Artificial intelligence in the diagnosis of cerebrovascular diseases using magnetic resonance imaging: A scoping review

Abstract

The field of radiology is currently undergoing revolutionary changes owing to the increasing application of artificial intelligence (AI). This scoping review identifies and summarizes the technical methods and clinical applications of AI applied to magnetic resonance imaging of cerebrovascular diseases (CVDs). Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews was adopted and articles listed in PubMed and Cochrane databases from January 1, 2018 to December 31, 2023, were assessed. In total, 67 articles met the eligibility criteria. We obtained a general overview of the field, including lesion types, sample sizes, data sources, and databases and found that nearly half of the studies used multisequence magnetic resonance as the input. Both classical machine learning and deep learning were widely used. The evaluation metrics varied according to the five main algorithm tasks of classification, detection, segmentation, estimation, and generation. Cross-validation was primarily used with only one third of the included studies using external validation. We also illustrate the key questions of the CVD research studies and grade the clinical utility of their AI solutions. Although most attention is devoted to improving the performance of AI models, this scoping review provides information on the availability of algorithms, reliability of external validations, and consistency of evaluation metrics and may facilitate improved clinical applicability and acceptance.