An AI Based Screening Tool for Detection of Cerebral Venous Thrombosis on Non-Contrast Brain CT

Background: While non-contrast computed tomography (NCCT) is the primary ‎imaging modality in emergency settings, it has a low sensitivity for detection of ‎Cerebral Venous Thrombosis (CVT). This study aims to develop an Artificial ‎Intelligence (AI) tool to screen for CVT on routine NCCT scans.‎ Methods: We used a retrospectively collected dataset of 692 patients, including ‎‎258 with CVT from Iran Cerebral Venous Thrombosis Registry (ICVTR) (code: ‎‎9001013381) and 434 controls. NCCT images were processed using the pre-trained ‎CT-Foundation model to generate high-dimensional embeddings. These ‎embeddings were used to train several machine learning classifiers: Support Vector ‎Machine (SVM) with linear, polynomial, and RBF kernels, Logistic Regression, ‎Random Forest, an Artificial Neural Network (ANN), and a soft-voting Ensemble ‎model. Model performance was assessed using a 10-fold cross-validation protocol.‎ Results: The Random Forest model achieved the highest specificity of 0.856 ± ‎‎0.050, excelling at identifying non-CVT cases. Conversely, the Ensemble model ‎yielded the highest sensitivity of 0.614 ± 0.087. The SVM with a linear kernel ‎provided the best overall discriminative ability, with the highest AUC of 0.718 ± ‎‎0.053. No single model demonstrated superiority across all metrics, reflecting the ‎inherent challenge of detecting CVT on NCCT.‎ Conclusion: Our findings demonstrate the feasibility of using an AI-based ‎model to detect CVT on non-contrast CT scans. While not yet a replacement for ‎expert radiological interpretation, this approach serves as a promising automated ‎screening tool. It has the potential to reduce diagnostic delays and improve patient ‎outcomes by flagging suspicious cases in emergency workflows.‎