慢性阻塞性肺病的机器诊断使用一种新型的快速响应度仪
Leeran Talker1, Daniel Neville2, Laura Wiffen2
1TidalSense Limited, 15a Vinery Rd, Cambridge, CB1 3DN, UK.
Respiratory research
|June 2, 2023
概括
机器学习分析图数据准确识别慢性阻塞性肺部疾病 (COPD). 这种非侵入性方法在临床环境中对COPD的快速诊断充满希望.
科学领域:
- 呼吸系统医学 呼吸系统医学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 测量二氧化碳 (CO2) 波形的图学,用于机械通风和心肺复苏.
- 二氧化碳波形特征与V / Q不匹配,死空间,呼吸模式和呼吸道阻塞相关.
- 这项研究研究了用于COPD检测的图学.
研究的目的:
- 开发一种机器学习分类器,以区分COPD患者和非COPD患者,使用图数据.
- 为了评估COPD的头像学衍生特征的诊断准确性.
主要方法:
- 分析了来自四项研究的295名患者的图数据,生成了88186个图.
- 使用TidalSense的云平台进行实时几何分析,处理二氧化碳传感器数据.
- 设计了82个生理特征,用于训练和验证机器学习分类器 (XGBoost).
主要成果:
- 该XGBoost型号实现了高类平衡的AUROC为0.985.
- 对COPD诊断的积极预测值 (PPV) 为0.914,敏感度为0.915.
- 驱动分类的关键特征与阿尔法角度和呼吸平原相关,与螺旋计相关.
结论:
- 该N-TidalTM设备可以准确,近乎实时的COPD诊断.
- 来自图的特征显示,它们有可能成为COPD的新生物标志物.
- 支持该技术在未来的临床应用中用于COPD检测.
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