Systems and methods for artificial intelligence powered molecular workflow verifying slide and block quality for testing

1. A computer-implemented method for verifying slide and block quality for testing, the method comprising:

applying a machine learning model to determine an amount or a percentage of tissue with an attribute from a digital image; and

outputting a quality score corresponding to the determined amount or percentage of tissue with the attribute being below a predetermined value, wherein the quality score is equal to zero when the determined amount or percentage of tissue with the attribute is greater than the predetermined value and the quality score is a linear combination of a value of the attribute and one or more additional attribute values when the determined amount or percentage of tissue with the attribute is less than or equal to the predetermined value.

2. The computer-implemented method of claim 1 , wherein determining the amount or percentage includes summing and normalizing the digital image that indicates a presence of the attribute by a total amount of tissue.

3. The computer-implemented method of claim 1 , further comprising:

partitioning each digital image of a collection of digital images into a collection of tiles;

detecting and/or segmenting a tissue region from a background of the digital image to create a tissue mask; and

removing all tiles in the collection of tiles that comprise the background.

4. The computer-implemented method of claim 3 , wherein detecting and/or segmenting comprises using one or more thresholding-based methods and running a connected components algorithm.

5. The computer-implemented method of claim 3 , wherein detecting and/or segmenting comprises using one or more segmentation algorithms.

6. The computer-implemented method of claim 1 , wherein the digital image is associated with a tissue block, the method further comprising:

determining a tissue block with a highest quality score for subsequent testing.

7. The computer-implemented method of claim 6 , further comprising:

indicating to a user that the tissue block has at least one additional slide to prepare for testing.

8. The computer-implemented method of claim 6 , further comprising:

determining whether the quality score is below a threshold.

9. The computer-implemented method of claim 8 , further comprising:

indicating to a user to prepare a new tissue block for testing when the quality score is determined to be below the threshold.

10. The computer-implemented method of claim 1 , further comprising:

outputting a function of at least one variable corresponding to the quality score.

11. The computer-implemented method of claim 10 , wherein the function of the at least one variable is a linear function.

12. The computer-implemented method of claim 10 , wherein the function of the at least one variable is a nonlinear function.

13. The computer-implemented method of claim 1 , further comprising:

outputting a binary image indicating where the attribute is located.

14. The computer-implemented method of claim 1 , further comprising:

receiving a synoptic annotation comprising one or more label for each digital image.

15. The computer-implemented method of claim 14 , wherein the one or more label is a pixel-level label, a tile level label, a slide-level label, and/or a part specimen-level label.

16. The computer-implemented method of claim 1 , wherein the digital image is a digital histopathology image.

17. A system for using a machine learning model to verify slide and block quality for testing, the system comprising:

at least one memory storing instructions; and

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

applying a machine learning model to determine an amount or a percentage of tissue with an attribute from a digital image; and

outputting a quality score corresponding to the determined amount or percentage of tissue with the attribute being below a predetermined value, wherein the quality score is equal to zero when the determined amount or percentage of tissue with the attribute is greater than the predetermined value and the quality score is a linear combination of a value of the attribute and one or more additional attribute values when the determined amount or percentage of tissue with the attribute is less than or equal to the predetermined value.

18. The system of claim 17 , wherein determining the amount or percentage includes summing and normalizing the digital image that indicates a presence of the attribute by a total amount of tissue.

19. A non-transitory computer-readable medium storing instructions that, when executed by a processor, perform a method of using a machine learning model to verify slide and block quality for testing, the method comprising:

applying a machine learning model to determine an amount or a percentage of tissue with an attribute from a digital image; and

outputting a quality score corresponding to the determined amount or percentage of tissue with the attribute being below a predetermined value, wherein the quality score is equal to zero when the determined amount or percentage of tissue with the attribute is greater than the predetermined value and the quality score is a linear combination of a value of the attribute and one or more additional attribute values when the determined amount or percentage of tissue with the attribute is less than or equal to the predetermined value.

20. The method of claim 19 , wherein the method further comprises:

partitioning each digital image of a collection of digital images into a collection of tiles;

detecting and/or segmenting a tissue region from a background of the digital image to create a tissue mask; and

removing all tiles in the collection of tiles that comprise the background.