基于深度学习的胸部CT图像估计的肺生物学年龄在健康成年人中:一个模型开发和验证研究
Liping Zuo1, Na Zhu2, Bowen Wang1
1Qilu Hospital of Shandong University, Jinan, CN.
JMIR aging
|February 2, 2026
概括
这项研究开发了一个估计的肺生物学年龄 (ePBA) 生物标志物,使用胸部CT扫描的深度学习. 慢性肺炎患者的年龄差距 (ePBA减去慢性年龄) 预测肺功能下降和死亡风险增加.
科学领域:
- 放射学和医学成像学 医学成像学
- 人工智能在医学中的应用
- 老年学和衰老研究研究.
背景情况:
- 使用胸部CT估计的肺生物学年龄 (ePBA) 显示出预测疾病进展和死亡率的前景.
- 目前基于CT的ePBA模型由于对健康成年人有限的培训和验证数据集而缺乏通用性.
研究的目的:
- 从使用深度学习的健康成年人的多中心胸部CT扫描中开发出强大的ePBA生物标志物.
- 研究慢性阻塞性肺病 (COPD) 患者的年龄差距 (ePBA减去慢性年龄) 与肺功能和全因死亡率之间的关联.
主要方法:
- 在三个机构中利用了11187个健康成年人的胸部CT扫描,用于模型开发和外部验证.
- 使用多个深度学习模型来计算ePBA.
- 研究了138名住院COPD患者的年龄差异与肺功能 (FEV1%) 和全因死亡率的关联.
主要成果:
- 深度学习模型表现出可接受的性能,ePBA与时间年龄有很强的相关性.
- 在COPD患者中,在较大的年龄差距和降低FEV1% (rs=-0.18,P=.03) 之间发现了显著的关联.
- 在COPD患者中,年龄差异与全因死亡率 (HR: 1.16 [95%CI: 1.08, 1.25]) 的增加风险显著相关.
结论:
- 在胸部CT扫描上使用深度学习开发并验证了一种新的ePBA生物标志物.
- 来自ePBA的年龄差异显示了作为一种新的临床生物标志物的潜力,用于预测COPD患者的结果.
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