对公开可用的MRI数据集进行调查,以在人工智能研究中潜在使用
Katharine A Dishner1,2, Bala McRae-Posani1,3, Arka Bhowmik1
1Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA.
Journal of magnetic resonance imaging : JMRI
|October 27, 2023
概括
本综述调查了公开可用的磁共振成像 (MRI) 数据集,这些数据集对于在放射学中推进人工智能 (AI) 至关重要. 它强调了110个数据集的关键特征,以指导人工智能研究人员.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 承诺在放射学领域取得重大进展.
- 人工智能的广泛临床采用受到缺乏大型,注释良好的和具有代表性的医学图像数据集的阻碍.
- 数据安全性,互操作性,患者隐私和适当的数据使用在数据集创建中带来了挑战.
研究的目的:
- 审查公开可用的磁共振成像 (MRI) 数据集,用于放射学AI研究.
- 通过在放射学子领域调查主要的MRI数据集,为研究人员提供参考.
- 识别和列出对人工智能研究人员有价值的关键特性.
主要方法:
- 在互联网上搜索公开可访问的MRI数据集.
- 根据包括受试者数,人口统计,兴趣领域,技术特征和注释在内的参数评估数据集.
- 审查了大脑,身体和肌肉骨子领域的110个数据集.
主要成果:
- 该审查涵盖了110个数据集,包括12个感兴趣领域 (例如,脊椎,心脏) 的1,686,245名受试者.
- 识别和编目了与人工智能研究相关的重要数据集特征.
- 提供了对放射学AI可用的资源的结构化概述.
结论:
- 公共可用的MRI数据集对于培训和验证放射学AI模型至关重要.
- 这项调查作为一个有价值的参考,以促进该领域的AI研究和开发.
- 解决数据集的可访问性和质量是释放AI在放射学中的潜力的关键.
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