# Diagnostic accuracy of a deep learning model for pterygium detection in Barcelos, Brazilian Amazon

## TL;DR

A deep learning model accurately detects pterygium in eye photos taken with smartphones in the Brazilian Amazon, showing promise for improving eye care in remote areas.

## Contribution

This study demonstrates a deep learning model's high diagnostic accuracy for pterygium detection in a remote population using smartphone images.

## Key findings

- The model achieved 91.43% sensitivity and 90.24% specificity in detecting pterygium.
- Diagnostic metrics were comparable to those of experienced ophthalmologists.
- The model's area under the curve was 0.91, indicating strong diagnostic performance.

## Abstract

This pilot study evaluated the diagnostic accuracy of a deep learning model
for detecting pterygium in anterior segment photographs taken using
smartphones in the Brazilian Amazon. The model’s performance was benchmarked
against assessments made by experienced ophthalmologists, considered the
clinical gold standard.

In this cross-sectional study, 38 participants (76 eyes) from Barcelos,
Brazil, were enrolled. Trained nonmedical health workers captured
high-resolution anterior segment images using smartphones. These images were
analyzed using a deep learning model based on the MobileNet-V2 convolutional
neural network. Diagnostic metrics–including sensitivity, specificity,
accuracy, positive predictive value, negative predictive value, and area
under the receiver operating characteristic curve–were calculated and
compared with the ophthalmologists’ evaluations.

The deep learning model achieved a sensitivity of 91.43%, specificity of
90.24%, positive predictive value of 88.46%, negative predictive value of
92.79%, and an area under the curve of 0.91. Logistic regression revealed no
statistically significant association between pterygium and demographic
variables such as age or gender.

The deep learning model demonstrated high diagnostic performance in
identifying pterygium in a remote Amazonian population. These preliminary
findings support the potential use of artificial intelligence–based tools to
facilitate early detection and screening in underserved regions, thereby
enhancing access to ophthalmic care.