Systems and methods for machine learning (ML) model diagnostic assessments based on digital pathology data

A system for performing a diagnostic assessment based on digital pathology data comprising:

• at least one computer processor, wherein the at least one computer processor is configured to:
• obtain first digital pathology data comprising intensity information, the first digital pathology data being associated with a plurality of regions of interest in a biological sample;
• apply one or more first machine learning models to the first digital pathology data, the one or more first machine learning models identifying one or more first regions of interest among the plurality of regions of interest based on the intensity information;
• apply one or more second machine learning models to the first digital pathology data, the one or more second machine learning models identifying at least one pattern associated with at least one of the one or more first regions of interest;
• generate a diagnostic assessment based on the one or more first regions of interest and the at least one pattern;
• and
• detect drift in diagnostic assessments performed by at least one pathologist in a clinical trial by comparing the generated diagnostic assessment with the pathologist diagnostic assessments, wherein detecting drift comprises detecting a change over time in at least one of nomenclature, grading of lesions, and scoring of a bio marker.

An article of manufacture for performing a diagnostic assessment based on digital pathology data comprising:

• a non-transitory processor readable medium;
• and
• instructions stored on the medium;
• wherein the instructions are configured to be readable from the medium by at least one computer processor and thereby cause the at least one computer processor to operate so as to:
• obtain first digital pathology data comprising intensity information, the first digital pathology data being associated with a plurality of regions of interest in a biological sample;
• apply one or more first machine learning models to the first digital pathology data, the one or more first machine learning models identifying one or more first regions of interest among the plurality of regions of interest based on the intensity information;
• apply one or more second machine learning models to the first digital pathology data, the one or more second machine learning models identifying at least one pattern associated with at least one of the one or more first regions of interest;
• generate a diagnostic assessment based on the one or more first regions of interest and the at least one pattern;
• and
• detect drift in diagnostic assessments performed by at least one pathologist in a clinical trial by comparing the generated diagnostic assessment with the pathologist diagnostic assessments, wherein detecting drift comprises detecting a change over time in at least one of nomenclature, grading of lesions, and scoring of a bio marker.