Performance evaluation of a computer‐aided polyp detection system with artificial intelligence for colonoscopy

Author:

Chino Akiko1ORCID,Ide Daisuke1,Abe Seiichiro2ORCID,Yoshinaga Shigetaka2,Ichimasa Katsuro3,Kudo Toyoki43,Ninomiya Yuki5,Oka Shiro5ORCID,Tanaka Shinji5,Igarashi Masahiro1

Affiliation:

1. Department of Gastroenterology Cancer Institute Hospital of Japanese Foundation for Cancer Research Tokyo Japan

2. Endoscopy Division National Cancer Center Hospital Tokyo Japan

3. Digestive Disease Center Showa University Northern Yokohama Hospital Kanagawa Japan

4. Tokyo Endoscopic Clinic Tokyo Japan

5. Department of Endoscopy Hiroshima University Hospital Hiroshima Japan

Abstract

ObjectivesA computer‐aided detection (CAD) system was developed to support the detection of colorectal lesions by deep learning using video images of lesions and normal mucosa recorded during colonoscopy. The study's purpose was to evaluate the stand‐alone performance of this device under blinded conditions.MethodsThis multicenter prospective observational study was conducted at four Japanese institutions. We used 326 videos of colonoscopies recorded with patient consent at institutions in which the Ethics Committees approved the study. The sensitivity of successful detection of the CAD system was calculated using the target lesions, which were detected by adjudicators from two facilities for each lesion appearance frame; inconsistencies were settled by consensus. Successful detection was defined as display of the detection flag on the lesion for more than 0.5 s within 3 s of appearance.ResultsOf the 556 target lesions from 185 cases, detection success sensitivity was 97.5% (95% confidence interval [CI] 95.8–98.5%). The “successful detection sensitivity per colonoscopy” was 93% (95% CI 88.3–95.8%). For the frame‐based sensitivity, specificity, positive predictive value, and negative predictive value were 86.6% (95% CI 84.8–88.4%), 84.7% (95% CI 83.8–85.6%), 34.9% (95% CI 32.3–37.4%), and 98.2% (95% CI 97.8–98.5%), respectively.Trial registrationUniversity Hospital Medical Information Network (UMIN000044622).

Publisher

Wiley

Subject

Gastroenterology,Radiology, Nuclear Medicine and imaging

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3