Digitized Acoustic Analysis for Monitoring Hemodialysis Access Dysfunction: Insights from Vascular Imaging and Post-Angioplasty Data

<b>Background:</b> Hemodialysis access dysfunction can lead to missed treatments and increased mortality. Traditional monitoring methods, such as physical examination and ultrasound, have limitations, emphasizing the need for a more efficient approach. This study investigates the use of digitized acoustic data to identify and monitor vascular access dysfunction. <b>Methods:</b> This prospective study involved patients undergoing hemodialysis with either arteriovenous fistulas (AVF) or arteriovenous grafts (AVG) between June 2023 and February 2025. All patients underwent vascular imaging (either angiography or ultrasound) to confirm the degree of stenosis. Acoustic data were recorded using a standardized procedure at various puncture sites. Pre- and post-angioplasty data were also collected to assess the effects of vascular intervention. The digitized acoustic data were analyzed for changes in relative loudness, peak-to-valley ratios, and frequency distribution. <b>Results:</b> A total of 157 patients with 236 audio recordings (mean age: 67 ± 11 years; 58% male) were included. Significant acoustic differences were found at the arterial puncture and anastomosis sites in AVF patients with dysfunction, particularly in venous site dysfunction, which exhibited a more pronounced reduction in sound volume and an increased peak-to-valley ratio. After angioplasty, acoustic changes were observed in both arterial and venous sites, with values moving toward normal levels. However, no significant acoustic changes were observed in AVG patients. Additionally, frequency distribution ratios showed minimal clinical relevance. <b>Conclusions:</b> Digitized acoustic data, particularly from the arterial puncture and anastomosis sites, can be an effective tool for detecting and monitoring hemodialysis access dysfunction. These findings suggest potential for acoustic analysis in clinical practice, especially when integrated with AI models for better diagnostics.