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深度学习启用诊断腹腔大动脉动脉瘤使用脉冲体积记录波形:一个在研究

Sina Masoumi Shahrbabak¹, Byeng Dong Youn^2,3, Hao-Min Cheng⁴

¹Mechanical Engineering, University of Maryland, College Park, MD 20742, USA.

Sensors (Basel, Switzerland)

|November 13, 2025

概括

对非侵入性脉冲量记录 (PVR) 信号的深度学习分析显示,对诊断腹腔大动脉动脉瘤 (AAA) 有希望. 这种具有成本效益的方法可以使用临床技术实现可访问的AAA查.

科学领域:

生物医学工程生物医学工程
人工智能在医学中的应用
心血管诊断心血管诊断服务

背景情况:

腹腔大动脉瘤 (AAA) 诊断通常依赖于侵入性或复杂的成像技术.
早期检测AAA的非侵入性方法对于改善患者的治疗结果至关重要.
脉冲波形分析为可访问的心血管评估提供了一个潜在的途径.

研究的目的:

研究使用深度学习 (DL) 分析非侵入性脉冲量记录 (PVR) 信号诊断腹腔大动脉动脉瘤 (AAA) 的可行性.
开发和评估一个DL算法来估计PVR波形的AAA严重性.
将基于PVR的DL算法的性能与训练在侵入性动脉血压 (BP) 信号上的DL算法的性能进行比较.

主要方法:

使用系统性动脉循环模型生成合成动脉血压 (BP) 和脉冲体积记录 (PVR) 波形信号.
在合成队列中模拟了各种严重程度的腹腔大动脉动脉瘤 (AAA).
开发了一个卷积神经网络 (CNN),用于从PVR信号中对AAA严重性估计的连续性质对抗规范化.
对合成数据与体内发现进行了验证,并将DL算法性能与侵入性BP数据进行了比较.

主要成果:

支持DL的基于PVR的算法证明了强大的AAA检测,ROC曲线下的面积>0.89.9.
该算法在从PVR信号 (MAE:12.6%) 中估计AAA严重程度时显示出合理的准确性.
性能相当于,虽然略低于,一个相同的CNN训练在侵入性动脉血压信号 (MAE: 10.3%).

结论:

对非侵入性PVR波形信号的深度学习分析为AAA诊断提供了一种可行的,具有成本效益的方法.
这种方法有可能促进可访问的AAA选.
经营者不可知,护理点PVR分析可以显著提高AAA的早期检测和管理.

关键词:

腹部大动脉动脉瘤腹部大动脉动脉瘤动脉脉冲的波形是动脉脉冲的波形.深度学习是一种深度学习.机器学习是机器学习.医疗点诊断的诊断方法脉冲音量记录记录脉冲音量记录

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