深度学习方法用于分析胸部X射线,以预测心力衰竭中的心脏事件
Kenya Kusunose1, Yukina Hirata2, Natsumi Yamaguchi2
1Department of Cardiovascular Medicine, Tokushima University Hospital, Tokushima, Japan.
Frontiers in cardiovascular medicine
|June 5, 2023
概括
使用胸部X射线的深度学习模型可以预测心力衰竭 (HF) 患者的肺动脉压升高 (PAWP). 这种人工智能驱动的PAWP概率增加了超越HF结果的传统因素的显著预后价值.
科学领域:
- 人工智能在医学中的应用
- 心脏病学 心脏病学
- 放射学 放射学是一门学科.
- 医疗信息学 医疗信息学
背景情况:
- 应用到胸部X射线的深度学习 (DL) 模型在预测肺动脉压升高 (PAWP) 方面表现有希望,这是心力衰竭 (HF) 的关键指标.
- 准确预测PAWP对于评估HF严重程度和患者预后至关重要.
研究的目的:
- 调查DL衍生的PAWP升高概率在预测住院心力衰竭患者的临床结果中的预后作用.
- 评估基于DL的PAWP概率是否提供了与HF管理中的传统临床因素相比的额外预后信息.
主要方法:
- 分析了192名住院HF患者的队列.
- 一个先前存在的AI模型被用来计算从胸部X射线中升高PAWP的概率.
- 主要终点包括在中位数为58个月的随访期间的HF再入院和心脏病死亡率.
主要成果:
- 在心声学上,增加PAWP的概率与扩张性功能障碍相关.
- 较高的DL衍生的PAWP概率与不良的临床结果有关,包括死亡或再入院.
- 在考克斯模型中,基于DL的PAWP概率独立预测了无事件存活率,表现优于左心房压升高 (LAP),并改善了临床数据模型.
结论:
- 分析胸部X射线的DL模型可以有效地预测PAWP,并在心力衰竭中提供显著的预后价值.
- 将DL衍生的PAWP概率集成到风险预测模型中,可以提高HF患者临床决策的准确性.
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