Computer-Aided Triage of Body CT Scans with Deep Learning
利用深度学习对身体 CT 扫描进行计算机辅助分类
基本信息
- 批准号:10585553
- 负责人:
- 金额:$ 58.68万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-08-16 至 2027-07-31
- 项目状态:未结题
- 来源:
- 关键词:3-DimensionalAbdomenActive LearningAddressAlgorithmsAnatomyArchivesArtificial IntelligenceAttentionAutomated AnnotationCase StudyChestClassificationClinicalClinical Decision Support SystemsComplexComputer AssistedComputer-Assisted DiagnosisConsumptionDataData SetDatabasesDetectionDevelopmentDiagnosticDiseaseExpert SystemsFaceFoundationsFutureGoalsHealth systemHeterogeneityImageInstitutionLabelLungManualsMedical ImagingModelingNatural Language ProcessingNoduleOrganOutcomePatientsPelvisPerformanceRadiology SpecialtyReportingResearch PersonnelResolutionScanningShapesSiteStructureSupervisionSystemTestingTherapeutic EquivalencyTimeTrainingTriageVariantX-Ray Computed Tomographyartificial intelligence algorithmbody systemchest computed tomographycone-beam computed tomographycostdeep learningdeep learning modelfederated learningimaging modalityimprovedinnovationlarge datasetslearning strategyradiologisttool
项目摘要
PROJECT SUMMARY / ABSTRACT
Computed tomography (CT) imaging for the body can result in thousands of images spanning many organs
and myriad possible diseases. With growing patient load as well as increasing resolution and complexity of
scans, the task of CT interpretation has become daunting. To improve radiologist performance, many artificial
intelligence (AI) algorithms have been produced, but most are limited by their very narrow application to a
specific disease in a specific organ or have been trained on limited data due to the high cost and complexity of
manual annotation. As a result, there is an unmet need because existing AI solutions have not significantly
improved the workflow or performance of radiologists.
To meet these needs, we propose to develop a computer-aided diagnosis triage tool for CT of the chest,
abdomen, and pelvis (CAP) that would focus radiologists’ attention on regions with a high likelihood of
actionable disease while minimizing search efforts in regions of low likelihood.
Our hypothesis is that a triage tool will improve radiologist workflow while simultaneously maintaining or
improving performance. Our long-term goal is to create a clinical decision support system that will address
bottlenecks of radiologist workflow and performance. As key steps toward demonstrating feasibility for that
goal, we propose the following three specific aims:
1. Create framework for the assembly, deidentification, annotation, and sharing of over a million chest,
abdomen, pelvis (CAP) CT cases from two major health systems.
2. Develop a triage system trained using multi-site CT datasets through collaborative/federated learning.
3. Pilot use of the triage system at multiple sites to allow radiologists to perform efficiently and equivalently for
clinical tasks of assessing actionable disease in CAP CT.
Key innovations will include the use of weak supervision to label a massive number of cases from two health
systems. Labeling will be based on rule-based expert systems as well as natural language processing. Image
classification will be based on deep learning models capable of processing an entire 3D CT volume and trained
with federated learning to leverage the rich heterogeneity of data from the two health systems.
The expected outcome of this project will be evidence to support a new clinical workflow for radiologist
interpretation, which is the foundation for all medical imaging. For this project, we will maximize impact by
addressing CAP CT because of the large patient load and complex anatomy/disease, and by producing one of
the largest medical imaging datasets that can be shared for future research including grand challenges. In
addition, by leveraging existing data in patient archives and radiology reports, our approach has the potential to
be applicable to other body sites or imaging modalities in the future.
项目概要/摘要
身体计算机断层扫描 (CT) 成像可生成跨越多个器官的数千张图像
以及无数可能的疾病。
扫描后,CT 判读的任务变得繁重,为了提高放射科医生的表现,需要进行许多人工操作。
智能(AI)算法已经产生,但大多数都因其非常狭窄的应用而受到限制
特定器官的特定疾病或由于成本高且复杂而接受了有限数据的培训
因此,由于现有的人工智能解决方案还没有显着提高,因此存在未满足的需求。
改善放射科医生的工作流程或绩效。
为了满足这些需求,我们建议开发一种胸部CT计算机辅助诊断分诊工具,
腹部和骨盆 (CAP),这将使放射科医生的注意力集中在极有可能出现这种情况的区域
可采取行动的疾病区域,同时最大限度地减少低可能性的搜索工作。
我们的假设是,分诊工具将改善放射科医生的工作流程,同时保持或
我们的长期目标是创建一个能够解决问题的临床决策支持系统。
放射科医生工作流程和绩效的瓶颈是证明其可行性的关键步骤。
目标,我们提出以下三个具体目标:
1. 打造百万级宝箱的组装、去识别、标注、共享框架,
来自两个主要卫生系统的腹部、骨盆 (CAP) CT 病例。
2. 通过协作/联合学习,开发使用多站点 CT 数据集进行训练的分诊系统。
3. 在多个地点试点使用分诊系统,使放射科医生能够高效、等效地执行任务
评估 CAP CT 中可采取行动的疾病的临床任务。
关键创新将包括利用弱监督来标记来自两个卫生部门的大量病例
标签将基于基于规则的专家系统以及自然语言处理。
分类将基于能够处理整个 3D CT 体积并经过训练的深度学习模型
通过联合学习来利用两个卫生系统丰富的异质性数据。
该项目的预期结果将成为支持放射科医生新的临床工作流程的证据
解释,这是所有医学成像的基础 对于这个项目,我们将通过以下方式最大限度地发挥影响。
由于患者量大且解剖结构/疾病复杂,因此可以解决 CAP CT 问题,并通过生成以下之一
最大的医学成像数据集,可以为未来的研究(包括重大挑战)共享。
此外,通过利用患者档案和放射学报告中的现有数据,我们的方法有可能
未来可应用于其他身体部位或成像方式。
项目成果
期刊论文数量(0)
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科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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