以人工智能为基础的体积六个组织的身体组成量化从CT心脏衰减扫描死亡率预测:一个多中心研究
Jirong Yi1, Anna M Marcinkiewicz2, Aakash Shanbhag3
1Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
The Lancet. Digital health
|May 17, 2025
概括
人工智能现在可以从CT减弱校正扫描中测量身体成分,识别所有原因死亡风险的关键指标. 这些人工智能衍生的身体组成指标为患者的结果提供了有价值的预后信息.
科学领域:
- 放射学 放射学是指放射学
- 人工智能的人工智能
- 心血管成像 - 心血管成像
背景情况:
- CT减弱校正 (CTAC) 扫描是心脏输液成像的标准,但未得到充分利用.
- 目前的用途包括减弱校正和视觉评分.
- 新的AI方法可以从这些扫描中提取更多的诊断信息.
研究的目的:
- 开发一种基于人工智能的方法,用于从CTAC扫描中进行体积体质分析.
- 评估这些人体组成指标对所有死因的预后价值.
主要方法:
- 基于AI的细分和图像处理被应用于来自多站点国际注册的CTAC扫描.
- 测量了骨,骨肌肉和脂肪组织 (皮下,内脏,肌内,心上) 的体积测量.
- 考克斯回归和卡普兰-梅尔分析评估了人体成分测量和全因死亡率之间的关联.
主要成果:
- 人工智能处理每次扫描不到2分钟.
- 在9918名患者中,高内脏脂肪组织 (VAT),上心脂肪组织 (EAT) 和肌内脂肪组织 (IMAT) 与死亡风险增加有关.
- 较高的骨衰减和骨肌体积指数与死亡风险降低有关.
结论:
- CTAC扫描包含有价值的,可以自动测量的身体成分生物标志物.
- 这些人工智能衍生措施为所有死因的死亡率提供了重要的额外预后信息.
- 这种方法提高了常规心脏成像扫描的实用性.
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