Reliable AI for Vascular Health Diagnostics using World Model
THE PREDICTIVE ERA

Advanced AI assistance powered by Neurovascular World Model.

From detecting & localizing vessel occlusions to forecasting what comes next.

Our physics-grounded world model detects and localizes vessel occlusions — including the distal ones (M2 distal and M3 segments) — then quantifies the flow and predicts what comes next.

The Neurovascular World Model - AI that goes beyond detection

From seeing the unseen in stroke — to a world model that detects, quantifies and predicts.

01

Detect & Localize

Up to a quarter of stroke patients have distal, medium-vessel occlusions (DMVOs) that conventional AI frequently misses. Avyuct detects and localizes vessel occlusions across the arterial tree — including these distal ones — pinpointing them vessel by vessel. Trained on expert-adjudicated distal stroke datasets, our model brings sensitivity to the clots long considered 'invisible' to automated triage.

02

Quantify

A scan is a photograph — it shows anatomy, never the forces at work inside it. Yet blood flow, pressure and wall stress are what actually drive a stroke or a rupture. Avyuct estimates all three, for every vessel, from a routine CT angiogram — no extra scan, no extra contrast. It's the physics a picture can't show, made visible and measurable.

03

Predict

Roll the vasculature forward in time to forecast stroke and rupture risk. A scan shows only the present; the model carries the patient's state forward, projecting how an aneurysm or a narrowing is likely to progress. It's built to reveal risk before disease appears — a forward view no imaging tool offers today. The read becomes a forecast, for this patient.

The Challenge

The Challenge We're Solving

Standard medical AI triage models frequently miss subtle distal vessel occlusions, leading to delayed interventions when every second counts for brain tissue survival.

01

Global Crisis

12M+

new strokes per year worldwide — the #2 cause of death globally.

1.9M

neurons die every 60 seconds without immediate treatment.

02

The Detection Gap

25%

of strokes are distal vessel occlusions (DMVO) frequently missed.

40%+

of DMVO cases missed by unaided emergency radiologists.

Avyuct helps save lives, Here Are Our Use Cases

Specialized Al solutions for various medical imaging applications, each designed to enhance diagnostic accuracy and clinical workflow efficiency.

Launched! and Available now
Ischemic Stroke

Detection of occlusion in brain blood vessels for rapid intervention and improved patient outcomes.

Coming Soon
Haemorrhagic Stroke

Identification of stroke caused by brain haemorrhage through our custom AI algorithms.

Ongoing
Coronary Artery Disease (CAD)

AI-powered detection of coronary artery blockages.

Our WorkFlow

A streamlined process from data ingestion to actionable clinical insights, powered by our state-of-the-art machine learning algorithms.

Data Preparation

Data Preparation Standardize, clean and prepare the medical data for model training

Model Training

Train custom advanced machine learning models using prepared datasets to ensure accuracy and reliability.

Inferencing

Deploy the trained models to analyze new data and generate outcomes near real time.

Actionable Insights

Transform model outputs into clear, actionable clinical insights to support medical diagnostics decision-­making.

Why Choose Avyuct?

Our commitment to excellence in AI-powered medical analytics sets us apart in delivering superior healthcare solutions.
Reliable Accuracy

State-of-the-art AI ensures advanced and reliable diagnostics for distal stroke detection.

Anomaly Visualization

Advanced imaging insights across M2–M4 segments enable confident, rapid clinical decisions.

End-to-End Solution

Complete AI-powered medical analysis with comprehensive reporting, from scan to actionable insight.

Trusted by Medical Professionals

Built in collaboration with clinicians. Backed by five U.S. patent filings in AI for medical imaging.

Join the Vascular Health Revolution using AI

Partner with us to redefine the standard of care for distal stroke detection