ORIGINAL RESEARCH article
Front. Physiol.
Sec. Skin Physiology
Construction and Validation of a Multi-Function Artificial Intelligence–Assisted System for Pressure Injury Recognition
Provisionally accepted
- Changshu No.1 People's Hospital, Suzhou, China
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Background With the acceleration of population aging, the incidence of pressure injury (PI) continues to rise, making early identification and accurate staging essential for preventing disease progression and improving prognosis. Conventional manual assessment relies heavily on clinical experience and subjective judgment, limiting real-time, objective, and quantitative evaluation. Objective This study aimed to develop and validate an artificial intelligence model based on the YOLOv11 neural network that integrates automatic PI detection, intelligent staging, and wound size measurement, thereby enhancing the timeliness, accuracy, and objectivity of PI assessment. Methods A total of 1,815 PI images collected from the electronic PI management systems of two medical centers between January 2021 and June 2025 were included. According to the 2019 NPUAP guidelines, images were classified into six categories: Stage I, Stage II, Stage III, Stage IV, unstageable, and deep tissue injury. Transfer learning was applied to train YOLOv11 models of different scales. Lesion localization and staging performance were compared to identify the optimal model. Automatic wound size measurement was achieved by integrating ArUco marker recognition with pixel-to-centimeter conversion. Results For bounding box localization, the YOLOv11s model demonstrated superior performance, with a precision of 0.854, recall of 0.766, mAP50 of 0.842, mAP50–95 of 0.629, and an inference speed of 4.8 ms per image. On the test set, overall staging classification accuracy reached 92.64%, with a sensitivity of 79.79%, specificity of 95.56%, and a false-positive rate of 4.44%. The highest accuracy was observed for deep tissue injury (96.45%), while Stage III showed the lowest accuracy (85.04%). In wound size measurement, PI-3DAS demonstrated high agreement with the reference standard, with a length mean absolute error (MAE) of 0.155 cm and ICC of 0.996, and a width MAE of 0.137 cm and ICC of 0.994. The mean time for AI-based measurement was 0.691 s, representing a 36.8-fold reduction compared with manual measurement (25.414 s; P < 0.001). Conclusion The YOLOv11-based PI-3DAS system enables automated PI detection, staging, and non-contact wound size quantification with high accuracy and consistency, while substantially improving measurement efficiency.
Keywords: automated staging, deep learning, Pressure injury, size measurement, YOLO
Received: 22 Dec 2025; Accepted: 02 Feb 2026.
Copyright: © 2026 Wang, Xu, Xia, Dai, Xu and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xiaodan Xu
Jian Chen
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