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

项目概要/摘要 外周动脉疾病（PAD）是一种非常普遍的血管疾病，发病率和死亡率很高 风险。但是，由于初级保健意识较低，外周动脉疾病 (PAD) 诊断不足。临床上常规PAD诊断 设置不适合低成本、高通量和准确的 PAD 诊断。注意到 PAD 会改变动脉 脉搏波形，动脉脉搏波形分析（称为脉搏波形分析 (PWA)）具有 提高 PAD 诊断准确性和便利性的潜力。特别是，PWA 可以胜过 通过利用动脉脉搏波形（例如 ABI）中的离散特征建立的技术 完整的脉冲波形。另外，可以方便地利用动脉脉搏波形来实现PWA 在四肢部位（例如手臂和脚踝，已在 ABI 中使用）进行测量。然而，PWA 涉及基于试错的经验特征选择。因此，PWA 可以与现代深度学习相结合 学习 (DL) 技术，利用 DL 自动选择与任务相关的特征的能力。 成功训练用于 PAD 诊断的 DL 算法需要大量相关的标记数据集 从不同的 PAD 患者收集纵向 PAD 进展。然而，只有稀缺（并且可能 来自少数患者的非纵向）数据集实际上可能是可用的。现在动脉搏动 波形不仅受到 PAD 的影响，还受到解剖学和动脉生物力学特征的影响 对于患者来说，数据集不足会降低深度学习算法对抗干扰的鲁棒性 由于现实世界中的 PAD 存在广泛的解剖学和动脉生物力学特征 患者模糊了动脉脉搏波形中的 PAD 特征。为了解决这些障碍，我们 建议通过开发一种新型计算方法来实现基于 DL 的动脉 PWA 诊断 PAD 方法 使用稀缺数据集对深度学习算法进行鲁棒训练的方法。我们的基本想法是扩展传统的 领域对抗性学习指导深度学习训练，以促进独立潜在特征的开发 诊断 PAD 时持续的解剖和动脉生物力学紊乱的研究。具体目标包括： (i) 开发连续域对抗正则化 (CDAR) 方法，用于鲁棒的深度学习算法训练 稀缺数据集； (ii) 展示在 DL 的帮助下开发的支持 DL 的动脉 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 在一项资源高效的计算机模拟研究中，考虑了患者身高和动脉僵硬度。我们也会预估金额 实现准确、稳健的 PAD 诊断所需的数据集，为我们的后续体内研究提供信息。如果 成功后，CDAR 方法和支持 DL 的 PWA 可能广泛适用于一系列疾病的诊断 心血管疾病。该项目的成功将为我们提供强有力的资源支持 使用从以下来源收集的数据集对支持 DL 的 PWA 方法进行 PAD 诊断进行深入的体内评估 真实的 PAD 患者基于该项目结果所告知的样本量。

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.