在放射学中使用云计算的端到端可重现的AI管道
Dennis Bontempi1,2,3, Leonard Nuernberg1,2,3, Suraj Pai1,2,3
1Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
Nature communications
|August 13, 2024
概括
云基础设施使放射学中可复制的人工智能 (AI) 成为可能. 这种方法确保了从数据检索到结果分析的透明度,加速了人工智能工具的临床翻译.
科学领域:
- 医疗成像医学成像
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 算法显示出对放射学进行革命的希望.
- 发表的人工智能研究缺乏透明度和可重复性,这阻碍了临床翻译.
- 现有的报告准则提供有限的实际解决方案.
研究的目的:
- 展示基于云的基础设施的潜力,以实现透明和可重复的AI放射学管道.
- 为应对实施和分享人工智能研究临床翻译的挑战.
主要方法:
- 使用云托管的数据和计算实现了端到端可重复的AI管道.
- 证明了数据预处理,深度学习推断和后处理的管道功能.
- 从最近的文献中验证了两个基于人工智能的癌症成像生物标志物使用案例.
主要成果:
- 在云端AI管道执行中实现端到端可重复性.
- 从现有文献中确认的发现,并将验证扩展到新的数据.
- 为瘤学领域提供了透明和可扩展的AI管道示例.
结论:
- 基于云的基础设施有助于实现和共享透明,可重复的AI放射学管道.
- 这种方法可以加速人工智能解决方案在放射学和瘤学的临床转化.
- 提高透明度和可复制性对于在医学成像中推进AI至关重要.
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