Retinal Health Screening Using Artificial Intelligence With Digital Fundus Images: A Review of the Last Decade (2012–2023)

Abstract

Prolonged diabetic retinopathy (DR), glaucoma, and age-related macular degeneration (AMD) may lead to vision loss. Hence, early detection and treatment are crucial to prevent irreversible vision loss. Fundus retinal images have been widely used to help detect these diseases. Manual screening is susceptible to human errors, tedious, and expensive. Hence, artificial intelligence (AI) techniques have been widely employed to overcome these constraints. This paper reviewed the work published on automated retinal health detection models using various machine learning (ML) and deep learning (DL) techniques. We reviewed 142 papers and 262 studies (124 on glaucoma, 60 on AMD, and 78 on DR) from January 2012 to June 2024 using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We found that Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) models were widely used in DL and ML techniques, respectively. To the best of our knowledge, this is the first review developed for detecting AMD, DR, and glaucoma using AI techniques over the last decade. We have discussed the limitations of the present methods and also suggested future directions for accurately detecting eye diseases.

Keywords

• Retinal health
• automated detection
• deep learning
• machine learning
• glaucoma
• fundus