双模态深度学习与域内训练和关注婴儿大脑髓化预测
Mamilla Sri Harshitha1, Mythri G1, Anju Thomas1
1Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, 620015, Tiruchirappalli, Tamil Nadu, India.
Neuroinformatics
|February 18, 2026
概括
本研究引入了一种深度学习模型,用于使用MRI进行自动化髓成熟评估. 该框架准确地预测了髓发育,为儿科神经成像中手动评估提供了更快,更可靠的替代方案.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 发育神经科学的发展神经科学.
背景情况:
- 髓成熟对大脑发育至关重要,但难以准确评估.
- 目前用于骨髓蛋白评估的手动方法耗时,并且存在观察者间的变化.
研究的目的:
- 开发和验证一个新的深度学习框架,用于自动化骨髓成熟度评估.
- 提高小儿神经成像中髓进展评估的准确性和效率.
主要方法:
- 一个双输入深度学习框架,利用T1和T2加权的MRI模式.
- 在域内训练的DenseNet121功能提取与注意力机制用于增强功能优先级.
- 多模态MRI数据的早期融合,其次是髓年龄预测的回归.
主要成果:
- 实现了高精度,平均绝对误差为1.18个月,皮尔森相关系数为0.98.
- 具有0.96的确定系数 (R2) 和0.98.98的一致性相关系数 (CCC) 的强大表现.
- 使用Grad-CAM的视觉解释性证实了对临床相关的大脑区域的关注.
结论:
- 拟议的深度学习模型为髓成熟提供了准确和可解释的预测.
- 这个框架有可能被整合到儿科神经成像诊断的临床实践中.
- 自动化评估比传统的手动评估方法有显著的改进.
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