在MRI上使用深度神经网络来确定早产和成熟婴儿的区域大脑生长与年龄的关系
Farzad Beizaee1,2, Michele Bona3, Christian Desrosiers3
1Software and IT Department, École de Technologie Supérieure, Montreal, QC, H3C 1K3, Canada. farzad.beizaee.1@ens.etsmtl.ca.
Scientific reports
|August 15, 2023
概括
这项研究引入了一个可解释的深度学习工具,使用MRI扫描来准确评估新生儿大脑发育. 该模型预测了月经后的年龄,有助于评估早产婴儿的大脑成熟.
科学领域:
- 神经成像是一种神经成像.
- 发育神经科学的发展神经科学.
- 人工智能在医学中的应用
背景情况:
- 新生儿MRI对于评估早产婴儿大脑发育变得越来越重要.
- 手动分析这些复杂的数据往往是不可行的.
- 需要可解释的AI工具来帮助放射科医生评估大脑成熟.
研究的目的:
- 开发一种可解释的深度学习管道,用于预测新生儿在扫描时的月经后年龄.
- 提供对早产婴儿大脑发育的定量测量.
- 为了提高AI模型在临床神经成像中的可解释性.
主要方法:
- 训练了一个深度神经网络,将新生儿大脑分成87个区域,使用dHCP研究中的T2加权MRI数据.
- 从细分区域提取特征用于大脑年龄估计.
- 利用回归模型来预测月经后年龄,达到高准确度.
主要成果:
- 拟议的框架准确地预测了月经后的年龄,平均绝对误差为0.46周.
- 该模型的性能超过了最近的,更复杂的方法.
- 确定了额叶和额叶叶作为新生儿大脑年龄估计的关键结构.
结论:
- 一种可解释的深度学习方法可以从结构性MRI中可靠地预测新生儿的大脑年龄.
- 该工具提供了一种快速和定量方法,用于评估早产婴儿的大脑成熟度.
- 这些发现强调了特定大脑区域在发展轨迹评估中的重要性.
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