Machine Learning for Handheld Vascular Studies

Study overview

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

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

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Collaborators and Investigators

Duke University,

Study Record Dates

2025-12-31