用PET进行人工智能的流量和透成像的REgistry:理性和设计
Giselle Ramirez1, Mark Lemley1, Aakash Shanbhag2
1Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
概括
用PET (REFINE PET) 进行人工智能的流量和输液成像的REgistry是一个大型数据集,用于开发心脏PET/CT AI工具. 该资源有助于推进心血管疾病的诊断和风险分层.
科学领域:
- 心血管成像 - 心血管成像
- 人工智能在医学中的应用
- 医疗信息学 医疗信息学
背景情况:
- 用PET (REFINE PET) 进行人工智能的流量和 perfusion 影像的REgistry被创建以汇总多中心PET和CT图像,临床数据和结果.
- 这种全面的资源旨在促进心脏PET/CT处理方法的验证和开发,包括标准和新的方法.
研究的目的:
- 为人工智能开发建立一个大规模的多中心心脏PET/CT数据注册表.
- 为了使定量和人工智能驱动的心脏成像分析技术的验证和进步.
主要方法:
- REFINE PET注册表是一个国际性的多中心努力,收集临床和成像数据.
- 使用QPET软件处理PET扫描,CT扫描使用深度学习检测冠状动脉.
- 患者的随访包括主要不良心血管事件 (MACE),如死亡,心肌梗塞和再血管.
主要成果:
- 目前,该注册表包括14个站点的35588名患者的数据,并正在不断扩展.
- 综合临床数据和来自42个类别的2200多个成像变量可用.
- 该数据集支持对5972名患者和9252名MACE患者的侵入性血管学相关性分析,平均随访时间为4.2年.
结论:
- REFINE PET注册表有效地整合了临床,多模式成像和AI工具.
- 本资源促进了PET/CT心肌输液成像在诊断和风险分层中的应用.
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