Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

180
The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
180
Acute Respiratory Failure-II01:21

Acute Respiratory Failure-II

222
Type I Respiratory Failure, or hypoxemic respiratory failure, occurs when the partial pressure of oxygen (PaO2) in arterial blood falls below 60 mmHg while breathing room air without a corresponding increase in arterial carbon dioxide levels (PaCO2). This condition highlights a significant impairment in the lungs' capacity to oxygenate the blood.
The underlying physiological abnormalities that contribute to hypoxemic respiratory failure include:
222
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

237
Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
237

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Do Multimodal Vision-Language Models Enhance the Medical Diagnostic Process? A Systematic Review.

Healthcare (Basel, Switzerland)·2026
Same author

Association Between Veno-Venous Extracorporeal Membrane Oxygenation and Right Ventricular Dysfunction in Acute Respiratory Distress Syndrome Patients: A Multicenter Retrospective Propensity-Matched Study.

ASAIO journal (American Society for Artificial Internal Organs : 1992)·2026
Same author

Deep learning-derived pericardial adipose tissue by electrocardiogram-gated cardiac computed tomography predicts cardiovascular events beyond coronary calcium score.

American journal of preventive cardiology·2026
Same author

A call for action: The need to quantify the "-itis" in primary sclerosing cholangitis.

Journal of hepatology·2026
Same author

MRI Deep Learning for Differentiating Glioblastoma, IDH Wild-type from Central Nervous System Diffuse Large B-cell Lymphoma.

Cancer research communications·2026
Same author

Assessment of Survival and the Decision to Engage in Palliative Care when Facing a Defeat in the ICU.

Medical decision making : an international journal of the Society for Medical Decision Making·2026

相关实验视频

Updated: Jun 29, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.2K

自动ARDS监控与胸部X射线识别使用卷积神经网络.

Run Zhou Ye1, Kirill Lipatov2, Daniel Diedrich3

  • 1Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA.; Division of Endocrinology, Department of Medicine, Centre de Recherche du CHUS, Sherbrooke QC J1H 5N4, Canada.

Journal of critical care
|March 29, 2024
PubMed
概括

一个深度学习模型准确地区分肺炎,ARDS和正常肺在胸部X射线上. 这种卷积神经网络 (CNN) 显示了急性呼吸困扰综合征 (ARDS) 快速识别的前景.

关键词:
在 ARDS 中,ARDS 是指 ARDS 的类型.卷积神经网络是一种卷积神经网络.机器学习 机器学习放射学 放射学是一门学科.

更多相关视频

Surfactant Depletion Combined with Injurious Ventilation Results in a Reproducible Model of the Acute Respiratory Distress Syndrome ARDS
06:22

Surfactant Depletion Combined with Injurious Ventilation Results in a Reproducible Model of the Acute Respiratory Distress Syndrome ARDS

Published on: April 7, 2021

3.4K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.4K

相关实验视频

Last Updated: Jun 29, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.2K
Surfactant Depletion Combined with Injurious Ventilation Results in a Reproducible Model of the Acute Respiratory Distress Syndrome ARDS
06:22

Surfactant Depletion Combined with Injurious Ventilation Results in a Reproducible Model of the Acute Respiratory Distress Syndrome ARDS

Published on: April 7, 2021

3.4K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.4K

科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 肺部医学 肺部医学

背景情况:

  • 区分肺炎,ARDS和正常肺部对于患者管理至关重要.
  • 目前的诊断方法可能耗时,延迟治疗.

研究的目的:

  • 设计,验证和评估用于区分胸部X射线的深度学习模型.
  • 该模型旨在准确地将图像分为肺炎,ARDS或正常类别.

主要方法:

  • 一项诊断性能研究使用了来自重症监护室患者的15899张胸部X射线图像.
  • 一个两步卷积神经网络 (CNN) 管道被开发和测试.

主要成果:

  • 在区分三个肺部模式方面,CNN模型实现了高灵敏度 (91.8%-97.8%) 和特异性 (96.6%-98.8%).
  • 在单独的数据集上验证显示,ARDS识别的灵敏度为96.3%,特异性为96.6%.

结论:

  • 基于胸部X射线模式识别的深度学习可以帮助区分ARDS和正常肺部.
  • 与基于文本的监控工具相比,CNN模型有可能实现更快的ARDS识别.