AI Stroke Analysis- Icobrain CVA

Icometrix

Icometrix is a Belgian-based company that specializes in tracking the progression of neurological disorders. Their expertise lies in the analysis and quantification of disease-specific structures of the brain using MRI and CT scans to keep track of and predict the progression of the disease. They are mainly focused on conditions such as multiple sclerosis (MS),  Dementia & Alzheimer’s disease, Epilepsy, Traumatic brain injury (TBI), and Cerebro Vascular Accident (CVA).

One of their product specializing in CVA is Icobrain CVA specializing in AI stroke analysis.

ICOBRAIN CVA

is an AI-powered tool designed to aid healthcare professionals in making quicker and more precise treatment decisions for stroke patients. It achieves this by offering several key functionalities:

Quantitative Assessment:

It evaluates tissue perfusion by measuring the volume of core and perfusion lesions. This is done by analyzing abnormalities in parameters such as Tmax and CBF, and it calculates the mismatch volume and ratio.

AIF/VOF Graph:

The tool generates an AIF/VOF graph, providing valuable information on the accuracy of the selected arterial input function and the overall quality of the report.

Perfusion Maps:

ICOBRAIN CVA offers insights into the perfusion state of the tissue through visual perfusion maps, aiding clinicians in understanding the extent and distribution of perfusion abnormalities.

How it works?

It automates vascular function estimation in brain perfusion imaging analysis by selecting arterial input function (AIF) through the Deconvolution method. It achieved inter-rater performance for the vascular function estimation and, subsequently, for the parameter maps and core lesion quantification using its AI stroke analysis algorithms in non-peer-reviewed research.1

Regulatory Approval:

This product is approved by the CE MDD and FDA.

FAQs

References

  1. https://www.researchgate.net/publication/344485381_AIFNet_Automatic_Vascular_Function_Estimation_for_Perfusion_Analysis_Using_Deep_Learning ↩︎