小児鎌状赤血球症における中枢神経系血管症の人工知能ベース分析

In children with sickle cell anemia (SCA), central nervous system (CNS) complications such as chronic vasculopathy, silent cerebral infarcts, and overt stroke cause significant morbidity and mortality, and remain difficult to predict. Here, we coupled pediatric magnetic resonance angiography (MRA) neuroimaging data from a completed clinical trial with artificial intelligence (AI)-based techniques to investigate associations between vascular morphology and clinical risk groups. Using an automated computer vision workflow, we generated quantitative metrics describing individual vessels, including small vessels not captured by conventional radiologic scoring. We then applied open-source machine learning algorithms, including: clustering to identify natural groupings within the data, classification to differentiate scans by clinical risk category, and scaled event-based modeling to order vascular features according to relative changes observed across the cohort. Across these approaches, vessel remodeling at branch points and increased vessel tortuosity consistently emerged as among the earliest observed vascular features distinguishing high-risk groups, independent of transcranial Doppler ultrasound velocities. These retrospective findings describe associations between MRA-derived vascular features and clinical groups, but do not establish causality or predict future events. Our results demonstrate that quantitative vascular metrics be extracted from already-obtained imaging data, without requiring additional patient procedures, and may complement existing risk stratification methods. In addition, as current guidelines recommend neuroimaging in school-aged children with SCA, and as MRA technologies continue to advance, our results support larger-scale prospective studies to validate computer vision-based biomarkers. Ultimately, these approaches may inform future studies aimed at improving SCA-related CNS complications and support more refined clinical characterization.