AI-Assisted Chronic Wound Assessment
Computer vision and smart-bandage sensing to track how chronic wounds heal, as part of the SWODDYS project.
Chronic wounds such as venous and arterial ulcers heal slowly, and judging their progress still relies largely on visual inspection. In the SWODDYS project, together with Uniklinikum Regensburg and PreSens GmbH and funded by the Bayerische Forschungsstiftung, I work on making that assessment objective:
- Wound images: localizing the wound and segmenting its tissue types with computer vision.
- Wound chemistry: analysing pH, O2 and CO2 readings from sensors inside the bandage to follow recovery over time.
Outputs
- Paper: Interpreting Venous and Arterial Ulcer Images Through the Grad-CAM Lens, Diagnostics, 2025.
- Poster: Introducing the CicatrixVision (CiVi) Dataset: First steps into AI-assisted Chronic Wound Assessment. MaD Conference 2025 and AI Day, University of Bayreuth 2025.
- Poster: Exploring Correlation Between Tissue Composition and Healing in Chronic Wounds. AIBE Conference 2024.
Left: CiVi dataset poster (MaD Conference and AI Day Bayreuth, 2025). Right: tissue composition poster (AIBE Conference, 2024). Click a poster to open the PDF.
Student projects I supervised
- Alisha Mund: EDTRS Method for Generating Superpixels for Dermatological Wound Images
- Apu Das: Exploring Active Learning for Wound Tissue Segmentation
- Jule Lang: Evaluating Graph Neural Networks for Node Classification on Superpixel-Based Representations of Wound Images
- Ranganath Pujari: Superpixel-Based Wound Segmentation with Frozen Vision Foundation Models and Graph Neural Network Heads