AtherosclerosisDopplerZAMNCT02932176
Machine Learning for Handheld Vascular Studies
An observational study of Atherosclerosis and Wounds and Injuries, run by Duke University at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Atherosclerosis, Wounds and Injuries
- What is being tested?
- Non-invasive vascular testing and machine-learning algorithm
- Type of treatment
- Medical device
- How many people
- 155 participants
- Where
- 1 siteUnited States
- Study dates
- 7 Sep 2016to 22 Jul 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Patients with a clinical indication and order for non-invasive vascular testing
Who may be able to join
- A clinically driven request for non-invasive vascular testing must be present
Who cannot join
- None (other than patient declines to participate)
Trial locations and contacts
1 site in 1 country.
United States1 site
Duke University Medical Center
Durham, North Carolina, 27710
About this study
Official title: Development and Validation of a Novel Machine-learning Algorithm to Assist in Handheld Vascular Diagnostics
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
Read the detailed description
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.
Furthermore, the investigators will investigate whether time domain features are necessary for accurate sound classification. Other studies have shown that specific features of audio waveforms can classify the data. The investigators will employ some of the most effective machine-learning algorithms for classification such as SVM, logistic regression, and Naïve Bayes, among others. The investigators will start with a binary classification problem in which individuals will be classified as healthy or unhealthy. Then, the investigators will move in complexity to multi-class classification problems in which individuals will be categorized into different groups according to defined abnormal arterial conditions. Data enrichment using physics-based models employing physiologically accurate finite element models of fluid flow in arteries to generate synthetic sound signals corresponding to various arterial conditions. Physics-based simulations would allow the investigators to produce a wealth of training data that can span many known arterial conditions. This capability can augment the classification accuracy and generalization of our algorithms, as clinical data may not be exhaustive enough to incorporate all the known arterial conditions. The investigators will study the performance of the trained algorithms on patient data. To this end, the investigators will partition the data into training and testing samples. The training samples will be used for training of the algorithms, while the testing set will be used to assess generalization capability. The investigators will compute misclassification rates for each algorithm as a metric for performance.
- Conditions
- Atherosclerosis, Wounds and Injuries
- Being tested
- Non-invasive vascular testing, machine-learning algorithm
- Keywords
- Arteries
- Medical subject headings
- Atherosclerosis, Wounds and Injuries
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Non-invasive vascular testingDevice
Also called Continuous wave Doppler, plethysmography
Results of clinically indicated non-invasive vascular testing will be used to develop a machine learning algorithm
machine-learning algorithmDevice
| Study group | Role | What they receive |
|---|---|---|
| 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 Receives: Device: Non-invasive vascular testing; Device: machine-learning algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
Algorithm generated Doppler classification
Measured: 1 year
Secondary outcomes (4)
Presence or absence of pulse
Measured: 1 year
Quality of pulse
Measured: 1 year
Presence or absence of Doppler signal
Measured: 1 year
Quality of Doppler signal
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Oct 2016 (estimated)
- Study start7 Sep 2016
- Primary completion22 Jul 2026
- Study completion22 Jul 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for AtherosclerosisStudies of the same condition, open and under way.
- More trials run by Duke UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 155 participants (actual)
Sponsor and investigators
- Lead sponsor
- Duke University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02932176
- Sponsor's protocol ID
- Pro00070090
- Acronym
- DopplerZAM
- Status last verified
- Jul 2026
- First submitted
- 19 Sep 2016
- Last updated
- 27 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 27 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of a Novel Machine-learning Algorithm to Assist in Handheld Vascular Diagnostics
- ClinicalTrials.gov ID
- NCT02932176
- Acronym / sponsor ID
- DopplerZAM / Pro00070090
- Conditions
- Atherosclerosis, Wounds and Injuries
- Keywords
- Arteries
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 155 participants (actual)
- Lead sponsor
- Duke UniversityUniversity, hospital or other
- Study start
- 7 Sep 2016 (actual)
- Primary completion
- 22 Jul 2026 (actual)
- Study completion
- 22 Jul 2026 (actual)
- First posted
- 13 Oct 2016 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
Detailed description
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.
Furthermore, the investigators will investigate whether time domain features are necessary for accurate sound classification. Other studies have shown that specific features of audio waveforms can classify the data. The investigators will employ some of the most effective machine-learning algorithms for classification such as SVM, logistic regression, and Naïve Bayes, among others. The investigators will start with a binary classification problem in which individuals will be classified as healthy or unhealthy. Then, the investigators will move in complexity to multi-class classification problems in which individuals will be categorized into different groups according to defined abnormal arterial conditions. Data enrichment using physics-based models employing physiologically accurate finite element models of fluid flow in arteries to generate synthetic sound signals corresponding to various arterial conditions. Physics-based simulations would allow the investigators to produce a wealth of training data that can span many known arterial conditions. This capability can augment the classification accuracy and generalization of our algorithms, as clinical data may not be exhaustive enough to incorporate all the known arterial conditions. The investigators will study the performance of the trained algorithms on patient data. To this end, the investigators will partition the data into training and testing samples. The training samples will be used for training of the algorithms, while the testing set will be used to assess generalization capability. The investigators will compute misclassification rates for each algorithm as a metric for performance.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Non-invasive vascular testing | – | Device: Non-invasive vascular testing, Device: machine-learning algorithmAll 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 |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients with a clinical indication and order for non-invasive vascular testing
Key inclusion criteria (1 listed)
- A clinically driven request for non-invasive vascular testing must be present
Key exclusion criteria (1 listed)
- None (other than patient declines to participate)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Non-invasive vascular testingAlso: Continuous wave Doppler, plethysmographyResults of clinically indicated non-invasive vascular testing will be used to develop a machine learning algorithm | Device | Non-invasive vascular testing |
| machine-learning algorithm | Device | Non-invasive vascular testing |
Primary outcome
Algorithm generated Doppler classification
Time frame: 1 year
4 secondary outcomes
Presence or absence of pulse
Time frame: 1 year
Quality of pulse
Time frame: 1 year
Presence or absence of Doppler signal
Time frame: 1 year
Quality of Doppler signal
Time frame: 1 year
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Aug 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Atherosclerosis and Wounds and Injuries, run by Duke University at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 27 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Algorithm generated Doppler classification (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Aug 2026.