AI 中文总结
针对手动测角术低效、信度差及现有软件方法精度不足的问题，提出结合2D特征提取与视图合成的神经渲染新流程，引入离散密度爬山算法修正3D地标，实现自动数字测角，为手部功能评估提供可靠工具。
AI 中文摘要
手部及手指疾病是肌肉骨骼残疾的主要诱因，临床亟需精准方法量化关节运动。关节活动范围（ROM）是诊断、康复监测及评估手术效果的指标。目前，测角仪是评估手指屈伸的标准工具，但手动测角术劳动强度大，且因检查者操作差异导致评估者间信度不一致。虽存在数字替代方案，但现有基于软件的方法常缺乏临床所需的精度。为解决这些局限，本文提出一种基于神经渲染的3D手部关节定位与姿态估计新流程。与以往方法不同，该方法结合2D特征提取与视图合成，显著提升了精度与临床可行性；此外，本文引入离散密度爬山算法，可对3D空间中的投影地标进行有效修正。该系统克服了手动测量的低效性与现有软件的不准确性，为客观功能评估提供了可靠工具。
英文摘要
Hand and finger disorders are leading contributors to musculoskeletal disability, creating a clinical need for precise methods to quantify joint motion. Range of motion (ROM) serves as the metric for diagnosis, rehabilitation monitoring, and evaluating surgical outcomes. Currently, the goniometer is the standard tool for assessing finger flexion and extension. However, manual goniometry is labor-intensive and suffers from inconsistent inter-rater reliability due to variations in examiner technique. While digital alternatives exist, current software-based approaches often lack the necessary accuracy for clinical usage. To address these limitations, we present a novel pipeline for 3-D hand joint location and pose estimation using neural rendering. Unlike previous methods, our approach combines 2-D feature extraction with view synthesis to significantly improve accuracy and clinical viability. Furthermore, we introduce a discrete density hill climbing algorithm that facilitates the meaningful correction of projected landmarks in 3-D space. This system overcomes the inefficiencies of manual measurement and the inaccuracies of existing software, providing a robust tool for objective functional assessment.
CommentsAccepted for publication in the Proceedings of the 48th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2026), Full Paper #1985
