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

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

  • By

  • 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

  • December 8, 2025

  • 0 min

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  • 1

    The study developed a deep learning framework, MBA Net, to estimate brain age in acute ischemic cerebrovascular disease (AICVD) patients.

  • 2

    MBA Net predicts contextual brain age (CBA) by masking acute infarcts on T2-FLAIR images, improving accuracy in brain age estimation.

  • 3

    The brain age gap (BAG), the difference between CBA and chronological age, independently predicts stroke recurrence at 3 months and 5 years.

  • 4

    Incorporating BAG into existing prediction models significantly enhances their discriminative performance for stroke recurrence.

  • 5

    These findings suggest that brain age may serve as a valuable biomarker for secondary stroke prevention strategies.

Original Source(s)

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

  • by 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

  • December 8, 2025

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