Deep Learning Applications in OCT and OCT Angiography for Retinal Disease Diagnosis: A Review

Authors

  • Jalaluddin Muhammad-Sukki Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor, Malaysia
  • Audrey Huong Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor, Malaysia https://orcid.org/0000-0002-4505-5860
  • Ser Lee Loh Centre for Robotics and Industrial Automation, Fakulti Teknologi dan Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
  • Firdaus Muhammad-Sukki School of Computing, Engineering & the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT United Kingdom
  • Xavier Ngu Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor, Malaysia

Keywords:

Classification, Deep learning, Optical coherence tomography, Retinal disease

Abstract

Retinal diseases are among the most pressing global health challenges for which novel diagnostic approaches are urgently needed. Optical Coherence Tomography (OCT) and OCT Angiography (OCTA) are novel imaging techniques that provide high-resolution visualization of retinal microvascular abnormalities. Nonetheless, interpreting complex OCT/OCTA images requires extensive knowledge and experience, prompting the need for automated analysis using deep learning technology. This review provides a critical overview of recent progress in deep learning applications for various OCT and OCTA data analysis, including the model training paradigms, optimization techniques, and image processing applications. Three hundred seventy-three articles were found based on the keywords “retinal disease”, “OCT” or “OCTA”, and “deep learning” in literature searches. Following title, abstract, and keyword screening, 170 articles were reviewed in full text for eligibility, with 27 studies included in the final review. Selected articles were reviewed to determine the dataset used, the employed deep learning and image preprocessing methods, and the evaluated performance metrics. This paper summarizes the applications of deep learning in improving the performance of retinal disease classification and identifies future directions for enhancing the reliability of AI-based models. Finally, this review outlines the challenges associated with current research and existing strategies, and suggests future directions to enhance the medical relevance of deep-learning diagnostic systems.

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Published

25-07-2026

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Muhammad-Sukki, J., Huong, A., Loh, S. L., Muhammad-Sukki, F., & Ngu, X. (2026). Deep Learning Applications in OCT and OCT Angiography for Retinal Disease Diagnosis: A Review. Applications of Modelling and Simulation, 10, 196–212. Retrieved from http://arqiipubl.com/ojs/index.php/AMS_Journal/article/view/1234

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Vol. 10 (2026)

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Review Article

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Copyright (c) 2026 Jalaluddin Muhammad-Sukki, Audrey Huong, Ser Lee Loh, Firdaus Muhammad-Sukki, Xavier Ngu

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