PT - JOURNAL ARTICLE AU - Tan, Aaron Hao AU - Donaldson, Laura AU - Moolla, Luqmaan AU - Pereira, Austin AU - Margolin, Edward TI - Deep learning model to identify homonymous defects on automated perimetry AID - 10.1136/bjo-2021-320996 DP - 2023 Oct 01 TA - British Journal of Ophthalmology PG - 1516--1521 VI - 107 IP - 10 4099 - http://bjo.bmj.com/content/107/10/1516.short 4100 - http://bjo.bmj.com/content/107/10/1516.full SO - Br J Ophthalmol2023 Oct 01; 107 AB - Background Homonymous visual field (VF) defects are usually an indicator of serious intracranial pathology but may be subtle and difficult to detect. Artificial intelligence (AI) models could play a key role in simplifying the detection of these defects. This study aimed to develop an automated deep learning AI model to accurately identify homonymous VF defects from automated perimetry.Methods VFs performed on Humphrey field analyser (24–2 algorithm) were collected and run through an in-house optical character recognition program that extracted mean deviation data and prepared it for use in the proposed AI model. The deep learning AI model, Deep Homonymous Classifier, was developed using PyTorch framework and used convolutional neural networks to extract spatial features for binary classification. Total collected dataset underwent 7-fold cross validation for model training and evaluation. To address dataset class imbalance, data augmentation techniques and state-of-the-art loss function that uses complement cross entropy were used to train and enhance the proposed AI model.Results The proposed model was evaluated using 7-fold cross validation and achieved an average accuracy of 87% for detecting homonymous VF defects in previously unseen VFs. Recall, which is a critical value for this model as reducing false negatives is a priority in disease detection, was found to be on average 92%. The calculated F2 score for the proposed model was 0.89 with a Cohen’s kappa value of 0.70.Conclusion This newly developed deep learning model achieved an overall average accuracy of 87%, making it highly effective in identifying homonymous VF defects on automated perimetry.Data are available upon reasonable request.