aceDeep Learning-Based Detection of Sessile Serrated Lesions in Colonoscopy Images: Optimization of a YOLOv3 Framework With Mucus Cap Feature Enhancement for Sessile Serrated Lesion Detection
Background and aim Sessile serrated lesions (SSLs) are difficult to detect during colonoscopy via standard white-light imaging (WLI) because they are flat, have indistinct borders, and are often hidden by mucus caps. Although narrowband imaging (NBI) can help, its effectiveness is inconsistent. This study evaluated the feasibility of an artificial intelligence (AI)-assisted approach incorporating mucus cap features and both WLI and NBI for SSL detection and diagnosis. Methods This study developed an AI model for…
Adding mucus cap screening to a YOLOv3-based model raised lesion-level detection of sessile serrated lesions from 77.4% to 95.0% in the study's colonoscopy images.
The authors state that prospective and multicenter studies are required before reliable clinical deployment, and describe the work as an incremental step toward generalizability.
Evidence
- Peer-reviewedJournal of Gastroenterology and Hepatology2026-10-07
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Truvace Impact Record TRV-2026-1754, v1: “Deep Learning-Based Detection of Sessile Serrated Lesions in Colonoscopy Images: Optimization of a YOLOv3 Framework With Mucus Cap Feature Enhancement for Sessile Serrated Lesion Detection.” Truvace, 2026-10-17. /record/TRV-2026-1754 (accessed at citation time). sha256 2c04a105d8b02fd6…
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