背景: 臂神經叢受損大部分皆由於外傷所致。其分類主要分為上幹型臂神經叢受損（第5、6 頸椎神經受損）及全幹型臂神經受損（第5、6、7、8頸椎及第一胸椎神經受損）。治療仍以恢復肩部、肘關節及手部的功能為主，且術後的評估亦多以傳統主觀的認定或者MRC量表來評估，其復原的功能少有具體量化或更科學的方式來評估。本論文分成三大部分。首先，介紹目前常見的臂神經叢手術以及創新的合併健側頸7神經移植及功能性游離皮瓣手術及相關的結果。其次，以生物力學原理為基礎，透過科學量化的方式，評估臂神經叢損傷病患的功能恢復情況。最後，將病患分類數值化，並應用類神經網路建模的工程手段進行分類比較預測術後的復原情況，建立可隨時修正的分析模式以利將來臨床應用。
方法: (1) 收集40位臂神經叢損傷患者，追蹤六年。其中20位接受hemi-CC7 transfer (group 1)，另外20位接受Total-CC7 移植(group 2)，進行肩部及肘關節功能評估。(2) 收集43位臂神經叢損傷患者並分為三組，包括單一神經移植(n=14)，雙神經移植(n=22)及三神經移植(n=7)等。以量化的方式量測術後肩關節的屈曲(flexion)，外展(abduction)及外旋(external rotation)等力量，並分析其組間差異及恢復功能與復原週期的關係。(3) 篩選34位上臂型臂神經叢損傷患者接受神經移植的族群，透過類神經網路軟體建立三種分析模式，包括線性回歸(linear regression)，徑向基函數(radial basis function)及多層感知器(multilayer perceptron)等進行分類及預測，期望找出最佳預測及分類模式。
結果: (1) 接受Total-CC7移植(group 2)手術的患者在手部功能評估結果為M3及M4者佔65%，明顯比接受Hemi-CC7 (group 1)手術的患者(35%)多。尤其在同時接受肩胛上神經(suprascapular)及腋神經(axillary)手術者，具有較佳的肩關節功能。(2) 在單一神經移植，雙神經移植及三神經移植等三個族群中的MRC分數並未具有明顯差異。而在外旋及肘關節屈曲(elbow flexion)強度的量化評估中，採單一神經移植者均較雙神經移植及三神經移植者佳。然而，隨著復健及追蹤週期越久，在進行雙神經移植患者的肌肉強度均具有改善的趨勢。(3) 採用類神經網路的三種分析模式，結果以多層感知模式，在MRC分數及肩部外展及外旋功能，均能得到最佳的分類及預測結果。
結論: (1) 接受Total-CC7移植的復原功能不錯，但相對於Hemi-CC7移植者具有較多的donor complication。而同時進行肩胛上神經及腋神經移植手術者，能有效恢復肩部的功能。(2) 在具有相同的MRC分數的病患，事實上可能存在著不同量化肌力的差異，移植的神經數，並不一定為決定術後功能的主要因素。(3) 在使用類神經網路進行臂神經叢手術之術後功能評估及預測模式中，採用多層感知模式能有效預測其結果，本研究建立的分析模式可為決定手術方式的參考。

Background: Brachial plexus injury (BPI) is a devastating injury, mostly caused by high energy trauma. There are 2 major types of BPI: upper arm type (C5-6 injury) and total arm type (C5-6-7-8-T1 injury). The priorities of surgical treatments are to restore the shoulder / elbow function, and hand function as well. There are three topics in this study: (1) Surgical technique and outcome evaluation comparing the total contralateral C7 root transfer to hemi-CC7 transfer, on total root avulsion BPI, (2) To quantify the muscle strength for post-surgical BPI patients' evaluation comparing the outcomes of single- double-, and triple- nerve transfer, and (3) To establish a model to predict the prognosis of BPI patients after reconstruction by artificial neural networks (ANN).

Materials and Methods:
(1) Forty patients who received neurotization for BPI were enrolled in this prospective study. Group 1 (n=20) received hemi-CC7 transfer for hand function, while group 2 (n=20) received total-CC7 transfer. Additional neurotization included spinal accessory, phrenic, and intercostal nerve transfer for shoulder and elbow function. The results were evaluated with an average of 6 years follow-up.
(2) Forty-three patients who received neurotization for BPI were enrolled. Patients were divided into 3 subgroups: single nerve transfer (n=14), double nerve transfer group (n=22), and triple nerve transfer group (n=7). Quantitative strength of shoulder flexion, abduction, external rotation, and elbow flexion were measured by the handheld dynamometer. One-way ANOVA and simple linear regression tests were used to examine the differences among three groups and the relationships between progress of motor recovery and follow-up period.
(3) A sample of 40 patients who have upper arm type and treated with nerve transform were collected. The commercial neural network software, NeuroSolutions, was used for building the model to predict the prognosis of BPI patients. Fifteen parameters from clinical record were set as the input variables, while the MRC scores were set as the output variables. Three common neural network models, including linear regression, radial basis function, and multilayer perceptron, were built and used to compare the accuracy of classification results for shoulder abduction and external rotation.

Results:
(1) Group 1 had fewer donor site complications (15%) than group 2 (45%); group 2 had significantly better hand M3 and M4 motor function (65%) than group 1 (35%; p =0.02). There was no difference in sensory recovery. Significantly, better shoulder function was obtained by simultaneous neurotization on both suprascapular and axillary nerves.
(2) There were no significant differences in MRC scores among three groups. Results of quantitative strength (%non-involved) showed the patients in single group had the trend of larger strength of external rotator than double and triple groups (p=0.04). Patients in single group had larger strength of elbow flexion than those in double (p<0.01) and in triple (p=0.01) groups. Significant relationship between improvement of muscle strength and follow-up period were found in patients with double nerve transfer group.
(3) The results of mean square error (MSE) and correction for the three ANN models showed that the multilayer perceptron algorithm was most accurate for MRC classification of both shoulder abduction and shoulder external rotation followed by radial basis function model and linear regression neural network model.

Conclusions:
(1) Total-CC7 transfer had better hand recovery but more donor complications than hemi-CC7. Neurotization on both supra-scapular and axillary nerves improved shoulder recovery.
(2) Although the MRC scores were similar, the different outcomes among nerve transfer number were found in quantitative strength values. Number of transferred nerve may not the determination of outcomes.
(3) The multilayer perceptron model provided better prediction outcomes at the shoulder abduction and external rotation functions than that from the linear regression model and radial basis function model. It may be helpful for decision-making of surgical type.

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