Monitoring in real-time. Detecting changes in hemodynamic features. Alerting response. Enabling early treatment.
First medical device with an
AI- alert of an impending stroke
based on biological mimicry.
Avertto learns personalized baseline hemodynamic features using unique long-term big data.
Avertto’s AI continuously analyzes stroke probability by detecting deviations from individual baseline activity.
Avertto identifies a stroke risk, notifying and calls responsive care. GPS enables a universally location, accessible healthcare and immediate treatment.
Template matching is utilized to assess pulse waves in compared with prototypical waveform
Deep LearningSelf-learns each patient’s typical baseline activity
OcclusionsDetected as deviations from baseline activity
False alertsAvoided by artifact removal
These techniques enable alerting stroke before permanent damage caused
Unique personalised threshold optimization.
AI learned personalized baseline hemodynamic activity
Continuously outputs an LVO probability
Alarm triggered above individualized threshold
Using 40% threshold probability, 100% accuracy LVOs detected. No false alarms
Avertto hits its first-year targets, gaining an additional 5M ILS for their innovative stroke monitoring tech. This funds the next multi-center blinded clinical trials, with a 70% funding match, propelling them toward their seed funding.
Avertto claimed the Asper Prize from Hebrew University’s ASPER-HUJI Innovate, winning NIS 100,000 for their stroke-preventing technology. Competing with 44 other startups, Avertto's system for real-time stroke detection and prevention through blood flow monitoring stood out, aiming for timely treatment and aligning with UN SDGs.
StrokeAlert is chosen as one of the 5 finalists in the Aviram Awards from 780 startups, recognized for its potential impact in MENA. Committed to improving lives globally, it aims to detect strokes with technology that enables timely treatment, embodying our mission for humanity’s betterment.
StrokeAlert secures 2.5M ILS to develop a stroke monitoring system, focusing on tech development, clinical trials, and market research. The female-led company (Dr. Ben-Pazi) qualifies for 75% funding coverage, with a potential additional 5M ILS upon meeting first-year milestones.
Chosen among 85 women-led startups for a 75K Euro EIC grant, the StrokeAlert System stands out for its affordable, unique monitoring, showing promise for broad adoption. Its prototype stands out in this emerging field, marking a successful proof-of-concept with transformative potential in stroke prevention.