Automated medical diagnostic image quality control using AI-based techniques
使用基于人工智能的技术进行自动化医疗诊断图像质量控制
基本信息
- 批准号:570437-2021
- 负责人:
- 金额:$ 15.08万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Alliance Grants
- 财政年份:2021
- 资助国家:加拿大
- 起止时间:2021-01-01 至 2022-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Computed Tomography (CT) and Magnetic resonance imaging (MRI) images are critical diagnostic tools routinely use for diagnosis, treatment planning, and monitoring disease progression. CT and MRI images are particularly critical for evaluating the damage caused to the lungs, brain, and blood vessels, particularly as we are continuing to discover the systemic effects that Covid -19 infections have on the body. The accuracy and timeliness of patient diagnosis on radiology scanners is a direct function of the quality of the images produced during a patient scan. Poor quality images can result in a missed or incorrect diagnosis. In the best-case scenario, an image acquired by a mis-calibrated scanner will result in a costly, time consuming, repeated patient scan, and if undetected it can result in a missed diagnosis which may seriously risk patients' health and, in some cases, lead to death. The overall goal of the proposed research is to develop a novel solution for performing automated Quality Control (QC), utilizing AI-based analysis of clinical images. Through a partnership with the University of British Columbia (UBC), Advanced Quality Systems (AQS), and Interior Health Authority (IHA), the proposed solution will use artificial intelligence (AI)-based analysis of clinical images that will directly increase the ability and availability of Canadian CT and MRI scanners to be used in patient diagnosis, treatment, research and development. As the Canada recovers from the Covid-19 pandemic, this research will help address the increased need for rapid, accurate clinical diagnosis during a period of reduced healthcare staffing levels imposed by infection control measures and staff burnout.
计算机断层扫描(CT)和磁共振成像(MRI)图像是重要的诊断工具,通常用于诊断、治疗计划和监测疾病进展。CT和MRI图像对于评估对肺、脑和血管造成的损害尤为关键,特别是在我们不断发现新冠肺炎感染对身体造成的全身影响的情况下。放射学扫描仪上患者诊断的准确性和及时性是患者扫描过程中产生的图像质量的直接函数。质量差的图像可能会导致漏诊或误诊。在最好的情况下,由错误校准的扫描仪获取的图像将导致昂贵、耗时、重复的患者扫描,如果未被检测到,可能会导致漏诊,这可能会严重威胁患者的健康,在某些情况下,还会导致死亡。拟议研究的总体目标是开发一种新的解决方案,利用基于人工智能的临床图像分析来执行自动质量控制(QC)。通过与不列颠哥伦比亚大学(UBC)、高级质量系统(AQS)和内政部卫生局(IHA)的合作,拟议的解决方案将使用基于人工智能(AI)的临床图像分析,这将直接提高加拿大CT和MRI扫描仪用于患者诊断、治疗、研究和开发的能力和可用性。随着加拿大从新冠肺炎大流行中恢复过来,这项研究将有助于解决由于感染控制措施和工作人员倦怠而导致的医疗人员编制减少期间对快速、准确临床诊断的日益增长的需求。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Feldman, Rebecca其他文献
Immunohistochemistry-Enabled Precision Medicine
- DOI:
10.1007/978-3-030-16391-4_4 - 发表时间:
2019-01-01 - 期刊:
- 影响因子:0
- 作者:
Gatalica, Zoran;Feldman, Rebecca;Spetzler, David - 通讯作者:
Spetzler, David
Appendix-derived Pseudomyxoma Peritonei (PMP) Molecular Profiling Toward Treatment of a Rare Malignancy
- DOI:
10.1097/coc.0000000000000376 - 发表时间:
2018-08-01 - 期刊:
- 影响因子:2.6
- 作者:
Gleeson, Elizabeth M.;Feldman, Rebecca;Bowne, Wilbur B. - 通讯作者:
Bowne, Wilbur B.
Molecular profiling of head and neck squamous cell carcinoma.
- DOI:
10.1002/hed.24290 - 发表时间:
2016-04 - 期刊:
- 影响因子:2.9
- 作者:
Feldman, Rebecca;Gatalica, Zoran;Knezetic, Joseph;Reddy, Sandeep;Nathan, Cherie-Ann;Javadi, Nader;Teknos, Theodoros - 通讯作者:
Teknos, Theodoros
An integrated public health response to an outbreak of Murray Valley encephalitis virus infection during the 2022-2023 mosquito season in Victoria.
在维多利亚州2022-2023蚊子季期间,对默里河谷脑炎病毒感染的爆发的综合反应。
- DOI:
10.3389/fpubh.2023.1256149 - 发表时间:
2023 - 期刊:
- 影响因子:5.2
- 作者:
Braddick, Maxwell;O'Brien, Helen M.;Lim, Chuan K.;Feldman, Rebecca;Bunter, Cathy;Neville, Peter;Bailie, Christopher R.;Butel-Simoes, Grace;Jung, Min-Ho;Yuen, Aidan;Hughes, Nicole;Friedman, N. Deborah - 通讯作者:
Friedman, N. Deborah
Impact of gender and mutational differences in hormone receptor expressing non-small cell lung cancer.
- DOI:
10.3389/fonc.2023.1215524 - 发表时间:
2023 - 期刊:
- 影响因子:4.7
- 作者:
Hsu, Robert;Chen, Denaly;Xia, Bing;Feldman, Rebecca;Cozen, Wendy;Raez, Luis E.;Borghaei, Hossein;Kim, Chul;Nagasaka, Misako;Mamdani, Hirva;Vanderwalde, Ari M.;Lopes, Gilberto;Socinski, Mark A.;Wozniak, Antoinette J.;Spira, Alexander I.;Liu, Stephen V.;Nieva, Jorge J. - 通讯作者:
Nieva, Jorge J.
Feldman, Rebecca的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Feldman, Rebecca', 18)}}的其他基金
Image acquisition and analysis tools for magnetic resonance imaging near brain injury
用于脑损伤附近磁共振成像的图像采集和分析工具
- 批准号:
RGPIN-2020-06005 - 财政年份:2022
- 资助金额:
$ 15.08万 - 项目类别:
Discovery Grants Program - Individual
Image acquisition and analysis tools for magnetic resonance imaging near brain injury
用于脑损伤附近磁共振成像的图像采集和分析工具
- 批准号:
RGPIN-2020-06005 - 财政年份:2021
- 资助金额:
$ 15.08万 - 项目类别:
Discovery Grants Program - Individual
Image acquisition and analysis tools for magnetic resonance imaging near brain injury
用于脑损伤附近磁共振成像的图像采集和分析工具
- 批准号:
RGPIN-2020-06005 - 财政年份:2020
- 资助金额:
$ 15.08万 - 项目类别:
Discovery Grants Program - Individual
Image acquisition and analysis tools for magnetic resonance imaging near brain injury
用于脑损伤附近磁共振成像的图像采集和分析工具
- 批准号:
DGECR-2020-00444 - 财政年份:2020
- 资助金额:
$ 15.08万 - 项目类别:
Discovery Launch Supplement
Localized surface gradient coil for carotid plaque compostion identification
用于颈动脉斑块成分识别的局部表面梯度线圈
- 批准号:
347915-2007 - 财政年份:2008
- 资助金额:
$ 15.08万 - 项目类别:
Postgraduate Scholarships - Doctoral
Localized surface gradient coil for carotid plaque compostion identification
用于颈动脉斑块成分识别的局部表面梯度线圈
- 批准号:
347915-2007 - 财政年份:2007
- 资助金额:
$ 15.08万 - 项目类别:
Postgraduate Scholarships - Doctoral
相似国自然基金
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
- 批准号:
- 批准年份:2024
- 资助金额:万元
- 项目类别:外国青年学者研究基金项目
基于循证医学本体论的临床元数据语言研究
- 批准号:30972549
- 批准年份:2009
- 资助金额:24.0 万元
- 项目类别:面上项目
完善城镇居民基本医疗保险的"基本医疗服务包"研究
- 批准号:70873131
- 批准年份:2008
- 资助金额:24.0 万元
- 项目类别:面上项目
基于计算模型的医用X线最优曝光控制技术的研究
- 批准号:60472004
- 批准年份:2004
- 资助金额:26.0 万元
- 项目类别:面上项目
相似海外基金
Non-invasive Condition Monitoring of Ventricular Assistive Devices Using Automated Advanced Acoustic Methods
使用自动化先进声学方法对心室辅助装置进行无创状态监测
- 批准号:
10629554 - 财政年份:2023
- 资助金额:
$ 15.08万 - 项目类别:
Optimization and Validation of a Cost-effective Image-Guided Automated Extracapsular Extension Detection Framework through Interpretable Machine Learning in Head and Neck Cancer
通过可解释的机器学习在头颈癌中优化和验证具有成本效益的图像引导自动囊外扩展检测框架
- 批准号:
10648372 - 财政年份:2023
- 资助金额:
$ 15.08万 - 项目类别:
Automated enhancement and correction of brain MRI images that leverages the entire imaging exam
利用整个成像检查自动增强和校正脑部 MRI 图像
- 批准号:
10760852 - 财政年份:2023
- 资助金额:
$ 15.08万 - 项目类别:
Automated Sonographic Detection of Pulmonary Embolism Using Machine Learning Algorithm
使用机器学习算法自动超声检测肺栓塞
- 批准号:
10741242 - 财政年份:2023
- 资助金额:
$ 15.08万 - 项目类别:
Developing and Evaluating Multi-Modal Clinical Diagnostic Reasoning Models for Automated Diagnosis Generation
开发和评估用于自动诊断生成的多模式临床诊断推理模型
- 批准号:
10724044 - 财政年份:2023
- 资助金额:
$ 15.08万 - 项目类别:
Automated High-purity Exosome isolation-based AD diagnostics system (AHEADx)
基于自动化高纯度外泌体分离的 AD 诊断系统 (AHEADx)
- 批准号:
10738697 - 财政年份:2023
- 资助金额:
$ 15.08万 - 项目类别:
Automated medical diagnostic image quality control using AI-based techniques
使用基于人工智能的技术进行自动化医疗诊断图像质量控制
- 批准号:
570437-2021 - 财政年份:2022
- 资助金额:
$ 15.08万 - 项目类别:
Alliance Grants
M-ISIC: A Multimodal Open-Source International Skin Imaging Collaboration Informatics Platform for Automated Skin Cancer Detection
M-ISIC:用于自动皮肤癌检测的多模式开源国际皮肤成像协作信息学平台
- 批准号:
10528944 - 财政年份:2022
- 资助金额:
$ 15.08万 - 项目类别:
M-ISIC: A Multimodal Open-Source International Skin Imaging Collaboration Informatics Platform for Automated Skin Cancer Detection
M-ISIC:用于自动皮肤癌检测的多模式开源国际皮肤成像协作信息学平台
- 批准号:
10689201 - 财政年份:2022
- 资助金额:
$ 15.08万 - 项目类别:
An automated system to differentiate Kawasaki disease from febrile illness with real life clinical datasets in New York City
利用纽约市真实临床数据集区分川崎病和发热性疾病的自动化系统
- 批准号:
10477176 - 财政年份:2022
- 资助金额:
$ 15.08万 - 项目类别: