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

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Abstract

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.

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Funding

This work was partially supported by the H2020 specific targeted research project SeeFar: Smart glasses for multifacEted visual loss mitigation and chronic disEase prevention indicator for healthier, saFer, and more productive workplAce foR ageing population. (H2020-SC1- DTH-2018-1, GA No 826429) (www.see-far.eu).

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Authors and Affiliations

  1. D-Eye s.r.l., Padua, Italy

    Alexa Berto, Fabio Scarpa & Alberto Scarpa

  2. Department of Information Engineering, University of Padova, 35131, Padua, Italy

    Fabio Scarpa

  3. Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH), 70013, Heraklion, Greece

    Nikos Tsiknakis, Georgios Manikis & Kostas Marias

  4. Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, 45115, Ioannina, Greece

    Dimitrios I. Fotiadis

  5. Department of Materials Science and Engineering, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, 45110, Ioannina, Greece

    Dimitrios I. Fotiadis

  6. Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71004, Heraklion, Greece

    Kostas Marias

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Berto, A., Scarpa, F., Tsiknakis, N. et al. Automated analysis of fundus images for the diagnosis of retinal diseases: a review. Res. Biomed. Eng. 40, 225–251 (2024). https://doi.org/10.1007/s42600-023-00320-9

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