SYSTEMS AND METHODS FOR PROCESSING ELECTRONIC IMAGES WITH METADATA INTEGRATION

1 . A computer-implemented method for processing electronic medical images, comprising:

receiving a plurality of medical images of at least one pathology specimen, the pathology specimen being associated with a patient;

receiving a gross description, the gross description comprising data about the medical images;

extracting data from the gross description;

determining, using a machine learning system, at least one associated location on the medical images for one or more pieces of data extracted; and

outputting a visual indication of the gross description data displayed in relation to the medical images.

2 . The method of claim 1 , further comprising:

determining if the gross description is structured or unstructured;

upon determining that the gross description is structured, providing the gross description to a rule-based AI system; and

upon determining the gross description is unstructured, providing the gross description to a natural language processing based machine learning system.

3 . The method of claim 1 , further comprising:

receiving a corresponding radiologic image associated with a patients; and

determining a sample location of the medical images relative to the radiologic image.

4 . The method of claim 3 , further comprising:

displaying the sample location of the medical image relative to the radiologic image.

5 . The method of claim 1 , further comprising:

receiving a corresponding three-dimensional figure associated with a patient; and

determining a sample location of the medical images relative to the three-dimensional figure.

6 . The method of claim 1 , further comprising:

comparing the associated location of the data on the medical images with an external system, wherein any discrepancies are marked.

7 . The method of claim 1 , further comprising:

determining that diseased tissue is present in two or more of the plurality of medical images; and

determining a location of the diseased tissue in three-dimensions based on the determined location of diseased tissue within the medical images.

8 . The method of claim 7 , further comprising:

estimating an area and/or volume of the diseased tissue.

9 . The method of claim 1 , further comprising:

determining a new coordinate system for measurement data of lesions within the medical images.

10 . The method of claim 1 , further comprising:

inferring genomic characteristics about a tumor based on data describing one or more alternative tumors within the patient.

11 . A system for processing electronic medical images, the system comprising:

at least one memory storing instructions; and

at least one processor configured to execute the instructions to perform operations comprising:

receiving a plurality of medical images of at least one pathology specimen, the pathology specimen being associated with a patient;

receiving a gross description, the gross description comprising data about the medical images;

extracting data from the gross description;

determining, using a machine learning system, at least one associated location on the medical images for one or more pieces of data extracted; and

outputting a visual indication of the gross description data displayed in relation to the medical images.

12 . The system of claim 11 , further comprising:

determining if the gross description is structured or unstructured;

upon determining that the gross description is structured, providing the gross description to a rule-based AI system; and

upon determining the gross description is unstructured, providing the gross description to a natural language processing based machine learning system.

13 . The system of claim 11 , further comprising:

receiving a corresponding radiologic image associated with a patients; and

determining a sample location of the medical images relative to the radiologic image.

14 . The system of claim 13 , further comprising:

displaying the sample location of the medical image relative to the radiologic image.

15 . The system of claim 11 , further comprising:

receiving a corresponding three-dimensional figure associated with a patient; and

determining a sample location of the medical images relative to the three-dimensional figure.

16 . The system of claim 11 , further comprising:

comparing the associated location of the data on the medical images with an external system, wherein any discrepancies are marked.

17 . The system of claim 11 , further comprising:

determining that diseased tissue is present in two or more of the plurality of medical images; and

determining a location of the diseased tissue in three-dimensions based on the determined location of diseased tissue within the medical images.

18 . The system of claim 17 , further comprising:

estimating an area and/or volume of the diseased tissue.

19 . The system of claim 17 , further comprising:

determining a new coordinate system for measurement data of lesions within the medical images.

20 . A non-transitory computer-readable medium storing instructions that, when executed by a processor, perform operations processing electronic medical images, the operations comprising:

receiving a plurality of medical images of at least one pathology specimen, the pathology specimen being associated with a patient;

receiving a gross description, the gross description comprising data about the medical images;

extracting data from the gross description;

determining, using a machine learning system, at least one associated location on the medical images for one or more pieces of data extracted; and

outputting a visual indication of the gross description data displayed in relation to the medical images.