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

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

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

Non-invasive imaging in heart transplant recipients: state of art and future direction.

European journal of nuclear medicine and molecular imaging·2026
Same author

A Thematic Analysis of Ethical Issues Faced by Nursing Students in Clinical Practice.

SAGE open nursing·2026
Same author

Incremental Prognostic Value of Subendocardial Myocardial Flow Reserve in Patients With Normal Perfusion.

Circulation·2026
Same author

Advancing digital health literacy in cancer care: Recommendations from two nominal group technique workshops in the TRANSiTION project.

Open research Europe·2026
Same author

Mental Health Screening Tools for Cancer Patients, and Their Caregivers: An Umbrella Review.

Psycho-oncology·2026
Same author

Impact of re-transurethral resection of bladder staging on risk stratification of high-grade T1 non-muscle-invasive bladder cancer across European Association of Urology 2021 risk groups.

BJU international·2026

相关实验视频

Updated: Jul 6, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

人工智能优化癌症成像:用户体验研究

Iman Hesso1, Lithin Zacharias1, Reem Kayyali1

  • 1Pharmacy Department, Faculty of Health, Science, Social Care and Education, Kingston University London, Kingston Upon Thames, United Kingdom.

JMIR cancer
|October 10, 2024
PubMed
概括

医学成像中的人工智能 (AI) 可以改善癌症诊断和治疗. INCISIVE项目开发了一个AI工具箱,收集医疗保健专业人士对更好的临床整合特征和实施障碍的反.

关键词:
德尔菲的方法方法德尔菲方法.强大的AI工具箱用户体验设计研讨会人工智能的人工智能是人工智能.癌症 癌症 癌症 癌症 癌症癌症成像检查 癌症成像检查用户体验用户体验

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

2.7K

相关实验视频

Last Updated: Jul 6, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

2.7K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 欧盟倡议的INCISIVE项目旨在利用人工智能增强癌症成像.
  • 该项目重点是开发人工智能工具箱,以提高诊断准确性,特异性,灵敏性,可解释性和成本效益.

研究的目的:

  • 了解医疗保健专业人员 (HCP) 对INCISIVE AI工具箱的需求,挑战和期望.
  • 确定AI在医学成像中的实施潜在障碍.

主要方法:

  • 这是一项混合方法研究,涉及用户体验 (UX) 设计研讨会和两阶段的Delphi研究.
  • 招聘采用了INCISIVE联盟网络中的有目的抽样策略.
  • 数据分析包括描述性统计 (SPSS) 和定性分析 (NVivo).

主要成果:

  • 研讨会确定了人工智能工具箱所需的功能和实施障碍.
  • 德尔菲研究在特征排名 (W=0.741) 和障碍 (W=0.705) 方面达到了强烈共识.
  • 关键发现突出了AI在诊断,分期,治疗反应预测和护理整合方面的潜力,同时指出了资源有限和数据变化等障碍. 医疗人员强调需要人工智能可解释性.

结论:

  • 该研究为最终用户提供了关于INCISIVE AI工具箱设计和实施的见解.
  • 结果指导AI可解释性特征的发展,用于诊断,分期和治疗/后续服务.
  • 结合最终用户的观点旨在确保INCISIVE AI解决方案满足需求,并推动其在临床实践中的采用.