Park Kanggil, Lee Ji Young, Choi Ahin, Byeon Jeong-Sik, Kim Namkug
Department of Biomedical Engineering, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, 88, Olympic-Ro 43Gil, Songpa-Gu, Seoul, 05505, Republic of Korea.
Division of Gastroenterology, Health Screening and Promotion Center, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43Gil, Songpa-Gu, Seoul, 05505, Republic of Korea.
J Imaging Inform Med. 2025 Aug 5. doi: 10.1007/s10278-025-01632-1.
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.
在结肠镜检查中，足够的退镜时间至关重要，因为它与息肉检出率直接相关。然而，传统的退镜时间测量可能会受到非观察活动的影响而产生偏差，从而导致对操作质量的评估不准确。本研究旨在开发一种深度学习（DL）模型，该模型通过排除非观察阶段来准确测量净退镜时间，并生成关键操作事件的定量视觉总结。我们开发了一种基于深度学习的自动时间视频分割模型，该模型在40个完整长度的结肠镜检查视频和从221例结肠镜检查操作中提取的825个盲肠片段上进行训练。该模型对四个关键事件进行分类：盲肠、干预、外部和窄带成像（NBI）模式。利用时间视频分割结果，我们计算了净退镜时间，并从每个片段中提取代表性图像用于视频总结。使用四个标准的时间视频分割指标评估模型性能，以及其与内部和外部测试数据集上内镜医师记录时间的相关性。在内部和外部测试中，DL模型在时间视频分割性能方面的总F1分数均超过93%。净退镜时间与内镜医师记录的时间显示出很强的相关性（内部数据集，r = 0.984，p < 0.000；外部数据集，r = 0.971，p < 0.000）。此外，该模型成功生成了代表性图像，内镜医师的视觉评估证实这些图像提供了关键事件的准确总结。与人工审查相比，所提出的模型为评估操作质量提供了一种更高效、标准化和客观的方法。该模型有潜力加强临床实践并改善结肠镜检查的质量保证。
