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<p>Acute ischemic cerebrovascular disease (AICVD) exhibits high recurrence rates, necessitating novel biomarkers for refined risk stratification. While MRI-derived brain age correlates with stroke incidence, its prognostic utility for recurrence is unestablished. We developed the Mask-based Brain Age estimation Network (MBA Net), a deep learning framework designed for AICVD patients. MBA Net predicts contextual brain age (CBA) in non-infarcted regions by masking acute infarcts on T2-FLAIR images, thereby mitigating the confounding effects of dynamic infarcts during acute-phase neuroimaging. The model was trained on data from 5353 healthy individuals and then applied to a multicenter cohort of 10,890 AICVD patients. Brain age gap (BAG), defined as the deviation between CBA and chronological age, independently predicted stroke recurrence at both 3 months and 5 years, outperforming chronological age. Incorporating BAG into established prediction models significantly improved discriminative performance. These findings support brain age’s potential utility in AI-driven precision strategies for secondary stroke prevention.</p>

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Deep learning-based brain age predicts stroke recurrence in acute ischemic cerebrovascular disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月8日

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  • Hongyu Zhou,
  • Ziyang Liu,
  • Jing Jing,
  • Hongqiu Gu,
  • Lingling Ding,
  • Yingyu Jiang,
  • Hao Liu,
  • Jinxin Zhao,
  • Wanlin Zhu,
  • Yuesong Pan,
  • Yong Jiang,
  • Xia Meng,
  • Xuewei Xie,
  • Zhe Zhang,
  • Jian Cheng,
  • Yubo Fan,
  • Yilong Wang,
  • Xingquan Zhao,
  • Hao Li,
  • Zixiao Li,
  • Tao Liu,
  • Yongjun Wang

摘要

Acute ischemic cerebrovascular disease (AICVD) exhibits high recurrence rates, necessitating novel biomarkers for refined risk stratification. While MRI-derived brain age correlates with stroke incidence, its prognostic utility for recurrence is unestablished. We developed the Mask-based Brain Age estimation Network (MBA Net), a deep learning framework designed for AICVD patients. MBA Net predicts contextual brain age (CBA) in non-infarcted regions by masking acute infarcts on T2-FLAIR images, thereby mitigating the confounding effects of dynamic infarcts during acute-phase neuroimaging. The model was trained on data from 5353 healthy individuals and then applied to a multicenter cohort of 10,890 AICVD patients. Brain age gap (BAG), defined as the deviation between CBA and chronological age, independently predicted stroke recurrence at both 3 months and 5 years, outperforming chronological age. Incorporating BAG into established prediction models significantly improved discriminative performance. These findings support brain age’s potential utility in AI-driven precision strategies for secondary stroke prevention.