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

ABSTRACT:

Background

Pressure ulcers (PUs) and incontinence-associated dermatitis (IAD) are prevalent conditions in clinical settings, posing significant challenges due to their similar presentations but differing treatment needs. Accurate differentiation between PUs and IAD is essential for appropriate patient care, yet it remains a burden for nursing staff and wound care experts.

Objective

This study aims to develop and introduce a robust multimodal deep learning framework for the classification of PUs and IAD, along with the fine-grained categorization of their respective wound severities, to enhance diagnostic accuracy and support clinical decision-making.

Methods

We collected and annotated a dataset of 1555 wound images, achieving consensus among 4 wound experts. Our frame

SUBMITTER: Brehmer A 

PROVIDER: S-EPMC12223690 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature