用AI实现烧伤三维重建与定量分析，让评估更客观精准。
AI-Driven Three-Dimensional Reconstruction and Quantitative Analysis for Burn Injury Assessment
- 通过多视角摄影和深度学习，重建患者特异性3D烧伤表面。
- 可计算烧伤面积、体积变化等指标，支持治疗过程动态追踪。
- 适合临床医生、康复团队用于急症与门诊的客观决策支持。
准确、可重复的烧伤评估对治疗规划、愈合监测和医法律证至关重要，但传统目视检查和2D摄影主观性强且难以进行纵向比较。本文提出一个集成多视角摄影测量、3D表面重建和深度学习分割的AI烧伤评估与管理平台，基于消费级相机获取的多角度图像，重建患者特异性的3D烧伤表面，并将烧伤区域映射至解剖结构，以真实单位计算表面积、总体表面积（TBSA）、深度相关的几何代理指标及体积变化。多次重建结果空间对齐，可量化愈合进程，实现对创面收缩和深度减小的客观追踪。平台还支持结构化患者建档、引导式图像采集、3D分析可视化、治疗建议生成及自动化报告输出。仿真评估显示重建稳定、指标计算一致，且呈现临床合理的纵向趋势，证明该方法在急性及门诊护理中具有可扩展、非侵入式的客观、几何感知评估与决策支持潜力。
原文摘要 · Abstract (English)
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.
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