Escobar-Grisales D, Rozenfeld Olchik M, Arias-Vergara T, Brzoskowski Dos Santos V, Lara Bressanelli A, Mata I, Orozco-Arroyave JR, García AM (2026)
Publication Type: Journal article
Publication year: 2026
Book Volume: 16
Pages Range: 1125-1136
Journal Issue: 6
DOI: 10.1177/1877718X261467587
BackgroundDigital speech analysis affords robust markers of Parkinson's disease (PD). However, most studies target late-onset PD (LOPD), neglecting early-onset PD (EOPD) -an increasingly prevalent subtype. This proof-of-concept study tackles such gap.MethodsWe used machine learning to discriminate persons with EOPD (with symptom onset before age 50) and LOPD (with symptom onset after age 50) from healthy controls (HCs) through prosodic and articulatory features from natural speech.ResultsMaximal classification between patients and HCs was afforded by combined prosodic and articulation features in LOPD (AUC = 0.90) and by articulation alone in EOPD (AUC = 0.79), with chance-level discrimination between patient groups (AUC = 0.55). Motor severity (MDS-UPDRS-III) scores predicted by these features correlated with actual motor severity scores in both LOPD (r = 0.52, p < 0.001) and EOPD (r = 0.27, p < 0.001).ConclusionsDigital speech markers offer markers of PD irrespective of age of onset.Plain language summary titleVoice recordings capture motor symptoms in Parkinson's disease irrespective of age of onset.
APA:
Escobar-Grisales, D., Rozenfeld Olchik, M., Arias-Vergara, T., Brzoskowski Dos Santos, V., Lara Bressanelli, A., Mata, I.,... García, A.M. (2026). Digital profiling of dysarthria in late- and early-onset Parkinson's disease. Journal of Parkinson's Disease, 16(6), 1125-1136. https://doi.org/10.1177/1877718X261467587
MLA:
Escobar-Grisales, Daniel, et al. "Digital profiling of dysarthria in late- and early-onset Parkinson's disease." Journal of Parkinson's Disease 16.6 (2026): 1125-1136.
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