Neuvara

Neuvara provides AI-driven diagnostic support for rare neurological diseases by integrating scanner-aware MRI representations with clinical data such as patient history and symptoms.

Updated 1 month ago

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Rare neurological diseases often require years of investigation before a definitive diagnosis, because subtle imaging signals are obscured by variability in MRI protocols and limited integration of clinical context. This delays treatment and increases the burden on patients and specialists.

Solution

Neuvara offers an AI platform that processes MRI scans with scanner-aware normalization to reduce protocol-induced variability, then combines the harmonized imaging data with patient history, symptoms, and scanner metadata. The system generates region-level heatmaps and similarity matches to previously confirmed cases, providing explainable evidence that clinicians can review. Outputs are designed for human‑in‑the‑loop workflows, allowing specialists to validate predictions before acting on them. The platform is currently undergoing retrospective, multi‑scanner validation to ensure robust performance on rare disease patterns. By focusing on multimodal evidence and explainability, Neuvara aims to shorten diagnostic timelines while maintaining clinical oversight.

Target Audience

Primary users are neurologists, radiologists, and academic researchers focused on diagnosing rare neurological disorders, particularly those operating in institutions with diverse MRI equipment and extensive clinical datasets.

Features

Scanner‑aware MRI harmonization that accounts for manufacturer, field strength, and sequence parameters before analysis
Shared 3D volumetric backbone that learns neuroimaging representations applicable to multiple rare disease tasks
Fusion of imaging features with clinical metadata (history, symptoms, scanner profile) to produce context‑aware predictions
Explainable heatmaps highlighting region‑level signals that contribute to each diagnostic suggestion
Cohort similarity search that retrieves cases with confirmed diagnoses matching the patient’s imaging and clinical profile
Research‑stage validation framework including retrospective studies, model cards, and clinician‑reviewed output workflows
Human‑in‑the‑loop review interface that presents AI evidence for specialist confirmation before clinical use