ArticleScientific reports2025
Explainable artificial intelligence to diagnose early Parkinson's disease via voice analysis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Altered Neuromuscular Time-Frequency Organization During Sit-to-Walk in Parkinson's Disease: An Explainable Artificial Intelligence Approach Using Surface Electromyography.Sensors (Basel, Switzerland) · 2026Article
- 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
- Non-Invasive Voice-Based Early Detection of Parkinson's Disease via Spectral Feature Analysis and Machine Learning Techniques.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- 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
- LLM research on public biosignals data is needed to protect patients.NPJ digital medicine · 2026Review
- GLP-1 agonists in neurodegeneration: a multimodal biomarker-guided approach.Trends in molecular medicine · 2026Review
- QuantumNeuroXAI: a quantum-inspired deep learning framework with explainability for brain signal analysis and neurological disorder detection.Scientific reports · 2026Article
- Harnessing artificial intelligence: Revolutionizing clinical care for Parkinson's disease.Journal of Parkinson's disease · 2026Review
- The epistemic economy of vocal biomarker startups: a qualitative interview study of data practices and governance.Frontiers in digital health · 2026Article
- Systematic review of unveiling the potential of AI using machine learning and deep learning methods in neurodegenerative diseases.PeerJ. Computer science · 2026Review
- Wearable-Sensor-Based Analysis of Aerial Archimedean Spirals for Early Detection of Parkinson's Disease.Sensors (Basel, Switzerland) · 2025Article
- 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
- An Explainable Ensemble and Deep Learning Framework for Accurate and Interpretable Parkinson's Disease Detection from Voice Biomarkers.Diagnostics (Basel, Switzerland) · 2025Article
- Effects of Ketogenic Diet on Quality of Life in Parkinson Disease: An Integrative Review.Nutrients · 2025Review
- Multi-modal deep learning framework for early detection of Parkinson's disease using neurological and physiological data for high-fidelity diagnosis.Scientific reports · 2025Article
- Multi-Channel Spectro-Temporal Representations for Speech-Based Parkinson's Disease Detection.Journal of imaging · 2025Article
- Explainable machine learning for early detection of Parkinson's disease in aging populations using vocal biomarkers.Frontiers in aging neuroscience · 2025Article
- Harnessing artificial intelligence for brain disease: advances in diagnosis, drug discovery, and closed-loop therapeutics.Frontiers in neurology · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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