Kishimoto-Urata Megumi, Urata Shinji, Nishijima Hironobu, Baba Shintaro, Fujimaki Yoko, Kondo Kenji, Yamasoba Tatsuya
Department of Otolaryngology, Graduate School of Medicine The University of Tokyo Tokyo Japan.
Laryngoscope Investig Otolaryngol. 2023 Aug 25;8(5):1189-1195. doi: 10.1002/lio2.1145. eCollection 2023 Oct.
To investigate whether machine learning (ML)-based algorithms, namely logistic regression (LR), random forest (RF), k-nearest neighbor (k-NN), and gradient-boosting decision tree (GBDT), utilizing early post-onset parameters can predict facial synkinesis resulting from Bell's palsy or Ramsay Hunt syndrome more accurately than the conventional statistics-based LR.
This retrospective study included 362 patients who presented to a facial palsy outpatient clinic. Median follow-up of synkinesis-positive and -negative patients was 388 (range, 177-1922) and 198 (range, 190-3021) days, respectively. Electrophysiological examinations were performed, and the rate of synkinesis in Bell's palsy and Ramsay Hunt syndrome was evaluated. Sensitivity and specificity were assessed using statistics-based LR; and electroneurography (ENoG) value, the difference in the nerve excitability test (NET), and scores of the subjective Yanagihara scaling system were evaluated using early post-onset parameters with ML-based LR, RF, -NN, and GBDT.
Synkinesis rate in Bell's palsy and Ramsay Hunt syndrome was 20.2% (53/262) and 40.0% (40/100), respectively. Sensitivity and specificity obtained with statistics-based LR were 0.796 and 0.806, respectively, and the area under the receiver operating characteristic curve (AUC) was 0.87. AUCs measured using ML-based LR of "ENoG," "difference in NET," "Yanagihara," and all three components ("all") were 0.910, 0.834, 0.711, and 0.901, respectively.
ML-based LR model shows potential in predicting facial synkinesis probability resulting from Bell's palsy or Ramsay Hunt syndrome and has comparable reliability to the conventional statistics-based LR.
研究基于机器学习（ML）的算法，即逻辑回归（LR）、随机森林（RF）、k近邻（k-NN）和梯度提升决策树（GBDT），利用发病初期参数预测贝尔麻痹或拉姆齐·亨特综合征所致面部联动是否比传统基于统计的LR更准确。
这项回顾性研究纳入了362例到面瘫门诊就诊的患者。面部联动阳性和阴性患者的中位随访时间分别为388天（范围177 - 1922天）和198天（范围190 - 3021天）。进行了电生理检查，并评估了贝尔麻痹和拉姆齐·亨特综合征中面部联动的发生率。使用基于统计的LR评估敏感性和特异性；使用基于ML的LR、RF、k-NN和GBDT的发病初期参数评估神经电图（ENoG）值、神经兴奋性测试（NET）的差异以及主观柳原评分系统的得分。
贝尔麻痹和拉姆齐·亨特综合征中面部联动发生率分别为20.2%（53/262）和40.0%（40/100）。基于统计的LR获得的敏感性和特异性分别为0.796和0.806，受试者操作特征曲线（AUC）下面积为0.87。使用基于ML的LR对“ENoG”、“NET差异”、“柳原”以及所有三个成分（“全部”）测量的AUC分别为0.910、0.834、0.711和0.901。
基于ML的LR模型在预测贝尔麻痹或拉姆齐·亨特综合征所致面部联动概率方面显示出潜力，并且与传统基于统计的LR具有相当的可靠性。
3级。
