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This paper is in the following e-collection/theme issue:

Decision Support for Health Professionals (1685) Imaging Informatics (350) Machine Learning (2297) Artificial Intelligence (2440) Image Recognition and AI in Dermatology (22)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/62774, first published .
Convolutional Neural Network Models for Visual Classification of Pressure Ulcer Stages: Cross-Sectional Study

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

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

Authors of this article:

Changbin Lei1 Author Orcid Image ;   Yan Jiang2 Author Orcid Image ;   Ke Xu3 Author Orcid Image ;   Shanshan Liu3 Author Orcid Image ;   Hua Cao4 Author Orcid Image ;   Cong Wang3 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

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