A comprehensive review of artificial intelligence models for screening major retinal diseases

• Published in: The Artificial intelligence review Vol. 57; no. 5; p. 111
• Main Authors: Hassan, Bilal, Raja, Hina, Hassan, Taimur, Akram, Muhammad Usman, Raja, Hira, Abd-alrazaq, Alaa A., Yousefi, Siamak, Werghi, Naoufel
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.05.2024; Springer; Springer Nature B.V
• Subjects: Abnormalities; Artificial Intelligence; Automation; Blindness; Clinical medicine; Computer Science; Deep learning; Diabetes; Diabetic retinopathy; Edema; Glaucoma; Image analysis; Machine learning; Macular degeneration; Medical research; Medicine, Experimental; Ophthalmology; Retinal diseases; Retinal images; Visual impairment; Glaucoma; Fundus; Age-related macular degeneration (AMD); Optical coherence tomography (OCT); Diabetic retinopathy (DR); Artificial intelligence
• ISSN: 1573-7462; 0269-2821; 1573-7462
• DOI: 10.1007/s10462-024-10736-z

This paper provides a systematic survey of artificial intelligence (AI) models that have been proposed over the past decade to screen retinal diseases, which can cause severe visual impairments or even blindness. The paper covers both the clinical and technical perspectives of using AI models in hosipitals to aid ophthalmologists in promptly identifying retinal diseases in their early stages. Moreover, this paper also evaluates various methods for identifying structural abnormalities and diagnosing retinal diseases, and it identifies future research directions based on a critical analysis of the existing literature. This comprehensive study, which reviews both the conventional and state-of-the-art methods to screen retinopathy across different modalities, is unique in its scope. Additionally, this paper serves as a helpful guide for researchers who want to work in the field of retinal image analysis in the future.