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相关概念视频

Hormones and Bone Tissue01:17

Hormones and Bone Tissue

The endocrine system produces and secretes hormones, which interact with the skeletal system. These hormones control bone growth, maintain bone once it is formed, and remodel it.
Hormones That Influence Osteoblasts and/or Maintain the Matrix
Several hormones are necessary for controlling bone growth and maintaining the bone matrix. The pituitary gland secretes growth hormone (GH), which, as its name implies, controls bone growth. This happens in several ways: first, it triggers chondrocyte...

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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SEPoolConvNeXt:一种深度学习框架,用于使用T1和T2加权MRI自动分类新生儿大脑发育.

Gulay Maçin1, Melahat Poyraz2, Zeynep Akca Andi3

  • 1Department of Radiology, Beyhekim Training and Research Hospital, Konya 42090, Turkey.

Journal of clinical medicine
|October 29, 2025
PubMed
概括
此摘要是机器生成的。

一个新的深度学习模型,SEPoolConvNeXt,使用MRI扫描准确地分类新生儿大脑发育,优于现有方法. 这种人工智能工具有助于早期发现婴儿发育异常.

关键词:
用T1加权成像技术进行成像.用T2加权成像技术进行成像.大脑发育大脑的发育大脑的发展卷积神经网络 (CNN) 是一种神经网络.深度学习是一种深度学习.磁共振成像 (MRI) 的使用.

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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:

  • 新生儿和婴儿的大脑发育涉及快速的髓化和皮质成熟,对于评估规范轨迹和检测异常至关重要.
  • 使用MRI对大脑发育进行自动分类是具有挑战性的,因为有重叠的发育阶段和性别特异的变异.

研究的目的:

  • 介绍SEPoolConvNeXt,这是一个新的深度学习框架,用于细粒度分类新生儿大脑发育.
  • 评估SEPoolConvNeXt的性能与已建立的卷积神经网络 (CNN) 模型相比,使用T1和T2加权的MRI序列.

主要方法:

  • 开发了SEPoolConvNeXt,集成剩余连接,分组卷积,并引导注意力进行高效和差别分析.
  • 使用了29516张MRI图像的数据集,分为四个子组 (T1男性,T1女性,T2男性,T2女性),分为14个年龄类 (0-12个月).
  • 在相同的实验条件下,比较了SEPoolConvNeXt与19个预训练CNN的性能.

主要成果:

  • SEPoolConvNeXt的测试准确度始终在95%以上,明显超过了基线CNN (平均约70.7%).
  • 在所有子组中观察到高精度,T2女性达到99.47%-100%,T1男性超过98%.
  • 该模型表现出强大的概括性,大多数子组在组合评估中超过98-99%的准确性.

结论:

  • SEPoolConvNeXt为评估新生儿大脑成熟提供了一个强大,高效和生物相关的框架.
  • 该模型能够整合性别和年龄特定的发育轨迹,为人工智能辅助的神经发育评估提供了基础.
  • 这种方法对临床翻译有希望,特别是在监测高风险婴儿 (如早产婴儿) 时.