Impact of Synthetic Lesional MR Images in Automated Focal Cortical Dysplasia Detection in Low-Data Scenarios

Focal cortical dysplasia is among the most common causes of drug-resistant epilepsy, yet its subtle MRI signature is frequently missed even by experienced neuroradiologists. Training AI detectors requires large labeled datasets that are extraordinarily difficult to accumulate for rare neurological conditions. This study demonstrates that generative models can produce synthetic MRI scans realistic enough to fool specialist radiologists, and that mixing them with real data meaningfully improves detection sensitivity. The approach offers a template for data augmentation across rare disease imaging — from rare tumors to congenital anomalies — where waiting for large natural datasets is clinically unacceptable and synthetic data may be the most practical path forward. Authors: Prabhjot Kaur, Hakim Ouaalam, Sedat Kandemirli, Sanjay P. Prabhu, Simon K. Warfield Paper: https://arxiv.org/abs/2606.07381v1