Artificial Intelligence for Improved Breast Cancer Screening Accuracy: External Validation, Refinement, and Clinical Translation

人工智能提高乳腺癌筛查准确性:外部验证、细化和临床转化

基本信息

  • 批准号:
    10544496
  • 负责人:
  • 金额:
    $ 51.77万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-01-01 至 2024-12-31
  • 项目状态:
    已结题

项目摘要

PROJECT SUMMARY Screening mammography saves lives but human interpretation alone is imperfect and is associated with significant harms including ~30,000 missed breast cancers and ~3.8 million false-positives exams each year in the U.S. alone. Traditional computer-aided detection failed to improve screening accuracy, in part due to the static nature of software trained and tested on small datasets decades ago. Recent advances in improved computer processing power, cloud-based data storage capabilities, and availability of large imaging datasets have led to renewed excitement for applying artificial intelligence (AI) to mammography interpretation. We propose a unique academic-industry partnership to validate, refine, scale, and clinically translate our proven 2D mammography AI algorithm to 3D mammography interpretation. Our team helped organize and lead the Dialogue for Reverse Engineering Assessments and Methods (DREAM) Digital Mammography Challenge, an open crowdsourced AI algorithmic challenge that provided >640,000 digital 2D mammogram images and associated clinical metadata to >1,200 coding teams worldwide. Our industry partner for this grant, DeepHealth, Inc., was the top performing team in the DREAM Challenge. With >50% of U.S. facilities now offering 3D mammography for screening, the 50-to-100-fold increase in imaging data represents a new critical barrier for both radiologists and AI algorithm developers. To date, there have been few publications addressing AI-enhanced interpretation of 3D mammography, the emerging screening exam of choice. We will validate our post-DREAM algorithm for 2D mammography (which currently rivals human interpretation alone) using UCLA's Athena Breast Health Network, one of the largest population-based breast imaging registries. We will enhance our 2D AI algorithm with expert radiologist supervision and examine the impact of adding novel non-imaging data parameters, including genetic mutation and tumor molecular subtype data, to help train the AI model to identify more clinically significant cancers. We will use several novel technical algorithmic approaches to scale from 2D to 3D mammography which, in our preliminary studies, have shown improved accuracy beyond radiologist interpretation alone. Finally, we will perform a series of interpretive studies to identify the optimal interface between “black box” outputs and radiologist interpreters, which remains an understudied topic. With >40 million U.S. women undergoing screening each year, seemingly small improvements in overall accuracy would still imply significantly improved population-based outcomes. In summary, we have assembled an unparalleled multidisciplinary team with expertise in machine/deep learning, breast cancer screening accuracy, medicine, oncology, radiology, imaging technology assessment, and biostatistics. We have a proven track record of strong collaboration and are well positioned to validate, enhance, scale, and translate our proven 2D AI algorithm for improved 3D mammography accuracy. Our new end user tool will help tip the balance of routine screening towards greater benefits than harms.
项目总结

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 }}

CHRISTOPH I LEE其他文献

CHRISTOPH I LEE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('CHRISTOPH I LEE', 18)}}的其他基金

Population-Based Evaluation of Artificial Intelligence for Mammography Prior to Widespread Clinical Translation
在广泛临床转化之前对乳腺 X 线摄影人工智能进行基于人群的评估
  • 批准号:
    10651842
  • 财政年份:
    2022
  • 资助金额:
    $ 51.77万
  • 项目类别:
Population-Based Evaluation of Artificial Intelligence for Mammography Prior to Widespread Clinical Translation
在广泛临床转化之前对乳腺 X 线摄影人工智能进行基于人群的评估
  • 批准号:
    10445206
  • 财政年份:
    2022
  • 资助金额:
    $ 51.77万
  • 项目类别:
Racial and Socioeconomic Disparities in Breast Cancer Diagnostic Work Up and Outcomes
乳腺癌诊断工作和结果的种族和社会经济差异
  • 批准号:
    10394189
  • 财政年份:
    2021
  • 资助金额:
    $ 51.77万
  • 项目类别:
Racial and Socioeconomic Disparities in Breast Cancer Diagnostic Work Up and Outcomes
乳腺癌诊断工作和结果的种族和社会经济差异
  • 批准号:
    10094564
  • 财政年份:
    2021
  • 资助金额:
    $ 51.77万
  • 项目类别:
Racial and Socioeconomic Disparities in Breast Cancer Diagnostic Work Up and Outcomes
乳腺癌诊断工作和结果的种族和社会经济差异
  • 批准号:
    10654528
  • 财政年份:
    2021
  • 资助金额:
    $ 51.77万
  • 项目类别:
Artificial Intelligence for Improved Breast Cancer Screening Accuracy: External Validation, Refinement, and Clinical Translation
人工智能提高乳腺癌筛查准确性:外部验证、细化和临床转化
  • 批准号:
    10320906
  • 财政年份:
    2020
  • 资助金额:
    $ 51.77万
  • 项目类别:
Artificial Intelligence for Improved Breast Cancer Screening Accuracy: External Validation, Refinement, and Clinical Translation
人工智能提高乳腺癌筛查准确性:外部验证、细化和临床转化
  • 批准号:
    9912472
  • 财政年份:
    2020
  • 资助金额:
    $ 51.77万
  • 项目类别:
Project 2
项目2
  • 批准号:
    10705584
  • 财政年份:
    2011
  • 资助金额:
    $ 51.77万
  • 项目类别:
Project 2
项目2
  • 批准号:
    10411222
  • 财政年份:
    2011
  • 资助金额:
    $ 51.77万
  • 项目类别:

相似海外基金

Rational design of rapidly translatable, highly antigenic and novel recombinant immunogens to address deficiencies of current snakebite treatments
合理设计可快速翻译、高抗原性和新型重组免疫原,以解决当前蛇咬伤治疗的缺陷
  • 批准号:
    MR/S03398X/2
  • 财政年份:
    2024
  • 资助金额:
    $ 51.77万
  • 项目类别:
    Fellowship
Re-thinking drug nanocrystals as highly loaded vectors to address key unmet therapeutic challenges
重新思考药物纳米晶体作为高负载载体以解决关键的未满足的治疗挑战
  • 批准号:
    EP/Y001486/1
  • 财政年份:
    2024
  • 资助金额:
    $ 51.77万
  • 项目类别:
    Research Grant
CAREER: FEAST (Food Ecosystems And circularity for Sustainable Transformation) framework to address Hidden Hunger
职业:FEAST(食品生态系统和可持续转型循环)框架解决隐性饥饿
  • 批准号:
    2338423
  • 财政年份:
    2024
  • 资助金额:
    $ 51.77万
  • 项目类别:
    Continuing Grant
Metrology to address ion suppression in multimodal mass spectrometry imaging with application in oncology
计量学解决多模态质谱成像中的离子抑制问题及其在肿瘤学中的应用
  • 批准号:
    MR/X03657X/1
  • 财政年份:
    2024
  • 资助金额:
    $ 51.77万
  • 项目类别:
    Fellowship
CRII: SHF: A Novel Address Translation Architecture for Virtualized Clouds
CRII:SHF:一种用于虚拟化云的新型地址转换架构
  • 批准号:
    2348066
  • 财政年份:
    2024
  • 资助金额:
    $ 51.77万
  • 项目类别:
    Standard Grant
BIORETS: Convergence Research Experiences for Teachers in Synthetic and Systems Biology to Address Challenges in Food, Health, Energy, and Environment
BIORETS:合成和系统生物学教师的融合研究经验,以应对食品、健康、能源和环境方面的挑战
  • 批准号:
    2341402
  • 财政年份:
    2024
  • 资助金额:
    $ 51.77万
  • 项目类别:
    Standard Grant
The Abundance Project: Enhancing Cultural & Green Inclusion in Social Prescribing in Southwest London to Address Ethnic Inequalities in Mental Health
丰富项目:增强文化
  • 批准号:
    AH/Z505481/1
  • 财政年份:
    2024
  • 资助金额:
    $ 51.77万
  • 项目类别:
    Research Grant
ERAMET - Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
ERAMET - 快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
  • 批准号:
    10107647
  • 财政年份:
    2024
  • 资助金额:
    $ 51.77万
  • 项目类别:
    EU-Funded
Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
  • 批准号:
    10106221
  • 财政年份:
    2024
  • 资助金额:
    $ 51.77万
  • 项目类别:
    EU-Funded
Recite: Building Research by Communities to Address Inequities through Expression
背诵:社区开展研究,通过表达解决不平等问题
  • 批准号:
    AH/Z505341/1
  • 财政年份:
    2024
  • 资助金额:
    $ 51.77万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了