, AI-assisted colonoscopy was only activated in half of the cases, and multiple concerns were raised by staff and endoscopists. Future studies will help elucidate the patients and endoscopists that would benefit most from AI-assisted colonoscopy.
Le texte complet de cet article est disponible en PDF.Abbreviations : AI, ADR, APC, CADe, CRC, RCT, SSLDR
Plan
| DISCLOSURE: The following author disclosed financial relationships: P. S. Ge: Consultant for Alira Health, Boston Scientific, Ovesco America, and Neptune Medical. All other authors disclosed no financial relationships. |
|
| DIVERSITY, EQUITY, AND INCLUSION: We worked to ensure gender balance in the recruitment of human subjects. We worked to ensure that the language of the study questionnaires reflected inclusion. One or more of the authors of this paper self-identifies as an under-represented gender minority in science. |
Vol 98 - N° 1
P. 100 - juillet 2023 Retour au numéro
Article précédent
- Computer-aided diagnosis of early-stage colorectal cancer using nonmagnified endoscopic white-light images (with videos)
- Daiki Nemoto, Zhe Guo, Shinichi Katsuki, Takahito Takezawa, Ryo Maemoto, Keisuke Kawasaki, Ken Inoue, Takashi Akutagawa, Hirohito Tanaka, Koichiro Sato, Teppei Omori, Kunihiro Takanashi, Yoshikazu Hayashi, Yuki Nakajima, Yasuyuki Miyakura, Takayuki Matsumoto, Naohisa Yoshida, Motohiro Esaki, Toshio Uraoka, Hiroyuki Kato, Yuji Inoue, Boyuan Peng, Ruiyao Zhang, Takashi Hisabe, Tomoki Matsuda, Hironori Yamamoto, Noriko Tanaka, Alan Kawarai Lefor, Xin Zhu, Kazutomo Togashi
- Computer-aided polyp detection (CADe) in real life: not the “CADe-llac” we were promised
- Fares Ayoub, Neil Sengupta
Bienvenue sur EM-consulte, la référence des professionnels de santé.
L’accès au texte intégral de cet article nécessite un abonnement.
Déjà abonné à cette revue ?