# Artificial intelligence-based model for the interpretation and reporting of standard automated perimetry

## TL;DR

An AI model was developed to interpret perimetry tests for glaucoma, showing high accuracy in detecting visual field abnormalities.

## Contribution

A novel AI-based model (Inception V3) was applied to standard automated perimetry in a remote region, demonstrating high diagnostic accuracy.

## Key findings

- The AI model achieved 80% sensitivity and 94.64% specificity in detecting altered perimetry results.
- The area under the ROC curve was 0.93, indicating strong diagnostic performance.

## Abstract

Standard automated perimetry has been the standard method for measuring
visual field changes for several years. It can measure an individual’s
ability to detect a light stimulus from a uniformly illuminated background.
In the management of glaucoma, the primary objective of perimetry is the
identification and quantification of visual field abnormalities. It also
serves as a longitudinal evaluation for the detection of disease
progression. The development of artificial intelligence--based models
capable of interpreting tests could combine technological development with
improved access to healthcare.

In this observational, cross-sectional, descriptive study, we used an
artificial intelligence-based model [Inception V3] to interpret gray-scale
crops from standard automated perimetry that were performed in an
ophthalmology clinic in the Brazilian Amazon rainforest between January 2018
and December 2022.

The study included 1,519 standard automated perimetry test results that were
performed using Humphrey HFA-II-i-750 (Zeiss Meditech). The Subsequently,
70%, 10%, and 20% of the dataset were used for training, validation, and
testing, respectively. The model achieved 80% (68.23%-88.9%) sensitivity and
94.64% (88.8%-98%) specificity for detecting altered perimetry results.
Furthermore, the area under the receiver operating characteristic curve was
0.93.

The integration of artificial intelligence in the diagnosis, screening, and
monitoring of pathologies represents a paradigm shift in ophthalmology,
enabling significant improvements in safety, efficiency, availability, and
accessibility of treatment.