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KeratoDetect: Keratoconus Detection Algorithm Using Convolutional Neural Networks.

Alexandru Lavric1, Popa Valentin1

  • 1Computers, Electronics and Automation Department, Stefan cel Mare University of Suceava, Suceava 720229, Romania.

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Summary
This summary is machine-generated.

A new algorithm, KeratoDetect, accurately identifies keratoconus (KTC) using corneal topography and convolutional neural networks. This AI tool aids ophthalmologists in rapid patient screening, improving diagnostic accuracy for the rising number of KTC cases.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Keratoconus (KTC) is a progressive corneal disorder with increasing diagnoses.
  • Current diagnostic and treatment methods require enhancement.
  • Understanding KTC's pathological mechanisms is crucial.

Purpose of the Study:

  • To develop an automated algorithm for keratoconus detection.
  • To leverage deep learning for analyzing corneal topography.
  • To provide a tool for rapid and accurate keratoconus screening.

Main Methods:

  • Implementation of the KeratoDetect algorithm.
  • Utilizing a convolutional neural network (CNN) for feature extraction from corneal topography data.
  • Training and testing the algorithm on a dedicated dataset.

Main Results:

  • The KeratoDetect algorithm achieved 99.33% accuracy on the test dataset.
  • The CNN effectively learned features indicative of keratoconus.
  • High performance in distinguishing affected from unaffected eyes.

Conclusions:

  • KeratoDetect offers a highly accurate method for keratoconus detection.
  • The algorithm can significantly assist ophthalmologists in early screening.
  • Improved diagnostic speed and accuracy can facilitate timely treatment.