Machine Learning Techniques for Ophthalmic Data Processing: A Review

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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Deep Learning
  • Diagnostic Techniques, Ophthalmological*
  • Glaucoma / diagnostic imaging
  • Humans
  • Image Interpretation, Computer-Assisted*
  • Machine Learning*
  • Retinal Diseases / diagnostic imaging
  • Tomography, Optical Coherence