评估机器学习和人工智能方法分析多发性硬化症语音记录的临床效用：一项初步研究
Assessing clinical utility of Machine Learning and Artificial Intelligence approaches to analyze speech recordings in Multiple Sclerosis: A Pilot Study
摘要：
背景资料：多发性硬化症的早期诊断以及准确的疾病进展监测是成功的疾病管理的重要组成部分。先前的研究已经确定多发性硬化症与言语差异相关。早期使用客观声学测量的研究发现了可测量的构音障碍。目的：确定机器学习和深度学习/人工智能方法的潜在临床实用性，用于使用语音记录辅助多发性硬化症的诊断、生物标志物提取和进展监测。研究方法：一个语料库的65 MS阳性和66个健康的人大声朗读相同的文字被用于有针对性的声学特征提取，利用自动音素分割。训练、调整和评估一系列二元分类模型的准确度和曲线下面积。结果如下：随机森林模型表现最好，在验证数据集上实现了0.82的准确度，在训练数据集上的5 k倍循环中的曲线下面积为0.76。7个声学特征中有5个具有统计学意义。结论：机器学习和人工智能在自动分析语音记录以帮助MS诊断和进展跟踪方面似乎很有前途。需要进一步临床验证这些方法及其映射到多发性硬化症的进展，以及验证英语人群的实用性。
英文摘要：
Background: An early diagnosis together with an accurate disease progression monitoring of multiple sclerosis is an important component of successful disease management. Prior studies have established that multiple sclerosis is correlated with speech discrepancies. Early research using objective acoustic measurements has discovered measurable dysarthria. Objective: To determine the potential clinical utility of machine learning and deep learning/AI approaches for the aiding of diagnosis, biomarker extraction and progression monitoring of multiple sclerosis using speech recordings. Methods: A corpus of 65 MS-positive and 66 healthy individuals reading the same text aloud was used for targeted acoustic feature extraction utilizing automatic phoneme segmentation. A series of binary classification models was trained, tuned, and evaluated regarding their Accuracy and area-under-curve. Results: The Random Forest model performed best, achieving an Accuracy of 0.82 on the validation dataset and an area-under-curve of 0.76 across 5 k-fold cycles on the training dataset. 5 out of 7 acoustic features were statistically significant. Conclusion: Machine learning and artificial intelligence in automatic analyses of voice recordings for aiding MS diagnosis and progression tracking seems promising. Further clinical validation of these methods and their mapping onto multiple sclerosis progression is needed, as well as a validating utility for English-speaking populations.
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