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

Applications of AI (876) Imaging Informatics (449) Artificial Intelligence (4584) Dermatology (89) Machine Learning from Digital Images in Dermatology (24) Pressure Ulcers and Decubitus Wounds (34)

Published on in Vol 4 (2025)

This is a member publication of University of Duisburg-Essen

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67356, first published .
Nurse uses tablet to review patient's knee injury during physical therapy session.

Fine-Grained Classification of Pressure Ulcers and Incontinence-Associated Dermatitis Using Multimodal Deep Learning: Algorithm Development and Validation Study

Fine-Grained Classification of Pressure Ulcers and Incontinence-Associated Dermatitis Using Multimodal Deep Learning: Algorithm Development and Validation Study

Authors of this article:

Alexander Brehmer1 Author Orcid Image ;   Constantin Seibold1 Author Orcid Image ;   Jan Egger1, 2, 3 Author Orcid Image ;   Khalid Majjouti4 Author Orcid Image ;   Michaela Tapp-Herrenbrück4 Author Orcid Image ;   Hannah Pinnekamp5 Author Orcid Image ;   Vanessa Priester5 Author Orcid Image ;   Michael Aleithe6 Author Orcid Image ;   Uli Fischer5 Author Orcid Image ;   Bernadette Hosters4 Author Orcid Image ;   Jens Kleesiek1, 3 Author Orcid Image
Article Authors Cited by (5) Tweetations Metrics

Journals

  1. Pinnekamp H, Priester V, Steidle J, Majjouti K, Brehmer A, Tapp-Herrenbrück M, Aleithe M, Kleesiek J, Hosters B, Fischer U. Effects of a transformer-based AI-based application to support incontinence-associated dermatitis and pressure injury assessment, nursing care and documentation: Controlled pilot intervention study. International Journal of Nursing Studies Advances 2026;10:100479 View
  2. 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
  3. 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
  4. Sun Y, Liu C, Zhang J, Wei Z, Zhou Z, Fan C, Yue F. Unimodal and Multimodal Deep Learning for Pressure Injury Identification: Scoping Review (Preprint). JMIR Medical Informatics 2026 View

Conference Proceedings

  1. Soleh O, Hendry H, Sembiring I, Sutedja I, Jonas D, Kiantara R. 2025 4th International Conference on Creative Communication and Innovative Technology (ICCIT). Explainable CNN Model for Real Time Diabetic Wound Severity Classification in Telemedicine View