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Machine Learning for Handheld Vascular Studies
Sponsor: Duke University
Summary
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
Official title: Development and Validation of a Novel Machine-learning Algorithm to Assist in Handheld Vascular Diagnostics
Key Details
Gender
All
Age Range
Any - Any
Study Type
OBSERVATIONAL
Enrollment
180
Start Date
2016-09-07
Completion Date
2026-12-31
Last Updated
2026-03-05
Healthy Volunteers
Yes
Conditions
Interventions
Non-invasive vascular testing
Results of clinically indicated non-invasive vascular testing will be used to develop a machine learning algorithm
machine-learning algorithm
Locations (1)
Duke University Medical Center
Durham, North Carolina, United States