Can a deep-learning system really equal professional human graders in detecting retinal diseases?
Ruth Steer |
With several groups and researchers developing artificial intelligence (AI) and deep learning systems for ophthalmic applications, diagnosis by machine is on the cards. Researchers at the Singapore National Eye Center and National University Singapore School of Computing have brought us one step closer with a deep-learning system that detects diabetic retinopathy and related eye diseases (glaucoma and AMD). But how does it compare with professional human graders? Using 494,661 retinal images from multiethnic (Chinese, Indian, Malay, Hispanic, African-American and White) patients with diabetes, the system demonstrated high sensitivity (≥90.5 percent) and specificity (≥87.2 percent) for identifying retinal diseases, comparable with the professional graders (≥88.5 percent and ≥99.3 percent, respectively). Daniel Ting, lead author on the corresponding paper (1), tells us more.
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