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

• URL: http://arxiv.org/abs/2201.10511v1
• Date: Tue, 25 Jan 2022 18:12:54 GMT
• Title: Initial Investigations Towards Non-invasive Monitoring of Chronic Wound Healing Using Deep Learning and Ultrasound Imaging
• Authors: Maja Schlereth (1,2), Daniel Stromer (2), Yash Mantri (3), Jason Tsujimoto (3), Katharina Breininger (1), Andreas Maier (2), Caesar Anderson (4), Pranav S. Garimella (5), Jesse V. Jokerst (6) ((1) Department Artificial Intelligence in Biomedical Engineering, FAU Erlangen-N\"urnberg, Erlangen, (2) Pattern Recognition Lab, FAU Erlangen-N\"urnberg, Erlangen, (3) Department of Bioengineering, University of California, San Diego, (4) Department of Emergency Medicine, San Diego, (5) Division of Nephrology and Hypertension, Department of Medicine, San Diego, (6) Department of Nanoengineering, University of California, San Diego)
• Abstract summary: We present initial results of a deep learning-based automatic segmentation of cross-sectional wound size in ultrasound images. We conclude that deep learning-supported analysis of non-invasive ultrasound images is a promising area of research.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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