预测呼吸道疾病死亡风险使用开源人工智能在亚洲卫生查人口的胸部X射线图上预测呼吸道疾病死亡风险
Jong Hyuk Lee1,2, Seung Ho Choi3, Hugo J W L Aerts4,5
1Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea.
Radiology. Artificial intelligence
|April 2, 2025
概括
开源的深度学习算法CXR-Lung-Risk有效地使用胸部X射线图预测亚洲人群的呼吸道疾病死亡率. 该工具增强了超越临床因素的风险分层,有助于早期检测和管理.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 公共卫生和流行病学
背景情况:
- 胸部X射线图对于诊断呼吸系统疾病至关重要.
- 准确的死亡风险分层对于患者管理至关重要.
- 深度学习算法在医学图像分析方面表现有前途.
研究的目的:
- 评估CXR-肺风险算法对呼吸道疾病死亡率的预后能力.
- 评估算法在亚洲健康查人口中的表现.
- 探索CXR-肺风险与传统临床因素的附加值.
主要方法:
- 从一个单中心亚洲队列的胸部X射线图的回顾性分析.
- 对预测死亡率的CXR-肺风险算法的外部验证.
- 竞争风险分析和概率比测试用于预后评估.
- 风险评分轨迹的探索性时间序列聚类.
主要成果:
- CXR-肺风险显著预测呼吸道疾病死亡率 (HR:2.01) 与临床因素的预后改善 (P <.001).
- 一个持续增加CXR-肺风险得分的小组显示出明显较差的结果 (HR: 3.26; P = .02).
- 该算法在接受健康查的亚洲人口中表现出有效性.
结论:
- 开源的CXR-肺风险模型是预测亚洲队列中呼吸道疾病死亡率的宝贵工具.
- 该算法允许使用基线和后续胸部X射线进行双层风险分层方法.
- 对CXR-肺风险得分的纵向分析提供了进一步的预后见解.
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