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Demographic Confounding in Voice-Based Parkinson Disease Screening: Methodological Analysis of the Bridge2AI Voice
Shikhar Shukla1, Parvati Naliyatthaliyazchayil1, Judy W Gichoya2
1Department of Biomedical Informatics and Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, 535 W Michigan St., IT 475J, Indianapolis, IN, 46202, United States, 1 3172740439.
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 P=.36; dementia: 0.905 vs 0.895, P=.75), indicating a demographic shortcut; because both cohorts share the same 148-person control group, this parallel pattern is one dataset-level artifact, not 2 independent confirmations. Within ages 60-80 years, the AST exceeded age-only for both conditions (PD: 0.787 vs 0.568, ΔAUC +0.225, 95% CI +0.089 to +0.361; P=.001, n=129; dementia: 0.809 vs 0.598, ΔAUC +0.215, 95% CI +0.056 to +0.375; P=.008, n=95), though the dementia result reflects residual site confounding. Three convergent estimates of PD-specific signal, post hoc age-restricted (0.787), de novo retrained (0.798), and propensity-matched (US, 28 pairs; 0.760; P=.02 vs age-only), all fell substantially below the 0.843 full-cohort AUC. Under the most conservative adjustment (US-only, ages 60-80 years), AST AUC was 0.726, which we consider the least confounded estimate. Cross-version preprocessing testing produced AUC 0.618, indicating brittle representations. Of 13 published voice-PD studies, only 7 reported case and control age distributions, and none reported a demographic-only baseline or an age-restricted evaluation.
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.
