Development and Validation of a Novel Machine-learning Algorithm to Assist in Handheld Vascular Diagnostics

Status: Recruiting
Location: See location...
Intervention Type: Device
Study Type: Observational
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

Eligibility
Participation Requirements
Sex: All
Healthy Volunteers: t
View:

• A clinically driven request for non-invasive vascular testing must be present

Locations
United States
North Carolina
Duke University Medical Center
RECRUITING
Durham
Contact Information
Primary
Leila Mureebe, MD
leila.mureebe@duke.edu
Time Frame
Start Date: 2016-09-07
Estimated Completion Date: 2025-12-31
Participants
Target number of participants: 180
Treatments
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
Related Therapeutic Areas
Sponsors
Leads: Duke University

This content was sourced from clinicaltrials.gov