Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications

Abstract

Artificial intelligence (AI) technology is promising in the field of healthcare. With the developments of big data and image-based analysis, AI shows potential value in ophthalmology applications. Recently, machine learning and deep learning algorithms have made significant progress. Emerging evidence has demonstrated the capability of AI in the diagnosis and management of anterior segment diseases. In this review, we provide an overview of AI applications and potential future applications in anterior segment diseases, focusing on cornea, refractive surgery, cataract, anterior chamber angle detection, and refractive error prediction.

Emulation of optical and electrical synaptic functions in MoS2/SnSe2 van der Waals heterojunction memtransistors

“…With the rapid development of technologies such as artificial intelligence, 1) it is difficult for traditional von Neumann computing architecture hardware to meet the computing power requirements of modern applications. 2) Due to the human brain's superior ability of adaptive learning and parallel computing, a neuromorphic computing system based on the human brain structure provides a novel and effective solution to break the von Neumann bottleneck.…”

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“…The integration of AI in ophthalmology provides benefits by offering effective solutions for diagnostics, treatment planning, and patient monitoring [ 4 ]. AI has the capability to screen for and diagnose a variety of eye conditions such as diabetic retinopathy (DR) [ 5 – 7 ], glaucoma [ 8 , 9 ], age-related macular degeneration (AMD) [ 10 – 12 ], retinopathy of prematurity (ROP) [ 13 ], cataracts [ 14 ], and other anterior segment diseases [ 15 – 17 ], and the study showed that the sensitivity was 97.5% for detecting DR [ 5 ] and 100% for detecting AMD [ 18 ]. AI has significantly automated the screening and prioritization of conditions, including DR, ROP, and glaucoma.…”