Systems and methods for analyzing electronic images for quality control

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

receiving a digital image corresponding to a target specimen associated with a pathology category, wherein the digital image is an image of human or animal tissue and/or an image algorithmically generated to replicate human or animal tissue;

determining a quality control (QC) machine learning model, the QC machine learning model being configured to predict a quality designation based on a presence or lack of one or more artifacts and/or;

providing the digital image as an input to the QC machine learning model;

receiving the quality designation associated with the digital image as an output from the QC machine learning model;

determining, based on the output from the QC machine learning model, whether the quality designation is either an approved designation or a rejected designation, wherein the quality designation is indicative or artifacts such as errors or imperfections in the digital image, wherein the quality designation includes an approval scale and/or a rejection scale that provides a finer indication of the image quality;

determining a quality assurance (QA) machine learning model configured to predict a disease designation based on one or more biomarkers, the QA machine learning model being different than the QC machine learning model;

providing the digital image to the QA machine learning model;

receiving a disease designation for the digital image as an output from the QA machine learning model;

receiving an external designation for one of the digital image or the target specimen and comprising a disease property selected from at least one of a cancer detection, a cancer grade, a cancer origin, a diagnosis, a presence or absence of a microorganism, a specimen type, a cancer type, a cancer status, a tumor size, a lesions risk level, or a grade;

evaluating the external designation by comparing the disease designation to the external designation;

outputting a comparison result based on evaluating the external designation by comparing the disease designation to the external designation; and

rejecting the external designation based on the evaluation, based on the external designation deviating from the disease designation beyond a predetermined threshold.

2. The computer-implemented method of claim 1 , wherein outputting the quality designation of the digital image further comprises generating a report comprising at least one of a type of artifact found, a number of artifacts found, clinical impact of an artifact found, a time to rectify an artifact, potential pattern, or correlation in a plurality of artifacts.

3. The computer-implemented method of claim 1 , wherein the pathology category is one of histology, cytology, frozen section, or immunohistochemistry.

4. The computer-implemented method of claim 2 , wherein the quality designation includes a quality score determination.

5. The computer-implemented method of claim 1 , further comprising automatically generating a notification based on quality characteristic, the notification comprising an artifact presence, absence, or degree designation.

6. The computer-implemented method of claim 1 , wherein outputting the quality designation of the digital image further comprises a visual indicator corresponding to an artifact of the digital image.

7. The computer-implemented method of claim 1 , wherein the QA machine learning model is generated by processing a plurality of training images, associated with the pathology category, to predict a disease designation based on one or more biomarkers.

8. The computer-implemented method of claim 1 , wherein the one or more artifacts comprise a missing tissue, a glass crack, a bubble, a blur amount, a missing tissue, a folded tissue, a line, a scratch, dust, pen, or a stain amount.

9. A system for processing electronic images, the system comprising:

at least one memory storing instructions; and

at least one processor executing the instructions to perform operations comprising:

receiving a digital image corresponding to a target specimen associated with a pathology category, wherein the digital image is an image of human or animal tissue and/or an image algorithmically generated to replicate human or animal tissue;

determining a quality control (QC) machine learning model, the QC machine learning model being configured to predict a quality designation based on a presence or lack of one or more artifacts and/or;

providing the digital image as an input to the QC machine learning model;

receiving the quality designation associated with the digital image as an output from the QC machine learning model;

determining, based on the output from the QC machine learning model, whether the quality designation is either an approved designation or a rejected designation, wherein the quality designation is indicative or artifacts such as errors or imperfections in the digital image, wherein the quality designation includes an approval scale and/or a rejection scale that provides a finer indication of the image quality;

determining a quality assurance (QA) machine learning model configured to predict a disease designation based on one or more biomarkers, the QA machine learning model being different than the QC machine learning model;

providing the digital image to the QA machine learning model;

receiving a disease designation for the digital image as an output from the QA machine learning model;

receiving an external designation for one of the digital image or the target specimen and comprising a disease property selected from at least one of a cancer detection, a cancer grade, a cancer origin, a diagnosis, a presence or absence of a microorganism, a specimen type, a cancer type, a cancer status, a tumor size, a lesions risk level, or a grade;

evaluating the external designation by comparing the disease designation to the external designation;

outputting a comparison result based on evaluating the external designation by comparing the disease designation to the external designation; and

rejecting the external designation based on the evaluation, based on the external designation deviating from the disease designation beyond a predetermined threshold.

10. The system of claim 9 , wherein outputting the quality designation of the digital image further comprises generating a report comprising at least one of a type of artifact found, a number of artifacts found, clinical impact of an artifact found, a time to rectify an artifact, potential pattern, or correlation in a plurality of artifacts.

11. The system of claim 9 , wherein the pathology category is one of histology, cytology, frozen section, or immunohistochemistry.

12. The system of claim 9 , wherein the quality designation includes a quality score determination.

13. The system of claim 9 , further comprising automatically generating a notification based on quality characteristic, the notification comprising an artifact presence, absence, or degree designation.

14. The system of claim 9 , wherein outputting the quality designation of the digital image further comprises a visual indicator corresponding to an artifact of the digital image.

15. The system of claim 9 , wherein the QA machine learning model is generated by processing a plurality of training images, associated with the pathology category, to predict a disease designation based on one or more biomarkers.

16. The system of claim 9 , wherein the one or more artifacts comprise a missing tissue, a glass crack, a bubble, a blur amount, a missing tissue, a folded tissue, a line, a scratch, dust, pen, or a stain amount.

17. 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 digital image corresponding to a target specimen associated with a pathology category, wherein the digital image is an image of human or animal tissue and/or an image algorithmically generated to replicate human or animal tissue;

determining a quality control (QC) machine learning model, the QC machine learning model being configured to predict a quality designation based on a presence or lack of one or more artifacts and/or;

providing the digital image as an input to the QC machine learning model;

receiving the quality designation associated with the digital image as an output from the QC machine learning model;

determining, based on the output from the QC machine learning model, whether the quality designation is either an approved designation or a rejected designation, wherein the quality designation is indicative or artifacts such as errors or imperfections in the digital image, wherein the quality designation includes an approval scale and/or a rejection scale that provides a finer indication of the image quality;

determining a quality assurance (QA) machine learning model configured to predict a disease designation based on one or more biomarkers, the QA machine learning model being different than the QC machine learning model;

providing the digital image to the QA machine learning model;

receiving a disease designation for the digital image as an output from the QA machine learning model;

receiving an external designation for one of the digital image or the target specimen and comprising a disease property selected from at least one of a cancer detection, a cancer grade, a cancer origin, a diagnosis, a presence or absence of a microorganism, a specimen type, a cancer type, a cancer status, a tumor size, a lesions risk level, or a grade;

evaluating the external designation by comparing the disease designation to the external designation;

outputting a comparison result based on evaluating the external designation by comparing the disease designation to the external designation; and

rejecting the external designation based on the evaluation, based on the external designation deviating from the disease designation beyond a predetermined threshold.

18. The non-transitory computer-readable medium of claim 17 , wherein outputting the quality designation of the digital image further comprises generating a report comprising at least one of a type of artifact found, a number of artifacts found, clinical impact of an artifact found, a time to rectify an artifact, potential pattern, or correlation in a plurality of artifacts.

19. The computer-implemented method of claim 1 , wherein the one or more artifacts is a blur, missing tissue, line, folded tissue, bubbles, cracks in glass, scratches, dust, pen marking, and/or stains of the digital image.

20. The computer-implemented method of claim 1 , further concluding:

determining, using the QC machine learning model, a report based on the quality designation, the report including types of artifacts found, number of each type of artifact, clinical impact of artifacts, time to rectify artifact, and/or potential patterns or correlations in artifacts.