生物年龄模型使用可解释的基于CT的心脏代谢生物标志物用于长寿的表型预测
Perry J Pickhardt1,2, Michael W Kattan3, Matthew H Lee4
1The Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA. ppickhardt2@uwhealth.org.
Nature communications
|February 7, 2025
概括
一个新的基于CT的生物年龄模型使用人工智能通过分析肌肉,脂肪和骨密度来预测寿命. 这种先进的模型在长寿预测方面显著优于传统的人口统计学.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 老年学是一门学科.
背景情况:
- 预测寿命对于个性化医学至关重要.
- 当前的方法通常依赖于标准的人口统计数据,这些数据有局限性.
研究的目的:
- 开发和验证基于计算机断层扫描 (CT) 的生物年龄模型,以提高寿命预测.
- 评估这种人工智能驱动模型与传统的人口预测指标的性能.
主要方法:
- 在123,281名成年人的腹部CT扫描上利用可解释AI算法的自动管道.
- 量化了骨肌肉,腹部脂肪,大动脉化,骨密度和腹部器官.
- 根据最终模型权重的预测准确度选择CT生物标志物.
主要成果:
- 在预测长寿方面,CT生物年龄模型显著优于人口统计数据 (IPA=29.2对21.7).
- 获得了更高的准确性 (10年AUC=0.880对比0.779),并在最高风险四分位数中确定了更高的生存风险 (HR=8.73,当排除早期癌症诊断时增加到24.79).
- 关键的贡献生物标志物包括肌肉密度,大动脉斑块负担,内脏脂肪密度和骨密度.
结论:
- 一个个性化的,基于CT的生物年龄模型为机会性风险评估提供了一个强大的工具.
- 这种人工智能驱动的方法与标准的人口因素相比,可以更准确地预测寿命.
- 该模型能够从CT扫描中分析多个生理标记,这提高了它在主动健康管理中的临床实用性.
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