Stenosis Characterization and Identification for Dialysis Vascular Access

An effective AI model for automatically detecting arteriovenous fistula stenosis

In this study, a novel artificial intelligence (AI) model is proposed to detect stenosis in arteriovenous fistulas (AVFs) using inexpensive and non-invasive audio recordings. The proposed model is a combination of two new input features based on short-time Fourier transform (STFT) and sample entropy, as well as two associated classification models (ResNet50 and ANN). The model's hyper-parameters were optimized through the use of the design of the experiment (DOE). The proposed AI model demonstrates high performance with all essential metrics, including sensitivity, specificity, accuracy, precision, and F1-score, exceeding 0.90 at detecting stenosis greater than 50%. These promising results suggest that our approach can lead to new insights and knowledge in this field. Moreover, the robust performance of our model, combined with the affordability of the audio recording device, makes it a valuable tool for detecting AVF stenosis in home-care settings.

Quantification and Visualization of Reliable Hemodynamics Evaluation Based on Non-Contact Arteriovenous Fistula Measurement

The condition of arteriovenous fistula (AVF) blood flow is typically checked by using auscultation; however, auscultation should require a qualitative judgment dependent on the skills of doctors, and further attention to contact infection is required. For these reasons, this study developed a non-contact and non-invasive medical device to measure the pulse wave of AVFs by applying optical imaging technology. As a first step toward realization of the quantification judgment based on non-contact AVF measurement, we experimentally validated the developed system, whereby the hemodynamics of 168 subjects were visually and quantitatively evaluated based on clinical tests. Based on the evaluation results, the fundamental statistical characteristics of the non-contact measurement, including the average and median values, and distribution of measured signal-to-noise power ratio, were demonstrated. The clinical test results contributed to the future construction of quantified criteria for the AVF condition with the non-contact measurement.

VASCULAR STENOSIS DETECTION USING TEMPORAL-SPECTRAL DIFFERENCES IN CORRELATED ACOUSTIC MEASUREMENTS

Central venous stenosis is often undiagnosed in patients with hemodialysis vascular access, partly due to imaging difficulties. Noninvasive, point-of-care detection could rely on detecting regions of turbulent blood flow caused by blood velocity changes. Here we present flexible microphone arrays for time-correlated measures of blood flow sounds and a new signal processing approach to calculate time correlation between spectral features. Continuous wavelet transform was used to produce an auditory spectral flux analytic signal, which was thresholded to identify systolic start and end phases. Microphone arrays were tested on pulsatile flow phantoms with blood flow rates of 850-1,200 mL/min and simulated stenosis from 10-85%. Measured results showed an inversion in the time onset of systolic spectral content for sites proximal and distal to stenosis for hemodynamically significant stenoses (+22 ms for stenosis<50% and -20 to -38 ms for stenosis>50%). Equivalent blood velocity increases were calculated as 142-155 cm/s in stenotic phantoms, which are within the physiologic range as measured by ultrasound.

Flexible, Skin Coupled Microphone Array for Point of Care Vascular Access Monitoring

Point-of-care screening for hemodialysis vascular access dysfunction requires tools that are objective and efficient. Listening for bruits during physical exam is a subjective examination which can detect stenosis (vascular narrowing) when properly performed. Phonoangiograms (PAGs)-mathematical analysis of bruits-increases the objectivity and sensitivity and permits quantification of stenosis location and degree of stenosis (DOS). This work describes a flexible and body-conformal multi-channel sensor and associated signal processing methods for automated DOS characterization of vascular access. The sensor used an array of thin-film PVDF microphones integrated on polyimide to record bruits at multiple sites along a vascular access. Nonlinear signal processing was used to extract spectral features, and cardiac cycle segmentation was used to improve sensitivity. PAG signal processing algorithms to detect stenosis location and severity are also presented. Experimental results using microphone arrays on a vascular access phantom demonstrated that stenotic lesions were detected within 1 cm of the actual location and graded to three levels (mild, moderate, or severe). Additional PAG features were also used to define a simple binary classifier aimed at patients with failing vascular accesses. The classifier achieved 90% accuracy, 92% specificity, and 91% sensitivity at detecting stenosis greater than 50%. These results suggest that point-of-care screening using microphone arrays can identify at-risk patients using automated signal analysis.