US 11,854,197 B2
Classification of medical images using machine learning to account for body orientation
Sun Young Park, San Diego, CA (US); and Dustin Michael Sargent, San Diego, CA (US)
Assigned to MERATIVE US L.P., Ann Arbor, MI (US)
Filed by Merative US L.P., Ann Arbor, MI (US)
Filed on Nov. 9, 2021, as Appl. No. 17/522,196.
Prior Publication US 2023/0146953 A1, May 11, 2023
Int. Cl. G06T 7/00 (2017.01); G06T 7/70 (2017.01); G06N 3/04 (2023.01); A61B 6/00 (2006.01); A61B 6/03 (2006.01)
CPC G06T 7/0012 (2013.01) [A61B 6/032 (2013.01); A61B 6/504 (2013.01); A61B 6/5217 (2013.01); G06N 3/04 (2013.01); G06T 7/70 (2017.01); G06T 2207/10081 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30101 (2013.01); G06T 2207/30196 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for identifying a medical condition in a patient, the method comprising:
generating, using a trained machine learning image generator, a set of training images, wherein the set of training images is generated based on three-dimensional imaging data from a plurality of patients, wherein each training image is based on a two-dimensional projection of the three-dimensional imaging data of a particular patient, and wherein each training image is labeled with a projection angle of the corresponding two-dimensional projection;
training, using the set of training images, a machine learning image classifier model to identify patient rotation angles in x-ray images;
processing a set of x-ray images with the machine learning image classifier model to identify a patient rotation angle for each x-ray image, wherein each x-ray image is labeled with a disease state;
training a machine learning medical condition classifier model to identify a medical condition, wherein the machine learning medical condition classifier model is trained using the set of x-ray images labeled with the medical condition state and the patient rotation angle determined by the image classifier model; and
applying the machine learning medical condition classifier model to determine an indication of the medical condition in an input x-ray image acquired from a patient.