ArticleScientific reports2023
A machine learning method to process voice samples for identification of Parkinson's disease.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 65 citations in OpenAlex.
- A Pilot Study of VGGish-CNN as a Model for the Classification of Parkinson's Disease from a Control Group Using Speech Impairment, Integrated with Explainable Artificial Intelligence.Bioengineering (Basel, Switzerland) · 2026Article
- Pilot Study Employing a Machine Learning Approach as a Potential Method for Predicting Parkinson's Disease Using Voice as a Digital Biomarker and the SHAP Approach for Feature Engineering.Bioengineering (Basel, Switzerland) · 2026Article
- Voice and Speech in Atypical Parkinsonian Disorders.Movement disorders clinical practice · 2026Review
- Embracing variability: toward proactive and precision-based voice science.Logopedics, phoniatrics, vocology · 2026Review
- Characteristics and Validity of Commercially Available Technologies Analyzing Voice Features to Assess Parkinson's Disease.Movement disorders clinical practice · 2026Review
- Applications of Artificial Intelligence in Neurological Voice Disorders.World journal of otorhinolaryngology - head and neck surgery · 2025Review
- Voice-Based Detection of Parkinson's Disease Using Machine and Deep Learning Approaches: A Systematic Review.Bioengineering (Basel, Switzerland) · 2025Review
- Voice-Based Early Diagnosis of Parkinson's Disease Using Spectrogram Features and AI Models.Bioengineering (Basel, Switzerland) · 2025Article
- Voice-Based Assessment of Extrapyramidal Symptoms Using Deep Learning.Sensors (Basel, Switzerland) · 2025Article
- Prediction of Parkinson Disease Using Long-Term, Short-Term Acoustic Features Based on Machine Learning.Brain sciences · 2025Article
- Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson's Disease Detection.Journal of imaging · 2025Article
- Smartphone-derived multidomain features including voice, finger-tapping movement and gait aid early identification of Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Voice biomarkers as prognostic indicators for Parkinson's disease using machine learning techniques.Scientific reports · 2025Article
- Explainable artificial intelligence to diagnose early Parkinson's disease via voice analysis.Scientific reports · 2025Article
- A quantum inspired machine learning approach for multimodal Parkinson's disease screening.Scientific reports · 2025Article
- Pre-trained convolutional neural networks identify Parkinson's disease from spectrogram images of voice samples.Scientific reports · 2025Article
- Development of Neurodegenerative Disease Diagnosis and Monitoring from Traditional to Digital Biomarkers.Biosensors · 2025Review
- Explainable machine learning for early detection of Parkinson's disease in aging populations using vocal biomarkers.Frontiers in aging neuroscience · 2025Article
- Parkinson's disease detection using spectrogram-based multi-model feature fusion networks.Frontiers in neurology · 2025Article
- Ranking pre-trained speech embeddings in Parkinson's disease detection: Does Wav2Vec 2.0 outperform its 1.0 version across speech modes and languages?Computational and structural biotechnology journal · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Machine learning approaches have been used for the automatic detection of Parkinson's disease with voice recordings being the most used data type due to the simple and non-invasive nature of acquiring such data. Although voice recordings captured via telephone or mobile devices allow much easier and wider access for data collection, current conflicting performance results limit their clinical applicability. This study has two novel contributions. First, we show the reliability of personal telephone-collected voice recordings of the sustained vowel /a/ in natural settings by collecting samples from 50 people with specialist-diagnosed Parkinson's disease and 50 healthy controls and applying machine learning classification with voice features related to phonation. Second, we utilize a novel application of a pre-trained convolutional neural network (Inception V3) with transfer learning to analyze the spectrograms of the sustained vowel from these samples. This approach considers speech intensity estimates across time and frequency scales rather than collapsing measurements across time. We show the superiority of our deep learning model for the task of classifying people with Parkinson's disease as distinct from healthy controls.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.