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相关概念视频

Vertebral Column: Regions and Curvature01:16

Vertebral Column: Regions and Curvature

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The vertebral column or spine is a flexible column that supports the head, neck, and body and  allows for their movements. It also protects the spinal cord.
Regions of the Vertebral Column
In an adult, the spine is subdivided into five regions: the cervical, the thoracic, the lumbar, the sacral, and the coccygeal region. The spine initially develops as a series of 33 vertebrae; after 20 years of age, the nine bones in the sacral region, five sacral, and four coccygeal bones fuse to form...
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Spinal Cord: Cross-sectional Anatomy01:16

Spinal Cord: Cross-sectional Anatomy

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The cross-sectional anatomy of the spinal cord offers a detailed view of its complex structure and function within the central nervous system. At the core of the spinal cord lies the gray matter, characterized by its butterfly or "H"-shaped appearance in cross-section. This central region is enveloped by white matter, with the overall structure divided into symmetrical halves by the dorsal median sulcus and the ventral median fissure.
Gray Matter and its Components
Central to the gray matter is...
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相关实验视频

Updated: May 5, 2026

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model
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基于深度学习的定量椎脊椎成熟分析.

Fulin Jiang1, Abbas Ahmed Abdulqader2, Yan Yan2

  • 1College of Computer Science, Chongqing University, Chongqing University Three Gorges Hospital, Chongqing, 400044, China.

Head & face medicine
|March 27, 2025
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概括

这项研究引入了一个两阶段的神经网络,用于自动化椎脊椎成熟 (QCVM) 阶段. 该系统提高了地标定位的准确性,并帮助初级牙科专家进行分阶段,增强了临床诊断.

关键词:
人工智能的人工智能是人工智能.自动化地标位置定位侧侧脑图片 侧侧脑图矯正牙科 矯正牙科是一種矯正牙科.定量性宫脊椎成熟 (QCVM) 是指

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Precision Measurements and Parametric Models of Vertebral Endplates
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相关实验视频

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科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 矯正牙科 矯正牙科是一種矯正牙科.

背景情况:

  • 定量椎脊椎成熟 (QCVM) 的分期对于正牙治疗规划至关重要.
  • 目前用于标志性标识和成熟分阶段的方法通常是主观的和耗时的.
  • 深度学习模型在头脑测量分析中处理解剖学变异方面表现有前途.

研究的目的:

  • 开发一种先进的双阶段卷积神经网络 (CVnet),用于精确地定位椎标志.
  • 为了提高定量椎成熟阶段 (QCVM) 的准确性.
  • 评估系统的性能及其对初级牙科专家诊断准确性的影响.

主要方法:

  • 一个双阶段卷积神经网络 (CVnet) 在 2100 个脑表图像上被设计和训练 (训练,验证,测试的比例为 8:1:1).
  • 该系统评估了各种感兴趣的区域 (ROI) 尺寸,以定位19个椎脊柱的地标,并对QCVM阶段进行分类.
  • 用成功检测率和学生t测试来测量里程碑定位的准确性;用6名初级牙科医生测试QCVM诊断的准确性.

主要成果:

  • 最佳的ROI校准产生了0.66±0.46毫米的平均地标定位误差,在2毫米范围内98.10%的成功检测率.
  • 该系统实现了69.52%的QCVM阶段识别精度.
  • 年轻的正牙医在使用该系统时在分期准确度上有10.95%的改善.

结论:

  • 一个两阶段的神经网络有效地自动化了椎脊椎地标识别和QCVM分期.
  • 开发的方法简化了工作流程,并提高了骨成熟度估计的准确性.
  • 该系统为一致的治疗计划提供了有价值的临床支持,特别是对经验较少的从业者来说.