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相关概念视频

Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

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相关实验视频

Updated: May 14, 2026

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
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基于深度卷积神经网络的正牙诱导外部根吸收的自动分级系统.

Shuxi Xu1,2,3, Houli Peng1,2,3, Lanxin Yang1,2,3

  • 1College of Stomatology, Chongqing Medical University, Chongqing, 401147, China.

Journal of imaging informatics in medicine
|February 23, 2024
PubMed
概括

深度学习模型在 ортодонтической治疗后准确地评分牙根吸收. 使用EfficientNet-B4的自动化系统实现了94%的准确性,超过了人类正牙医,并帮助临床诊断.

关键词:
人工智能的人工智能是人工智能.卷积神经网络是一种卷积神经网络.诊断系统的诊断系统.矯正牙科 矯正牙科是一種矯正牙科.根的再吸收方式

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科学领域:

  • 牙科 牙科是指牙科的专业.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • ортодонтически诱导的外部根吸收 (OIERR) 是经常发生的 ортодонтической治疗的并发症.
  • 准确的OIERR分级对于及时的临床管理和治疗调整至关重要.

研究的目的:

  • 开发和评估深层卷积神经网络 (CNN) 用于自动化OIERR分级.
  • 在临床实践中构建可靠的基于人工智能的OIERR评估系统.

主要方法:

  • 六个预先训练的CNN (EfficientNet变体和MobileNet-V3) 在2146个牙切片上进行了训练和验证.
  • 通过交叉验证来评估性能,并与正牙医的评估进行比较.
  • 使用Grad-CAM可视化模型决策领域.

主要成果:

  • 所有六个CNN模型在OIERR分级中都表现出高性能,平均准确率为0.92.
  • 开发的人工智能系统超过了正牙医的平均精度 (0.82).
  • 使用五倍交叉验证的EfficientNet-B4实现了最高准确度 (0.94),确定了尖顶区域为关键.

结论:

  • 在OIERR评分方面,CNN表现出色,表现优于人类专家.
  • 拟议的人工智能系统为正牙医提供了一个有希望的,可靠的诊断支持工具.
  • 自动化OIERR分级可以提高临床决策和患者护理.