Novel WEB Decision Support System for cardiac image interpretation and reporting

用于心脏图像解释和报告的新型 WEB 决策支持系统

基本信息

  • 批准号:
    8219341
  • 负责人:
  • 金额:
    $ 103.02万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2011
  • 资助国家:
    美国
  • 起止时间:
    2011-02-07 至 2014-01-31
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): Today's cardiac imaging field requires diagnosticians to master an ever-expanding knowledge base (KB) while the time to master this knowledge, apply it to specific tasks and reimbursement are steadily shrinking. These constraints pose a serious healthcare problem that inevitably leads to physician's errors. Thus new tools are required to assist physicians to timely apply comprehensive, up-to-date objective knowledge and the available patient data to specific clinical problems. The long-term objective of our Fast-Track proposal is to improve the care of cardiac patients and reduce the cost of cardiac image interpretation by developing new tools for a WEB-accessible cardiac toolbox that provides decision support to increase the accuracy of detecting coronary heart disease (CHD). Specifically, we propose a WEB-based system where acquired ECG-gated myocardial perfusion SPECT (MPS) raw images are uploaded to be automatically reconstructed and analyzed to extract regional quantitative parameters of myocardial perfusion and function. These parameters are converted to certainty factors of abnormality and submitted to an imaging decision support system (DSS) that is continuously updated with the latest scientific/clinical knowledge to reach an impression of the patient's heart status. These conclusions reached by the DSS and justifications for each conclusion are used to automatically generate a web-based structured report for the diagnostician to easily review, learn from the justifications, and either modify and/or approve for optimal accuracy of the diagnosis and prognosis of CHD. Specifically we propose to: 1) develop a novel left ventricular (LV) quantitative algorithm that automatically extracts parameters of left LV regional perfusion and function used to diagnose CHD; 2) develop the LV expert (LVX) DSS and 3) design and implement the LV quantification and DSS algorithms using the .net platform so that they can be integrated into our Syntermed infrastructure and deployed over the web and/or used as conventional stand- alone work-stations. In Phase I we will develop a proof-of-principle system where LV perfusion information from MPS studies is analyzed and DSS interpreted for automatic report generation and physician review. In Phase II, the system will be: a) extended to include quantification and DSS of myocardial function, ischemia, viability and clinical risk factors, b) extended to include a methodology to continuously update LVX's KB, c) automated to link all the reconstruction, processing, quantification, interpretation, and reporting applications, and d) deployed in .net on the web with database and eCommerce accounting capability. Using this process we expect to confirm our primary hypothesis that diagnosticians using our decision support will provide a faster, more accurate diagnosis and prognosis of CHD than those provided by the same diagnosticians without the aid of this system. The system will be commercialized using Syntermed's successful strategy of other Emory software through: 1) licensing to major instrumentation manufacturers, 2) direct sales to clients that use PC workstations and 3) per WEB-access fee using the existing Syntermed Live network. PUBLIC HEALTH RELEVANCE: Physicians are required to master an ever-expanding knowledge base and take into account an ever increasing amount of patient-specific clinical information while time available to master this knowledge base and apply it to specific tasks is steadily shrinking. There is also an increasing shortage of cardiac diagnosticians [Fye04] who primarily interpret nuclear cardiology studies and an ever increasing number of aging "Baby Boomers" who are becoming patients [Kni02]. This project is to develop software tools that will use the latest pertinent clinical and imaging knowledge from the medical literature and domain experts and make it WEB-available to physicians to support their medical decisions so they can make faster and more accurate diagnosis and avoid misdiagnosis and patient mismanagement.
描述(由申请人提供):今天的心脏成像领域要求诊断医生掌握不断扩大的知识库(KB),而掌握这些知识并将其应用于特定任务和报销的时间正在稳步缩短。这些限制构成了一个严重的医疗问题,不可避免地导致医生的错误。因此,需要新的工具来帮助医生及时应用全面的、最新的客观知识和可用的患者数据来解决具体的临床问题。我们的快速通道提案的长期目标是通过开发可访问web的心脏工具箱的新工具来改善心脏病患者的护理并降低心脏图像解释的成本,该工具箱提供决策支持,以提高检测冠心病(CHD)的准确性。具体而言,我们提出了一个基于web的系统,该系统将获取的ecg门控心肌灌注SPECT (MPS)原始图像上传,并进行自动重构和分析,以提取心肌灌注和功能的区域定量参数。这些参数被转换为异常的确定因素,并提交给成像决策支持系统(DSS),该系统不断更新最新的科学/临床知识,以达到对患者心脏状态的印象。DSS得出的这些结论和每个结论的理由被用来自动生成一个基于网络的结构化报告,供诊断医生轻松审查,从理由中学习,修改和/或批准,以获得最佳的诊断准确性和冠心病预后。具体而言,我们建议:1)开发一种新的左室(LV)定量算法,自动提取左室区域灌注和功能参数,用于诊断冠心病;2)开发LV专家(LVX)决策支持系统;3)使用。net平台设计并实现LV量化和决策支持系统算法,以便它们可以集成到synterminology基础设施中,并在网络上部署和/或用作传统的独立工作站。在第一阶段,我们将开发一个原理验证系统,其中分析来自MPS研究的左室灌注信息,并解释DSS以自动生成报告和医生审查。在第二阶段,该系统将:a)扩展到包括心肌功能,缺血,活力和临床风险因素的量化和DSS, b)扩展到包括持续更新LVX知识库的方法,c)自动化连接所有重建,处理,量化,解释和报告应用程序,d)部署在网络上的。net上,具有数据库和电子商务会计功能。通过这个过程,我们希望证实我们的主要假设,即使用我们的决策支持的诊断医生比没有使用该系统的诊断医生提供更快、更准确的冠心病诊断和预后。该系统将利用synterminology的其他Emory软件的成功策略,通过以下方式实现商业化:1)授权给主要仪器制造商,2)直接销售给使用PC工作站的客户,3)使用现有synterminology Live网络的每个web访问费用。

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 }}

ERNEST V GARCIA其他文献

ERNEST V GARCIA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('ERNEST V GARCIA', 18)}}的其他基金

Quantification of myocardial blood flow using Dynamic PET/CTA fused imagery to determine physiological significance of specific coronary lesions
使用动态 PET/CTA 融合图像对心肌血流量进行量化,以确定特定冠状动脉病变的生理意义
  • 批准号:
    9755481
  • 财政年份:
    2018
  • 资助金额:
    $ 103.02万
  • 项目类别:
Quantification of myocardial blood flow using Dynamic PET/CTA fused imagery to determine physiological significance of specific coronary lesions
使用动态 PET/CTA 融合图像对心肌血流量进行量化,以确定特定冠状动脉病变的生理意义
  • 批准号:
    9980994
  • 财政年份:
    2018
  • 资助金额:
    $ 103.02万
  • 项目类别:
Novel WEB Decision Support System for cardiac image interpretation and reporting
用于心脏图像解释和报告的新型 WEB 决策支持系统
  • 批准号:
    8054462
  • 财政年份:
    2011
  • 资助金额:
    $ 103.02万
  • 项目类别:
Novel WEB Decision Support System for cardiac image interpretation and reporting
用于心脏图像解释和报告的新型 WEB 决策支持系统
  • 批准号:
    8427296
  • 财政年份:
    2011
  • 资助金额:
    $ 103.02万
  • 项目类别:
UNIFIED APPROACH TO QUANTIFY & VISUALIZE CARDIAC IMAGES
统一的量化方法
  • 批准号:
    2028437
  • 财政年份:
    1988
  • 资助金额:
    $ 103.02万
  • 项目类别:
UNIFIED APPROACH TO QUANTIFY & VISUALIZE CARDIAC IMAGERY
统一的量化方法
  • 批准号:
    2220264
  • 财政年份:
    1988
  • 资助金额:
    $ 103.02万
  • 项目类别:
UNIFIED APPROACH TO QUANTIFY & VISUALIZE CARDIAC IMAGERY
统一的量化方法
  • 批准号:
    2220265
  • 财政年份:
    1988
  • 资助金额:
    $ 103.02万
  • 项目类别:
UNIFIED APPROACH TO QUANTIFY & VISUALIZE CARDIAC IMAGERY
统一的量化方法
  • 批准号:
    3360022
  • 财政年份:
    1988
  • 资助金额:
    $ 103.02万
  • 项目类别:
UNIFIED APPROACH TO QUANTIFY & VISUALIZE CARDIAC IMAGERY
统一的量化方法
  • 批准号:
    3360026
  • 财政年份:
    1988
  • 资助金额:
    $ 103.02万
  • 项目类别:
UNIFIED APPROACH TO QUANTIFY & VISUALIZE CARDIAC IMAGERY
统一的量化方法
  • 批准号:
    3360024
  • 财政年份:
    1988
  • 资助金额:
    $ 103.02万
  • 项目类别:

相似海外基金

Interplay between Aging and Tubulin Posttranslational Modifications
衰老与微管蛋白翻译后修饰之间的相互作用
  • 批准号:
    24K18114
  • 财政年份:
    2024
  • 资助金额:
    $ 103.02万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
EMNANDI: Advanced Characterisation and Aging of Compostable Bioplastics for Automotive Applications
EMNANDI:汽车应用可堆肥生物塑料的高级表征和老化
  • 批准号:
    10089306
  • 财政年份:
    2024
  • 资助金额:
    $ 103.02万
  • 项目类别:
    Collaborative R&D
The Canadian Brain Health and Cognitive Impairment in Aging Knowledge Mobilization Hub: Sharing Stories of Research
加拿大大脑健康和老龄化认知障碍知识动员中心:分享研究故事
  • 批准号:
    498288
  • 财政年份:
    2024
  • 资助金额:
    $ 103.02万
  • 项目类别:
    Operating Grants
Baycrest Academy for Research and Education Summer Program in Aging (SPA): Strengthening research competencies, cultivating empathy, building interprofessional networks and skills, and fostering innovation among the next generation of healthcare workers t
Baycrest Academy for Research and Education Summer Program in Aging (SPA):加强研究能力,培养同理心,建立跨专业网络和技能,并促进下一代医疗保健工作者的创新
  • 批准号:
    498310
  • 财政年份:
    2024
  • 资助金额:
    $ 103.02万
  • 项目类别:
    Operating Grants
関節リウマチ患者のSuccessful Agingに向けたフレイル予防対策の構築
类风湿性关节炎患者成功老龄化的衰弱预防措施的建立
  • 批准号:
    23K20339
  • 财政年份:
    2024
  • 资助金额:
    $ 103.02万
  • 项目类别:
    Grant-in-Aid for Scientific Research (B)
Life course pathways in healthy aging and wellbeing
健康老龄化和福祉的生命历程路径
  • 批准号:
    2740736
  • 财政年份:
    2024
  • 资助金额:
    $ 103.02万
  • 项目类别:
    Studentship
NSF PRFB FY 2023: Connecting physiological and cellular aging to individual quality in a long-lived free-living mammal.
NSF PRFB 2023 财年:将生理和细胞衰老与长寿自由生活哺乳动物的个体质量联系起来。
  • 批准号:
    2305890
  • 财政年份:
    2024
  • 资助金额:
    $ 103.02万
  • 项目类别:
    Fellowship Award
I-Corps: Aging in Place with Artificial Intelligence-Powered Augmented Reality
I-Corps:利用人工智能驱动的增强现实实现原地老龄化
  • 批准号:
    2406592
  • 财政年份:
    2024
  • 资助金额:
    $ 103.02万
  • 项目类别:
    Standard Grant
McGill-MOBILHUB: Mobilization Hub for Knowledge, Education, and Artificial Intelligence/Deep Learning on Brain Health and Cognitive Impairment in Aging.
McGill-MOBILHUB:脑健康和衰老认知障碍的知识、教育和人工智能/深度学习动员中心。
  • 批准号:
    498278
  • 财政年份:
    2024
  • 资助金额:
    $ 103.02万
  • 项目类别:
    Operating Grants
Welfare Enhancing Fiscal and Monetary Policies for Aging Societies
促进老龄化社会福利的财政和货币政策
  • 批准号:
    24K04938
  • 财政年份:
    2024
  • 资助金额:
    $ 103.02万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了