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

  • University of Tennessee Health Science Center
  • National University of Sciences and Technology (NUST)
  • Weill Cornell Medicine-Qatar

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

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 languageBritish English
Article number111
JournalArtificial Intelligence Review
Volume57
Issue number5
DOIs
StatePublished - May 2024

Keywords

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

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