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Related Concept Videos

Diabetic Retinopathy01:27

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

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Methodology for Biomimetic Chemical Neuromodulation of Rat Retinas with the Neurotransmitter Glutamate In Vitro
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Artificial intelligence for retinal diseases.

Jennifer I Lim1, Aleksandra V Rachitskaya2, Joelle A Hallak3

  • 1University of Illinois at Chicago, College of Medicine, Department of Ophthalmology and Visual Sciences, Chicago, IL, United States.

Asia-Pacific Journal of Ophthalmology (Philadelphia, Pa.)
|August 29, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise in diagnosing and managing retinal diseases like diabetic retinopathy (DR) and age-related macular degeneration (AMD). AI can predict disease progression and aid in treatment analysis, potentially reducing blindness.

Keywords:
AlgorithmsArtificial intelligenceBiomarkersDeep learningScreening

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Area of Science:

  • Ophthalmology
  • Medical Artificial Intelligence
  • Biomedical Imaging

Background:

  • Common retinal diseases pose a significant threat to vision globally.
  • Early diagnosis and effective management are crucial for preventing vision loss.

Purpose of the Study:

  • To review worldwide applications of artificial intelligence (AI) in diagnosing and managing common retinal diseases.
  • To explore AI's potential impact on treatment outcome analysis and disease management.

Main Methods:

  • Conducted an online literature review of AI applications for retinal diseases using PubMed Central.
  • Included studies on screening, diagnosis, monitoring, and treatment outcomes for conditions like AMD, DR, ROP, and SCR.
  • Focused on AI's use with retinal imaging techniques such as OCT and OCTA.

Main Results:

  • AI demonstrates utility in screening for DR, AMD, ROP, and SCR.
  • AI algorithms can predict disease progression and treatment response using validated datasets.
  • AI facilitates rapid, quantitative analysis of retinal biomarkers from OCT and OCTA, and may assist in robotic surgery planning.

Conclusions:

  • AI tools can assist clinicians in disease screening, monitoring, and quantitative analysis of treatment outcomes.
  • AI may help reduce socioeconomic disparities affecting retinal disease outcomes.
  • The public health impact of AI in preventing blindness from retinal diseases requires further determination.