Artificial Intelligence‐Powered Acoustic Analysis System for Dysarthria Severity Assessment

Abstract

Dysarthria is common in movement disorders, such as Wilson's disease (WD), Parkinson's disease, or Huntington's disease. Dysarthria severity assessment is often indispensable for the management of these diseases. However, such assessment is usually labor‐intensive, time‐consuming, and expensive. To seek efficient and cost‐effective solutions for dysarthria assessment, an artificial intelligence (AI)‐powered acoustic analysis system is proposed and its performance in a valuable sample of WD, an ideal disease model with mainly mixed dysarthria, is verified. A test‐retest reliability analysis yields excellent reproducibility in the acoustic measures (mean intraclass correlation coefficient [ICC] = 0.81). Then, a system for dysarthria assessment is trained with WD patients (n = 65) and sex‐matched healthy controls (n = 65) using a machine learning approach. The system achieves reasonable performance in evaluating dysarthria severity with either stepwise classification or regression (all areas under the curve >80%; mean absolute error = 6.25, r = 0.79, p < 0.0001). The diadochokinesis and sustained phonation tasks contribute the most to prediction, and the corresponding acoustic features can provide significant and independent contributions. The present study demonstrates the feasibility and good performance of the AI‐powered acoustic analysis framework, offering the potential to facilitate early screening and subsequent management of dysarthria.