Artificial intelligence could be harnessed to diagnose refractive error from retinal fundus images.
Researchers trained an algorithm to predict refractive error with high accuracy from a total of 226,870 retinal fundus images, and validated it on two datasets: UK Biobank and the US Age-Related Eye Disease Study (AREDS).
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The Clinical Management Guidelines (CMGs) set out the evidence to inform your clinical practice and support professional judgement with respect to diagnosis and management.
