Towards noninvasive early detection and classification of peripheral arterial disease stenosis severity by perfusion dynamics and artificial intelligence
Authors: Livneh A , Heitner T , Landesberg A 
Published: October 2022

Introduction

Recent guidelines suggest Ankle-Brachial Index (ABI) measurement for assessing and reducing cardiovascular risk. PAD patients suffer from generalized arterial stiffening (arteriosclerosis) and develop threatening arterial stenosis foci. ABI cannot differentiate between the two, and detecting focal stenosis is especially challenging in the elderly and diabetics suffering from severe arteriosclerosis. Thus, better diagnostic tools are required for early detection of severe arterial stenosis in order to improve patient management reduce atherosclerotic cardiovascular risk and amputation rates.

Purpose

We hypothesized that quantifying the peripheral perfusion dynamics would provide more sensitive indices for monitoring atherosclerotic disease severity. Thus, we compared the utility of ABI, impedance-plethysmography (IPG), and photoplethysmography (PPG) in detecting and classifying arterial-stenosis using machine learning and artificial intelligence.

Methods

Arterial stenosis was emulated in supine lying healthy consenting volunteers (n=31) by inflating a blood-pressure cuff around their thigh to 45 and 90 mmHg for six minutes. We quantified the perfusion dynamics by IPG and PPG signal analyses in the time-frequency domain. The induced stenosis levels were classified by artificial intelligence using novel indices.

Results

Hallux O2-saturation remained intact (96±3%, p=0.4) even during 90 mmHg thigh-cuff compression. The baseline ABI (1.19±0.01) remained unchanged during 45 mmHg compression, slightly declined (1.12±0.02) upon 90 mmHg compression; but, all ABI values were above the 0.9 threshold. The pulse transit time, a well-established index of arteriosclerosis, remained unchanged. Conversely, upon stenosis emulation (compression), the IPG immediately recorded a significant proportional phase delay between the compressed and contralateral legs and a significant proportional correlation decline. At baseline, the IPG phase-difference between legs over the 2–4 Hz band was 1.79±0.48° and grew to 29.5±2.4° and 71.2±1.8° upon 45 and 90 mmHg compression (p&lt;0.001). The IPG inter leg correlation at the 4–6 Hz band declined from 0.962±0.005 at baseline to 0.857±0.012 and 0.676±0.015 during 45 and 90 mmHg compression (p&lt;0.001). The PPG phase difference grew significantly and proportionally upon compression (p&lt;0.001, n=28) but without a significant correlation decline. A neural-network classifier achieved 100% accuracy in classifying baseline and each compression level based on the IPG phase and correlation. The best classifier achieved only 81.25% accuracy, 83.33% sensitivity, and 90% Specificity based on the PPG.

Conclusion

Our perfusion-dynamic indices outperformed the ABI, and the IPG outperformed the PPG in detecting and classifying emulated stenosis. Differentiation between arteriosclerosis and arterial stenosis may be of immense clinical merit. Therefore, this simple, noninvasive method merits clinical evaluation.

Funding Acknowledgement

Type of funding sources: None.