.png){kind=icon}
返回
Artificial intelligence for retinal diseases
.png){kind=icon}
OA
AI
DOI：10.1016/j.apjo.2024.100096.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Purpose: To discuss the worldwide applications and potential impact of artificial intelligence (AI) for the diagnosis, management and analysis of treatment outcomes of common retinal diseases. Methods: We performed an online literature review, using PubMed Central (PMC), of AI applications to evaluate and manage retinal diseases. Search terms included AI for screening, diagnosis, monitoring, management, and treatment outcomes for age-related macular degeneration (AMD), diabetic retinopathy (DR), retinal surgery, retinal vascular disease, retinopathy of prematurity (ROP) and sickle cell retinopathy (SCR). Additional search terms included AI and color fundus photographs, optical coherence tomography (OCT), and OCT angiography (OCTA). We included original research articles and review articles. Results: Research studies have investigated and shown the utility of AI for screening for diseases such as DR, AMD, ROP, and SCR. Research studies using validated and labeled datasets confirmed AI algorithms could predict disease progression and response to treatment. Studies showed AI facilitated rapid and quantitative interpretation of retinal biomarkers seen on OCT and OCTA imaging. Research articles suggest AI may be useful for planning and performing robotic surgery. Studies suggest AI holds the potential to help lessen the impact of socioeconomic disparities on the outcomes of retinal diseases. Conclusions: AI applications for retinal diseases can assist the clinician, not only by disease screening and monitoring for disease recurrence but also in quantitative analysis of treatment outcomes and prediction of treatment response. The public health impact on the prevention of blindness from DR, AMD, and other retinal vascular diseases remains to be determined.
Keyword:
Artificial intelligence
Algorithms
Screening
Biomarkers
Deep learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.5
论文数:
2.0K
被引数:
2.3K
机构
引用论文
1,7-Induction of chirality in Mukaiyama aldol reactions using π-allyltricarbonyliron lactone and lactam complexes as chiral templates1,7-手性诱导在Mukaiyama醛醇反应中的应用，使用π-烯丙基三羰基铁内酯和内酰胺复合物作为手性模板.png)
Tetrahedron
TetrahedronIF0
PREAI
分享收藏Clinically applicable deep learning for diagnosis and referral in retinal disease深度学习在视网膜疾病诊断和转诊中的临床应用NATURE MEDICINE
NATURE MEDICINEIF50
PREAI
分享收藏FedEYE: A scalable and flexible end-to-end federated learning platform for ophthalmologyFedEYE: 面向眼科的可扩展且灵活的端到端联合学习平台PATTERNSOAAI
PATTERNSIF7.4
OAAI分享收藏Health Economic Implications of Artificial Intelligence Implementation for Ophthalmology in Australia: A Systematic Review澳大利亚实施人工智能对眼科的健康经济影响: 系统评价OAAI
OAAI分享收藏分享收藏
查看更多内容