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

基于深度学习的动脉脉搏波形分析方法用于外周动脉疾病诊断

• 批准号: 10411311
• 负责人: Jin-Oh Hahn
• 金额: $ 7.25万
• 依托单位: UNIV OF MARYLAND, COLLEGE PARK
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10411311
• 关键词: Address; Affect; Aging; Anatomy; Ankle; Arteries; Awareness; Biomechanics; Blood Pressure; Cardiovascular Diseases; Characteristics; Classification; Clinical; Computing Methodologies; Data Set; Detection; Diagnosis; Disadvantaged; Disease Progression; Equipment; Feasibility Studies; Fostering; Height; Human; Image; Label; Lead; Learning; Limb structure; Measures; Methods; Modernization; Morbidity - disease rate; Morphology; Patients; Performance; Peripheral arterial disease; Physiologic pulse; Primary Health Care; Procedures; Resort; Resources; Sample Size; Severities; Signal Transduction; Site; Techniques; Time; Training; Trees; United States; Vascular Diseases; algorithm training; arm; arterial stiffness; base; cost; deep learning; deep learning algorithm; disease diagnosis; efficacy evaluation; feature selection; follow-up; in silico; in vivo; indexing; large datasets; mathematical model; mortality risk; novel; screening; success; virtual patient

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.

项目摘要/摘要 摘要外周动脉疾病是一种发病率高、病死率高的血管疾病。 风险。但是，PAD被低估了，初级保健意识低。临床常规PAD诊断方法的探讨 设置不适合低成本、高吞吐量和准确的PAD诊断。注意到PAD改变了动脉 脉搏波，动脉脉搏波的分析(称为脉搏波分析(PWA))具有 有可能提高PAD诊断的准确性和便利性。特别是，PWA可以表现得更好 通过利用动脉的脉搏波形(例如ABI)中的离散特征构建的技术 完整的脉搏波形。另外，利用动脉脉搏波可以方便地实现脉搏波变换 在四肢部位(例如，已经在ABI使用的手臂和脚踝)进行测量。然而，PWA 涉及基于试错法的经验特征选择。因此，PWA可能会与现代深度相结合 学习(DL)技术，利用DL自动选择与任务相关的功能的能力。 成功训练用于PAD诊断的DL算法需要关联大量的标记数据集 从不同的PAD患者收集的纵向PAD进展。然而，只有稀有的(可能是 非纵向的)来自少量患者的数据集实际上可能是可用的。现在那个动脉脉搏 波形不仅受到PAD的影响，而且还受到PAD的解剖和动脉生物力学特性的影响 患者、数据集的不足会降低DL算法对干扰的健壮性 由于在真实的PAD中遇到了广泛的解剖和动脉生物力学特性 患者在动脉脉搏波中模糊了PAD的特征。为了解决这些障碍，我们 提出通过开发一种新的计算方法来实现使能DL的动脉PWA方法来诊断PAD 稀缺数据集下数据挖掘算法的稳健训练方法。我们的基本想法是将传统的 以领域对抗性学习指导数字图书馆的训练，促进潜在特征的独立开发 持续的解剖学和动脉生物力学障碍在诊断PAD中的作用。具体目标包括：(I) 提出了一种连续域对抗正则化(CDAR)方法，用于稳健的DL算法训练 稀缺的数据集；以及(Ii)展示在以下方面开发的具有DL功能的动脉PWA的潜力 CDAR用于检测、定位和评估PAD针对相关干扰的严重程度 与患者身高和动脉僵硬的关系，这是一项资源高效的计算机研究。我们还会估算出 需要的数据集，以实现准确和强大的PAD诊断，为我们的后续活体研究提供信息。如果 成功的CDAR方法和支持DL的PWA可能广泛适用于范围的诊断 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-- 使用从以下项目收集的数据集对启用DL的PWA方法进行密集的体内评估以进行PAD诊断 真实的PAD患者根据样本量通知本项目的结果。