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Artificial intelligence in retinal image analysis: Development, advances, and challenges.

Anthony C Oganov1, Ian Seddon2, Sayena Jabbehdari3

  • 1Department of Ophthalmology, Renaissance School of Medicine, Stony Brook, NY, USA.

Survey of Ophthalmology
|April 28, 2023
PubMed
Summary

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This summary is machine-generated.

Artificial intelligence (AI) enhances the screening, diagnosis, and monitoring of retinal diseases like diabetic retinopathy. AI models show promise in clinical settings, with ongoing research into advanced applications and imaging integration.

Area of Science:

  • Ophthalmology
  • Medical Artificial Intelligence
  • Retinal Imaging Analysis

Background:

  • Diagnostic technologies are rapidly advancing, offering new insights into retinal conditions.
  • Artificial intelligence (AI) presents significant potential for improving the detection and management of various retinal pathologies.

Purpose of the Study:

  • To review the current applications of AI in the screening, diagnosis, and monitoring of retinal diseases.
  • To evaluate the performance of AI models in both research and clinical settings for retinal pathologies.
  • To discuss future directions and challenges in the use of AI for retinal conditions.

Main Methods:

  • Review of existing literature on AI applications in retinal imaging.
  • Analysis of methodologies employed in AI models for retinal pathology detection.
Keywords:
Artificial IntelligenceFAFluorescein angiographyFundus photographyOCTOptical coherence tomographyRetina

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  • Evaluation of AI model performance metrics in research and clinical studies.

    • Main Results:

    • AI is being utilized for screening, diagnosis, and monitoring of conditions including diabetic retinopathy, diabetic macular edema, central serous chorioretinopathy, and age-related macular degeneration.
    • AI models demonstrate varying degrees of success in research and clinical contexts, highlighting their potential utility.
    • Performance evaluation indicates a growing capability of AI in analyzing retinal images.

    Conclusions:

    • AI holds considerable promise for revolutionizing the management of retinal diseases.
    • Future research should focus on integrating multiple imaging modalities and addressing implementation challenges.
    • Continued investigation is crucial for optimizing AI algorithms and ensuring their effective clinical translation.