医学成像数据的发展 基于成像的观测研究的数据标准化:OMOP通用数据模型扩展
Woo Yeon Park1, Kyulee Jeon2,3, Teri Sippel Schmidt4
1Biomedical Informatics and Data Science, Johns Hopkins University, 855 N Wolfe St, Rangos 616, Baltimore, MD, USA. wpark11@jhu.edu.
Journal of imaging informatics in medicine
|February 5, 2024
概括
本研究介绍了医学成像通用数据模型 (MI-CDM) 扩展,以协调人工智能研究的医疗保健数据. 它可以对疾病生物标志物和治疗疗效的成像特征进行可靠的分析.
科学领域:
- 医疗信息学医学信息学
- 医疗保健中的人工智能
- 生物医学成像分析分析
背景情况:
- 人工智能 (AI) 和深度学习方面的进步需要大型,协调的数据集来开发强大的医疗模型.
- 现有的数据模型,如观察医学结果伙伴关系共同数据模型 (OMOP CDM),需要扩展,以充分支持复杂的成像研究.
研究的目的:
- 为OMOPCDM提出和描述医学成像通用数据模型 (MI-CDM) 的扩展.
- 解决在标准化框架内整合和分析医学成像数据的结构和语义要求.
主要方法:
- 在OMOP CDM中引入两个新的表格来管理成像数据.
- 增加了两个新的词汇,以标准化与成像相关的术语.
- 开发连接DICOM数据源和追踪成像特征来源的能力.
主要成果:
- MI-CDM扩展方便了DICOM数据的链接和成像特征来源的跟踪.
- 通过成像功能来定义表型,扩展可计算的成像生物标志物.
- 为成像研究和结果研究提供了统一的方法.
结论:
- MI-CDM扩展增强了OMOP CDM在AI驱动的医学成像研究中的实用性.
- 它支持开发标准化,可计算的成像生物标志物,并促进表型定义.
- 为利用成像数据在医疗保健研究和结果分析中提供全面的解决方案.
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