A Clinically Relevant and Understandable Approach to Developing Digital Speech Biomarkers for Neurodegenerative Disorders

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Communication ability declines in neurodegenerative diseases, impacting quality of life and necessitating personalized management approaches.
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The study developed an acoustic-based framework using AI to extract clinically relevant speech markers for assessing communication disorders.
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Markers identified subtle subclinical changes and differentiated communicative impairment patterns in amyotrophic lateral sclerosis and Parkinson's disease.
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The framework supports early detection and phenotyping of communication disorders, enhancing personalized, measurement-based care.
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Existing assessments are subjective and lack sensitivity; the proposed method offers objective analysis to improve diagnosis and monitoring.

Frontiers In Digital Health

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