Review of Machine Learning Applications Using Retinal Fundus Images

Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to...

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Bibliographic Details
Published in:Diagnostics (Basel) 2022-01, Vol.12 (1), p.134
Main Authors: Jeong, Yeonwoo, Hong, Yu-Jin, Han, Jae-Ho
Format: Article
Language:English
Subjects:
Alzheimer's disease
Blood vessels
Deep learning
Diabetes
Diabetic retinopathy
fundus image
Glaucoma
Hemorrhage
Human performance
Machine learning
Macular degeneration
Ophthalmology
Optics
Photoreceptors
Retina
retinal image
Review
Tomography
Tuberculosis
Visual impairment
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Summary:Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics12010134