A deep learning model using topography scans showed the ability to effectively differentiate keratoconus vs. normal corneas with an accuracy of 99.3% and to stage the disease with an accuracy of 88%.
“Unfortunately, the program was unable to learn any useful parameter that would help predicting the likelihood of success vs. disease progression following corneal cross-linking, which may partially be attributed to the reduced sample size given the rarity in disease progression,” Henry Liu, MD, said at the virtual Association for Research in Vision and Ophthalmology meeting.
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