Machine learning and especially deep learning techniques are dominating medical image and data analysis. This article reviews machine learning approaches proposed for diagnosing ophthalmic diseases during the last four years. Three diseases are addressed in this survey, namely diabetic retinopathy, age-related macular degeneration, and glaucoma. The review covers over 60 publications and 25 public datasets and challenges related to the detection, grading, and lesion segmentation of the three considered diseases. Each section provides a summary of the public datasets and challenges related to each pathology and the current methods that have been applied to the problem. Furthermore, the recent machine learning approaches used for retinal vessels segmentation, and methods of retinal layers and fluid segmentation are reviewed. Two main imaging modalities are considered in this survey, namely color fundus imaging, and optical coherence tomography. Machine learning approaches that use eye measurements and visual field data for glaucoma detection are also included in the survey. Finally, the authors provide their views, expectations and the limitations of the future of these techniques in the clinical practice.

中文翻译：

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眼科数据处理的机器学习技术：综述。

机器学习，尤其是深度学习技术在医学图像和数据分析中占主导地位。本文回顾了最近四年用于诊断眼科疾病的机器学习方法。本次调查涉及三种疾病，即糖尿病性视网膜病变，与年龄有关的黄斑变性和青光眼。该综述涵盖了60多种出版物和25个公共数据集，以及与三种被考虑疾病的检测，分级和病变分割有关的挑战。每个部分总结了与每种病理相关的公共数据集和挑战以及已应用于该问题的当前方法。此外，综述了用于视网膜血管分割的最新机器学习方法，以及视网膜层和流体分割的方法。本次调查考虑了两种主要的成像方式，即彩色眼底成像和光学相干断层扫描。调查还包括使用眼测量和视野数据进行青光眼检测的机器学习方法。最后，作者提供了他们的观点，期望以及这些技术在临床实践中的局限性。