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 |
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.
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