Neurology Unit, Department of Clinial and Experimental Sciences, University of Brescia, Italy.
Department of Brain and Behavioural Sciences, Medical and Genomic Statistics Unit, University of Pavia, Pavia, Italy.
Brain Stimul. 2021 Mar-Apr;14(2):241-249. doi: 10.1016/j.brs.2021.01.004. Epub 2021 Jan 13.
To evaluate the performance of a Random Forest (RF) classifier on Transcranial Magnetic Stimulation (TMS) measures in patients with Mild Cognitive Impairment (MCI).
We applied a RF classifier on TMS measures obtained from a multicenter cohort of patients with MCI, including MCI-Alzheimer's Disease (MCI-AD), MCI-frontotemporal dementia (MCI-FTD), MCI-dementia with Lewy bodies (MCI-DLB), and healthy controls (HC). All patients underwent TMS assessment at recruitment (index test), with application of reference clinical criteria, to predict different neurodegenerative disorders. The primary outcome measures were the classification accuracy, precision, recall and F1-score of TMS in differentiating each disorder.
160 participants were included, namely 64 patients diagnosed as MCI-AD, 28 as MCI-FTD, 14 as MCI-DLB, and 47 as healthy controls (HC). A series of 3 binary classifiers was employed, and the prediction model exhibited high classification accuracy (ranging from 0.72 to 0.86), high precision (0.72-0.90), high recall (0.75-0.98), and high F1-scores (0.78-0.92), in differentiating each neurodegenerative disorder. By computing a new classifier, trained and validated on the current cohort of MCI patients, classification indices showed even higher accuracy (ranging from 0.83 to 0.93), precision (0.87-0.89), recall (0.83-1.00), and F1-scores (0.85-0.94).
TMS may be considered a useful additional screening tool to be used in clinical practice in the prodromal stages of neurodegenerative dementias.
评估随机森林 (RF) 分类器在轻度认知障碍 (MCI) 患者经颅磁刺激 (TMS) 测量中的性能。
我们将 RF 分类器应用于 MCI 多中心队列患者的 TMS 测量，包括 MCI-阿尔茨海默病 (MCI-AD)、MCI-额颞叶痴呆 (MCI-FTD)、MCI-路易体痴呆 (MCI-DLB) 和健康对照组 (HC)。所有患者在招募时 (基准测试) 接受 TMS 评估，并应用参考临床标准，以预测不同的神经退行性疾病。主要结局指标是 TMS 在区分每种疾病方面的分类准确性、精度、召回率和 F1 评分。
共纳入 160 名参与者，其中 64 名患者被诊断为 MCI-AD、28 名患者被诊断为 MCI-FTD、14 名患者被诊断为 MCI-DLB、47 名患者为健康对照组 (HC)。我们采用了一系列 3 个二分类器，预测模型表现出较高的分类准确性 (范围为 0.72 至 0.86)、较高的精度 (0.72-0.90)、较高的召回率 (0.75-0.98) 和较高的 F1 评分 (0.78-0.92)，可区分每种神经退行性疾病。通过计算一个基于当前 MCI 患者队列训练和验证的新分类器，分类指标显示出更高的准确性 (范围为 0.83 至 0.93)、精度 (0.87-0.89)、召回率 (0.83-1.00) 和 F1 评分 (0.85-0.94)。
TMS 可被视为神经退行性痴呆前驱期临床实践中有用的附加筛选工具。
