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Detection of trachoma using machine learning approaches.

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Artificial intelligence models can help diagnose trachomatous inflammation-follicular (TF), a leading cause of blindness. These AI tools reduce the need for expert graders, improving efficiency in trachoma elimination efforts.

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Area of Science:

  • Ophthalmology
  • Infectious Diseases
  • Artificial Intelligence

Background:

  • Trachoma remains a leading infectious cause of global blindness.
  • Disease rarity complicates clinical training for monitoring surveys.
  • Artificial intelligence (AI) is explored to aid in diagnosing trachomatous inflammation-follicular (TF).

Purpose of the Study:

  • To evaluate the potential of AI models to augment or replace human graders in TF diagnosis.
  • To assess the performance of Convolutional Neural Network (CNN) models in identifying TF from images.

Main Methods:

  • Utilized a dataset of 2300 images with a 5% TF positivity rate.
  • Developed and trained ResNet101 and VGG16 CNN classifiers.
  • Applied data augmentation and oversampling techniques, including external dataset augmentation.

Main Results:

  • Models achieved high sensitivity (95%) and reduced false negatives.
  • AI grading reduced the need for skilled graders by 66-75%.
  • Basic augmentation techniques improved performance more than clinically-grounded methods.

Conclusions:

  • AI models show promise in reducing the burden on human graders for TF diagnosis.
  • Current models are not sufficient for independent clinical deployment but can assist experts.
  • Future improvements require larger datasets with varied image quality and capture methods.