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Automated system may be similar as manual grading in detecting glaucoma
An automated system for the detection of glaucoma may have a comparable performance for classification as compared with color fundus images viewed by trained ophthalmologists, according to a study.Because undiagnosed glaucoma is a large contributor to preventable blindness, a number of known risk factors could provide guidelines to potentially target at-risk patients for early detection and treatment. Algorithms trained to detect topographic changes suggestive of glaucoma from color fundus photographs could be employed to screen huge populations for the presence of glaucoma.