拇指腕掌關節（Trapeziometacarpal Joint, TMC）是人體中運動最靈活且結構最複雜的關節之一，約佔上肢活動功能的50%。然而，由於長期承受高負荷，TMC 關節易發生退化性骨關節炎。現有研究雖已探討TMC 關節的形態與力學特性，但仍面臨主要限制：僅基於靜態影像分析、採用材料性質單一反映個體差異，造成分析結果偏差。

本研究整合四維電腦斷層掃描（4DCT）動態影像、深度學習技術、形狀分析與逆向有限元素分析，建立創新工作流程。透過深度學習技術捕捉 TMC 關節於 4DCT 下的運動過程，運用深度學習進行自動化影像分割與三維重建，分析 TMC 退化性關節炎臨床壓力檢測之拍攝夾角與關節活動平面關係。同時，結合統計形狀模型（Statistical Shape Model）進行群體分析，建立具泛化性的關節模型，並以逆向有限元素法連結影像數據與關節周遭韌帶材料參數。

研究成果不僅能準確描述 TMC 關節動態行為，更建立標準化且具個體化特徵的分析流程，為臨床診斷、手術規劃和輔具設計提供理論基礎。此方法學框架可進一步擴展至其他關節研究，為生物力學領域開創新範式。

This research investigates the Trapeziometacarpal (TMC) joint, which accounts for approximately 50% of upper limb functionality but is susceptible to degenerative osteoarthritis due to its complex structure and high load-bearing requirements. Traditional studies, limited by static image analysis and simplified biomechanical models, have failed to fully capture individual variations and dynamic behaviors. To address these limitations, this study presents an innovative workflow integrating 4D computed tomography (4DCT), deep learning technologies, and inverse finite element analysis.

The methodology incorporates several key components: a multi-view ensemble learning architecture combining four mainstream CNN models for efficient image segmentation; Statistical Shape Model (SSM) analysis for establishing a parameterized standard model with soft tissue components; and a Python-Abaqus based inverse optimization workflow utilizing Delaunay triangulation for accurate ligament material coefficient prediction. The research validated new stress-view methods through a Custom Stress View Evaluation System (CSVES), which enabled simultaneous force measurements during standardized stress postures.

The results demonstrate successful establishment of a standardized yet personalized analysis framework, achieving high accuracy in predicting ligament material parameters (error < 0.1%) and providing superior joint displacement measurements. This comprehensive approach not only validates the reliability of new arthritis diagnostic methods but also establishes a foundation for clinical diagnosis, surgical planning, and assistive device design, potentially extending to other joint studies in biomechanics.

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