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US 12,213,810 B2Granted
Systems and methods for automated coronary plaque characterization and risk assessment using intravascular optical coherence tomography
AssigneeResearch Development Foundation
InventorsThomas E. Milner, Vikram Lal Baruah, Aydin Zahedivash, Austin McElroy, Marc D. Feldman, Taylor Brent Hoyt
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Abstract
Exemplary embodiments of the present disclosure include apparatus and methods to classify the plaque tissue present in the coronary artery using intravascular optical coherence tomography (IVOCT) images.
Claims26
A system comprising:
- an imaging device comprising an optical coherence tomography light source, wherein the imaging device is configured to obtain an image of intravascular tissue comprising plaque;
- and
- a non-transitory computer readable medium configured to:
- analyze a pixel of the image with a first neural network configured to classify the plaque as a first tissue type of a plurality of tissue types;
- analyze the pixel of the image with a second neural network configured to classify the plaque as a second tissue type of the plurality of tissue types;
- and
- analyze the pixel of the image with a third neural network configured to classify the plaque as a third tissue type of the plurality of tissue types, wherein the non-transitory computer readable medium is configured to optimize the first, second and third neural networks by evaluating a plurality of features of the image with nodes of the first, second and third neural networks to calculate sensitivity and specificity of the plurality of features using a receiver operating characteristic (ROC) curve.
4 more elements
A method of characterizing coronary plaque, the method comprising:
- obtaining an image of a sample site using an optical coherence tomography light source emitting light from an optical fiber, wherein the image comprises intravascular tissue comprising plaque;
- analyzing quantitative data of a pixel of the image with a first neural network configured to classify the plaque as a first tissue type of a plurality of tissue types, wherein the first neural network comprises a first plurality of nodes and reads a first plurality of features;
- analyzing quantitative data of the pixel of the image with a second neural network configured to classify the plaque as a second tissue type of the plurality of tissue types, wherein the second neural network comprises a second plurality of nodes and reads a second plurality of features;
- and
- analyzing quantitative data of the pixel of the image with a third neural network configured to classify the plaque as a third tissue type of the plurality of tissue types, wherein the third neural network comprises a third plurality of nodes and reads a third plurality of features, wherein the quantitative data includes classifying features comprising one or more of the following:
- contrast, energy, correlation, homogeneity, entropy, and maximum probability.
3 more elements
Dependencies are inferred from the claim text.