The prototype device for non-invasive diagnosis of arteriovenous fistula condition

What is it about?

In our study, we have proposed low-cost device which was created using a machine-learning approach. The general concept was to recognize patients with significant stenosis based on patterns developed and assign them to appropriate classes. This activity was performed as a sequence of successive operations: recording of AVF acoustical signal (bruit), signal processing, feature extraction, selection and reduction of features, classification, and interpretation of the classification results. As a final achievement we have constructed a prototype of microprocessor device with the intelligent assessment system which based on the six-step patient classification (letters A-F, from the best condition to the worst). This classification agree with medical assessment with approximately 81% accuracy.

Featured Image

Why is it important?

Based on available literature, up to date, the need for fully automated devices offering the AVF condition assessment has not been satisfied. Both patients and doctors need an easy-to-use and reliable support system in assessing the condition of the fistula. To the best of the authors' knowledge, there is no such tool yet. The proposed device based on machine learning technique can be such support. The strength of this research is almost ideal coincidence of the k-means clusters and the six-step medical scale created by a Dialyze Center staff. Our classifiers uses the very same six-step, medically justified scale.

Perspectives