Classification of Shunt Murmurs for Diagnosis of Arteriovenous Fistula Stenosis

Higashi, Daisuke; Nishijima, Ken‐ichi; Furuya, Kazuo; Tanaka, Keiko; Shin, Satoko

doi:10.23919/apsipa.2018.8659641

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Comparison with prior attempts to solve this problem shows that out approach is competitive and takes a novel approach to the problem. Prior attempts to classify stenosis by applying an SVM to shunt murmurs resulted in low accuracy (55%) and underperformed human judges [ 27 ]. In contrast, our positive predictive value of 0.957 is slightly higher than the 0.917 achieved by a previous study of AVF classification using PPG signals, suggesting a potential use of this approach as a screening tool [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

Tensor decomposition and machine learning for the detection of arteriovenous fistula stenosis: An initial evaluation

Poushpas¹,

Normahani²,

Kisil³

et al. 2023

PLoS ONE

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Duplex ultrasound (DUS) is the most widely used method for surveillance of arteriovenous fistulae (AVF) created for dialysis. However, DUS is poor at predicting AVF outcomes and there is a need for novel methods that can more accurately evaluate multidirectional AVF flow. In this study we aimed to evaluate the feasibility of detecting AVF stenosis using a novel method combining tensor-decomposition of B-mode ultrasound cine loops (videos) of blood flow and machine learning classification. Classification of stenosis was based on the DUS assessment of blood flow volume, vessel diameter size, flow velocity, and spectral waveform features. Real-time B-mode cine loops of the arterial inflow, anastomosis, and venous outflow of the AVFs were analysed. Tensor decompositions were computed from both the ‘full-frame’ (whole-image) videos and ‘cropped’ videos (to include areas of blood flow only). The resulting output were labelled for the presence of stenosis, as per the DUS findings, and used as a set of features for classification using a Long Short-Term Memory (LSTM) neural network. A total of 61 out of 66 available videos were used for analysis. The whole-image classifier failed to beat random guessing, achieving a mean area under the receiver operating characteristics (AUROC) value of 0.49 (CI 0.48 to 0.50). In contrast, the ‘cropped’ video classifier performed better with a mean AUROC of 0.82 (CI 0.66 to 0.96), showing promising predictive power despite the small size of the dataset. The combined application of tensor decomposition and machine learning are promising for the detection of AVF stenosis and warrant further investigation.

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Section: Discussionmentioning

confidence: 99%

Tensor decomposition and machine learning for the detection of arteriovenous fistula stenosis: An initial evaluation

Poushpas¹,

Normahani²,

Kisil³

et al. 2023

PLoS ONE

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“…As a countermeasure, in the medical field, a population with a higher pre-test probability in other items can be extracted and applied to the judgment machine. In this arteriovenous fistula sound classification approach, a large classifier that combines the classical statistical method based on physical sound features [ 26 ] and a learning model that detects abnormal sounds by unsupervised learning with this learning model is considered to be an effective classification method [ 27 , 28 , 29 ].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Hemodialysis Arteriovenous Bruit by Deep Learning

Ota

Nishiura

Ishihara

et al. 2020

Sensors

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Physical findings of auscultation cannot be quantified at the arteriovenous fistula examination site during daily dialysis treatment. Consequently, minute changes over time cannot be recorded based only on subjective observations. In this study, we sought to supplement the daily arteriovenous fistula consultation for hemodialysis patients by recording the sounds made by the arteriovenous fistula and evaluating the sounds using deep learning methods to provide an objective index. We sampled arteriovenous fistula auscultation sounds (192 kHz, 24 bits) recorded over 1 min from 20 patients. We also extracted arteriovenous fistula sounds for each heartbeat without environmental sound by using a convolutional neural network (CNN) model, which was made by comparing these sound patterns with 5000 environmental sounds. The extracted single-heartbeat arteriovenous fistula sounds were sent to a spectrogram and scored using a CNN learning model with bidirectional long short-term memory, in which the degree of arteriovenous fistula stenosis was assigned to one of five sound types (i.e., normal, hard, high, intermittent, and whistling). After 100 training epochs, the method exhibited an accuracy rate of 70–93%. According to the receiver operating characteristic (ROC) curve, the area under the ROC curves (AUC) was 0.75–0.92. The analysis of arteriovenous fistula sound using deep learning has the potential to be used as an objective index in daily medical care.

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