Abstract：Objective To develop computer vision models for endoscopic bowel preparation scoring based on deep
learning. Methods A total of 2394 endoscopic images from the Gastrointestinal Endoscopy Centre of the First Affiliated Hospital
of Soochow University (n=600) and the HyperKvasir database (n=1794) were collected, scored by endoscopists according to
the Boston bowel preparation scale (BBPS, 0-3, four categories). They were randomly divided into training sets (1439 pieces),
verification sets (478 pieces) and test sets (477 pieces) according to 6∶2∶2. Three deep learning networks (DenseNet169,
DenseNet121, EfficientNet B3) were used to develop the bowel preparation classification models by transfer learning. Metrics
such as confusion matrices in the test set were used for model evaluation. Meanwhile, the models were compared with senior and
junior endoscopists. Results The three deep learning-based bowel preparation classification models were successfully developed.
The classification accuracy of all models was high, and the average classification accuracy was 0.897, which was similar to the
clarification performance of junior endoscopist (0.914) and lower than that of senior endoscopist (0.941). Among them, the best
performing model was the DenseNet169 model, which had the highest classification accuracy (0.914) and the highest average
precision (0.892). In addition, the visual interpretation of the models’ classification results was presented in the form of heat maps
by using gradient-weighted class activation mapping. Conclusion The endoscopic bowel preparation classification model developed
using deep learning is feasible, and the classification and generalization ability of the model can be further improved by expanding
the sample source through multicenter studies.
沈文娟，徐昶，林嘉希，许春芳，陆建英，朱锦舟. 基于深度学习的内镜肠道准备
评分模型的建立[J]. 中国医疗设备, 2023, 38(11): 11-15.
SHEN Wenjuan, XU Chang, LIN Jiaxi, XU Chunfang, LU Jianying, ZHU Jinzhou. Computer Vision Models for Endoscopic Bowel Preparation Scoring Based on Deep Learning. China Medical Devices, 2023, 38(11): 11-15.
