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.

Authors

Hongna Lu, Pan Jiang, Tianyin Chen, QinQin Hu, Jiahua Wu, Xi Deng, Mengyao Ge, Ting Weng, Liangshun Zhang, Wenying Guo, Xiaolin Chen, Feng Xu

Published in

Journal of gastroenterology and hepatology. Oct 07, 2026. Epub Oct 07, 2026.

Abstract

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.
This study developed an AI model for SSL detection by training it on 1980 colonoscopic images (2023-2025). The model enhances standard detection frameworks by specifically integrating mucus cap features and optimizing WLI performance. Model performance was evaluated using detection rate, accuracy, and AUC.
At the image level, the SSL detection model achieved an accuracy of 98.7% (sensitivity 100.0%, specificity 97.4%). Lesion-level detection improved from 77.4% with the conventional model to 95.0% after incorporation of mucus cap screening, representing an improvement of 17.6%, with detection rates of 95.9% for mucus cap-free lesions and 98.4% for mucus cap-containing lesions. For morphology-based typing, the WLI-based model achieved an accuracy of 85.8% (AUC 0.89), whereas the NBI-based model showed superior performance with an accuracy of 91.6% (AUC 0.94).
This study provides real-world clinical evidence validating AI-based models for SSL detection and diagnosis. Although representing an incremental step rather than a novel framework, such evidence is essential for improving model generalizability and enabling stable extrapolation across clinical settings. Further prospective and multicenter studies are required before reliable clinical deployment.

PMID:
42844151
Bibliographic data and abstract were imported from PubMed on 08 Oct 2026.

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