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AI can detect Parkinson's disease by analyzing subtle changes in the voice
Last reviewed: 02.07.2025
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Algorithms that can detect subtle changes in a person's voice are becoming a potential new tool for diagnosing Parkinson's disease, researchers in Iraq and Australia report.
Key points of the study:
Speech is one of the first indicators of Parkinson's disease (PD), which is considered the fastest growing neurological disorder in the world, affecting more than 8.5 million people. However, traditional diagnostic methods are often complex and slow, delaying early detection of the disease.
Researchers from Middle Technical University (MTU) in Baghdad and the University of South Australia (UniSA) recently published a report on advances in artificial intelligence (AI) for diagnosing Parkinson's disease.
Early Voice Changes as an Indicator of Parkinson's Disease
Associate Professor Ali Al-Naji, a medical engineer at MTU and adjunct professor at UniSA, says AI-powered voice analysis could change the approach to early diagnosis and remote monitoring of the neurodegenerative disorder.
- Symptoms: PD causes changes in the voice, including variations in pitch, articulation, and rhythm, due to decreased control of the vocal muscles.
- Analysis methods: AI algorithms analyze these acoustic features, allowing disease-related voice patterns to be identified long before visible symptoms appear.
How does artificial intelligence work?
- Technologies Used: Machine learning and deep learning. Algorithms are trained on large datasets containing voice recordings of Parkinson's patients and healthy people.
- Voice Parameter Analysis: Extract characteristics such as pitch, speech distortions, and changes in vowel pronunciation.
- Accuracy: In one study, voice classification accuracy reached 99%.
Benefits of early diagnosis
- Improved quality of life: Early detection allows for timely treatment, which slows the progression of symptoms.
- Remote Monitoring: The AI system can be used to monitor patients from a distance, reducing the need for clinic visits.
Potential limitations and further research
The researchers acknowledge that more research is needed on larger, more diverse samples to ensure the algorithms are robust across different populations.
This approach represents a step forward in the diagnosis of Parkinson's disease, opening up new prospects for earlier and more convenient detection of the disease.