Systems and methods to process electronic images to identify diagnostic tests

A computer-implemented method for training a machine-learning system to determine an applicability of a diagnostic test, the method comprising:

• receiving, by one or more processors, one or more digital images associated with a pathology specimen;
• receiving, by the one or more processors, a set of patient data associated with the one or more digital images;
• identifying, by the one or more processors, at least one tissue region of interest for analysis in the one or more digital images;
• determining and removing at least one non-applicable region from the one or more digital images, the at least one non-applicable region comprising at least one area not identified as a tissue region of interest;
• providing, by the one or more processors, the at least one tissue region of interest and the set of patient data to a machine-learning model as training data, wherein the machine-learning model is trained using the at least one tissue region of interest and the set of patient data to identify one or more applicable diagnostic tests;
• generating, by the one or more processors, at least one threshold for each of the one or more applicable diagnostic tests;
• and
• generating, by the one or more processors, a set of parameters for each of the one or more applicable diagnostic tests, the set of parameters including the at least one threshold.

A system for training a machine-learning system to determine an applicability of a diagnostic test, the system comprising:

• at least one memory storing instructions;
• and
• at least one processor configured to execute the instructions to perform operations comprising:
• receiving, by one or more processors, one or more digital images associated with a pathology specimen;
• receiving, by the one or more processors, a set of patient data associated with the one or more digital images;
• identifying, by the one or more processors, at least one tissue region of interest for analysis in the one or more digital images;
• determining and removing at least one non-applicable region from the one or more digital images, the at least one non-applicable region comprising at least one area not identified as a tissue region of interest;
• providing, by the one or more processors, the at least one tissue region of interest and the set of patient data to a machine-learning model as training data, wherein the machine-learning model is trained using the at least one tissue region of interest and the set of patient data to identify one or more applicable diagnostic tests;
• generating, by the one or more processors, at least one threshold for each of the one or more applicable diagnostic tests;
• and
• generating, by the one or more processors, a set of parameters for each of the one or more applicable diagnostic tests, the set of parameters including the at least one threshold.

A non-transitory computer readable medium storing instructions that, when executed by a processor, cause the processor to perform a method for training a machine-learning system to determine an applicability of a diagnostic test, the method comprising:

• receiving, by one or more processors, one or more digital images associated with a pathology specimen;
• receiving, by the one or more processors, a set of patient data associated with the one or more digital images;
• identifying, by the one or more processors, at least one tissue region of interest for analysis in the one or more digital images;
• determining and removing at least one non-applicable region from the one or more digital images, the at least one non-applicable region comprising at least one area not identified as a tissue region of interest;
• providing, by the one or more processors, the at least one tissue region of interest and the set of patient data to a machine-learning model as training data, wherein the machine-learning model is trained using the at least one tissue region of interest and the set of patient data to identify one or more applicable diagnostic tests;
• generating, by the one or more processors, at least one threshold for each of the one or more applicable diagnostic tests;
• and
• generating, by the one or more processors, a set of parameters for each of the one or more applicable diagnostic tests, the set of parameters including the at least one threshold.