BMC medical informatics and decision making · 2026
Deep-learning-based diagnostic models for transcutaneous oxygen pressure-defined peripheral arterial disease and lower extremity wound healing in patients with diabetic foot.
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Tsai Ming-Feng, Shih Chen-Shen, Chen Yu-Fan, Chu Yu-Chang, Yu Chieh-Ming, Yao Wen-Teng, Chan Ya-Shu, Yu Chia-Meng, Huang Shu-Tien, Liu Liong-Rung, Chiu Lang-Hua, Lin Yueh-Hung, Yang Chin-Yi, Ho Kung-Chen, Huang Wen-Chen, Tung Kwang-Yi, Hung Fei-Hung, Chiu Hung-Wen
Аннотация
BACKGROUND: Few studies have evaluated peripheral artery disease (PAD) and wound healing in patients with lower extremity wounds using a convolutional neural network (CNN)-based deep learning algorithm. We aimed to establish a CNN deep-learning model based on transcutaneous oxygen pressure (TcPO2)-annotated wound images for detecting PAD and wound healing in diabetic patients with lower extremity wounds.
METHODS: An extensive database of 1,407 original images from 77 patients with lower extremity wounds were collected to produce CNN deep-learning models (i.e., GoogleNet, ResNet 101V2 and EfficientNet). A framework was constructed, including image pre-processing and TcPO2-based grouping, to establish an optimal training model and to validate each model's performance for detecting PAD or wound healing.
RESULTS: In the established CNN deep-learning models, the ResNet101V2 model with original wound images showed the best performance for detecting PAD (sensitivity 93.08%, accuracy 86.20%) or wound healing (sensitivity 96.76%, accuracy 88.14%), although the performance of GoogleNet and EfficientNet models also demonstrated high sensitivity and accuracy.
CONCLUSIONS: CNN deep-learning algorithm based on objective TcPO2 values and image preprocessing is a promising model for detecting PAD and wound healing for lower extremity wounds, providing an easily implemented and more objective and reliable computation tool for physicians to automatically identify PAD and monitor wound healing.
METHODS: An extensive database of 1,407 original images from 77 patients with lower extremity wounds were collected to produce CNN deep-learning models (i.e., GoogleNet, ResNet 101V2 and EfficientNet). A framework was constructed, including image pre-processing and TcPO2-based grouping, to establish an optimal training model and to validate each model's performance for detecting PAD or wound healing.
RESULTS: In the established CNN deep-learning models, the ResNet101V2 model with original wound images showed the best performance for detecting PAD (sensitivity 93.08%, accuracy 86.20%) or wound healing (sensitivity 96.76%, accuracy 88.14%), although the performance of GoogleNet and EfficientNet models also demonstrated high sensitivity and accuracy.
CONCLUSIONS: CNN deep-learning algorithm based on objective TcPO2 values and image preprocessing is a promising model for detecting PAD and wound healing for lower extremity wounds, providing an easily implemented and more objective and reliable computation tool for physicians to automatically identify PAD and monitor wound healing.