ArticleJournal of medical Internet research2026
Demographic Confounding in Voice-Based Parkinson Disease Screening: Methodological Analysis of the Bridge2AI Voice Dataset.
Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Explicit Mechanistic Causal Analyses or Interventional Trials Are Required for Objective, Clinical, Voice-Based Parkinson Disease Characterization.Journal of medical Internet research · 2026Article
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4 authors.
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Abstract
Background: Voice-based deep learning models for Parkinson disease (PD) and dementia screening report areas under the curve (AUCs) of 0.85-0.97, but rarely audit demographic confounding. Because speech changes substantially with age, case-control age imbalance alone can produce high classification performance independent of disease. Objective: We audited the Bridge to Artificial Intelligence (Bridge2AI) Voice Dataset v3.0.0 with three objectives: (1) quantify age and site confounding in voice screening for PD and dementia, (2) evaluate whether a disease-specific acoustic signal persists after demographic adjustment, and (3) propose minimum reporting standards. Methods: We fine-tuned an audio spectrogram transformer (AST; 86.4 million parameters) using 5-fold participant-level cross-validation for PD (n=253) and dementia (n=221). Logistic regression on age and sex provided a demographic-only baseline under identical splits. Confounding was assessed by (1) restricting evaluation to ages 60-80 years, (2) restricting to US participants because all Canadian PD (n=62) and all Canadian dementia (n=70) participants were cases with no Canadian controls, (3) retraining AST from scratch on the age-restricted subgroup, (4) 1:1 nearest-neighbor propensity-score matching on age and sex in the US age-restricted PD subgroup, and (5) applying v3.0.0-trained models to v2.0.1 spectrograms of the same participants. Results: On the full cohort, age alone was statistically indistinguishable from the AST (PD: AUC 0.875 vs 0.843, DeLong Conclusions: Demographic confounding dominates full-cohort voice-screening metrics on the Bridge2AI Voice Dataset for both PD and dementia. After age restriction, site control, and propensity matching, residual AST performance for PD (AUC 0.726-0.798) remained significantly above demographic baselines, supporting a disease-specific acoustic signal substantially weaker than full-cohort metrics suggest; for dementia, insufficient US cases precluded similar estimates. Voice biomarker studies should report case and control demographic distributions, demographic-only baselines under identical splits, and age-restricted performance alongside full-cohort metrics.
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