Developed by machine learning specialists and neurobehavioral researchers. Built for the youth.
We analyze variation in the pupil's response to visual stimuli through an administered visuospatial working memory task given to patients.
Grounded in extensive scientific research highlighting correlations between pupillary responses and attentional span. Our work is published in Nature Scientific Reports, ACM, and IEEE.
We capture eye biometrics, including pupil-size and eye gaze, and analyze this data using ML-based algorithms.
Deep learning to capture eye biometrics, such as eye gaze and pupil-size.
State-of-the-art ML algorithms to output detailed analysis of biometrics, including a probability of a patient having ADHD.
Detailed statistics and platform for clinicians and psychiatrists to analyze oculometric data in depth and make more informed diagnoses using our detailed report.
We're venture-backed by Soma Capital and grants from the Columbia and Stanford. We're working with doctors and researchers at leading instituions across New York City. We've published our work in Nature Scientific Reports, ACM, and IEEE.
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