Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Contrast-enhanced ultrasound in critical care: a problem-solving approach.

Frontiers in medicine·2026
Same author

Preferential nitrogen retention characterizes current eutrophication in United States lakes.

Nature communications·2026
Same author

Power from Potential: A Survey of Electrostatic Actuators for Haptics.

IEEE transactions on haptics·2026
Same author

NIPT-based prenatal screening of maternal Xq28 copy number variations in a cohort of 80,371 pregnancies.

Archives of gynecology and obstetrics·2026
Same author

Prevalence of pre-existing neutralizing antibodies to AAV5 and AAV8 in patients with Wilson's disease.

Molecular therapy. Advances·2026
Same author

Au anchoring on 3D hierarchically porous Ti<sub>3</sub>C<sub>2</sub>T <sub><i>x</i></sub> MXene aerogels for enhanced selectivity, response and kinetics in room-temperature trimethylamine sensing.

RSC advances·2026

相关实验视频

Updated: Jan 12, 2026

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
06:56

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

2.8K

边缘引导深度学习模型使用MRI预测胎儿大脑年龄

Haitao Gan1, Qingsong Gao1, Zhi Yang1

  • 1School of Computer Science, Hubei University of Technology, Wuhan, China.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
|November 4, 2025
PubMed
概括

一个新的深度学习模型使用MRI准确地预测胎儿大脑年龄,并结合了边缘细节以提高精度. 这种方法与临床医生的表现相美,有助于精确的胎儿发育评估.

关键词:
这就是为什么MRI是MRI.深度学习是一种深度学习.边缘信息 边缘信息胎儿大脑年龄 胎儿大脑年龄

更多相关视频

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.4K
Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy
08:49

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

4.2K

相关实验视频

Last Updated: Jan 12, 2026

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
06:56

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

2.8K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.4K
Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy
08:49

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

4.2K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 胎儿的发育 胎儿的发育

背景情况:

  • 对于胎儿大脑年龄预测的深度学习模型往往忽略了局部边缘细节,可能会限制准确性.
  • 现有的方法可能无法从MRI扫描中完全捕捉胎儿大脑发育的细微差别.

研究的目的:

  • 开发一种新的深度学习模型,集成全球前沿信息,以提高胎儿大脑年龄的预测.
  • 从MRI数据提高胎儿年龄评估的准确性和可靠性.

主要方法:

  • 使用了1630个胎儿大脑MRI扫描 (妊娠22-38周) 的回顾性数据集.
  • 使用平均绝对误差 (MAE) 和R平方 (R2) 训练和优化了结合全球边缘信息的神经网络.
  • 数据集被分为训练 (4/5) 和测试 (1/5) 集,用于模型验证.

主要成果:

  • 边缘引导的深度学习模型实现了高精度,平均绝对误差 (MAE) 为0.79周,R2值为0.94.
  • 该模型与现有的胎儿年龄预测方法相比,表现优越.
  • 该方法增强了用于胎儿年龄估计的回归模型的稳定性和可靠性.

结论:

  • 提出的边缘引导深度学习模型显著优于现有的胎儿大脑年龄预测方法.
  • 这种新的方法为准确的临床评估胎儿大脑发育提供了有价值的工具.