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Integrating Artificial Intelligence for Optimal Analysis of CardiacPET/CT

集成人工智能以优化心脏 PET/CT 分析

基本信息

批准号：
10593858
负责人：
Marcelo F DI CARLI
金额：
$ 73.71万
依托单位：
CEDARS-SINAI MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-22 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10593858
关键词：
Adoption Anatomy Artificial Intelligence Atherosclerosis Automation Blood flow Cardiac Cardiometabolic Disease Cause of Death Clinical Clinical Data Collaborations Complex Computer software Computing Methodologies Coronary Coronary Arteriosclerosis Data Detection Development Diabetes Mellitus Diagnostic Diffuse Disease Dose Engineering Fatty acid glycerol esters Goals Growth Hybrids Image Image Analysis Intelligence Ischemia Joints Kinetics Knowledge Measurement Measures Methods Microvascular Dysfunction Modality Modeling Myocardial Myocardial perfusion Nature Obesity Epidemic PET/CT scan Patient Care Patient risk Patients Performance Perfusion Physicians Positron-Emission Tomography Protocols documentation Psyche structure Quality Control Radioisotopes Radiology Specialty Research Research Personnel Risk Assessment Scanning Site Statistical Models Supervision Survival Analysis Technical Expertise Techniques Technology Testing Translating Visual Widespread Disease Work X-Ray Computed Tomography adverse outcome artificial intelligence algorithm attenuation base biomedical imaging cardiovascular imaging chest computed tomography clinical application clinical imaging coronary artery calcium deep learning diagnostic accuracy disability disease diagnosis disorder risk diverse data experience heart imaging image processing imaging Segmentation imaging biomarker imaging modality improved medical specialties multidisciplinary multimodal data multimodality new technology novel outcome prediction perfusion imaging prognostic prototype risk stratification single photon emission computed tomography supervised learning tool unsupervised learning

项目摘要

PROJECT SUMMARY Coronary artery disease (CAD) is the leading cause of death and disability in the US and globally. The epidemic of obesity, diabetes, and cardiometabolic disease is changing the nature of CAD, with diffuse and microvascular disease emerging as key drivers of adverse outcomes. Radionuclide myocardial perfusion imaging is the most widely used modality for CAD assessment and is still primarily performed with SPECT. But SPECT evaluates only relative perfusion and is inherently insensitive in the setting of diffuse or microvascular disease. PET, with its unique ability to accurately quantify absolute myocardial blood flow, allows robust detection of obstructive CAD, diffuse atherosclerosis, balanced ischemia, and coronary microvascular dysfunction. Cardiac PET is also always obtained with additional chest CT for attenuation correction purposes. However, this modality requires a high level of on-site technical expertise to maximize its broad capabilities. We have applied highly efficient, image-based artificial intelligence (AI) approaches extensively to SPECT and CT, demonstrating improved diagnostic accuracy and risk stratification. These tools can be harnessed to enhance the utility of cardiac PET/CT. We propose to efficiently translate the latest AI advances and our recent SPECT developments to fully automate cardiac PET/CT analysis, including novel tools for quality control, high- performance image segmentation, new quantitative variables, and direct outcome prediction from images, using PET/CT data from multiple centers. The overall aim is to develop is to develop practical AI algorithms for comprehensive cardiac PET/CT analysis and to validate them in a multi-center setting. For this work, we propose the following 3 specific aims: (1) To develop and test automated end-to-end PET quantification, (2) To develop and test automated end-to-end chest CT quantification, (3) To develop and validate explainable AI models for enhanced patient assessment from images and clinical data, employing latest advances in survival analysis, supervised and unsupervised learning, and knowledge transfer. This research will result in personalized tools, which will improve the accuracy of patient assessment by PET/CT beyond what is possible by the current practice of subjective interpretation and mental integration of diverse data. Explainable methods combining image and clinical data to make AI conclusions more tangible will allow clinical adoption of this technology. The new tools can dramatically simplify PET/CT protocols, reduce subjectivity, reduce burden on the physicians, and maximize the information derived from the multimodal scans. They will fit directly into existing workflows, facilitating deployment in diverse clinical settings. The new AI methods for image analysis and explainable integration of multimodality data will generalize to other diseases and problems in biomedical imaging.

项目总结