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相关概念视频

Computed Tomography01:10

Computed Tomography

4.4K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Ultrasonography01:17

Ultrasonography

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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
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相关实验视频

Updated: Jun 21, 2025

Author Spotlight: Developing a Bedside Protocol for Kidney and Genitourinary Ultrasonography
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Author Spotlight: Developing a Bedside Protocol for Kidney and Genitourinary Ultrasonography

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一个基于深度学习的新型人工智能系统,用于解释计算机断层扫描中的结石病.

Jin Kim1, Chan Woo Kwak2, Saangyong Uhmn1

  • 1Department of Computer Engineering, Hallym University, Chuncheon, South Korea.

European urology focus
|July 13, 2024
PubMed
概括

在CT扫描中检测结石的人工智能 (AI) 系统在现实世界急诊室设置中显示了94%的准确性. 这种人工智能在诊断和石材参数计算的速度上明显优于人类专家.

关键词:
人工智能的人工智能是人工智能.深度学习是一种深度学习.诊断成像诊断成像的使用数字信息 数字信息.目标检测检测的目标检测.断层扫描 (Tomography) 是一个专业的技术.乌罗石质病是一种质病.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 泌尿器科 泌尿器科 泌尿器科 泌尿器科

背景情况:

  • 尿病的诊断依赖于计算机断层扫描 (CT) 扫描.
  • 精确的石材参数计算 (体积,密度) 对于处理决策至关重要.
  • 当前的诊断方法对于急诊室 (ER) 场景可能耗时.

研究的目的:

  • 开发一种人工智能 (AI) 系统,用于CT图像中检测尿病.
  • 使用深度学习实现石材参数 (体积,密度) 的实时计算.
  • 将人工智能系统的性能与急诊室泌尿科医生的性能进行比较.

主要方法:

  • 一个深度学习模型 (YOLOv4架构) 在39,433个轴向CT图像上进行了训练.
  • 数据集分为培训 (70%),内部验证 (10%) 和测试 (20%) 集.
  • 在100张ER CT图像上使用图形处理单元 (GPU) 进行了外部验证.

主要成果:

  • 人工智能系统在验证集上达到95%的准确率,在外部ER验证上达到94%的准确率.
  • 该系统表现出高速度,在13秒内分析了150张CT图像,比人类专家快得多.
  • 人工智能实时计算石块体积需要0.2秒,而泌尿科医生需要77秒.

结论:

  • 开发的AI系统在临床环境中提供精确 (94%) 和快速检测尿病.
  • 人工智能系统显示了在ER环境中提高诊断效率的潜力.
  • 在消费级GPU上的高级深度学习可以促进快速的尿病诊断.