Machine Learning in the Detection of the Glaucomatous Disc and Visual Field

被引:2
|
作者
Smits, David J. [1 ]
Elze, Tobias [2 ]
Wang, Haobing [3 ]
Pasquale, Louis R. [4 ]
机构
[1] Harvard Med Sch, Massachusetts Eye & Ear Infirm, Dept Ophthalmol, Boston, MA 02115 USA
[2] Harvard Med Sch, Massachusetts Eye & Ear Infirm, Schepens Eye Res Inst, Boston, MA 02115 USA
[3] Harvard Med Sch, Massachusetts Eye & Ear Infirm, Boston, MA 02115 USA
[4] Icahn Sch Med Mt Sinai, Dept Ophthalmol, New York, NY USA
关键词
Glaucoma; machine learning; artificial intelligence; teleophthalmology; visual fields; optic nerve photos; OPEN-ANGLE GLAUCOMA; INDEPENDENT COMPONENT ANALYSIS; BASE-LINE CHARACTERISTICS; DIABETIC-RETINOPATHY; OCULAR HYPERTENSION; AUTOMATED SEGMENTATION; MACULAR DEGENERATION; HEMIFIELD TEST; PROGRESSION; PREVALENCE;
D O I
10.1080/08820538.2019.1620801
中图分类号
R77 [眼科学];
学科分类号
100212 ;
摘要
Glaucoma is the leading cause of irreversible blindness worldwide. Early detection is of utmost importance as there is abundant evidence that early treatment prevents disease progression, preserves vision, and improves patients' long-term quality of life. The structure and function thresholds that alert to the diagnosis of glaucoma can be obtained entirely via digital means, and as such, screening is well suited to benefit from artificial intelligence and specifically machine learning. This paper reviews the concepts and current literature on the use of machine learning for detection of the glaucomatous disc and visual field.
引用
收藏
页码:232 / 242
页数:11
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