CALIPER: Multimodal Disease Staging and Velocity Estimation from Clinical Snapshots

Multimodal disease progression is often observed through sparse clinical snapshots rather than dense trajectories. Alzheimer's disease provides a canonical case, with amyloid (A), tau (T), and neurodegeneration (N) biomarkers measured irregularly and incompletely. Individuals at the same disease time may exhibit different modality-specific stages and velocities: a modality that is further advanced need not be changing faster. Comparing these changes also requires accounting for state-dependent response sensitivity and residual variation. We introduce Caliper, a generative framework for modality-specific staging and Fisher-calibrated population-velocity readouts from incomplete multimodal snapshots. An anchored observation model learns a shared disease clock, modality-specific stage offsets, and heteroscedastic emissions. A reverse Schr\"odinger bridge constructs endpoint-conditioned population currents and regime responsibilities from pseudo-ordered snapshots. Differentiating modality-stage maps along these currents yields model-implied local velocities, calibrated using the mean-response component of Fisher information. In model-matched simulations, Caliper recovers modality-stage ordering and relative calibrated speeds; matched ablations show higher mean relative-speed correlation and leading-modality accuracy with local Fisher scaling than with fixed or omitted scales. In ADNI and SCAN-linked NACC development data, the clock captures clinical severity, and imaging-and-age regime assignments achieve higher mean anchor-recovery accuracy than a same-anchor logistic reference. Matched ADNI follow-up comparisons show modality-pair-dependent ordering gains over uncalibrated stage speed. Caliper separates disease position from locally standardized change, enabling comparison of heterogeneous multimodal profiles at the same disease time.