Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study

By Pu Wang, T. Berzin, Jeremy R. Glissen Brown, Shishira S. Bharadwaj, A. Becq, Xun Xiao, Pei-xi Liu, Liangping Li, Yan Song, Di Zhang and 4 more. Gut, 2019

720 citations recorded by the source corpus.

Objective The effect of colonoscopy on colorectal cancer mortality is limited by several factors, among them a certain miss rate, leading to limited adenoma detection rates (ADRs). We investigated the effect of an automatic polyp detection system based on deep learning on polyp detection rate and ADR. Design In an open, non-blinded trial, consecutive patients were prospectively randomised to undergo diagnostic colonoscopy with or without assistance of a real-time automatic polyp detection system providing a simultaneous visual notice and sound alarm on polyp detection. The primary outcome was ADR. Results Of 1058 patients included, 536 were randomised to standard colonoscopy, and 522 were randomised to colonoscopy with computer-aided diagnosis. The artificial intelligence (AI) system significantly increased ADR (29.1%vs20.3%, p<0.001) and the mean number of adenomas per patient (0.53vs0.31, p<0.001). This was due to a higher number of diminutive adenomas found (185vs102; p<0.001), while there was no statistical difference in larger adenomas (77vs58, p=0.075). In addition, the number of hyperplastic polyps was also significantly increased (114vs52, p<0.001). Conclusions In a low prevalent ADR population, an automatic polyp detection system during colonoscopy resulted in a significant increase in the number of diminutive adenomas detected, as well as an increase in the rate of hyperplastic polyps. The cost–benefit ratio of such effects has to be determined further. Trial registration number ChiCTR-DDD-17012221; Results.

Subfields: Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/1bc626e9952ef3d2134acf7c88e6a65e2c443f42

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Citations in the corpus

Performance of artificial intelligence for colonoscopy regarding adenoma and polyp detection: a meta-analysis. (2020, methodology): “A total of 6 RCTs 10,11,21–24 , all published between 2019 and 2020, tested the impact of CNN based systems on detection of colorectal neoplasia.”
Key challenges for delivering clinical impact with artificial intelligence (2019, methodology): “…detection algorithm for diagnostic colonoscopy demonstrating a significant increase in detection of diminutive adenomas and hyperplastic polyps [59]; a simulated prospective, double-blind RCT of an algorithm to detect acute neurologic events [60]; and an unmasked RCT of a system to provide…”
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging (2019, methodology): “[34] perform an excellent subset analysis of a colonoscopy polyp detector, with comparative performance analysis presented by polyp size, location, shape, and underlying pathology (e.”
Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension (2020, neutral): “Examples of such applications are wide ranging and include AI systems for screening and triage, 15,16 diag nosis, 17–20 prognostication, 21,22 decision support, 23 and treat ment recommendation.”
Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review (2020, background): “CO (ADR, PDR, number of adenomas per patient) Sichuan Provincial People’s Hospital (China) 1058 patients, 8 physicians RCT Wang et al, 2019 [69]”