Duke University
The use of handheld arterial 'stethoscopes' (continuous wave Doppler devices) are ubiquitous in clinical practice. However, most users have received no formal training in their use or the interpretation of the returned data. This leads to delays in diagnosis and errors in diagnosis. The investigators intend to create a novel machine-learning algorithm to assist clinicians in the use of this data. This study will allow the investigators to collect sound files from the use of the devices and compare the algorithms output to established, existing vascular testing. There will be no invasive procedures, and use of these stethoscopes is part of routine clinical care. If successful, this data and algorithm will be later deployed via smartphone app for point of case testing in a separate study
There are three main research tasks for this project: 1) the identification of discriminant features of Doppler audio for patient classification, 2) the selection and training of classification algorithms, and 3) CWD audio data enrichment using physics-based models. The investigators will determine which discriminant features are optimal for patient classification from ultrasound Doppler audio. To this end, the investigators will employ signal features in the frequency domain such as bandwidth, peak frequency, mean power, mean frequency, and time harmonic distortion, among others. Furthermor…
Inclusion Criteria: * A clinically driven request for non-invasive vascular testing must be present Exclusion Criteria: * None (other than patient declines to participate)
Results of clinically indicated non-invasive vascular testing will be used to develop a machine learning algorithm
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