OptiDON-Guard: An Ensemble Model for Identification of Dysthyroid Optic Neuropathy Using Optical Coherence Tomography Angiography

Purpose: To explore the value of machine learning models based on optical coherence tomography angiography (OCTA) in identifying dysthyroid optic neuropathy (DON). Methods: A retrospective, dual-center, cross-sectional study was conducted involving 346 thyroid eye disease patients (with/without DON) from two institutions. Participants underwent ophthalmic examinations, including OCTA scans centered on the macula and optic nerve head. The OCTA data from Center 1 were split into a training set and an internal validation set, while the data from Center 2 served as an external test set. Features were extracted from OCTA images, and a wrapper-based feature selection strategy identified the optimal subset. A two-tiered stacking modeling framework was used, incorporating baseline model construction, data augmentation using pseudo-labeling, and ensemble integration. Model performance (area under the curve [AUC], accuracy, sensitivity, specificity, precision) was evaluated alongside receiver operating characteristic, calibration, and decision curve analyses. Additionally, the SHapley Additive exPlanation method was used to interpret feature contributions. Results: The ensemble model, OptiDON-Guard, demonstrated superior performance to individual models on both internal validation and external test sets. In the internal validation set, it achieved an AUC of 0.872, accuracy of 0.943, sensitivity of 0.667, specificity of 0.979, and precision of 0.943. In the external test set, it maintained strong performance with an AUC of 0.854, accuracy of 0.816, sensitivity of 0.767, specificity of 0.842, and precision of 0.854. Conclusions: The OptiDON-Guard is a reliable, interpretable, and clinically applicable tool for identifying DON. Translational Relevance: The OptiDON-Guard offers a valuable clinical tool to enhance the diagnostic precision of DON.