机器学习通过语音特征辅助诊断多种疾病，效果因病种而异。
The order in speech disorder: a scoping review of state of the art machine learning methods for clinical speech classification
- 系统梳理91项研究，评估语音识别模型在疾病诊断中的表现
- 帕金森病、构音障碍等病症诊断准确率超90%，精神类疾病差异较大
- 适合临床研究者与医疗AI开发者参考，推动语音辅助诊断落地
背景：语音模式已成为多种病因相关疾病的潜在诊断标志物。机器学习（ML）为利用这些模式实现精准诊断提供了可能。目的：综述现有研究中机器学习在神经、喉部及精神疾病语音诊断中的应用进展。方法：系统检索564篇文献，最终纳入91项研究，涵盖从声带病变到精神与神经疾病等多种病症。根据报告的诊断准确率对语音分类方法进行0-10分评分。结果：喉部疾病、构音障碍及帕金森病相关语音变化的诊断准确率均较高，表明语音具备强诊断潜力。抑郁症、精神分裂症、轻度认知障碍和阿尔茨海默病也表现出高准确率，但研究间存在波动。强迫症与自闭症则需更深入研究以确认语音特征与疾病的关系。结论：基于语音模式的机器学习模型在多种精神、喉部及神经系统疾病诊断中具有前景，但效果因病种而异，仍需进一步研究。未来若融入临床实践，有望革新多种疾病的评估与诊断方式。
原文摘要 · Abstract (English)
Background:Speech patterns have emerged as potential diagnostic markers for conditions with varying etiologies. Machine learning (ML) presents an opportunity to harness these patterns for accurate disease diagnosis. Objective: This review synthesized findings from studies exploring ML's capability in leveraging speech for the diagnosis of neurological, laryngeal and mental disorders. Methods: A systematic examination of 564 articles was conducted with 91 articles included in the study, which encompassed a wide spectrum of conditions, ranging from voice pathologies to mental and neurological disorders. Methods for speech classifications were assessed based on the relevant studies and scored between 0-10 based on the reported diagnostic accuracy of their ML models. Results: High diagnostic accuracies were consistently observed for laryngeal disorders, dysarthria, and changes related to speech in Parkinsons disease. These findings indicate the robust potential of speech as a diagnostic tool. Disorders like depression, schizophrenia, mild cognitive impairment and Alzheimers dementia also demonstrated high accuracies, albeit with some variability across studies. Meanwhile, disorders like OCD and autism highlighted the need for more extensive research to ascertain the relationship between speech patterns and the respective conditions. Conclusion: ML models utilizing speech patterns demonstrate promising potential in diagnosing a range of mental, laryngeal, and neurological disorders. However, the efficacy varies across conditions, and further research is needed. The integration of these models into clinical practice could potentially revolutionize the evaluation and diagnosis of a number of different medical conditions.
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