Evidence map›Paper›PMID 41965731›Full record

ArticleBMC medical informatics and decision making2026

Deep-learning-based diagnostic models for transcutaneous oxygen pressure-defined peripheral arterial disease and lower extremity wound healing in patients with diabetic foot.

Ming-Feng Tsai, Chen-Shen Shih, Yu-Fan Chen, Yu-Chang Chu, Chieh-Ming Yu, Wen-Teng Yao, Ya-Shu Chan, Chia-Meng Yu, Shu-Tien Huang, Liong-Rung Liu and 8 more

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Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

18 authors.

Ming-Feng TsaiGraduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, No.250, Wuxing St., Xinyi Dist., Taipei, 110, Taiwan.
Chen-Shen ShihGraduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, No.250, Wuxing St., Xinyi Dist., Taipei, 110, Taiwan.
Yu-Fan ChenDivision of Plastic Surgery, Department of Surgery, MacKay Memorial Hospital, Taipei, 104, Taiwan.
Yu-Chang ChuGraduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, No.250, Wuxing St., Xinyi Dist., Taipei, 110, Taiwan.
Chieh-Ming YuDivision of Plastic Surgery, Department of Surgery, MacKay Memorial Hospital, Taipei, 104, Taiwan.
Wen-Teng YaoDivision of Plastic Surgery, Department of Surgery, MacKay Memorial Hospital, Taipei, 104, Taiwan.
Ya-Shu ChanSchool of Health Care Administration, College of Management, Taipei Medical University, Taipei, 110, Taiwan.
Chia-Meng YuDivision of Plastic Surgery, Department of Surgery, MacKay Memorial Hospital, Taipei, 104, Taiwan.
Shu-Tien HuangGraduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, No.250, Wuxing St., Xinyi Dist., Taipei, 110, Taiwan.
Liong-Rung LiuGraduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, No.250, Wuxing St., Xinyi Dist., Taipei, 110, Taiwan.
Lang-Hua ChiuGraduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, No.250, Wuxing St., Xinyi Dist., Taipei, 110, Taiwan.
Yueh-Hung LinDepartment of Medicine, MacKay Medical University, New Taipei City, 252, Taiwan.
Chin-Yi YangDepartment of Dermatology, New Taipei Municipal TuCheng Hospital, New Taipei City, 236, Taiwan.
Kung-Chen HoDepartment of Medicine, MacKay Medical University, New Taipei City, 252, Taiwan.
Wen-Chen HuangDivision of Plastic Surgery, Department of Surgery, MacKay Memorial Hospital, Taipei, 104, Taiwan.
Kwang-Yi TungDivision of Plastic Surgery, Department of Surgery, MacKay Memorial Hospital, Taipei, 104, Taiwan.
Fei-Hung HungHealth Data Analytics and Statistics Center, Office of Data Science, Taipei Medical University, Taipei, 110, Taiwan.
Hung-Wen ChiuGraduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, No.250, Wuxing St., Xinyi Dist., Taipei, 110, Taiwan. hwchiu@tmu.edu.tw.

Funding

National Science and Technology Council, Taiwan NSTC113-2221-E-038-006
6 · The paper itself

Abstract

backgroundFew 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.

methodsAn 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.

resultsIn 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.

conclusionsCNN 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.

Indexed as

Blood Gas Monitoring, TranscutaneousDeep LearningDiabetic FootLower ExtremityPeripheral Arterial DiseaseWound HealingConvolutional Neural NetworksHumansConvolutional neural networkLower extremity woundPeripheral artery diseaseTranscutaneous oxygen pressureWound healing

Identifiers

PMID41965731
PMCPMC13185308

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.