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September 30, 2025BioengineeringOpen Access

Voice-Based Early Diagnosis of Parkinson’s Disease Using Spectrogram Features and AI Models

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Achieving 97% accuracy with deep learning models marks a significant advancement in early PD diagnosis.
The study utilized a publicly available voice dataset containing 81 samples to train and evaluate machine learning models.
Feature extraction from speech signals employed techniques like MFCC and spectrograms to capture vocal impairments.
Integration of acoustic features with deep learning methods highlights considerable potential for monitoring Parkinson's Disease.

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Automated system distinguishes PD from non-PD individuals using voice features, suggesting improved diagnosis.

Quamar et al. (2025) studied this question.

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