在患有状细胞贫血的儿童中,区域大脑年龄下降
Amy E Mirro1, Landon Christian Power2, Jinli Wang1
1Washington University in St. Louis, Saint Louis, Missouri, United States.
Blood advances
|December 1, 2025
概括
机器学习工具DeepBrainNet揭示了状细胞贫血 (SCA) 儿童的区域大脑年龄下降,这表明大脑发育发生了改变. 该工具还使用MRI扫描准确地分类SCA存在.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 状细胞贫血 (SCA) 可以影响大脑发育.
- 通过机器学习计算的大脑年龄差距,可以作为大脑健康的生物标志物.
研究的目的:
- 在SCA儿童中测试DeepBrainNet机器学习工具.
- 与对照组相比,调查SCA儿童是否表现出较年轻的预测大脑年龄.
- 评估DeepBrainNet使用MRI数据对SCA进行分类的能力.
主要方法:
- DeepBrainNet被用来预测210个儿童的大脑MRI (90个SCA,40个控制) 的大脑年龄.
- 计算了全球和区域大脑年龄差距,并在各组之间进行了比较.
- DeepBrainNet 的架构被修改,以创建用于 SCA 检测的分类器.
主要成果:
- 在SCA和对照组之间,全球大脑年龄差距没有显著差异.
- 与对照组相比,在没有中风史的SCA儿童中观察到区域性减少的大脑年龄差距 (-1.26年,p=0.021).
- 经过修改的DeepBrainNet模型在分类没有中风史的SCA中获得了69%的准确性和0.7 AUC.
结论:
- 没有中风史的SCA儿童在脑年龄方面呈现区域性下降,表明大脑发育发生变化.
- DeepBrainNet可以有效地使用标准MRI序列对儿童进行SCA分类.
- 机器学习工具显示出在SCA中改善风险预测和治疗评估的潜力.
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