Artificial Intelligence in Cerebrovascular Imaging: A Targeted Review of Aneurysm Detection and Rupture Risk...

Area of Science:

• Neuroimaging
• Vascular Neurology
• Artificial Intelligence in Medicine

Background:

• Cerebral aneurysms pose significant risks, with traditional imaging limited in predicting rupture.
• There is a critical need for advanced, individualized diagnostic strategies to improve patient outcomes.
• Artificial intelligence (AI) presents transformative potential in cerebrovascular imaging and aneurysm management.

Purpose of the Study:

• To review and synthesize the application of AI in cerebrovascular imaging for aneurysm detection and rupture risk prediction.
• To provide an integrated framework for radiomics, machine learning (ML), and deep learning (DL) in aneurysm analysis.
• To highlight emerging AI solutions and address challenges for clinical translation.

Main Methods:

• Narrative review of published studies on AI in cerebrovascular imaging.
• Examination of radiomics for quantitative feature extraction.
• Analysis of ML and deep learning (DL) models for detection and risk prediction.

Main Results:

• Radiomics, ML, and DL offer enhanced capabilities for aneurysm detection and rupture risk assessment.
• AI methods show potential to outperform traditional risk scores like PHASES and ELAPSS.
• Current AI approaches are investigational and require rigorous prospective validation.

Conclusions:

• AI, encompassing radiomics, ML, and DL, holds significant promise for personalized aneurysm care.
• Clinical validation and standardization are essential for the successful integration of AI into practice.
• AI-driven precision medicine can potentially reduce aneurysm rupture rates and improve patient outcomes.