Clinical Applications of Artificial Intelligence in Glaucoma

被引:18
|
作者
Yousefi, Siamak [1 ,2 ,3 ]
机构
[1] Univ Tennessee, Dept Ophthalmol, Hlth Sci Ctr, Memphis, TN USA
[2] Univ Tennessee, Dept Genet Genom & Informat, Hlth Sci Ctr, Memphis, TN USA
[3] 930 Madison Ave,Suite 726, Memphis, TN 38163 USA
关键词
Artificial Intelligence; Convolutional Neural Network (CNN); Deep Learning; Glaucoma; Machine Learning; Ophthalmology; OPTICAL COHERENCE TOMOGRAPHY; FIBER LAYER THICKNESS; INDEPENDENT COMPONENT ANALYSIS; STANDARD AUTOMATED PERIMETRY; MACHINE LEARNING CLASSIFIERS; VISUAL-FIELD PROGRESSION; OPEN-ANGLE GLAUCOMA; NEURAL-NETWORK; DIABETIC-RETINOPATHY; OCULAR HYPERTENSION;
D O I
10.18502/jovr.v18i1.12730
中图分类号
R77 [眼科学];
学科分类号
100212 ;
摘要
Ophthalmology is one of the major imaging-intensive fields of medicine and thus has potential for extensive applications of artificial intelligence (AI) to advance diagnosis, drug efficacy, and other treatment-related aspects of ocular disease. AI has made impressive progress in ophthalmology within the past few years and two autonomous AI -enabled systems have received US regulatory approvals for autonomously screening for mid-level or advanced diabetic retinopathy and macular edema. While no autonomous AI-enabled system for glaucoma screening has yet received US regulatory approval, numerous assistive AI-enabled software tools are already employed in commercialized instruments for quantifying retinal images and visual fields to augment glaucoma research and clinical practice. In this literature review (non-systematic), we provide an overview of AI applications in glaucoma, and highlight some limitations and considerations for AI integration and adoption into clinical practice.
引用
收藏
页码:97 / 112
页数:16
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