Artificial Intelligence in Eye Disease

Dear Colleagues,

While the use of artificial intelligence (AI) is rapidly spreading to the medical world amid the vortex of the fourth industrial revolution, the use of AI in ophthalmology is attracting attention for diagnosis of various ophthalmic diseases, including optic nerve diseases, which are difficult to diagnose. Particularly, it could help to diagnose with high accuracy by introducing the AI when applied to fundus photographs, optical coherence tomography, and visual field to achieve strong classification performance in the detection of ocular and retinal diseases. In ocular imaging, AI can be used as a possible solution for screening, diagnosing, and monitoring patients with major eye disease in primary care and community settings. For instance, through deep learning algorithms that read retinal images, various diseases can be observed, such as bleeding, macular abnormalities—e.g., drusen—choroidal abnormalities, retinal vessel abnormalities, nerve fiber layer defects, and glaucomatous optic nerve papilla changes. Thus, deep learning architecture can be applied to learn to recognize eye diseases, thereby raising the diagnosis rate with a clinically acceptable performance. In other words, AI serves as a safety device for both patients and doctors, and as an auxiliary tool to quickly judge the results. It prevents the possibility of misdiagnosis that can occur in the first place, provides treatment efficiency, and increases patient reliability. Consequently, AI could potentially revolutionize the way that ophthalmology is practiced in the future. Thus, the aims of this Special Issue are to highlight the recent progress and trends in utilizing AI techniques, such as machine learning and deep learning for detecting, screening, diagnosing, and monitoring numerous eye diseases not only in diverse clinical practice but also in basic research of ophthalmology.

Prof. Dr. Jae-Ho Han
Guest Editor

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