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基于变压器的多模态MRI融合用于预测月经后年龄和新生儿大脑发育分析

Haiyan Zhao¹, Hongjie Cai¹, Manhua Liu²

¹School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.

Medical image analysis

|March 10, 2024

概括

准确的脑年龄评估对于新生儿发育至关重要. 这项研究引入了一个深度学习框架,使用MRI精确估计月经后年龄 (PMA) 并确定早产婴儿的发育迟缓.

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:

新生儿大脑发育涉及迅速的结构和功能变化,影响未来的认知能力.
准确评估大脑年龄对于确定发育成熟度和预测新生儿病理风险至关重要.
通过磁共振成像 (MRI) 评估新生儿大脑,由于其复杂性,多维性和噪声而存在挑战.

研究的目的:

为准确的月经后年龄 (PMA) 估计和新生儿大脑发育分析提出多模式深度学习框架.
为了提高准确性,利用T2加权结构MRI (T2-sMRI) 和扩散MRI (dMRI) 数据.
通过突出地图提高大脑发育评估的可解释性.

主要方法:

开发了一个双流密集网络,以学习T2-sMRI和dMRI的模式特征.
一个使用自我注意机制的变压器模块集成了用于PMA预测和预期/预期分类的功能.
在大脑模板上生成 Saliency 地图,以可视化和解释模型预测.

主要成果:

该框架在预产新生儿中实现了PMA估计0.5周的平均绝对误差 (MAE).
过早出生的受试者表现出大脑发育的延迟,延迟随着早产的增加而增加.
该方法以95%的准确度准确地分类了按期出生和早产的受试者,突出了显著的群体差异.

结论:

拟议的多模式深度学习框架准确估计新生儿的大脑年龄,并分类早产婴儿.
这种方法为大脑发育轨迹提供了宝贵的见解,并有助于识别与典型成熟的偏差.
这些发现强调了先进的人工智能技术在新生儿神经成像中的潜力,用于早期风险预测和发育监测.

关键词:

卷积神经网络是一种卷积神经网络.多模态核磁共振 (MRI) 是一种多模态核磁共振.月经后的年龄月经后的年龄过早分娩过早分娩是什么变压器变压器变压器

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