关于为肺栓塞 (VQ4PEDB) 构建人工智能辅助注释性肺通风 perfusion 剪影的大型数据库
Amir Jabbarpour1, Eric Moulton2,3, Sanaz Kaviani3,4
1Department of Physics, Carleton University, Ottawa, ON, Canada.
Frontiers in nuclear medicine
|August 1, 2025
概括
一个新的多中心数据集,VQ4PEDB,结合了通风 perfusion (V/Q) scintigraphy图像和临床报告来训练人工智能 (AI) 模型来诊断肺栓塞 (PE). 本资源旨在提高PE和其他肺部疾病的诊断准确度.
科学领域:
- 核医学是一种核医学.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 透气- perfusion (V/Q) 扫描对于诊断肺栓塞 (PE) 非常重要.
- 解释V/Q扫描需要专业知识,而人工智能可以提高诊断准确度.
- 开发强大的AI模型需要全面的,多中心的数据集.
研究的目的:
- 开发一个多中心数据集 (VQ4PEDB),整合V/Q成像和临床报告.
- 促进创建和验证用于PE诊断的AI模型.
- 为了解决V/Q成像数据采集,非识别和注释方面的挑战.
主要方法:
- 建立了一个全面的成像注册表,包含V/Q图像,临床报告和其他来自加拿大和美国站点的相关数据.
- 在V7达尔文平台上利用众包注释来进行V/Q图像细分.
- 将注释数据输入深湖,用于人工智能模型培训,并进行质量保证检查.
主要成果:
- 编制了来自多个中心的3,122个V/Q研究,包括来自各种扫描仪制造商的平面和SPECT图像.
- 标注了1500项研究,识别和细分了3511个血管 perfusion 缺陷.
- 将V/Q研究分为PE的概率类别,包括正常和异常发现.
结论:
- VQ4PEDB是一个独特而广泛的数据库,用于PE诊断中的V/Q核光学.
- 数据库包括各种通风剂和扫描仪类型,提高了其通用性.
- VQ4PEDB将成为开发和验证肺病诊断人工智能工具的宝贵资源.
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