Real-World Colonoscopy Video Integration to Improve Artificial Intelligence Polyp Detection Performance and Reduce Manual Annotation Labor

Background/Objectives: Artificial intelligence (AI) integration in colon polyp detection often exhibits high sensitivity but notably low specificity in real-world settings, primarily due to reliance on publicly available datasets alone. To address this limitation, we proposed a semi-automatic annotation method using real colonoscopy videos to enhance AI model performance and reduce manual labeling labor. Methods: An integrated AI model was trained and validated on 86,258 training images and 17,616 validation images. Model 1 utilized only publicly available datasets, while Model 2 additionally incorporated images obtained from real colonoscopy videos of patients through a semi-automatic annotation process, significantly reducing the labeling burden on expert endoscopists. Results: The integrated AI model (Model 2) significantly outperformed the public-dataset-only model (Model 1). At epoch 35, Model 2 achieved a sensitivity of 90.6%, specificity of 96.0%, overall accuracy of 94.5%, and an F1 score of 89.9%. All polyps in test videos were successfully detected, demonstrating considerable enhancement in detection performance compared to the public dataset-only model. Conclusions: Integrating real-world colonoscopy video data using semi-automatic annotation markedly improved diagnostic accuracy and significantly reduced manual annotation workload. However, the findings need validation through multicenter external datasets to ensure generalizability.