Convolutional Neural Network Models for Visual Classification of Pressure Ulcer Stages: Cross-Sectional Study

Journals

1. Wei Y, Liu X, Pei J, Zhang H, Han L. Artificial Intelligence in Pressure Injury Diagnosis: A Critical Appraisal for Clinical Practice. Advances in Wound Care 2025 View
2. Jogi S, Shanbhag V, Chauhan L, Chhauda S, Dubey U, Shenoy A, Devi E. Design and development of an mHealth application for pressure ulcer care and caregiver support. Frontiers in Digital Health 2025;7 View
3. Zhang L, Yu H, Yang Y. Large Separable Kernel Attention–Driven Multidimensional Feature Cross-Level Fusion Classification Network of Knee Cartilage Injury: Algorithm Development and Validation. JMIR Medical Informatics 2025;13:e79748 View
4. Guo F, Cao N, Nie J, Guo W, Wang J. Research on real-time detection and staging technology for pressure injuries in critically ill patients based on the YOLOv8 deep learning model. Frontiers in Public Health 2026;14 View
5. Wang B, Wang Y, Sun T, Chen Y, Liu L, Li C, Yan J. Integrating Wound Images and Clinical Text for Pressure Injury Assessment and Treatment Recommendation. Bioengineering 2026;13(6):642 View
6. Zhou L, Zhang Z, Wang J, Zhang H, Zhong C, Zong T, Sun L, Luo M, Qiao L, Hu H, Zhang W, Wang C, Yang R, Zhou Y, Wang L. Deep learning-based pressure injury staging: a multicentre study involving 59 hospitals. Journal of Global Health 2026;16 View
7. Lott J, McShannon D, Dietrich N. A selective deep learning framework for pressure injury staging with calibrated confidence and automated clinical documentation. Intensive and Critical Care Nursing 2026;97:104466 View
8. Takahashi T, Miyo K, Tamai N. Benchmarking Zero-Shot Generative Pre-Trained Transformer-Based Multimodal Large Language Models for Pressure Injury Staging. Advances in Wound Care 2026 View