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基于使用细分变压器进行定量敏感度映射的脑年龄预测.

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IEEE journal of biomedical and health informatics

|December 13, 2023

概括

这项研究引入了一个新的AI网络 (STAN) 以使用定量敏感度映射 (QSM) 脑部扫描来预测大脑年龄. 该方法准确估计大脑年龄,并显示了识别高级大脑衰老过程的潜力.

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科学领域:

神经成像是一种神经成像.
人工智能的人工智能
生物标志物发现发现

背景情况:

大脑衰老涉及复杂的结构和功能变化,包括髓和铁沉积的改变.
大脑年龄作为评估个体神经发育进展的定量标记.
定量敏感度映射 (QSM) 对铁和髓敏感,因此非常适合用于大脑年龄估计.

研究的目的:

引入一个创新的3D卷积网络,分段转换器年龄网络 (STAN),用于从QSM数据中预测大脑年龄.
评估基于QSM的大脑年龄预测的准确性和可靠性.

主要方法:

开发一个两阶段的3D卷积网络 (STAN) 用于大脑年龄预测.
培训和测试STAN对712名健康参与者的QSM图像进行了训练和测试.
在健康个体和帕金森病患者中,比较预测的大脑年龄和时间年龄.

主要成果:

在大脑年龄预测方面,STAN模型实现了高准确性,平均绝对误差 (MAE) 为4.124年,R2为0.933.
与健康受试者相比,帕金森病患者在预测和时间大脑年龄之间的差距明显更大 (P<0.01).

结论:

基于QSM的预测大脑年龄是评估大脑衰老的可靠和准确的表型.
这种方法有可能成为探索高级大脑衰老和相关神经疾病的生物标志物.