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Related Experiment Video

Updated: Sep 17, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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

Alexander Brehmer¹, Constantin Seibold¹, Jan Egger^1,2,3

¹Institute for Artificial Intelligence in Medicine, Essen University Hospital, Girardetstr. 2, Essen, 45131, Germany, 0201 72377829.

|July 3, 2025

Summary

A new deep learning framework accurately differentiates pressure ulcers (PUs) and incontinence-associated dermatitis (IAD), outperforming human experts. This AI tool aids clinicians in diagnosing these similar-presenting wounds for better patient care.

Keywords:

computer vision deep learning image classification incontinence-associated dermatitis multi modal data pressure ulcer synthetic image generation wound classification

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Published on: August 18, 2022

Area of Science:

Artificial Intelligence in Healthcare
Medical Image Analysis
Deep Learning for Diagnostics

Background:

Pressure ulcers (PUs) and incontinence-associated dermatitis (IAD) are common clinical conditions with similar appearances but distinct treatments.
Accurate differentiation between PUs and IAD is crucial for effective patient management but challenging for healthcare professionals.

Purpose of the Study:

To develop a multimodal deep learning framework for classifying PUs and IAD.
To enable fine-grained categorization of wound severities for enhanced diagnostic accuracy.
To provide a decision-support tool for clinicians in distinguishing between PUs and IAD.

Main Methods:

A dataset of 1555 wound images was annotated by wound care experts.
A multimodal deep learning framework integrating images and patient data was developed.
Four models (CNNs and Transformers) were evaluated using various preprocessing, augmentation, and training techniques.

Main Results:

The TinyViT transformer model achieved 93.23% F1-score in binary PU/IAD classification, surpassing human experts.
TinyViT excelled in PU severity classification (75.43% F1-score), while ConvNeXtV2 led in IAD classification (53.20% F1-score).
Multimodal data integration improved binary classification; ensembling enhanced overall accuracy.

Conclusions:

The multimodal deep learning framework accurately differentiates PUs from IAD, exceeding human expert performance.
This AI tool can reduce diagnostic uncertainty, optimize treatment, and improve patient outcomes.
Potential clinical applications include integration into EHR systems or mobile diagnostic tools.