Artificial intelligence in cerebral cavernous malformations: a scoping review

Abstract

Objectives: Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being applied in medical research, including studies on cerebral cavernous malformations (CCM). This scoping review aims to analyze the scope and impact of AI in CCM, focusing on diagnostic tools, risk assessment, biomarker identification, outcome prediction, and treatment planning.

Methods: We conducted a comprehensive literature search across different databases, reviewing articles that explore AI applications in CCM. Articles were selected based on predefined eligibility criteria and categorized according to their primary focus: drug discovery, diagnostic imaging, genetic analysis, biomarker identification, outcome prediction, and treatment planning.

Results: Sixteen studies met the inclusion criteria, showcasing diverse AI applications in CCM. Nearly half (47%) were cohort or prospective studies, primarily focused on biomarker discovery and risk prediction. Technical notes and diagnostic studies accounted for 27%, concentrating on computer-aided diagnosis (CAD) systems and drug screening. Other studies included a conceptual review on AI for surgical planning and a systematic review confirming ML's superiority in predicting clinical outcomes within neurosurgery.

Discussion: AI applications in CCM show significant promise, particularly in enhancing diagnostic accuracy, risk assessment, and surgical planning. These advancements suggest that AI could transform CCM management, offering pathways to improved patient outcomes and personalized care strategies.

Keywords: CCM; Cerebral cavernous malformations; artificial intelligence; cavernous angioma; computer aided diagnosis; deep learning; machine learning.