Constructing an artificial intelligence assisted system for colonoscopy quality control based on various deep learning architectures(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2024年第11期
- Page:
- 1443-1452
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Constructing an artificial intelligence assisted system for colonoscopy quality control based on various deep learning architectures
- Author(s):
- CHEN Jian1; ZHANG Zihao2; WANG Ganhong3; WANG Zhenni1; XIA Kaijian4; XU Xiaodan1
- 1. Department of Gastroenterology, Changshu No.1 Peoples Hospital/Changshu Hospital Affiliated to Soochow University, Suzhou 215500, China 2. Shanghai Haoxiong Education Technology Co., Ltd., Shanghai 200434, China 3. Department of Gastroenterology, Changshu Hospital of Traditional Chinese Medicine, Suzhou 215500, China 4. Changshu Key Laboratory of Medical Artificial Intelligence and Big Data, Suzhou 215500, China
- Keywords:
- Keywords: deep learning Transformer colonoscopy quality control colonoscopy model deployment
- PACS:
- R574.6
- DOI:
- DOI:10.3969/j.issn.1005-202X.2024.11.018
- Abstract:
- Abstract: Objective To develop deep learning models for colonoscopy quality control using various deep learning architectures, and to delve into the decision-making mechanisms. Methods The colonoscopy images were selected from two datasets separately constructed by the HyperKvasir and Changshu Hospital Affiliated to Soochow University, encompassing intestines of varying degrees of cleanliness, polyps, and cecums. After image preprocessing and enhancement, transfer learning was carried out using the pre-trained models based on convolutional neural network (CNN) and Transformer. The model training adopted cross-entropy loss functions and Adam optimizer, and simultaneously implemented learning rate scheduling. To enhance model transparency, a thorough interpretability analysis was conducted using Grad-CAM, Guided Grad-CAM, and SHAP. The final model was converted to ONNX format and deployed on various equipment terminals to achieve real-time colonoscopy quality control. Results In a dataset of 3 831 colonoscopy images, EfficientNet model outperformed the other models on the test set, achieving an accuracy of 0.992 which was higher than those of the other models based on CNN (DenseNet121, ResNet50, VGG19) and Transformer (ViT, Swin, CvT), with a precision, recall rate, and F1 score of 0.991, 0.989, and 0.990. On an external test set of 358 images, EfficientNet model had an average AUC, precision, and recall rate of 0.996, 0.948, and 0.952, respectively. Although EfficientNet model is high-performing, some misjudgments still occurred. Interpretability analysis highlighted key image areas affecting decision-making. In addition, EfficientNet model was successfully converted to ONNX format and deployed on multiple platforms and devices, and it ensured real-time colonoscopy quality control with an inference speed of over 60 frames per second. Conclusion Among the 7 models developed for colonoscopy quality control based on CNN and Transformer, EfficientNet demonstrated exemplary performance across all categories and is deployed for real-time predictions on multiple terminals, aiming to provide patients with better medical care.
Last Update: 2024-11-26