Deep learning-based brain age predicts stroke recurrence in acute ischemic cerebrovascular disease
Hongyu Zhou1,2, Ziyang Liu3, Jing Jing1,2,4
1Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
NPJ Digital Medicine
|December 8, 2025
Summary
Brain age gap (BAG) predicts stroke recurrence in acute ischemic cerebrovascular disease (AICVD) patients. This novel biomarker, derived from AI-based brain age estimation, aids in secondary stroke prevention strategies.
Area of Science:
- Neurology
- Radiology
- Artificial Intelligence in Medicine
Background:
- Acute ischemic cerebrovascular disease (AICVD) has high recurrence rates, requiring better risk stratification biomarkers.
- Current MRI-based brain age has unestablished prognostic value for AICVD recurrence.
Purpose of the Study:
- To develop and validate a deep learning model (MBA Net) for estimating brain age in AICVD patients.
- To assess the prognostic utility of brain age gap (BAG) for predicting stroke recurrence in AICVD.
Main Methods:
- Developed Mask-based Brain Age estimation Network (MBA Net) using T2-FLAIR MRI to predict contextual brain age (CBA) in non-infarcted regions.
- Trained MBA Net on healthy individuals (n=5353) and applied to a multicenter AICVD cohort (n=10,890).
- Calculated BAG as the difference between CBA and chronological age.
Main Results:
- BAG independently predicted stroke recurrence at 3 months and 5 years post-AICVD.
- BAG demonstrated superior prognostic performance compared to chronological age alone.
- Integrating BAG into existing models significantly enhanced their discriminative ability for recurrence.
Conclusions:
- Brain age gap (BAG) is a promising independent predictor of stroke recurrence in AICVD.
- AI-driven brain age estimation offers potential for precision medicine in secondary stroke prevention.
- MBA Net provides a novel tool for assessing neuroimaging-based risk in AICVD patients.
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