口腔疾病為全球最常見的非傳染性疾病之一，其中約有3.1億人口全口缺牙患者。近年來，All-on-4®全口重建植牙技術廣泛應用於無牙患者之治療，其核心概念為於上下顎骨中植入四根植體並搭配支架與贋復物以完成固定式修復。然而，現今臨床上之植體配置與決策多仰賴醫師之個人經驗與主觀偏好，缺乏一套結合力學理論與客觀分析的植牙配置準則，易導致不穩定或高風險之放置結果，進而影響其長期穩定性與成功率。
本研究的目的為提出一套結合有限元素分析與最佳化方法之下顎All-on-4®植體配置決策支援系統，可應用於真實患者，以生物力學指標作為其目標函數進行植體配置最佳化。本研究收集了10位臨床患者之下顎骨錐狀射束電腦斷層掃描 資料以重建三維幾何模型，並匯入至有限元素分析軟體Abaqus進行力學模擬與分析計算植體周邊平均應力，並透過粒子群演算法，反覆迭代最小化該應力指標。同時，本研究招募了10位專科醫師與10位實習醫師對相同10位患者進行植體配置，進行組內與組間之可行性與一致性分析，並探討醫師決策與基於生物力學的電腦決策之間的差異。
相較於現行All-on-4®概念的植牙準則與現行之植牙配置建議，本研究之最佳化系統在10個臨床個案中降低了平均應力達52.19%，顯著其可針對個人下顎幾何條件有效優化自訂之力學指標。與專科醫師和實習醫師的植牙配置相比，本系統所得之植體最佳配置在平均應力上分別低了50.11%和44.75%。醫師的植體配置結果顯示，實習醫師組的可行決策僅有64%，而專科醫師組的決策雖皆為可行，但具有主觀放置偏好的現象。
本研究提出了可應用於真實患者之下顎All-on-4®植體配置最佳化系統，可作為醫師的決策支援工具，在確保可行性的前提下，提供基於生物力學的最佳植體配置參考。

Edentulism, affecting over 310 million people worldwide, presents a significant public health concern. The All-on-4® implant technique has emerged as a widely adopted solution for full-arch rehabilitation. However, clinical decisions for implant placement still largely depend on personal experience, lacking standardized biomechanical guidelines, which may compromise long-term stability and success.
This study introduces a decision-support system that combines Finite Element Analysis (FEA) and Particle Swarm Optimization (PSO) to optimize mandibular implant configurations based on biomechanical performance, specifically minimizing peri-implant stress. Using CT data from 10 clinical patients, 3D mandibular models were reconstructed for FEA, and PSO was applied to determine optimal implant positions, sizes, and angles. These results were compared with implant plans from 10 specialists and 10 interns.
Compared with traditional All-on-4® placement rules and expert decisions, the proposed system achieved a 52.19% reduction in average peri-implant stress. When compared specifically to the configurations made by specialists and interns, the system's optimized plans reduced stress by 50.11% and 44.75%, respectively. While all specialist-designed placements were feasible, they exhibited notable subjective biases. Intern-designed plans had a lower feasibility rate of 64%.
This study demonstrates the clinical potential of FEA-based optimization to support more consistent, evidence-based implant planning, especially for less experienced clinicians.

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