How it works
Three simple steps from a routine eye exam to AI-powered screening
Eye Examination
Our AI-based solution analyses data-rich images captured with existing retinal imaging cameras.
Data-Rich Images
Each pixel has over 100 colors instead of just 3.
Proprietary AI Analysis
Automated detection uses proprietary ML and DL models.
A Game-Changing Collaboration
RetiSpec's solution draws on technology developed by Robert Vince, Ph.D., and Swati More, Ph.D., at the University of Minnesota's Center for Drug Design.
University of Minnesota
Center for Drug Design
Peer-Reviewed Publications
Our research has been published in leading scientific journals.
Hyperspectral analysis of amyloid beta evolutional changes in preclinical to late-stage AD
M. Flanagan, B. Danner, C. Zamudio et al.
Implementing cognitive assessment and RetiSpec retinal screening in community-based settings
S. Cohen, A. Kurzman, J. Giordano et al.
In Vivo Assessment of Retinal Biomarkers by Hyperspectral Imaging: Early Detection of AD
S. More, J.M. Beach, C. McClelland et al.
Hyperspectral Imaging Signatures Detect Amyloidopathy in Alzheimer’s Mouse Retina
S. More, R. Vince
Conference Presentations
Our team regularly presents at leading scientific and medical conferences.
How can we facilitate adoption and scale for early detection of AD?
Cohen S, Giordano J, Kurzman A et al.
Enhancing clinical trial efficiency using RetiSpec eye scan
Lehr J, Grapentine S, Lenox B et al.
Retinal hyperspectral imaging and blood-based biomarkers comparable performance
Grapentine S, Hazan A, Shaked E et al.