Deep-learning aging clock tracks the human ageing process and detects eye diseases, among others.
The \”eyeAge system\” was developed by a team of biomedical scientists to develop a non-invasive and accurate \”aging clock\”. It uses retinal images, deep-learning models, and trained deep-learning model of the eye fundus, the deepest part of the eye, for tracking human aging.
Researchers are affiliated with Buck Institute for Research on Aging, Google Research, Google Health, Zuckerberg San Francisco General Hospital, Post Graduate Institute of Medical Education, and Research (India), and University of California, San Francisco.
The eyeAge system tracks eye changes associated with aging, age-related diseases and other factors.
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Deep-learning aging clock tracks human aging, detects eye and other diseases from retinal images
