Evaluation of Net Withdrawal Time and Colonoscopy Video Summarization Using Deep Learning Based Automated Temporal Video Segmentation.

Adequate withdrawal time is crucial in colonoscopy, as it is directly associated with polyp detection rates. However, traditional withdrawal time measurements can be biased by non-observation activities, leading to inaccurate assessments of procedural quality. This study aimed to develop a deep learning (DL) model that accurately measures net withdrawal time by excluding non-observation phases and generates quantitative visual summaries of key procedural events. We developed a DL-based automated temporal video segmentation model trained on 40 full-length colonoscopy videos and 825 cecum clips extracted from 221 colonoscopy procedures. The model classifies four key events: cecum, intervention, outside, and narrow-band imaging (NBI) mode. Using the temporal video segmentation results, we calculated the net withdrawal time and extracted representative images from each segment for video summarization. Model performance was evaluated using four standard temporal video segmentation metrics, and its correlation with endoscopist-recorded times on both internal and external test datasets. In both internal and external tests, the DL model achieved a total F1 score exceeding 93% for temporal video segmentation performance. The net withdrawal time showed a strong correlation with endoscopist-recorded times (internal dataset, r = 0.984, p < 0.000; external dataset, r = 0.971, p < 0.000). Additionally, the model successfully generated representative images, and the endoscopists' visual assessment confirmed that these images provided accurate summaries of key events. Compared to manual review, the proposed model offers a more efficient, standardized and objective approach to assessing procedural quality. This model has the potential to enhance clinical practice and improve quality assurance in colonoscopy.