ArticleNPJ digital medicine2023
Deep learning analysis of blood flow sounds to detect arteriovenous fistula stenosis.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 23 citations in OpenAlex.
- Risk Factors of Arteriovenous Fistula Stenosis in Hemodialysis Patients Using Electronic Stethoscope Monitoring: Prospective Pilot Cohort Study.JMIR formative research · 2026Article
- Preliminary evaluation of a novel surface accelerometer (HAVSE) for dialysis access surveillance: An exploratory pilot study.The journal of vascular access · 2026Article
- Acoustic-based Stenosis Detection for Dialysis Patients using Explainable Machine Learning.Research square · 2026Article
- Wireless, non-invasive, high-resolution thrill sensor for continuous vascular access monitoring of hemodialysis patients.Nature communications · 2026Article
- Hemodialysis vascular sound index as a diagnostic tool for vascular access stenosis: a prospective matched observational study.BMC nephrology · 2026Observational
- Hemodialysis vascular access flow surveillance: Current evidence and future directions.Journal of the Chinese Medical Association : JCMA · 2025Review
- Article
- Foundation models for cardiovascular disease detection via biosignals from digital stethoscopes.NPJ cardiovascular health · 2024Article
- Prediction of Vascular Access Stenosis by Lightweight Convolutional Neural Network Using Blood Flow Sound Signals.Sensors (Basel, Switzerland) · 2024Article
- The dawn of multimodal artificial intelligence in nephrology.Nature reviews. Nephrology · 2024Article
- The application of blood flow sound contrastive learning to predict arteriovenous graft stenosis of patients with hemodialysis.PloS one · 2024Article
- Prediction of Arteriovenous Access Dysfunction by Mel Spectrogram-based Deep Learning Model.International journal of medical sciences · 2024Article
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Authors and funding
14 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For hemodialysis patients, arteriovenous fistula (AVF) patency determines whether adequate hemofiltration can be achieved, and directly influences clinical outcomes. Here, we report the development and performance of a deep learning model for automated AVF stenosis screening based on the sound of AVF blood flow using supervised learning with data validated by ultrasound. We demonstrate the importance of contextualizing the sound with location metadata as the characteristics of the blood flow sound varies significantly along the AVF. We found the best model to be a vision transformer trained on spectrogram images. Our model can screen for stenosis at a performance level comparable to that of a nephrologist performing a physical exam, but with the advantage of being automated and scalable. In a high-volume, resource-limited clinical setting, automated AVF stenosis screening can help ensure patient safety via early detection of at-risk vascular access, streamline the dialysis workflow, and serve as a patient-facing tool to allow for at-home, self-screening.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.