Development and validation of a three-dimensional deep learning-based system for assessing bowel preparation on colonoscopy video

Authors：Feng, Lina; Xu, Jiaxin; Ji, Xuantao; Chen, Liping; Xing, Shuai; Liu, Bo; Han, Jian; Zhao, Kai; Li, Junqi; Xia, Suhong; Guan, Jialun; Yan, Chenyu; Tong, Qiaoyun; Long, Hui; Zhang, Juanli; Chen, Ruihong; Tian, Dean; Luo, Xiaoping; Xiao, Fang^*; Liao, Jiazhi^*

Source：Frontiers in Medicine, 2023, 10: 1296249.

DOI：10.3389/fmed.2023.1296249

• colonoscopy
• bowel preparation
• artificial intelligence
• deep learning
• convolutional neural network (CNN)

BackgroundThe performance of existing image-based training models in evaluating bowel preparation on colonoscopy videos was relatively low, and only a few models used external data to prove their generalization. Therefore, this study attempted to develop a more precise and stable AI system for assessing bowel preparation of colonoscopy video.MethodsWe proposed a system named ViENDO to assess the bowel preparation quality, including two CNNs. First, Information-Net was used to identify and filter out colonoscopy video frames unsuitable for Boston bowel preparation scale (BBPS) scoring. Second, BBPS-Net was trained and tested with 5,566 suitable short video clips through three-dimensional (3D) convolutional neural network (CNN) technology to detect BBPS-based insufficient bowel preparation. Then, ViENDO was applied to complete withdrawal colonoscopy videos from multiple centers to predict BBPS segment scores in clinical settings. We also conducted a human-machine contest to compare its performance with endoscopists.ResultsIn video clips, BBPS-Net for determining inadequate bowel preparation generated an area under the curve of up to 0.98 and accuracy of 95.2%. When applied to full-length withdrawal colonoscopy videos, ViENDO assessed bowel cleanliness with an accuracy of 93.8% in the internal test set and 91.7% in the external dataset. The human-machine contest demonstrated that the accuracy of ViENDO was slightly superior compared to most endoscopists, though no statistical significance was found.ConclusionThe 3D-CNN-based AI model showed good performance in evaluating full-length bowel preparation on colonoscopy video. It has the potential as a substitute for endoscopists to provide BBPS-based assessments during daily clinical practice.

• Institution
  中国科学院; 华中科技大学; 1