Machine Learning for Handheld Vascular Studies

NCT ID: NCT02932176

Last Updated: 2025-01-27

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 180 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2016-09-07
• Study Completion Date: 2025-12-31

Brief 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

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

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

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

Non-invasive vascular testing

Intervention Type: DEVICE

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

machine-learning algorithm

Intervention Type: DEVICE

Interventions

Non-invasive vascular testing

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

Intervention Type: DEVICE

machine-learning algorithm

Intervention Type: DEVICE

Other Intervention Names

Continuous wave Doppler; plethysmography

Eligibility Criteria

Inclusion Criteria

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

Exclusion Criteria

• None (other than patient declines to participate)

• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Duke University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Duke University Medical Center

Durham, North Carolina, United States

Site Status: RECRUITING

Countries

United States

Central Contacts

Leila Mureebe, MD

Role: CONTACT

leila.mureebe@duke.edu

Facility Contacts

Leila Mureebe, MD

Role: primary

leila.mureebe@duke.edu

Other Identifiers

Pro00070090

Identifier Type: -

Identifier Source: org_study_id