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Review of Machine Learning Applications Using Retinal Fundus Images.

Yeonwoo Jeong1, Yu-Jin Hong2, Jae-Ho Han1,3

  • 1Department of Brain and Cognitive Engineering, Korea University, 145 Anam Rd., Seoul 02841, Korea.

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Summary

Deep learning automates medical diagnosis using retinal images, improving accuracy for conditions like diabetic retinopathy (DR) and glaucoma. This review covers AI advancements in ophthalmology for faster, cost-effective screening.

Keywords:
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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Automated medical diagnosis enhances efficiency and reduces misdiagnosis rates.
  • Deep learning enables machines to interpret complex medical data, driving automation.
  • Ophthalmology benefits from AI for analyzing retinal images to detect diseases.

Purpose of the Study:

  • To review state-of-the-art deep learning methods for automated screening and diagnosis in ophthalmology.
  • To investigate AI applications for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma.
  • To cover machine learning techniques for retinal vasculature extraction from fundus images.

Main Methods:

  • Review of recent literature on deep learning applied to color fundus images.
  • Analysis of AI frameworks for identifying and assessing severity of retinal diseases.
  • Inclusion of machine learning approaches for retinal vasculature segmentation.

Main Results:

  • Deep learning models show significant promise in automating the detection of DR, AMD, and glaucoma.
  • AI facilitates accurate analysis of retinal images for disease identification and severity assessment.
  • Machine learning techniques are effective for extracting crucial vascular information from fundus images.

Conclusions:

  • Deep learning offers powerful tools for advancing automated screening and diagnosis in ophthalmology.
  • AI-driven analysis of retinal images can lead to earlier and more accurate disease detection.
  • Further research is needed to address challenges in developing robust AI systems for clinical use.