Equitable Artificial Intelligence for Glaucoma Screening with Fair Identity Normalization

Abstract

AbstractObjectiveTo develop an equitable artificial intelligence model for glaucoma screening.DesignCross-sectional study.Participants7,418 optical coherence tomography (OCT) paired with reliable visual field (VF) measurements of 7,418 patients from the Massachusetts Eye and Ear Glaucoma Service between 2021 and 2023.MethodsWe developed fair identify normalization (FIN) module to equalize the feature importance across different identity groups to improve model performance equity. EfficientNet served as the backbone model to demonstrate the effect of FIN on model equity. The OCT-derived retinal nerve fiber layer thickness (RNFLT) maps and corresponding three-dimensional (3D) OCT B-scans were used as model inputs, and a reliable VF tested within 30 days of an OCT scan was used to categorize patients into glaucoma (VF mean deviation < -3 dB, abnormal glaucoma hemifield test (GHT) and pattern standard deviation (PSD) < 5%) or non-glaucoma (VF mean deviation ≥ -1 dB and normal GHT and PSD results). The area under the receiver operating characteristic curve (AUC) was used to measure the model performance. To account for the tradeoff between overall AUC and group disparity, we proposed a new metric called equity-scaled AUC (ES-AUC) to compare model performance equity. We used 70% and 30% of the data for training and testing, respectively.Main Outcome MeasuresThe glaucoma screening AUC in different identity groups and corresponding ES-AUC.ResultsUsing RNFLT maps with FIN for racial groups, the overall AUC and ES-AUC increased from 0.82 to 0.85 and 0.76 to 0.81, respectively, with the AUC for Blacks increasing from 0.77 to 0.81. With FIN for ethnic groups, the overall AUC and ES-AUC increased from 0.82 to 0.84 and 0.77 to 0.80, respectively, with the AUC for Hispanics increasing from 0.75 to 0.79. With FIN for gender groups, the overall AUC and ES-AUC increased from 0.82 to 0.84 and 0.80 to 0.82, respectively, with an AUC improvement of 0.02 for both females and males. Similar improvements in equity were seen using 3D OCT B scans. All differences regarding overall-and ES-AUCs were statistically significant (p < 0.05).ConclusionsOur deep learning enhances screening accuracy for underrepresented groups and promotes identity equity.