Machine Learning Techniques for Ophthalmic Data Processing: A Review

Mhd Hasan Sarhan, M. Ali Nasseri, Daniel Zapp, Mathias Maier, Chris P. Lohmann, Nassir Navab, Abouzar Eslami

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

Abstract

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.

Original languageEnglish
Article number9151176
Pages (from-to)3338-3350
Number of pages13
JournalIEEE Journal of Biomedical and Health Informatics
Volume24
Issue number12
DOIs
StatePublished - Dec 2020
Externally publishedYes

Keywords

  • Ophthalmic diagnostics
  • age-related macular degeneration
  • deep learning
  • diabetic retinopathy
  • glaucoma

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