Wound3DAssist: A Practical Framework for 3D Wound Assessment

2025-08-24

AI Summary

Chronic wound assessment in clinical practice relies on subjective, labor-intensive manual measurements, while existing 2D video-based methods suffer from perspective distortion, narrow field-of-view, and inability to recover depth—particularly problematic in anatomically complex regions. To address these limitations, we propose the first modular, monocular, consumer-grade video-based 3D wound assessment framework designed for clinical deployment. Our method integrates lightweight monocular 3D reconstruction, precise wound segmentation, tissue classification, and perilesional skin analysis, enabling contactless, fully automated, viewpoint-invariant, and motion-robust 3D modeling. Using only a short video captured with a standard smartphone, it generates millimeter-accurate 3D wound models. We validate the framework on digital simulations, silicone phantoms, and real patient data: each assessment completes in under 20 minutes, significantly improving objectivity, efficiency, and clinical practicality.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Addresses subjective manual wound assessment limitations
• Overcomes 2D measurement perspective distortion issues
• Enables 3D wound depth capture in complex anatomies

Innovation

• 3D reconstruction from monocular smartphone videos
• Modular workflow integrating segmentation and analysis
• Non-contact automatic measurements with millimeter accuracy