Regional brain age is decreased in children with sickle cell anemia

Abstract

ABSTRACT: DeepBrainNet, a machine learning tool, uses magnetic resonance imaging (MRI) to predict an individual's brain age, allowing calculation of the brain age gap (predicted chronological age) for use as a biomarker of brain health. We tested the DeepBrainNet tool in sickle cell anemia (SCA) on 210 brain MRIs from 130 children (90 with SCA; 40 healthy controls) to investigate the hypothesis that children with SCA would have a younger predicted brain age than healthy controls as an index of aberrant brain development. DeepBrainNet estimates brain age from 80 axial slices of each brain, with the median used as global brain age. Global brain age gap was not different between patients with SCA and controls (P = .956). However, the estimated difference in regional brain age gap between SCA without stroke and controls was -1.26 (95% confidence interval, -2.34 to -0.19; P = .021) years after adjusting for chronological age, indicating regionally reduced brain age in SCA. The DeepBrainNet model architecture was modified to create a SCA classifier that distinguished axial brain slices from controls vs SCA without stroke history (accuracy, 0.69; area under the curve, 0.7). We conclude that there is a regional decrease in brain age in children with SCA without stroke compared with controls, suggesting altered brain development. Furthermore, DeepBrainNet can be used to train a classifier to accurately classify the presence of SCA in children without infarcts with only the input of clinically available MRI sequences. These data highlight that machine learning tools could potentially be used to improve upon risk prediction algorithms and assessment of treatment effect with further development.