Managing chronic wounds remains a major healthcare challenge, with clinical assessment often relying on subjective and time-consuming manual documentation methods. Although 2D digital videometry frameworks aided the measurement process, these approaches struggle with perspective distortion, a limited field of view, and an inability to capture wound depth, especially in anatomically complex or curved regions. To overcome these limitations, we present Wound3DAssist, a practical framework for 3D wound assessment using monocular consumer-grade videos. Our framework generates accurate 3D models from short handheld smartphone video recordings, enabling non-contact, automatic measurements that are view-independent and robust to camera motion. We integrate 3D reconstruction, wound segmentation, tissue classification, and periwound analysis into a modular workflow. We evaluate Wound3DAssist across digital models with known geometry, silicone phantoms, and real patients. Results show that the framework supports high-quality wound bed visualization, millimeter-level accuracy, and reliable tissue composition analysis. Full assessments are completed in under 20 minutes, demonstrating feasibility for real-world clinical use.
@article{arxiv.2508.17635,
title = {Wound3DAssist: A Practical Framework for 3D Wound Assessment},
author = {Remi Chierchia and Rodrigo Santa Cruz and Léo Lebrat and Yulia Arzhaeva and Mohammad Ali Armin and Jeremy Oorloff and Chuong Nguyen and Olivier Salvado and Clinton Fookes and David Ahmedt-Aristizabal},
journal= {arXiv preprint arXiv:2508.17635},
year = {2025}
}