In:
Journal of Gastrointestinal Surgery, Springer Science and Business Media LLC, Vol. 25, No. 8 ( 2021-08), p. 2011-2018
Abstract:
Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists ( 〉 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov . (NCT047126265). Results In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p 〈 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p 〈 0.001), but no difference was found with regard to larger lesions. Conclusions A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration clinicaltrials.gov Identifier: NCT047126265
Type of Medium:
Online Resource
ISSN:
1091-255X
,
1873-4626
DOI:
10.1007/s11605-020-04802-4
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2021
detail.hit.zdb_id:
2057634-1
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