Initial Investigations Towards Non-invasive Monitoring of Chronic Wound Healing Using Deep Learning and Ultrasound Imaging

TL;DR

This paper explores the use of deep learning to automatically segment ultrasound images for monitoring chronic wound healing, aiming to improve non-invasive diagnostics and therapy response assessment.

Contribution

It introduces initial deep learning methods for automatic wound segmentation in ultrasound images, highlighting challenges and potential for personalized wound care.

Findings

Deep learning models achieved Dice scores of 0.34 and 0.27 for wound segmentation.

The approach shows promise for non-invasive wound monitoring.

Further improvements are needed for robustness.

Abstract

Chronic wounds including diabetic and arterial/venous insufficiency injuries have become a major burden for healthcare systems worldwide. Demographic changes suggest that wound care will play an even bigger role in the coming decades. Predicting and monitoring response to therapy in wound care is currently largely based on visual inspection with little information on the underlying tissue. Thus, there is an urgent unmet need for innovative approaches that facilitate personalized diagnostics and treatments at the point-of-care. It has been recently shown that ultrasound imaging can monitor response to therapy in wound care, but this work required onerous manual image annotations. In this study, we present initial results of a deep learning-based automatic segmentation of cross-sectional wound size in ultrasound images and identify requirements and challenges for future research on this application. Evaluation of the segmentation results underscores the potential of the proposed deep learning approach to complement non-invasive imaging with Dice scores of 0.34 (U-Net, FCN) and 0.27 (ResNet-U-Net) but also highlights the need for improving robustness further. We conclude that deep learning-supported analysis of non-invasive ultrasound images is a promising area of research to automatically extract cross-sectional wound size and depth information with potential value in monitoring response to therapy.