Method for Predict Stenosis of Arteriovenous Fistula Patients Based on Machine Learning

Artificial intelligence can help predict AVF stenosis by analyzing the digitalized sound waves of AVF. This analysis is convenient and non-invasive. Moreover, this technique can help the development of a remote monitor of AVF stenosis, which is especially important in the era of the COVID-19 pandemic.

Authors

Y.Y. Chen
Wei‐Tse Hsu (ORCID: https://orcid.org/0000-0001-9576-0288)
Christopher S. Chen (ORCID: https://orcid.org/0000-0003-2445-8449)
Wei‐Ta Chen

Institutions

The University of Sydney (AU)
Taipei Medical University Hospital (TW)
Taipei Medical University (TW)

Publication Details

Journal: Seminars in Dialysis
Published: 2025-07-01
DOI: https://doi.org/10.1111/sdi.70001
Primary Topic: Central Venous Catheters and Hemodialysis
Type: article
Field-Weighted Citation Impact: 0.00

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article

Method for Predict Stenosis of Arteriovenous Fistula Patients Based on Machine Learning

Y.Y. Chen, Wei‐Tse Hsu, Christopher S. Chen, Wei‐Ta Chen

Jul 1, 2025

Seminars in Dialysis

Central Venous Catheters and Hemodialysis

article

Method for Predict Stenosis of Arteriovenous Fistula Patients Based on Machine Learning

Y.Y. Chen, Wei‐Tse Hsu, Christopher S. Chen, Wei‐Ta Chen

July 1, 2025article en

Abstract

Artificial intelligence can help predict AVF stenosis by analyzing the digitalized sound waves of AVF. This analysis is convenient and non-invasive. Moreover, this technique can help the development of a remote monitor of AVF stenosis, which is especially important in the era of the COVID-19 pandemic.

Seminars in DialysisVol. 38(4)

The University of Sydney (AU), Taipei Medical University Hospital (TW), Taipei Medical University (TW)

Good health and well-being

Openalex Percentile: Top 74%

Central Venous Catheters and Hemodialysis

DOI Source OpenAlex

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