Identifying geographic atrophy

被引:3
作者
Clevenger, Leanne [1 ]
Rachitskaya, Aleksandra [1 ,2 ]
机构
[1] Cleveland Clin, Cole Eye Inst, Cleveland, OH USA
[2] 9500 Euclid Av i32, Cleveland, OH 44195 USA
关键词
age-related macular degeneration; artificial intelligence; geographic atrophy; optical coherence tomography; OPTICAL COHERENCE TOMOGRAPHY; PERSISTENT HYPERTRANSMISSION DEFECTS; MACULAR DEGENERATION; FUNDUS AUTOFLUORESCENCE; PROGRESSION; DISEASE; PEGCETACOPLAN; PSEUDODRUSEN; THICKNESS; PREDICT;
D O I
10.1097/ICU.0000000000000952
中图分类号
R77 [眼科学];
学科分类号
100212 ;
摘要
Purpose of review Age-related macular degeneration (AMD) is one of the leading causes of blindness and can progress to geographic atrophy (GA) in late stages of disease. This review article highlights recent literature which assists in the accurate and timely identification of GA, and monitoring of GA progression. Recent findings Technology for diagnosing and monitoring GA has made significant advances in recent years, particularly regarding the use of optical coherence tomography (OCT). Identification of imaging features which may herald the development of GA or its progression is critical. Deep learning applications for OCT in AMD have shown promising growth over the past several years, but more prospective studies are needed to demonstrate generalizability and clinical utility. Summary Identification of GA and of risk factors for GA development or progression is essential when counseling AMD patients and discussing prognosis. With new therapies on the horizon for the treatment of GA, identification of risk factors for the development and progression of GA will become critical in determining the patients who would be appropriate candidates for new targeted therapies.
引用
收藏
页码:195 / 202
页数:8
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