Deep Learning and Glaucoma



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Deep Learning and Glaucoma

Glaucoma is a leading cause of irreversible blindness worldwide. A recent global meta-analysis of 50 population-based studies reported the pooled glaucoma prevalence (age range, 40-80 years) to be 3.5%, corresponding to an estimated 64.3 million individuals worldwide.

Li, Zhixi et al. used deep learning system to detect referable GON (glaucomatous optic neuropathy) with high sensitivity and specificity.

The study recruited 21 trained ophthalmologists to classify the photographs. Referable GON was defined as vertical cup-to-disc ratio of 0.7 or more and other typical changes of GON. The reference standard was made until 3 graders achieved agreement. A separate validation dataset of 8000 fully gradable fundus photographs was used to assess the performance of this algorithm.

doi: 10.1016/j.ophtha.2018.01.023

In the validation dataset, this deep learning system achieved an AUC of 0.986 with sensitivity of 95.6% and specificity of 92.0%.

Overall, the current study demonstrated that deep learning can be applied to create an algorithm that is capable of identifying referable GON with high sensitivity and specificity in a large dataset. Further studies are required to explore the usefulness of this algorithm deployed in different population settings and for different ophthalmic conditions.

Reference

Li Z, He Y, Keel S, Meng W, Chang RT, He M. Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. Ophthalmology. 2018 Aug;125(8):1199–1206. doi: 10.1016/j.ophtha.2018.01.023. Epub 2018 Mar 2. PMID: 29506863.

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