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提高过静脉取风险预测:将成像生物标志物集成到机器学习模型中

Vishal S Mehta¹, YingLiang Ma², Nadeev Wijesuriya¹

¹Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.

|February 14, 2024

概括

将成像数据集成到机器学习模型中,可以显著提高对通过静脉取 (TLE) 过程中重大不良事件和漫长程序的预测. 这种增强有助于更好地评估TLE程序的风险.

科学领域:

心脏病学心脏病学
医疗成像医学成像
人工智能的人工智能

背景情况:

机器学习 (ML) 模型越来越多地用于医疗程序中的风险预测.
输血提取 (TLE) 是一项复杂的程序,存在相关风险,包括重大不良事件 (MAE) 和漫长的程序时间.

研究的目的:

评估将成像数据纳入ML模型是否提高了MAE和漫长TLE程序的预测准确性.
测试假设,胸部X射线中的特定放射特征可以改善TLE风险预测.

主要方法:

开发了一个深度学习的卷积神经网络,可以自动检测TLE前胸部X射线图 (CXRs) 的特征.
分析的关键特征包括角化,上腔静脉 (SVC) 中的线圈百分比,以及SVC中重叠的.
评估了ML模型的性能,并没有整合这些成像生物标志物.

主要成果:

深度学习模型在检测心脏结构和CXRs上的主要特征方面取得了高精度.
整合成像生物标志物显著改善了MAE的预测,提高了平衡的准确性,灵敏度,特异性和AUC.
长时间手术 (≥100分钟) 的预测也显示了所有绩效指标的显著改善.

结论:

整合成像生物标志物的风险预测工具大大提高了ML模型预测MAE和TLE中长时间程序的能力.
利用CXR衍生成像生物标志物代表了优化TLE患者安全和程序效率的宝贵进步.

关键词:

人工智能的人工智能是人工智能.复杂性复杂性复杂性计算机视觉计算机视觉计算机视觉机器学习机器学习风险预测风险预测通过静脉取的提取方法

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