Deep Learning-Enabled Arterial Pulse Waveform Analysis Approach to Peripheral Artery Disease Diagnosis

项目总结/摘要 外周动脉疾病（peripheral artery disease，PAD）是一种高发病率和高死亡率的血管疾病 风险但是，PAD诊断不足，初级保健意识低。临床常规PAD诊断 这种设置不适合低成本、高通量和准确的PAD诊断。注意到PAD改变了动脉 脉搏波形，动脉脉搏波形的分析（称为脉搏波形分析（PWA））具有 提高PAD诊断的准确性和便利性的潜力。特别是，PWA可以超越 建立在动脉脉搏波形中的离散特征上的技术（例如，ABI）通过利用动脉 完整的脉冲波形。另外，PWA可以方便地用动脉脉搏波形来实现 在肢体部位测量（例如，手臂和脚踝，这已经在ABI中使用）。然而，PWA 涉及基于试错的经验特征选择。因此，PWA可以与现代深 学习（DL）技术，以利用DL自动选择任务相关特征的能力。 用于PAD诊断的DL算法的成功训练需要大量标记数据集相关联 从不同的PAD患者中收集纵向PAD进展。然而，只有稀缺（可能 非纵向）来自少数患者的数据集实际上是可用的。现在动脉脉搏 波形不仅受PAD的影响，而且还受动脉的解剖和动脉生物力学特性的影响。 数据集的不充分会使DL算法对干扰的鲁棒性恶化 由于在现实世界PAD中遇到的广泛的解剖学和动脉生物力学特征 动脉脉搏波形中PAD特征模糊的患者。为了克服这些障碍，我们 我建议通过开发一种新的计算方法，实现DL启用动脉PWA的PAD诊断方法 一种在数据集稀少的情况下对DL算法进行鲁棒训练的方法。我们的基本想法是将传统的 领域对抗性学习指导深度学习训练，以促进潜在特征的独立开发 在诊断PAD时连续的解剖学和动脉生物力学紊乱。具体目标包括： 开发一种用于鲁棒DL算法训练的连续域对抗正则化（CDAR）方法， 稀缺数据集;以及（ii）证明借助以下方法开发的DL启用动脉PWA的潜力 CDAR用于检测、定位和评估PAD的严重程度，以对抗相关干扰 与患者身高和动脉僵硬度的关系。我们还将估计 我们需要一系列数据集，以实现准确和可靠的PAD诊断，为我们的后续体内研究提供信息。如果 成功的，CDAR方法和DL启用PWA可广泛适用于诊断范围 心血管疾病。这个项目的成功将为我们提供一个强有力的理由，资源- 使用收集的数据集对DL启用的PWA方法进行PAD诊断的强化体内评估， 基于本项目结果告知的样本量的真实的PAD患者。

PROJECT SUMMARY/ABSTRACT Peripheral artery disease (PAD) is a highly prevalent vascular disease entailing high morbidity and mortality risks. But, PAD is underdiagnosed with low primary care awareness. Conventional PAD diagnosis in clinical settings is not suited to low-cost, high-throughput, and accurate PAD diagnosis. Noting that PAD alters arterial pulse waveforms, the analysis of arterial pulse waveforms (called the pulse waveform analysis (PWA)) has the potential for advancing the accuracy and convenience of PAD diagnosis. In particular, PWA can outperform techniques built upon discrete features in the arterial pulse waveforms (e.g., ABI) by exploiting the arterial pulse waveforms in their entirety. In addition, PWA can be realized with arterial pulse waveforms conveniently measured at the extremity sites (e.g., arm and ankle, which are already being employed in ABI). Yet, PWA involves trial-and-error-based empirical feature selection. Hence, PWA may be combined with modern deep learning (DL) techniques to leverage the ability of DL to automatically select task-relevant features. Successful training of a DL algorithm for PAD diagnosis requires massive labeled datasets associated with longitudinal PAD progression collected from diverse PAD patients. However, only scarce (and possibly non-longitudinal) datasets from a small number of patients may be available in reality. Now that arterial pulse waveform is affected not only by PAD but also by the anatomical and arterial biomechanical characteristics of the patient, insufficiency in datasets can deteriorate the robustness of the DL algorithm against disturbances due to a wide range of anatomical and arterial biomechanical characteristics encountered in real-world PAD patients obscuring the signatures of PAD in the arterial pulse waveforms. To address these obstacles, we propose to realize a DL-enabled arterial PWA approach to PAD diagnosis by developing a novel computational method for robust training of DL algorithms with scarce datasets. Our basic idea is to extend the conventional domain-adversarial learning to guide DL training so as to foster the exploitation of latent features independent of continuous anatomical and arterial biomechanical disturbances in diagnosing PAD. Specific aims include: (i) to develop a continuous domain-adversarial regularization (CDAR) method for robust DL algorithm training with scarce datasets; and (ii) to demonstrate the potential of the DL-enabled arterial PWA developed with the aid of CDAR for detecting, localizing, and assessing the severity of PAD robustly against disturbances associated with patient height and arterial stiffness in a resource-efficient in silico study. We will also estimate the amount of datasets required to enable accurate and robust PAD diagnosis to inform our follow-up in vivo study. If successful, the CDAR method and the DL-enabled PWA may be broadly applicable to the diagnosis of a range of cardiovascular diseases. The success of this project will provide us with a strong justification for resource- intensive in vivo assessment of the DL-enabled PWA approach to PAD diagnosis using datasets collected from real PAD patients based on the sample size informed by the results of this project.