Machine Learning for Handheld Vascular Studies

Part of paid clinical trials in Durham, North Carolina.

Sponsor
Duke University
Study ID
NCT02932176
Status
Recruiting

Conditions

  • Atherosclerosis
  • Wounds and Injuries

Eligibility Criteria

Sex
ALL
Age
N/A - N/A
Healthy Volunteers
Accepted

Interventions

  • Non-invasive vascular testing — DEVICE
    Results of clinically indicated non-invasive vascular testing will be used to develop a machine learning algorithm
  • machine-learning algorithm — DEVICE

Study Details

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

Key Dates

Start date
Sep 7, 2016
Status verified
Mar 2026
Primary completion
Dec 31, 2026
Completion
Dec 31, 2026

Study Design

Enrollment
180 participants (estimated)

Arms

  • Arm: Non-invasive vascular testing
    All patients undergoing non-invasive vascular testing will be eligible for this study. The official results will be used to develop the algorithm and to evaluate the accuracy of the algorithm

Primary Outcome Measure

Algorithm generated Doppler classification [ Time Frame: 1 year ]

Central Contacts

Locations (1)

FacilityCityStateZIPSite coordinators
Duke University Medical CenterDurhamNorth Carolina27710
Leila Mureebe, MD

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