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Computer Vision Identification of Trachomatous Inflammation-Follicular Using Deep Learning.

Ashlin S Joye^1,2, Marissa G Firlie³, Dionna M Wittberg²

¹Casey Eye Institute, Oregon Health and Science University, Portland, OR.

|September 23, 2024

Summary

This summary is machine-generated.

Machine learning models accurately detect trachoma inflammation-follicular (TF) from eye images, offering a reliable and cost-effective alternative to human grading for global health surveys.

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Area of Science:

Ophthalmology
Public Health
Artificial Intelligence

Background:

Trachoma surveys are crucial for estimating disease prevalence and guiding antibiotic distribution.
Current methods rely on human graders, which are resource-intensive and prone to errors.

Purpose of the Study:

To develop and evaluate machine learning models for automated trachoma grading.
To reduce costs and improve the reliability of trachoma screening surveys.

Main Methods:

A deep convolutional neural network (MobileNetV3 large) was trained using 56,725 everted eyelid photographs from Ethiopian children aged 0-9 years.
Ground truth was established using the median estimates from three expert grader groups.

Main Results:

The model achieved high performance with an area under the receiver operating characteristic curve of 0.943, an F1 score of 0.923, 88% accuracy, 83% sensitivity, and 91% specificity.
Predicted TF prevalence (32%) closely matched the human consensus estimate (30%).

Conclusions:

Deep convolutional neural network models demonstrate strong performance in classifying trachoma inflammation-follicular (TF) and follicle counts from conjunctival images.
These models show potential for accurate, efficient, and large-scale trachoma screening.
Further validation in diverse populations is recommended before widespread implementation.