基于对非侵入性动脉脉冲波形的深度学习启用分析进行的外周动脉疾病诊断
Sina Masoumi Shahrbabak1, Sooho Kim2, Byeng Dong Youn3
1Mechanical Engineering, University of Maryland, College Park, MD, 20742, USA.
Computers in biology and medicine
|December 12, 2023
概括
对非侵入性脉冲体积记录 (PVR) 波形的深度学习分析显示,对诊断外围动脉疾病 (PAD) 有希望. 这种方法提供了一个负担得起的,非侵入性的替代传统诊断,与血压分析的准确性紧密相匹配.
科学领域:
- 生物医学工程 生物医学工程
- 心血管研究研究心血管研究
- 人工智能在医学中的应用
背景情况:
- 周围动脉疾病 (PAD) 的诊断通常依赖于侵入性或不那么敏感的方法.
- 非侵入性动脉脉冲波形为PAD检测提供了一个潜在的替代方案.
- 目前的诊断方法,如脚腕指数 (ABI),在准确性和灵敏性方面都有局限性.
研究的目的:
- 研究使用非侵入性动脉脉冲量记录 (PVR) 波形分析诊断外周动脉疾病 (PAD) 的可行性.
- 开发和评估基于PVR波形的PAD检测和严重性评估的深度学习 (DL) 算法.
- 为了比较DL启用PVR分析与DL启用血压 (BP) 分析和脚-手腕指数 (ABI) 的有效性.
主要方法:
- 使用数学模型生成合成动脉血压 (BP) 和脉冲体积记录 (PVR) 波形,模拟各种PAD严重程度.
- 开发了一种深度学习 (DL) 算法,用于分析手臂和骨PVR波形,用于PAD诊断.
- 使用诸如正负预测值,ROC曲线下的面积 (AUC),相关系数 (r) 和布兰德-阿尔特曼协议极限等指标评估了DL算法的性能,将PVR分析与BP分析和ABI进行比较.
主要成果:
- 启用DL的PVR波形分析在检测PAD方面表现出足够的准确性,性能接近动脉血压分析 (AUC:0.90对0.97).
- 在估计PAD严重程度时,DL启用PVR分析的有效性低于动脉血压分析 (r值:0.77对0.93).
- 支持DL的PVR波形分析在PAD检测和严重程度估计方面显著超过了脚-手臂指数 (ABI).
结论:
- 由深度学习驱动的非侵入性动脉脉冲波形分析是对外围动脉疾病 (PAD) 诊断的可行方法.
- 这种支持DL的PVR分析显示了作为PAD负担得起和非侵入性诊断工具的潜力.
- 这些发现表明,DL启用的PVR分析可以补充或甚至超过传统方法,如PAD评估的ABI.
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