IEEE J Biomed Health Inform. 2020 Dec;24(12):3338-3350. doi: 10.1109/JBHI.2020.3012134. Epub 2020 Dec 4.
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.
机器学习，尤其是深度学习技术，正在主导医学图像和数据分析。本文回顾了过去四年中提出的用于诊断眼科疾病的机器学习方法。本调查涵盖了三种疾病，即糖尿病视网膜病变、年龄相关性黄斑变性和青光眼。综述涵盖了超过 60 篇出版物和 25 个公共数据集，以及与三种疾病的检测、分级和病变分割相关的挑战。每一节都提供了与每个病理相关的公共数据集和挑战的摘要，以及应用于该问题的当前方法。此外，还回顾了用于视网膜血管分割的最新机器学习方法，以及视网膜层和液体积分的方法。本调查考虑了两种主要的成像方式，即彩色眼底成像和光相干断层扫描。还包括使用眼部测量和视野数据进行青光眼检测的机器学习方法。最后，作者提供了他们对这些技术在临床实践中的未来的看法、期望和局限性。
