深度学习低最佳螺旋测量以预测呼吸系统结果和死亡率.
Davin Hill1,2, Max Torop1, Aria Masoomi1
1Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA.
Research square
|July 9, 2025
概括
机器学习模型可以使用低于最佳的螺旋测量努力来预测呼吸结果和死亡率. 这种方法可能会从所有螺旋计试验中揭示有价值的数据,提高诊断能力.
科学领域:
- 肺部医学 肺部医学
- 医疗保健中的机器学习
- 呼吸系统诊断 呼吸系统诊断
背景情况:
- 螺旋测量对于诊断呼吸道疾病至关重要,通常依赖于满足质量控制 (QC) 标准的最大努力.
- 在预测呼吸道结果方面,低于最佳的螺旋测量努力 (低于最大或QC失败) 的实用性仍然不清楚.
研究的目的:
- 开发和评估一种机器学习模型,该模型能够使用非最佳螺旋测量数据预测呼吸结果和死亡率.
- 评估将所有螺旋测量力度,包括低于最佳的,纳入预测模型的价值.
主要方法:
- 一个新的基于螺旋图的对比学习框架 (Spiro-CLF) 被开发出来,可以从所有可用的体积-时间曲线 (最大和次优) 中创建肺功能表示.
- 斯皮罗-CLF模型是在英国生物库数据上训练的,并在独立的英国生物库和COPDGene队列上验证的.
- 模型的性能被评估为预测空气流量限制 (FEV1/FVC <0.7),预测低FEV1百分比 (FEV1 PP <80%),全因死亡率和呼吸系统表型.
主要成果:
- 在英国生物库中,Spiro-CLF有效地预测了FEV1/FVC<0.7 (AUROC 0.956) 和死亡率 (一致指数0.647) 使用低于最佳的努力,并将喘预测提高了9-42%.
- 在COPDGene队列中,虽然低于最佳的努力没有提高肺功能或死亡率预测,但Spiro-CLF表征显著预测了喘和其他呼吸系统表型 (P ≤ 2 × 10^-3).
- 显著的比例 (61.6%) 的螺旋测量工作是低于最佳的,突出了排除这些尝试的潜在数据损失.
结论:
- 一个机器学习模型 (Spiro-CLF) 可以使用低于最佳的螺旋测量数据成功预测呼吸系统表型和死亡率.
- 所有的螺旋计试验,包括低于最佳的螺旋计试验,都包含有价值的信息,可以增强对呼吸系统疾病的预测建模.
- 需要进一步的研究来探索这种方法在特定患者群体和情景中的临床实用性.
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