ParkScreen

ParkScreen is a multimodal Parkinson's disease screening decision aid that fuses three task-matched user recordings — sustained vowels /i/o/u/ for phonation (jitter, shimmer, HNR, F0 via Parselmouth), rapid /pa-ta-ka/ for articulation (DDK rate + regularity via intensity-envelope peak picking, no ASR), and an 8–12s smile-vs-neutral video for facial hypomimia (OpenFace AUs, Islam 2023 protocol). Two layers: an offline scientific spine trains per-channel LogReg classifiers on NeuroVoz with subject-level LOSO, and a real-time product layer scores user uploads with those fitted models, fuses via AUC-excess-weighted late fusion, and passes structured context into Claude Opus 4.7 to generate a hedged, disclaimer-bounded clinical-style report. Cross-channel disagreements are surfaced as "flagged for clinical review," never silently reconciled. On NeuroVoz (49 PD × 46 age-matched HC, N=95), phonation-only AUC is 0.567, DDK-only 0.740, and fused phonation+DDK 0.758 [0.662, 0.859] — fusion beats the strongest single channel. The separately-trained facial classifier hits in-distribution AUROC 0.812 on UFNet vs the paper's 0.830 SVM ensemble, and 0.708 on YouTubePD's external subset. Sensitivity analyses show DDK carries the load, dropping /a/e/ and steady-window-cropping vowels is what makes phonation non-degenerate, and a ~5-year PD-vs-HC age gap (p=0.013) accounts for a quantifiable slice of the AUC. This matters because PD is a motor-speech disorder — jitter/shimmer/HNR and DDK are direct mechanistic evidence, not proxy signals — and score-level fusion of small interpretable models is the honest choice at N=95, where deep fusion would overfit and obscure which channel is doing the work. The Claude layer solves the last mile: raw probabilities aren't clinically actionable, but a structured narrative with unit-labeled features, hedged language, mandatory disclaimers (screening-only + ON-medication caveat), and explicit disagreement flags is. Positioned as a decision aid, never a diagnosis.