Physiological metrics for determining stroke risk
By employing cerebral perfusion metrics and machine learning, the method offers precise stroke risk assessment, overcoming conventional methods' inaccuracies and guiding personalized interventions.
WO2026064659A1 Publication Date: 2026-03-26CALIFORNIA INST OF TECH +1
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Patent Information
- Application Number
- PCT/US2025/047228
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2024-11-01
- Filing Date
- 2025-09-19
- Publication Date
- 2026-03-26
AI Technical Summary
Technical Problem
Conventional techniques for identifying stroke risk are inadequate, often relying on general health and lifestyle factors, leading to inaccurate predictions and failing to guide personalized interventions.
Method used
Utilizing cerebral perfusion metrics, such as cerebral blood volume, flow, and oxygenation, obtained during hypercapnic stimulation, combined with machine learning models to predict stroke risk based on light reflection data from a headset worn by the user.
Benefits of technology
Provides precise and personalized stroke risk predictions, enabling targeted lifestyle changes or medical interventions to reduce the likelihood of stroke.
✦ Generated by Eureka AI based on patent content.
Abstract
Techniques for determining stroke risk are provided. In some embodiments, the techniques may involve causing, using one or more light sources disposed on a headset worn by a user, light to be emitted into a head of the user, obtaining, using one or more light detectors disposed on the headset, information indicative of light reflected from one more structures within the head of the user, wherein at least a portion of the obtained information is obtained during a time period of hypercapnic stimulation, based on the obtained information, determining a representation of one or more cerebral blood metrics, providing the representation of the one or more cerebral blood metrics as input to a computational model, and determining a stroke risk score based on output of the computational model.
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