AICURA turns fragmented, multimodal clinical data into one computable AI-ready substrate, so trials in neurodegeneration are designed, enriched, and run with far greater precision.
The signal that separates responders from non-responders, and fast progressors from slow, sits across imaging, fluid biomarkers, and longitudinal trajectories that trial systems never bring together. AICURA makes that data computable once, so every analysis and every model runs on the same integrated view of the patient.
Brings each patient's fragmented history, across imaging, biomarkers, and time, into a single computable view that models can actually use.
The environment clinical teams work in directly. They query, explore, and model against one harmonized substrate, without re-engineering the data for every analysis, and own both the data and the work built on it.
The models that turn the substrate into trial enrichment. State-of-the-art multimodal architectures today, extending to large pre-trained models across complex data modalities.
One of the tehrapeutic areas where patients differ most in how they progress and whether they respond, a computable substrate turns messy trial histories into sharper, smaller, faster studies.
Test eligibility, power, and design choices against real historical data before committing a protocol.
Select the patients a trial needs, cutting sample size and screen-failure rates without losing power.
Test inclusion and exclusion criteria against real data to cut screen failures before a protocol locks.
Derive progression markers from multimodal data to sharpen inclusion and exclusion endpoints