Artificial Intelligence (AI) has the potential to revolutionize the diagnosis and management of cerebral venous thrombosis (CVT), a condition that presents unique challenges due to its subtle and varied imaging findings. By focusing on essential radiological features, AI can detect key signs such as the “cord sign” or “empty delta sign”, recognize ischemic infarcts or hemorrhages in non-arterial distributions, and identify atypical hemorrhages in veins like Labbé’s or Trolard’s. Additionally, AI’s ability to consider bilateral thalamic edema, a hallmark of deep cerebral vein thrombosis (DCVT), and to address common diagnostic pitfalls ensures a more precise detection of CVT.

AI must also remain vigilant in high-risk scenarios, including local factors such as cranioencephalic trauma, infections, or brain tumors, and systemic conditions like hypercoagulable states, malignancies, and recent surgeries. Identifying these contexts allows AI to prioritize urgent cases and guide radiologists toward faster and more targeted diagnoses.

Although its effectiveness relies on high-quality training datasets and radiologist oversight, AI offers unparalleled opportunities to standardize image interpretation, reduce interobserver variability, and improve diagnostic accuracy. By addressing these challenges and leveraging its capabilities, AI has the potential to become an indispensable tool in modern neuroradiology, improving patient outcomes through earlier detection and timely intervention.