Rahimy Ehsan
Department of Ophthalmology, Palo Alto Medical Foundation, Palo Alto, California, USA.
Curr Opin Ophthalmol. 2018 May;29(3):254-260. doi: 10.1097/ICU.0000000000000470.
To describe the emerging applications of deep learning in ophthalmology.
Recent studies have shown that various deep learning models are capable of detecting and diagnosing various diseases afflicting the posterior segment of the eye with high accuracy. Most of the initial studies have centered around detection of referable diabetic retinopathy, age-related macular degeneration, and glaucoma.
Deep learning has shown promising results in automated image analysis of fundus photographs and optical coherence tomography images. Additional testing and research is required to clinically validate this technology.
描述深度学习在眼科领域的新兴应用。
近期研究表明，各种深度学习模型能够高精度地检测和诊断影响眼后段的多种疾病。大多数初步研究集中在可转诊的糖尿病视网膜病变、年龄相关性黄斑变性和青光眼的检测上。
深度学习在眼底照片和光学相干断层扫描图像的自动图像分析中已显示出有前景的结果。需要进行更多的测试和研究以对该技术进行临床验证。
