扳機指為手部骨科常見之疾病。至今，扳機指確切之病因仍尚未有完整結論，許多研究試圖從大體上去釐清問題的答案，然而大部分之研究皆僅利用健康之大體樣本，此外，現今的診斷方式仍須依靠臨床醫師之經驗去判別症狀並做出處置。另一方面，振動量測技術已被發展超過30年以上，然而其大部分的臨床應用皆在膝關節摩擦之振動分析，僅有非常少數是應用在身體其他部位。因此，本研究有兩個主要之實驗目的：(1)建立一個完整之扳機指大體模型和(2)應用振動量測技術於建立完成之大體模型中以釐清正常與扳機指訊號之本質與差異。本研究總共納入9隻中指和10隻無名指大體樣本，並開發出於肌腱中注入矽膠的方式來模擬扳機指中之結節，並結合過去研究中利用束帶加壓腱鞘的方式以全面性地去模擬肌腱和腱鞘之病態變化。本實驗分別模擬出肌腱和腱鞘之4個病態變化程度，此大體模型亦經過形態、壓印和滑動阻力之分析。此外，於測量滑動阻力之同時，固定一個三軸加速規於掌骨上以收集肌腱於等速滑動時之振動，這些振動訊號被進一步分析並擷取出可分辨出健康和扳機指訊號之特徵參數。根據結果，注入矽膠之肌腱有更大之厚度與體積且在力學特性上並無太大變化，此外，隨著扳機指之嚴重度上升，肌腱滑動阻力和伸直作功皆有顯著性變大，這些結果顯示本研究成功模擬出扳機現象於扳機指大體模型中並且與臨床之分級結果可以相互連結。於振動訊號分析中，隨著扳機指嚴重度增加，平均包絡震幅和碎形比例指數也有明顯上升，在眾多參數中，本研究利用相關性分析收斂出2個最具敏感度之特徵參數以建立後續二元密度熱區圖，圖中皆能清楚分辨出正常與第一、二級扳機指訊號之不同的熱區位置，此工具未來能輔助臨床醫師進行扳機指之診斷，尤其是對於難以確診之第一級扳機指。未來需要更多且大量之人體試驗來加以驗證此技術之有效性和準確度。

Trigger finger had long been a common disorder in hand orthopedics. Up to now, detailed etiology of the disorder has not been fully understood yet. To clarify the unknown causative factors regarding the disease, numerous experiments were done on human cadavers. However, most of these studies were conducted on normal fingers. In addition, the diagnosis often relies on clinicians’ experience to identify the symptoms. On the other hand, vibration arthrometry is a technique that has been developed for nearly 3 decades. However, the primary focus was on the detection of cartilage friction in knee joint. Very few studies were conducted on other parts of the body. Therefore, two aims were included in this study: (1) to create an authentic trigger finger cadaveric model and (2) to utilize vibration arthrometry to identify and analyze signal fundamentals and differences in the created cadaver model. A total of 9 middle and 10 ring cadaveric fingers were included in this study. Trigger finger cadaveric model was created by injecting silicone gel into the tendon to simulate nodule combining the previous method of applying a cable tie to the pulley for constriction. 4 degrees of severity were created using both methods, respectively. In addition, the cadaveric model were validated by morphological, indentation and gliding resistance tests. On the other hand, during the gliding resistance test, a tri-axial accelerometer was fixed to the metacarpal bone to detect the vibration during uni-speed excursion guided by a motor. The collected vibration signals were analyzed to extract features that differentiate the normal from triggering. In the results, larger volume and thickness were found in the injected tendon with no differences in mechanical properties. In addition, gliding resistance and work of extension were found to be larger as the severity of the triggering increased. These outcomes showed that the sign of triggering was simulated in this cadaveric model and a connection was discovered with the clinical grading system. On top of that, larger averaged envelope amplitude and fractal scaling index were displayed in the severer triggering conditions. 2 most sensitive features were extracted by the correlation analysis and further plotted into a bivariate density heat map with distinct zones of normal, grade I and II signals. This tool could help solve the problem of difficult diagnosis of grade I trigger finger clinically. Further studies should be carried out to validate and calibrate this technique in living human beings.

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