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

  • Bilal Hassan
  • , Hina Raja
  • , Taimur Hassan*
  • , Muhammad Usman Akram
  • , Hira Raja
  • , Alaa A. Abd-alrazaq
  • , Siamak Yousefi
  • , Naoufel Werghi
  • *Corresponding author for this work
  • Khalifa University of Science and Technology
  • University of Tennessee Health Science Center
  • Margalla Institute of Health Sciences
  • Education City

Research output: Contribution to journal › Article › peer-review

33   Link opens in a new tab Citations (SciVal)

Abstract

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.

Original languageEnglish
Article number111
Number of pages93
JournalArtificial Intelligence Review
Volume57
Early online date5 Apr 2024
DOIs
Publication statusPublished - 5 Apr 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Age-related macular degeneration (AMD)
  • Artificial intelligence
  • Diabetic retinopathy (DR)
  • Fundus
  • Glaucoma
  • Optical coherence tomography (OCT)

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