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相关概念视频

External Anatomy of the Kidney01:21

External Anatomy of the Kidney

505
The kidneys are a pair of bean-shaped organs in the human body that play a critical role in maintaining overall health. They filter out waste products from the blood, regulate blood pressure, maintain electrolyte balance, and stimulate the production of red blood cells.
The kidneys are located in the retroperitoneal space on either side of the vertebral column, protected posteriorly by the 11th and 12th ribs. The right kidney sits slightly lower than the left owing to the presence of the liver...
505
Internal Anatomy of the Kidney01:12

Internal Anatomy of the Kidney

1.1K
The kidneys are essential organs in the human body, performing a myriad of tasks that maintain homeostasis and overall health.
Anatomical Position and Dimensions
The kidneys are retroperitoneal organs positioned against the posterior abdominal wall on either side of the spine, roughly between the twelfth thoracic and third lumbar vertebrae. Each kidney is typically 10-12 cm long, 5-6 cm wide, and 3-4 cm thick, weighing about 150 grams.
Renal Cortex
The outermost region of the kidney is the...
1.1K

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相关实验视频

Updated: May 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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深度学习用于损伤检测:CT图像算法性能和外部验证:实验研究实验研究.

Chien-Hung Liao1, Chun-Hsiang Ouyang1, Chih-Chi Chen1,2,3,4,5,6

  • 1Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou; Chang Gung University, Taoyuan, Taiwan.

International journal of surgery (London, England)
|January 27, 2025
PubMed
概括
此摘要是机器生成的。

一个新的深度学习 (DL) 算法,RenoTrNet,在CT扫描上准确地检测脏创伤. 这种工具在改善临床环境中的创伤诊断方面表现有前途.

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Last Updated: May 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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科学领域:

  • 放射学 放射学是一门学科.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 在CT扫描上检测损伤是具有挑战性的,并且经常被忽视.
  • 深度学习 (DL) 在损伤检测中的应用尚未得到充分探索.
  • 这项研究解决了使用DLL改善脏创伤检测的需要.

研究的目的:

  • 开发和验证用于检测损伤的DL算法.
  • 使用机构创伤数据进行培训.
  • 使用北美放射学会 (RSNA) 数据集进行外部验证.

主要方法:

  • 开发了RenoTrNet,这是一个基于机构数据的DL模型.
  • 使用精度,灵敏度,特异性,PPV和NPV进行评估的性能.
  • 采用热图可视化用于模型解释性.

主要成果:

  • 内部验证:准确度为0.88,灵敏度为0.75,特异性为0.95.
  • 外部RSNA验证:准确度为0.93,灵敏度为0.73,特异性为0.94.
  • 外部验证显示高的NPV (0.98),但较低的PPV (0.45).

结论:

  • RenoTrNet DL算法在CT扫描上显示了损伤检测的高精度.
  • 该模型显示了在现实世界创伤诊断中临床部署的潜力.
  • 优化图像细分和计算效率可以提高临床效用.