Article... IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE Signal Processing in Medicine and Biology Symposium2018
Stenosis Characterization and Identification for Dialysis Vascular Access.
Article in ... IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE Signal Processing in Medicine and Biology Symposium, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- An effective AI model for automatically detecting arteriovenous fistula stenosis.Scientific reports · 2023Article
- Quantification and Visualization of Reliable Hemodynamics Evaluation Based on Non-Contact Arteriovenous Fistula Measurement.Sensors (Basel, Switzerland) · 2022Article
- Flexible, Skin Coupled Microphone Array for Point of Care Vascular Access Monitoring.IEEE transactions on biomedical circuits and systems · 2019Article
- VASCULAR STENOSIS DETECTION USING TEMPORAL-SPECTRAL DIFFERENCES IN CORRELATED ACOUSTIC MEASUREMENTS.... IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE Signal Processing in Medicine and Biology Symposium · 2019Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Vascular access dysfunction is the leading cause of hospitalization for hemodialysis patients and accounts for the most medical costs in this patient population. Vascular access flow is commonly hindered by blood vessel narrowing (stenosis). Current screening methods involving imaging to detect stenosis are too costly for routine use at the point of care. Noninvasive, real-time screening of patients at risk of vascular access dysfunction could potentially identify high-risk patients and reduce the likelihood of emergency surgical interventions. Bruits (sounds produced by turbulent blood flow near stenoses) can be interpreted by skilled clinical staff using conventional stethoscopes. To improve the sensitivity of detection, digital analysis of blood flow sounds (phonoangiograms or PAGs) is a promising approach for classifying vascular access stenosis using non-invasive auditory recordings. Here, we demonstrate auditory and spectral features of PAGs which estimate both the location and degree of stenosis (DOS). Auditory recordings from nine stenosis phantoms with variable DOS and hemodynamic flow rate were obtained using a digital recording stethoscope and analyzed to extract classification features. Autoregressive modeling and discrete wavelet transforms were used for multiresolution signal decomposition to produce 14 distinct features, most of which were linearly correlated with DOS. Our initial results suggest that the widely-used auditory spectral centroid is a simple way to calculate features which can estimate both the location and severity of vascular access stenosis.
Identifiers
What OpenQuestion 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 OpenQuestion graph.