AUTHOR=Li Wenjie , Chen Suhua , Chen Xin , Ji Xiangtian , Zhu Huan , Zhang Qihang , Zhu Chenyu , Wang Tao , Zhang Yan , Yang Jun 

TITLE=Accelerated brain age in Moyamoya disease patients: a deep learning approach and correlation with disease severity

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 19 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1668993

DOI=10.3389/fnins.2025.1668993

ISSN=1662-453X

ABSTRACT=IntroductionThis study aims to utilize a DenseNet based deep learning framework to predict brain age in patients with Moyamoya disease (MMD), examining the relationship between brain age and disease severity to enhance diagnostic and prognostic capabilities.MethodsWe analyzed unenhanced MRI scans from 432 adult MMD patients and 565 normal controls collected between January 2018 and December 2022. Data preprocessing involved converting DICOM files to NIFTI format and labeling based on established diagnostic criteria. A DenseNet121 architecture, implemented using PyTorch, was employed to predict brain age. Statistical analyses included correlation assessments and comparisons between predicted brain age, chronological age, and MRA scores.ResultsThe predicted brain age for MMD patients was significantly higher than their chronological age, averaging 37.9 years versus 35.8 years (p < 0.01). For normal controls, predicted brain age matched chronological age at 36.5 years. Delta age (difference between predicted brain age and chronological age) was significantly elevated in MMD patients (p < 0.001) and positively correlated with MRA scores, indicating a link between arterial stenosis severity and accelerated brain aging.DiscussionThe DenseNet based model effectively predicts brain age, revealing that MMD patients experience accelerated brain aging correlated with disease severity. These findings highlight the potential of brain age prediction as a biomarker for MMD, aiding in personalized treatment strategies and early intervention. Future research should explore multi-center datasets and longitudinal data to validate and extend these findings.