Deep Learning Applications in Ophthalmology

Ehsan Rahimy

Disclosures

Curr Opin Ophthalmol. 2018;29(3):254-260. 

In This Article

Abstract and Introduction

Abstract

Purpose of review: To describe the emerging applications of deep learning in ophthalmology.

Recent findings: Recent studies have shown that various deep learning models are capable of detecting and diagnosing various diseases afflicting the posterior segment of the eye with high accuracy. Most of the initial studies have centered around detection of referable diabetic retinopathy, age-related macular degeneration, and glaucoma.

Summary: Deep learning has shown promising results in automated image analysis of fundus photographs and optical coherence tomography images. Additional testing and research is required to clinically validate this technology.

Introduction

The growing integration of artificial intelligence in healthcare promises to reshape and disrupt the practice of clinical medicine in the coming years. Analysis of big data stands to impact fields such as genome analysis, to targeted therapeutic drug discovery, and commercialization of treatments, among many other applications. Within ophthalmology, artificial intelligence is already augmenting diagnostic imaging capabilities, which may soon lead to deployment of cost-efficient telemedicine screening programs worldwide. Although the majority of these early efforts have focused on the analysis of color fundus photographs or optical coherence tomography (OCT) scans for detection of posterior segment diseases such as diabetic retinopathy, age-related macular degeneration, and glaucoma, which are covered in this review, emerging artificial intelligence platforms are being dedicated to other ophthalmologic diseases, including retinopathy of prematurity,[1] cataracts,[2,3] corneal ectasia,[4,5] and oculoplastic reconstruction after basal cell carcinoma excision.[6]

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