AI-Driven Three-Dimensional Reconstruction and Quantitative Analysis for Burn Injury Assessment

TL;DR

This paper introduces an AI-powered platform that uses multi-view imaging and 3D reconstruction to objectively assess burn injuries over time, improving accuracy and reproducibility in clinical evaluation.

Contribution

It presents a novel integrated system combining photogrammetry, deep learning segmentation, and clinical workflow for scalable, objective burn assessment using standard cameras.

Findings

Stable 3D reconstructions with consistent metrics

Objective measurement of burn surface area and depth

Clinically plausible longitudinal healing trends

Abstract

Accurate, reproducible burn assessment is critical for treatment planning, healing monitoring, and medico-legal documentation, yet conventional visual inspection and 2D photography are subjective and limited for longitudinal comparison. This paper presents an AI-enabled burn assessment and management platform that integrates multi-view photogrammetry, 3D surface reconstruction, and deep learning-based segmentation within a structured clinical workflow. Using standard multi-angle images from consumer-grade cameras, the system reconstructs patient-specific 3D burn surfaces and maps burn regions onto anatomy to compute objective metrics in real-world units, including surface area, TBSA, depth-related geometric proxies, and volumetric change. Successive reconstructions are spatially aligned to quantify healing progression over time, enabling objective tracking of wound contraction and depth reduction. The platform also supports structured patient intake, guided image capture, 3D analysis and visualization, treatment recommendations, and automated report generation. Simulation-based evaluation demonstrates stable reconstructions, consistent metric computation, and clinically plausible longitudinal trends, supporting a scalable, non-invasive approach to objective, geometry-aware burn assessment and decision support in acute and outpatient care.