Automated analysis of fundus images for the diagnosis of retinal diseases: a review

• Published in: Research on biomedical engineering 2024-03, Vol.40 (1), p.225-251
• Main Authors: Berto, Alexa, Scarpa, Fabio, Tsiknakis, Nikos, Manikis, Georgios, Fotiadis, Dimitrios I., Marias, Kostas, Scarpa, Alberto
• Format: Article
• Language: English
• Subjects: Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Engineering; Review
• ISSN: 2446-4732; 2446-4740
• DOI: 10.1007/s42600-023-00320-9

Purpose Colour fundus images are widely used in diagnosis treatment decision of several retinal diseases such as diabetic retinopathy (DR), glaucoma and age-related macular degeneration (AMD). These very common conditions must be detected early and monitored to prevent progression and avoid permanent damage. Fundus images revealed to be useful also for the diagnosis of cataracts. In recent years, various automatic diagnostic support methods have been proposed in the literature, with the aim of facilitating widespread screening and obtaining quantitative, objective, and reproducible information. Methods In this review paper, an overview of traditional, machine learning and modern deep learning techniques for ophthalmic disease diagnosis (i.e. glaucoma, DR, AMD, and cataract) using retinal fundus images is presented. In addition, various publicly available image datasets used for such purposes are described. Results The current main challenges and findings are identified, as well as common aspects and discrepancies between the various methods developed for the various diseases. Conclusion The overview of what has been done about all pathologies, rather than only on a specific one, could favour a migration of the best solutions and, hopefully, the development of a more precise and clinically useful automatic analysis of all pathologies. Important critical insights and research trends are also discussed. Graphical abstract