Innovative Two-Phase Deep Learning System for Automated Classification of Pressure Injuries
By
Ting-Yu Lai
Yi-Jiun Chou
Chun-You Liu
Chien-Wei Chen
Ching-Ting Lin
Wei-Chun Wang
Yimin Hsu
Ming-Li Hsieh
Shih-Sheng Chang
March 27, 2026
Clinical Scorecard: Innovative Two-Phase Deep Learning System for Automated Classification of Pressure Injuries
At a Glance
Category Detail
Condition Pressure Injury (PI) Key Mechanisms Two-stage deep learning approach combining object detection (YOLOv9) and image classification (DenseNet161) Target Population Hospitalized patients, particularly in intensive care units Care Setting Clinical workflows for nursing assessments
Key Highlights
Developed an AI framework for automatic PI staging from clinical images Achieved an overall accuracy of 0.775 in staging on an independent test set Demonstrated improved diagnostic accuracy and reduced subjectivity in PI staging Framework provides standardised and reproducible PI staging Potential integration into nursing workflows as a clinical decision-support tool Guideline-Based Recommendations
Diagnosis
Utilize AI-driven classification systems for objective PI staging Management
Implement two-stage deep learning frameworks in clinical practice to enhance PI assessment Monitoring & Follow-up
Regularly evaluate the performance of AI models in real-world clinical settings Risks
Challenges in handling intra-wound heterogeneity and variability in image quality Patient & Prescribing Data
Patients with pressure injuries in clinical settings
AI system may assist in providing consistent and objective staging for treatment planning
Clinical Best Practices
Incorporate AI tools to support nursing assessments of pressure injuries Ensure high-quality image acquisition for accurate AI analysis Train clinical staff on the use of AI systems for enhanced decision-making Related Resources & Content
