Effectiveness of a Deep-learning Polyp Detection System in Prospectively Collected Colonoscopy Videos With Variable Bowel Preparation Quality.

Journal Article (Journal Article)

BACKGROUND: Colonoscopy is the gold standard for polyp detection, but polyps may be missed. Artificial intelligence (AI) technologies may assist in polyp detection. To date, most studies for polyp detection have validated algorithms in ideal endoscopic conditions. AIM: To evaluate the performance of a deep-learning algorithm for polyp detection in a real-world setting of routine colonoscopy with variable bowel preparation quality. METHODS: We performed a prospective, single-center study of 50 consecutive patients referred for colonoscopy. Procedural videos were analyzed by a validated deep-learning AI polyp detection software that labeled suspected polyps. Videos were then re-read by 5 experienced endoscopists to categorize all possible polyps identified by the endoscopist and/or AI, and to measure Boston Bowel Preparation Scale. RESULTS: In total, 55 polyps were detected and removed by the endoscopist. The AI system identified 401 possible polyps. A total of 100 (24.9%) were categorized as "definite polyps;" 53/100 were identified and removed by the endoscopist. A total of 63 (15.6%) were categorized as "possible polyps" and were not removed by the endoscopist. In total, 238/401 were categorized as false positives. Two polyps identified by the endoscopist were missed by AI (false negatives). The sensitivity of AI for polyp detection was 98.8%, the positive predictive value was 40.6%. The polyp detection rate for the endoscopist was 62% versus 82% for the AI system. Mean segmental Boston Bowel Preparation Scale were similar (2.64, 2.59, P=0.47) for true and false positives, respectively. CONCLUSIONS: A deep-learning algorithm can function effectively to detect polyps in a prospectively collected series of colonoscopies, even in the setting of variable preparation quality.

Full Text

Duke Authors

Cited Authors

  • Becq, A; Chandnani, M; Bharadwaj, S; Baran, B; Ernest-Suarez, K; Gabr, M; Glissen-Brown, J; Sawhney, M; Pleskow, DK; Berzin, TM

Published Date

  • July 2020

Published In

Volume / Issue

  • 54 / 6

Start / End Page

  • 554 - 557

PubMed ID

  • 31789758

Electronic International Standard Serial Number (EISSN)

  • 1539-2031

Digital Object Identifier (DOI)

  • 10.1097/MCG.0000000000001272


  • eng

Conference Location

  • United States