基于深度学习的大脑年龄预测了急性缺血性脑血管疾病中中风复发的情况
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
概括
大脑年龄差距 (BAG) 预测急性缺血性脑血管疾病 (AICVD) 患者中风复发. 这种新的生物标志物,来自基于人工智能的大脑年龄估计,有助于二次中风预防策略.
科学领域:
- 神经学 神经学
- 放射学 放射学是指放射学
- 人工智能在医学中的应用
背景情况:
- 急性缺血性脑血管疾病 (AICVD) 的复发率很高,需要更好的风险分层生物标志物.
- 目前基于MRI的脑年龄对于AICVD复发的预后价值尚未确定.
研究的目的:
- 开发和验证一个深度学习模型 (MBA Net) 来估计AICVD患者的大脑年龄.
- 评估大脑年龄差距 (BAG) 的预后效用,用于预测AICVD中风复发.
主要方法:
- 开发了基于面具的脑年龄估计网络 (MBA Net),使用T2-FLAIRMRI预测非心脏病发作区域的背景脑年龄 (CBA).
- 在健康个体 (n=5353) 上接受了MBA Net培训,并应用于多中心AICVD队列 (n=10,890).
- 计算BAG作为CBA和时间表年龄之间的差异.
主要成果:
- 在AICVD后3个月和5年,BAG独立预测了中风复发.
- 与单独的时间年龄相比,BAG显示出更好的预后性能.
- 将BAG集成到现有模型中显著提高了它们对复发的辨别能力.
结论:
- 大脑年龄差距 (BAG) 是AICVD中中风复发的有希望的独立预测因素.
- 人工智能驱动的大脑年龄估计为二次中风预防提供了精准医学的潜力.
- MBA Net提供了一种用于评估AICVD患者基于神经成像的风险的新工具.
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