Shen Matthew, Mortezaagha Pouria, Rahgozar Arya
Ottawa Hospital Research Institute, The Ottawa Hospital, Ottawa, Canada.
University of Ottawa School of Engineering Design and Teaching Innovation, University of Ottawa, Ottawa, Canada.
Sci Rep. 2025 Apr 5;15(1):11687. doi: 10.1038/s41598-025-96575-6.
Parkinson's disease (PD) is a neurodegenerative disorder affecting motor control, leading to symptoms such as tremors and stiffness. Early diagnosis is essential for effective treatment, but traditional methods are often time-consuming and expensive. This study leverages Artificial Intelligence (AI) and Machine Learning (ML) techniques, using voice analysis to detect early signs of PD. We applied a hybrid model combining Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Multiple Kernel Learning (MKL), and Multilayer Perceptron (MLP) to a dataset of 81 voice recordings. Acoustic features such as Mel-Frequency Cepstral Coefficients (MFCCs), jitter, and shimmer were analyzed. The model achieved 91.11% accuracy, 92.50% recall, 89.84% precision, 91.13% F1 score, and an area-under-the-curve (AUC) of 0.9125. SHapley Additive exPlanations (SHAP) provided data explainability, identifying key features driving the PD diagnosis, thus enhancing AI interpretability and trustability. Furthermore, a probability-based scoring system was developed to enable PD patients and clinicians to track disease progression. This AI-driven approach offers a non-invasive, cost-effective, and rapid tool for early PD detection, facilitating personalized treatment through vocal biomarkers.
帕金森病（PD）是一种影响运动控制的神经退行性疾病，会导致震颤和僵硬等症状。早期诊断对于有效治疗至关重要，但传统方法往往既耗时又昂贵。本研究利用人工智能（AI）和机器学习（ML）技术，通过语音分析来检测帕金森病的早期迹象。我们将一种结合了卷积神经网络（CNN）、循环神经网络（RNN）、多核学习（MKL）和多层感知器（MLP）的混合模型应用于一个包含81个语音记录的数据集。对诸如梅尔频率倒谱系数（MFCC）、基频微扰和谐波音等声学特征进行了分析。该模型的准确率达到91.11%，召回率为92.50%，精确率为89.84%，F1分数为91.13%，曲线下面积（AUC）为0.9125。SHapley加性解释（SHAP）提供了数据可解释性，确定了驱动帕金森病诊断的关键特征，从而提高了人工智能的可解释性和可信度。此外，还开发了一种基于概率的评分系统，以使帕金森病患者和临床医生能够跟踪疾病进展。这种由人工智能驱动的方法为帕金森病的早期检测提供了一种非侵入性、经济高效且快速的工具，通过声音生物标志物促进个性化治疗。
