X-Ray Bone Marrow Edema Estimation Without MRI

The patent uses X-ray images as a cheaper copy or alternative representation of the joint structure, attempting to capture bone marrow edema information without requiring the expensive MRI scan. The deep learning model learns to extract BME features from this simpler X-ray representation, effectively copying the diagnostic capability of MRI at lower cost.

The patent replaces the expensive, complex MRI machine with a more economical X-ray system combined with a software-based deep learning model. This substitution uses cheaper hardware (X-ray machine) and computational resources (training data and models) to achieve comparable diagnostic accuracy for bone marrow edema detection.

The patent changes the input parameter from MRI images to X-ray images for training the deep learning model. By adjusting the training data source and learning parameters, the model adapts to extract bone marrow edema features from X-ray images, achieving cost-effective diagnosis while maintaining acceptable precision through optimized training procedures.

The patent employs feedback mechanisms during model training where the deep learning model continuously refines its estimates based on training data and validation results. This feedback loop allows the model to improve its accuracy in estimating bone marrow edema from X-ray images, ensuring that cost-effectiveness does not come at the expense of diagnostic precision.