RT Journal Article
SR Electronic
T1 Deep learning model to identify homonymous defects on automated perimetry
JF British Journal of Ophthalmology
JO Br J Ophthalmol
FD BMJ Publishing Group Ltd.
SP 1516
OP 1521
DO 10.1136/bjo-2021-320996
VO 107
IS 10
A1 Tan, Aaron Hao
A1 Donaldson, Laura
A1 Moolla, Luqmaan
A1 Pereira, Austin
A1 Margolin, Edward
YR 2023
UL http://bjo.bmj.com/content/107/10/1516.abstract
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.