PDF(Pubmed)
Abstract：
UNASSIGNED: Parkinson\'s disease (PD) is the second most common neurodegenerative disease and affects millions of people. Accurate diagnosis and subsequent treatment in the early stages can slow down disease progression. However, making an accurate diagnosis of PD at an early stage is challenging. Previous studies have revealed that even for movement disorder specialists, it was difficult to differentiate patients with PD from healthy individuals until the average modified Hoehn-Yahr staging (mH&Y) reached 1.8. Recent researches have shown that dysarthria provides good indicators for computer-assisted diagnosis of patients with PD. However, few studies have focused on diagnosing patients with PD in the early stages, specifically those with mH&Y ≤ 1.5.
UNASSIGNED: We used a machine learning algorithm to analyze voice features and developed diagnostic models for differentiating between healthy controls (HCs) and patients with PD, and for differentiating between HCs and patients with mild PD (mH&Y ≤ 1.5). The models were independently validated using separate datasets.
UNASSIGNED: Our results demonstrate that, a remarkable diagnostic performance of the model in identifying patients with mild PD (mH&Y ≤ 1.5) and HCs, with area under the ROC curve 0.93 (95% CI: 0.851.00), accuracy 0.85, sensitivity 0.95, and specificity 0.75.
UNASSIGNED: The results of our study are helpful for screening PD in the early stages in the community and primary medical institutions where there is a lack of movement disorder specialists and special equipment.
UNASSIGNED: We used a machine learning algorithm to analyze voice features and developed diagnostic models for differentiating between healthy controls (HCs) and patients with PD, and for differentiating between HCs and patients with mild PD (mH&Y ≤ 1.5). The models were independently validated using separate datasets.
UNASSIGNED: Our results demonstrate that, a remarkable diagnostic performance of the model in identifying patients with mild PD (mH&Y ≤ 1.5) and HCs, with area under the ROC curve 0.93 (95% CI: 0.851.00), accuracy 0.85, sensitivity 0.95, and specificity 0.75.
UNASSIGNED: The results of our study are helpful for screening PD in the early stages in the community and primary medical institutions where there is a lack of movement disorder specialists and special equipment.
摘要：
■帕金森病（PD）是第二常见的神经退行性疾病，影响数百万人。早期的准确诊断和后续治疗可以减缓疾病进展。然而,在早期对PD进行准确诊断是一项挑战。以前的研究表明，即使对于运动障碍专家来说，在平均改良Hoehn-Yahr分期(mH&Y)达到1.8之前,很难区分PD患者和健康个体.最近的研究表明，构音障碍为PD患者的计算机辅助诊断提供了良好的指标。然而,很少有研究集中在早期诊断PD患者，特别是mH&Y≤1.5的那些。
■我们使用机器学习算法来分析语音特征，并开发了用于区分健康对照（HC）和PD患者的诊断模型，并用于区分HCs和轻度PD患者(mH&Y≤1.5)。使用单独的数据集对模型进行独立验证。
■我们的结果表明，该模型在识别轻度PD(mH&Y≤1.5)和HCs患者方面的卓越诊断性能，ROC曲线下面积为0.93（95%CI：0.851.00），准确度0.85，灵敏度0.95，特异性0.75。
■我们的研究结果有助于在缺乏运动障碍专家和特殊设备的社区和基层医疗机构的早期筛查PD。
■我们使用机器学习算法来分析语音特征，并开发了用于区分健康对照（HC）和PD患者的诊断模型，并用于区分HCs和轻度PD患者(mH&Y≤1.5)。使用单独的数据集对模型进行独立验证。
■我们的结果表明，该模型在识别轻度PD(mH&Y≤1.5)和HCs患者方面的卓越诊断性能，ROC曲线下面积为0.93（95%CI：0.851.00），准确度0.85，灵敏度0.95，特异性0.75。
■我们的研究结果有助于在缺乏运动障碍专家和特殊设备的社区和基层医疗机构的早期筛查PD。
