Clinical trial NCT02932176: Machine Learning for Handheld Vascular Studies
Official title: Development and Validation of a Novel Machine-learning Algorithm to Assist in Handheld Vascular Diagnostics
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Atherosclerosis
Wounds and Injuries
Keywords: Arteries
Lead sponsor: Duke University
Sponsor class: OTHER
First posted: 2016-10-13
Enrollment: 180
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with a clinical indication and order for non-invasive vascular testing

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.

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)

Primary outcomes:
measure: Algorithm generated Doppler classification
timeFrame: 1 year
Secondary outcomes:
measure: Presence or absence of pulse
timeFrame: 1 year
measure: Quality of pulse
timeFrame: 1 year
measure: Presence or absence of Doppler signal
timeFrame: 1 year
measure: Quality of Doppler signal
timeFrame: 1 year
Other outcomes:
Locations:
city: Durham
country: United States
facility: Duke University Medical Center
state: North Carolina
Study officials:
References: