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 外围动脉疾病（PAD）是一种高度普遍的血管疾病，需要高发病率和死亡率 风险。但是，PAD诊断不足，初级保健意识较低。临床的常规垫诊断 设置不适合低成本，高通量和准确的垫诊断。注意到垫子变化 脉冲波形，动脉脉冲波形的分析（称为脉冲波形分析（PWA））具有 潜在提高PAD诊断的准确性和便利性。特别是，PWA胜过 通过利用动脉的动脉脉冲波形（例如ABI）建立的技术 脉冲波形完整。此外，可以方便地使用动脉脉冲波形实现PWA 在末端部位（例如，手臂和脚踝，已在ABI中使用）。但是，PWA 涉及基于反复试验的经验特征选择。因此，PWA可能与现代深 学习（DL）技术以利用DL自动选择与任务相关的功能的能力。 成功培训DL算法以进行PAD诊断需要大量标记的数据集相关的数据集 纵向垫的进展从不同的垫患者那里收集。但是，只有稀缺（可能 现实中可能会提供来自少数患者的非态数据集。现在动脉脉冲 波形不仅受PAD的影响，还受到解剖学和动脉生物力学特征的影响 患者，数据集中的不足可能会恶化DL算法的鲁棒性针对障碍的鲁棒性 由于在现实世界中遇到的各种解剖学和动脉生物力学特征 患者遮盖了动脉脉冲波形中垫的特征。为了解决这些障碍，我们 建议通过开发一种新型计算来实现启用了启用DL的动脉PWA方法来诊断PAD诊断 使用稀缺数据集对DL算法进行强大训练的方法。我们的基本思想是扩展常规 领域 - 反面学习以指导DL培训，以促进对潜在特征的开发独立 诊断垫中连续的解剖学和动脉生物力学障碍。具体目的包括：（i） 开发一种连续的域 - 逆转正则化（CDAR）方法，用于使用 稀缺数据集； （ii）证明借助于 CDAR用于检测，本地化和评估PAD的严重程度，以与相关的障碍进行鲁棒性 在计算机研究中，在资源有效的资源效率下，患者身高和动脉僵硬。我们还将估计金额 为了使我们的体内研究的随访所需的数据集所需的数据集。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。该项目的成功将为我们提供有力的资源理由 - 使用从中收集的数据集对支持DL的PWA方法进行PAD诊断的大量评估 根据该项目结果所告知的样本量，真正的PAD患者。