Equity-Enhanced Glaucoma Progression Prediction from OCT with Knowledge Distillation
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Purpose
To develop an equitable deep learning model with knowledge distillation to enhance the demographic equity in glaucoma progression prediction.
Methods
We developed a novel deep learning model called FairDist which used baseline optical coherency tomography (OCT) scans to predict glaucoma progression. First, an equity-aware EfficientNet termed EqEffNet was trained for glaucoma detection. Next, the pretrained detection model was adapted for progression prediction using knowledge distillation which minimizes image and identity feature differences between the detection and progression models. Progression was defined based on longitudinal visual field maps from at least five visits up to six years. Model performance was measured by the area under the receiver operating characteristic curve (AUC), Sensitivity, Specificity, and equity was assessed using equity-scaled AUC (ES-AUC), which adjusts AUC by accounting for subgroup disparities, focusing on gender and racial groups.
Results
Two types of glaucoma progression including mean deviation (MD) Fast progression and Total Deviation (TD) Pointwise progression were explored. For MD Fast Progression, FairDist achieved the highest AUC and ES-AUC for gender (AUC: 0.738, ES-AUC: 0.693) and race (AUC: 0.778, ES-AUC: 0.677) compared with methods with and without integrating inequity mitigation strategies. For TD Pointwise progression, FairDist achieved the best AUC and ES-AUC for gender (AUC: 0.743, ES-AUC: 0.719) and race (AUC: 0.746, ES-AUC: 0.645) among all methods.
Conclusions
FairDist enhances both model performance and equity in glaucoma progression prediction after integrating the equity-aware learning and knowledge distillation components. The proposed deep learning model shows promise in improving glaucoma diagnosis while reducing disparities across demographic groups.
