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相关概念视频

Adjusting a Traverse01:12

Adjusting a Traverse

48
In the site survey of a four-sided traverse, internal angles are essential to ensure geometric accuracy. The survey revealed that the sum of the measured internal angles was 359 degrees and 48 minutes, which is 12 minutes less than the expected 360 degrees. This discrepancy signals an error likely arising from measurement inaccuracies during the fieldwork.To rectify this error, the adjustment process involved distributing the 12-minute shortfall equally across the four internal angles. By...
48
Design Example: Traverse Angle Computations01:25

Design Example: Traverse Angle Computations

55
Traverse angle computations are a critical component of surveying, used to compute the internal angles within a closed traverse. A traverse consists of a series of connected lines forming a closed loop, often used for land boundary delineation or mapping. Calculating the internal angles ensures accuracy in the traverse geometry and is essential for checking survey data integrity.The process begins with known azimuths and bearings of the traverse sides. Internal angles at each vertex are...
55

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相关实验视频

Updated: Jun 4, 2025

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
05:49

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

714

深度学习算法可实现高精度的自动化科布角测量.

Daichi Hayashi1,2, Nor-Eddine Regnard3,4, Jeanne Ventre4

  • 1Department of Radiology, Chobanian and Avedisian School of Medicine, Boston University, Boston, MA, USA. daichi.alex.hayashi@gmail.com.

Skeletal radiology
|December 17, 2024
PubMed
概括
此摘要是机器生成的。

深度学习可以准确地测量脊柱脊椎病患者的全脊椎放射图上的科布角. 这种人工智能工具显示出高精度,特别是在儿科病例中,有助于评估脊柱形.

关键词:
科布的角度 科布的角度深度学习是一种深度学习.放射图片 放射图片 放射图片脊椎形症是什么? 脊椎形症是什么?

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Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
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Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

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相关实验视频

Last Updated: Jun 4, 2025

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Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

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Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
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科学领域:

  • 放射学 放射学是一门学科.
  • 人工智能在医学中的应用
  • 脊柱成像分析 脊柱成像分析

背景情况:

  • 脊柱脊柱病的管理依赖于从脊柱放射图中准确的科布角测量.
  • 手动测量可能耗时,并且受观察者之间的变化影响.
  • 深度学习 (DL) 为自动化和客观的放射性分析提供了潜力.

研究的目的:

  • 评估深度学习算法的准确性,用于在全脊椎放射图上进行自动化的科布角测量.
  • 在多样化的患者队列中,将DL性能与专家手册注释进行比较.

主要方法:

  • 分析了2岁以上的患者的全脊椎放射图.
  • 科布的角度由三个专家肌肉骨放射科医生/骨科外科医生手动注释.
  • 基本真相是通过注释者之间的共识或协议建立的.
  • 一个深度学习软件 (BoneMetrics, Gleamer) 用于自动测量.
  • 使用平均绝对误差 (MAE) 与手动注释相比,对准确性进行了评估.

主要成果:

  • 这项研究包括345名患者 (179名儿科,166名成人).
  • 对于主要曲率,DL算法实现了2.6°的平均绝对误差 (MAE).
  • 儿科患者的准确性更高,MAE为1.9°,而成年人则为3.3°.

结论:

  • 深度学习算法在预测脊椎病患者的科布角度方面表现出高准确度.
  • 自动化DL测量可以可靠地评估脊柱形,可能提高临床实践的效率和一致性.