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

项目总结/文摘

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.