How clinicians use multimodal insights
Our workflow takes multimodal patient data, discovers population-level subgroups, places each new patient into the nearest subgroup, and delivers interpretable decision support to the treating neurologist.
Multimodal Fusion
Integrating genetics, imaging, clinical assessments, molecular bio-specimens, and wearable data from diverse populations to build comprehensive disease profiles.
Cluster Identification
Generative population modeling uncovers latent biomarker associations, grouping patients into distinct mechanistic subtypes such as tremor-dominant (slow progression), PIGD (rapid progression), and cognitive-behavioral clusters.
Patient Application
Population-level knowledge translates into individual care, providing the neurologist with differential diagnosis support, prognostic trajectory insights, and therapeutic trial stratification.
The Twin Approach
Translational AI enables patient-level precision therapeutics and support without replacing clinical judgment, bridging the gap between large-scale population learning and individualized decision-making.
Population subgroup discovery and patient placement
Rather than treating every Parkinson's patient identically, we use multimodal data to discover population-level subgroups: clusters of patients who share biology and disease trajectory.
When a new patient arrives, their multimodal features are projected into this population map. The system identifies the nearest subgroup and retrieves comparable patients, providing the clinician with outcome context drawn from real cases.
The pipeline
Input data
Imaging, wearable, clinical, and biospecimen features from the patient encounter.
Subgroup placement
The patient is mapped into a population-derived subgroup that shares biology and trajectory.
Similar patients
Nearest-neighbor lookup surfaces comparable cases and their longitudinal outcomes.
Decision support
Monitoring priorities, pathway burden, and intervention context presented to the clinician.
What the clinician sees
- Subgroup label
- A biomarker-defined cluster assignment with population context, indicating which patient group the individual most closely resembles.
- Nearest neighbors
- The closest patients in the population map with their longitudinal trajectories and treatment outcomes.
- Pathway burden
- Motor, cognitive, and autonomic pathway contributions weighted by the patient's multimodal signature.
- Monitoring priorities
- Domain-specific flags highlighting which measures warrant closest follow-up and suggested assessment intervals.
Decision support dashboard concept: patient summary, subgroup classification, nearest-neighbor matches, and follow-up recommendations.
Explore the interactive workflow in the Parkinson Viz dashboard.