Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives

Kai Jin; Juan Ye

doi:10.1016/j.aopr.2022.100078

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Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives

更新时间：2023-11-15

- Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives
- AOPR Vol. 2, Issue 3, Pages: 100078(2022)
- 作者机构：
  
  1. ,Hangzhou,China
- 作者简介：
- 基金信息：
- DOI：10.1016/j.aopr.2022.100078
  CLC：
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Kai Jin, Juan Ye. Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives. [J]. AOPR 2(3):100078(2022)
DOI：

Kai Jin, Juan Ye. Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives. [J]. AOPR 2(3):100078(2022) DOI： 10.1016/j.aopr.2022.100078.

摘要

Background,The ophthalmology field was among the first to adopt artificial intelligence (AI) in medicine. The availability of digitized ocular images and substantial data have made deep learning (DL) a popular topic.,Main text,At the moment, AI in ophthalmology is mostly used to improve disease diagnosis and assist decision-making aiming at ophthalmic diseases like diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), cataract and other anterior segment diseases. However, most of the AI systems developed to date are still in the experimental stages, with only a few having achieved clinical applications. There are a number of reasons for this phenomenon, including security, privacy, poor pervasiveness, trust and explainability concerns.,Conclusions,This review summarizes AI applications in ophthalmology, highlighting significant clinical considerations for adopting AI techniques and discussing the potential challenges and future directions.

Abstract

关键词

Keywords

Artificial intelligenceDeep learningOphthalmologyDiabetic retinopathyGlaucomaAge-related macular degeneration

references

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