US 12,213,810 B2
Systems and methods for automated coronary plaque characterization and risk assessment using intravascular optical coherence tomography
Thomas E. Milner, Elgin, TX (US); Vikram Lal Baruah, Vernon Hills, IL (US); Aydin Zahedivash, Austin, TX (US); Austin McElroy, Austin, TX (US); Marc D. Feldman, San Antonio, TX (US); and Taylor Brent Hoyt, San Antonio, TX (US)
Assigned to Research Development Foundation, Carson City, NV (US)
Appl. No. 16/308,081
Filed by Research Development Foundation, Carson City, NV (US)
PCT Filed Jun. 8, 2017, PCT No. PCT/US2017/036587
§ 371(c)(1), (2) Date Dec. 7, 2018,
PCT Pub. No. WO2017/214421, PCT Pub. Date Dec. 14, 2017.
Claims priority of provisional application 62/347,379, filed on Jun. 8, 2016.
Prior Publication US 2023/0083484 A1, Mar. 16, 2023
Int. Cl. A61B 5/00 (2006.01); G06T 7/00 (2017.01)
CPC A61B 5/7264 (2013.01) [A61B 5/0066 (2013.01); A61B 5/0084 (2013.01); G06T 7/0014 (2013.01); G06T 2207/10101 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 26 Claims
OG exemplary drawing
 
1. 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.