Review of Machine Learning Applications Using Retinal Fundus Images

Abstract

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.

Ophthalmology and Artificial Intelligence: Present or Future? A Diabetic Retinopathy Screening Perspective of the Pursuit for Fairness

“…The variable performance [even in FDA-approved algorithms (42)], economic inequality in developing countries, technophobia in the application of AI systems in daily practice, and ethical concerns remain challenges for the implementation of AI (28). Multidisciplinary groups including medical doctors, computer engineers, data scientists, and informatics technologists are necessary for implementing AI from benchmark algorithms to ethical autonomous healthcare tools (35) (43)(44)(45).…”

Cytomegalovirus Retinitis Screening Using Machine Learning Technology

“…In the recent year, the machine learning methods are mostly focused on deep learning models, given that deep learning is a state-of-the-art technology that is known to outperform conventional machine learning. 15 One of the most popular algorithms of deep learning is convolutional neural networks. 16 Convolutional neural network is a class of artificial neural network that is widely applied to image data because of its effectiveness for image classifications.…”

Autonomous Screening for Laser Photocoagulation in Fundus Images Using Deep Learning

“…Artificial Intelligence (AI) using fundus imaging has been increasingly employed in various ophthalmological applications [23], [24]. These applications include extraction of basic patient data, such as age and sex [25], detection of retinal pathologies, [26]- [28] and pathology development prediction [29]- [31].…”