Prediction of cardiac rejection via machine learning derived features from digital endomyocardial biopsy images

A method determining a prediction associated with a transplant patient, comprising:

• identifying one or more immune cell regions or one or more interstitial fibers of one or more digitized endomyocardial biopsy (EMB) images from one or more patients;
• generating a plurality of histological features associated with the one or more immune cell regions or the one or more interstitial fibers;
• determining a set of discriminant features from the plurality of histological features, wherein the set of discriminant features are a subset of the plurality of histological features that are highly determinative of a prediction;
• and
• operating a machine learning predictive model on the set of discriminant features to generate the prediction.

An apparatus configured to generate a prediction associated with a transplant patient, comprising:

• a memory configured to store an imaging data set comprising one or more digitized endomyocardial biopsy (EMB) images from one or more patients;
• and
• a machine learning pipeline, comprising:
• a segmentation stage configured to identify one or more immune cell regions or one or more interstitial fibers within the one or more digitized EMB images;
• a feature extraction stage configured to extract a plurality of histological features associated with the one or more immune cell regions or the one or more interstitial fibers;
• and
• a machine learning predictive model configured to operate on the plurality of histological features to generate one or more predictions for the one or more patients.