Artificial Intelligence in Intracranial Aneurysm Management: Recent Advances, Methods of Detection, Treatment Decisions, and Future Directions

Intracranial aneurysms (IAs) demonstrate significant risks due to their potential to rupture, leading to subarachnoid hemorrhage and high mortality rates. Accurate detection, diagnosis, and treatment of IAs are of great importance. These processes typically rely on imaging tools such as computed tomography angiography, magnetic resonance angiography, and digital subtraction angiography. However, these tools are subject to human mistakes and variability in interpretation. Artificial intelligence (AI), including machine learning and deep learning, is being investigated more and more to improve IA management by enhancing its accuracy and efficiency. AI methods, such as convolutional neural networks, have demonstrated potential in the detection and characterization of aneurysms with high accuracy. Additionally, AI models can predict aneurysm rupture risk and guide treatment decisions, improving patient outcomes through individualized therapeutic regimens. Future developments in AI algorithms are expected to further integrate AI into clinical practice, improving the detection, diagnosis, and treatment of IAs.