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A noninvasive approach for the early detection of diabetic retinopathy

  • University of Louisville
  • AtheroPoint™
  • Inc.
  • Idaho State University

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations

Abstract

This chapter introduces one of the most critical problems in ophthalmology, specifically the diagnosis and detection of diabetic retinopathy (DR). Developing a fast, accurate, and reliable method for the early detection of DR is of great clinical importance to prevent blindness in patients. For this reason, various methods for early detection of DR have been investigated and used such as a dilated eye examination, tonometry, fluorescein angiography, optical coherence tomography, and ultrawide-field retinal imaging. With the increased popularity of machine learning, researchers have formulated their own algorithms and methods to detect DR with various rates of success. This chapter overviews past and current diagnostic methods that have been developed for DR. In addition, this chapter addresses new methodologies being developed/researched and some challenges that researchers face in developing fast, accurate, and reliable diagnosis.

Original languageEnglish
Title of host publicationDiabetes and Retinopathy
Subtitle of host publicationVolume 2: Computer-Assisted Diagnosis
PublisherElsevier
Pages205-228
Number of pages24
Volume2
ISBN (Electronic)9780128174388
ISBN (Print)9780128174395
DOIs
StatePublished - 1 Jan 2020

Keywords

  • Convoluted neural network
  • Diabetic retinopathy
  • ImageNet Dataset
  • Optical coherence tomography
  • Simulation
  • Ultrawide-field device

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