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

 

Your competent? doctor determined your brain age 2 years ago, right? And then created PRECISE PROTOCOLS TO REDUCE YOUR BRAIN AGING! Oh no, NOTHING OCCURRED!

AI Uncovers Secrets of Brain Aging October 2023 

The latest here:

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

• 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 

npj Digital Medicine volume 8, Article number: 750 (2025) Cite this article

Abstract

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.