Machine Learning for Handheld Vascular Studies
| Status: | Recruiting |
|---|---|
| Conditions: | Peripheral Vascular Disease, Cardiology, Hospital |
| Therapuetic Areas: | Cardiology / Vascular Diseases, Other |
| Healthy: | No |
| Age Range: | Any |
| Updated: | 10/28/2018 |
| Start Date: | September 7, 2016 |
| End Date: | December 2018 |
| Contact: | Leila Mureebe, MD |
| Email: | leila.mureebe@duke.edu |
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
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
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.
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.
Inclusion Criteria:
- A clinically driven request for non-invasive vascular testing must be present
Exclusion Criteria:
- None (other than patient declines to participate)
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