ArticleFrontiers in physiology2026
Construction and validation of a multi-function artificial intelligence-assisted system for pressure injury recognition.
Article in Frontiers in physiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Deep learning-based pressure injury staging: a multicentre study involving 59 hospitals.Journal of global health · 2026Article
- Integrating Wound Images and Clinical Text for Pressure Injury Assessment and Treatment Recommendation.Bioengineering (Basel, Switzerland) · 2026Article
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Authors and funding
6 authors.
Funding
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Abstract
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 National Pressure Ulcer Advisory Panel (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 (v11n/s/m/l/x). 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, mAP 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. This system provides a portable and practical tool to support clinical nursing assessment, therapeutic follow-up, and remote PI management.
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