Quantitative image signature and machine learning-based prediction of outcomes in cerebral cavernous malformations

There is increasing interest in novel prognostic tools and predictive biomarkers to help identify, with more certainty, cerebral cavernous malformations (CCM) susceptible of bleeding if left untreated. We developed explainable quantitative-based machine learning models from magnetic resonance imaging (MRI) in a large CCM cohort to demonstrate the value of artificial intelligence and radiomics in complementing natural history studies for hemorrhage and functional outcome prediction.
Source: Journal of Stroke and Cerebrovascular Diseases - Category: Neurology Authors: Source Type: research