New Studies Validate Use of AI Technology Across US Stroke Networks

Brainomix announced new data from studies that were presented at the US-based Society of Neurointerventional Surgery's (SNIS) Annual Meeting in Colorado Springs. The latest research, in collaboration with the Mayo Clinic, West Virginia University, and Boston Medical Center, highlighted some of the AI-enabled technology that the Oxford-based company has developed within its pipeline for the Brainomix 360 Stroke platform, representing capabilities that will enable physicians to extract maximal value from universally available non-contrast CT scans across stroke networks.

The FDA-cleared Brainomix 360 Stroke platform is powered by AI algorithms that provide real-time interpretation of brain scans to aid treatment and transfer decisions for stroke patients, with the aim of enabling more patients to receive the right treatment, in the right place, at the right time.

Dr Michalis Papadakis, CEO and Co-Founder of Brainomix, said: "The studies presented this week at SNIS symbolize our ongoing commitment to scientific excellence and reflect the successful partnerships that we have forged with leading academic institutions across the United States to jointly explore the frontiers of stroke AI imaging. Together we are identifying exciting opportunities that may expand the capabilities of physicians to better manage stroke patients across all levels of stroke networks with our technology."

Dr Ansaar Rai, Professor and Chair, Department of Neuroradiology & Neurointerventional Surgery, West Virginia University, said: "The studies presented this week at SNIS highlight the clinical value of the Brainomix 360 Stroke platform, particularly relating to the e-ASPECTS and Triage Stroke algorithms that assess non-contrast CT scans, which are universally available to all stroke centers across the US. The results indicate that these algorithms generate clinically relevant results for frontline stroke physicians that could help them to better manage stroke patients eligible for mechanical thrombectomy across all centers within a network."

Key studies presented at SNIS included:

• "AI Derived Clot Characteristics Predict Recanalization Success in Large Vessel Acute Stroke Patients Undergoing Mechanical Thrombectomy" was a research collaboration between Brainomix and West Virginia University in which baseline non-contrast CT (NCCT) scans from patients with large vessel occlusion (LVO) were analyzed with Brainomix 360 e-ASPECTS to identify hyperdense clots and quantify their length. The results showed that clot biomarkers from NCCT are predictors of successful recanalization and could facilitate the planning of mechanical thrombectomy (MT) procedures.
• "Large Vessel Occlusion Detection in Acute Ischemic Stroke Using Non-Contrast CT" was a multisite collaboration with the Mayo Clinic, West Virginia University, and Boston Medical Center validating Triage Stroke as a reliable tool for LVO detection. The authors concluded that when coupled with clinical information such as NIHSS, the tool may help centers to identify thrombectomy candidates, especially those with constrained resources and basic imaging.
• "How Dark Is Too Dark? Using Net-Water Uptake to Predict Futile Recanalization Following Mechanical Thrombectomy" was another multisite collaboration, this one between the Mayo Clinic, West Virginia University and Brainomix. Ischemic core volume and depth of hypoattenuation using Net Water Uptake (NWU) were quantified from baseline CT scans using Brainomix 360 e-ASPECTS, with results showing that NWU extracted automatically from routine imaging may help identify patients at risk of futile recanalization –especially those with large cores.