Customizing a machine learning model to process a selected subset of features to predict geographic atrophy progression

A method for generating a customized machine learning model, the method comprising:

• receiving a plurality of fundus autofluorescence (FAF) images for a plurality of subjects;
• generating a plurality of mask images based on the plurality of FAF images;
• generating, for each FAF image of the plurality of FAF images, a plurality of image features based on the plurality of mask images, wherein the plurality of image features includes a plurality of shape features and a plurality of texture features;
• training a set of initial machine learning models to predict growth rate of a geographic atrophy (GA) lesion using a plurality of features, wherein the plurality of features includes the plurality of image features and a plurality of clinical features;
• generating a plurality of scores for the plurality of features in which the plurality of scores indicates a ranking of importance of the plurality of features to the predicted growth rate;
• and
• building a customized machine learning model that predicts growth rate using a subset of features selected from the plurality of features based on the plurality of scores.

A system for generating a customized machine learning model, the system comprising:

• at least one data processor;
• and at least one memory storing instructions, which when executed by the at least one data processor, cause the processor to:
• receive a plurality of fundus autofluorescence (FAF) images for a plurality of subjects;
• generate a plurality of mask images based on the plurality of FAF images;
• generate, for each FAF image of the plurality of FAF images, a plurality of image features based on the plurality of mask images, wherein the plurality of image features includes a plurality of shape features and a plurality of texture features;
• train a set of initial machine learning models to predict growth rate of a geographic atrophy (GA) lesion using a plurality of features, wherein the plurality of features includes the plurality of image features and a plurality of clinical features;
• generate a plurality of scores for the plurality of features in which the plurality of scores indicates a ranking of importance of the plurality of features to the predicted growth rate;
• and
• build a customized machine learning model that predicts growth rate using a subset of features selected from the plurality of features based on the plurality of scores.