Bridging Data Limitations in Neurosurgery with Synthetic Data Generation: Generative Adversarial Network-Powered Multiclass Classification of Aneurysm Occlusion Grades

Background: Predicting aneurysm occlusion outcomes on follow-up is challenging, especially in niche neurosurgical scenarios with limited data. The Woven EndoBridge (WEB) is an endovascular device specifically used for wide-neck cerebral bifurcation aneurysms. This study aims to use synthetic data generation to improve the multiclass classification of 6-month aneurysm occlusion grades. Methods: A preprocessed dataset (n = 78) of the clinical, procedural, and morphometric aneurysm and WEB parameters across three institutions was fed into a conditional Generative Adversarial Network (cGAN) to generate the augmented dataset (n = 1000). The augmented dataset was validated using similarity and divergence metrics and distance-to-closest-record to ensure fidelity and privacy integrity. Two machine learning classifiers—Random Forest (RF) and XGBoost (XGB)—were trained on four dataset configurations: original data (cross-validation), oversampled original data (cross-validation), cGAN-augmented data, and oversampled cGAN data. We recorded the area under the receiver operative characteristic curve (AUC) and precision-recall analyses; a selected model was used for model-agnostic explainable artificial intelligence (AI) analysis. Results: The cGAN-augmented dataset generalized to the original with minimal overfitting or divergence. Models trained on the cGAN data outperformed those using on the cross-validated original or oversampled data, with XGB achieving the highest performance (AUC: 0.62–0.91, avg. 0.78), maintaining discriminative power for rare occlusion grades. Larger neck volumes and WEB resizing lead to unfavorable outcomes. Counterfactual analysis showed poorer outcomes with WEB underfilling, and diminished returns and aneurysm remnant risk beyond 15%. Conclusions: Synthetic data generation can be useful with small dataset limitations in neurosurgery. The cGAN-augmented dataset improved the discriminative performance of the classifiers across all occlusion grades and provided more granularity. The model-agnostic analysis identified small aneurysm neck volumes as a key determinant of better occlusion grade, which could be optimized with higher WEB volumes.