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Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...
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Artificial Intelligence and Ophthalmic Clinical Registries.

Luke Tran1, Himal Kandel1, Daliya Sari1

  • 1From the Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, (L.T., H.K., D.S., C.H.C., S.L.W.) Sydney, New South Wales, Australia.

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Artificial intelligence (AI) shows promise for healthcare, but AI models need extensive data. This review explores AI applications in ophthalmic clinical registries, finding limited use of advanced AI and a need for standardized validation.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Health Informatics

Background:

  • Artificial intelligence (AI) offers solutions for increasing clinical demand and limited health resources.
  • AI models require large, representative datasets for accurate clinical predictions.
  • Ophthalmic clinical registries are valuable sources of real-world data for training AI.

Purpose of the Study:

  • To review current applications of artificial intelligence (AI) in ophthalmic clinical registry data.
  • To identify trends in AI algorithm types and data inputs used with registry data.
  • To assess the current state and future potential of AI in ophthalmic registries.

Main Methods:

  • Systematic literature search conducted in July 2024 across EMBASE, Medline, PubMed, Scopus, and Web of Science.
  • Included primary research articles applying AI to ophthalmic clinical registry data.
  • Analyzed AI algorithm types, data inputs, outputs, and validation methodologies.

Main Results:

  • Twenty-three primary research articles were identified, focusing on 14 ophthalmic registries.
  • Glaucoma and neovascular age-related macular degeneration were the most common conditions studied.
  • Supervised conventional machine learning models dominated (85%), with limited use of deep learning or NLP; significant heterogeneity in validation was observed.

Conclusions:

  • The application of AI to ophthalmic clinical registries is nascent, with limited deep learning utilization.
  • Poor data accessibility and a lack of standardized validation methods hinder AI development.
  • Future advancements require standardized methodologies and increased domain expert involvement for clinically deployable AI.