Abstract: Objective To explore the feasibility of using machine learning and audio technology and analyzing the auscultation data from arteriovenous fistula (AVF) to monitor AVF function. Methods A total of 50 patients with AVF stenosis which required percutaneous transluminal angioplasty (PTA) were recruited in this study. Digital sound of the AVF shunt was recorded using a wireless electronic stethoscope before (pre-PTA) and after PTA (post-PTA). The audio files were subsequently converted to spectrograms, and the acoustic signatures were extracted. More than 30 classification learners in MATLAB software were used to analyze the extracted acoustic features, and models to evaluate the AVF function were constructed. Finally, the performance of these models was compared.   Results  One hundred audio files were obtained from the 50 recruited patients and were pooled for the study. In the spectrogram comparison of AVF before PTA and after PTA, the spectrogram before PTA showed a larger high-frequency amplitude. There was a significant difference between the files before and after PTA (Z=-4.721, P＜0.001). The highest and lowest frequencies in a cardiac cycle were significant different before and after PTA, and the difference was greater before PTA than after PTA (Z=-6.169, P＜0.001). Among the models established by more than 30 kinds of classification learners, the model established by efficient linear support vector machine (SVM) and coarse Gaussian SVM had the best performance, with 81.11% accuracy after 5-fold cross validation. The test accuracy of the model constructed by quadratic discrimination, cubic kNN, medium neural network, and double-layer neural network can reach up to 90%.  Conclusions  Spectrum-based machine learning models can predict the saliency narrowness of AVF, so it is feasible to use it for the monitoring of AVF function. The model constructed by efficient linear SVM and rough Gaussian SVM had the best performance in this feasibility study.

Key words: Arteriovenous fistula, Percutaneous transluminal angioplasty, Machine learning, Audio, Artificial intelligence