Granted Patent B2

US 12,217,856 · App. 18/602,776 · Granted Feb 4, 2025

Inventors: Prasanth Perugupalli (Cary, NC); Jaya Jain (Shahpura, IN); Durgaprasad Dodle (Telangana, IN); Prateek Jain (Karnataka, IN); Suhash Gerald (Karnataka, IN); Dipankar Das (Bangalore, IN)

Assignee: Pramana, Inc.

G16H30/40G16H50/20G16H70/60

Abstract

Claims (50)

1. An apparatus for identifying regions of interest during slide digitization, wherein the apparatus comprises:

at least a processor; and

a memory communicatively connected to the at least a processor, wherein the memory comprises instructions configuring the at least a processor to:

receive identification data as a function of a user dataset associated with at least a pathology slide;

identify one or more regions of interest within the at least a pathology slide as a function of the user dataset;

identify at least one scan parameter as a function of the one or more regions of interest;

train a machine learning model iteratively with identification training data, wherein the identification training data correlates a plurality of pathology slides and the user dataset to a plurality of regions of interest;

calculate an accuracy score for the machine learning model, wherein the accuracy score identifies regions of interest within a plurality of pathology slides;

modify the pathology slide by the identification training data as a function of user feedback and accuracy score;

retrain the machine learning model by iteratively adding outputs of the machine learning model to the plurality of pathology slides and the user data set;

generate a digitized slide by scanning the at least a modified pathology slide as a function of the at least one scan parameter;

process images from the modified pathology slide, wherein processing the images comprises enhancing at least a region of interest by performing a color space conversation operation; and

display the digitized slide using a user interface at a display device.

2. The apparatus of claim 1 , wherein receiving the user dataset comprises receiving the user dataset for an electronic health record.

3. The apparatus of claim 1 , wherein the memory comprises instructions configuring the at least a processor to:

generate diagnostic data as a function of the user dataset; and

identify the one or more regions of interest as a function of the diagnostic data.

4. The apparatus of claim 1 , wherein the memory comprises instructions configuring the at least a processor to generate a plurality of slide data as a function of the digitized slide and the user dataset.

5. The apparatus of claim 4 , wherein the memory comprises instructions configuring the at least a processor to attach the plurality of slide data to the digitized slide as metadata.

6. The apparatus of claim 1 , wherein the user dataset comprises:

a plurality of textual data associated with the at least a pathology slide; and

a plurality of image data associated with the at least a pathology slide.

7. The apparatus of claim 1 , wherein the at least one scan parameter comprises a magnification level.

8. The apparatus of claim 1 , wherein the at least one scan parameter comprises a focus depth.

9. The apparatus of claim 1 , wherein scanning the at least a pathology slide comprises scanning the at least a pathology slide in a single pass.

10. A method for identifying regions of interest during slide digitization, wherein the method comprises:

receiving, using at least a processor, a user dataset associated with at least a pathology slide;

identifying, using the at least a processor, one or more regions of interest within the at least a pathology slide as a function of the user dataset;

identifying, using the at least a processor, at least one scan parameter as a function of the one or more regions of interest;

training a machine learning model iteratively with identification training data, wherein the identification training data correlates a plurality of pathology slides and the user dataset to a plurality of regions of interest;

calculating an accuracy score for the machine learning model, wherein the accuracy score identifies regions of interest within a plurality of pathology slides;

modify the pathology slide by the identification training data as a function of user feedback and accuracy score;

retrain the machine learning model by iteratively adding outputs of the machine learning model to the plurality of pathology slides and the user data set;

generate a digitized slide by scanning the at least a modified pathology slide as a function of the at least one scan parameter;

process images from the modified pathology slide, wherein processing the images comprises enhancing at least a region of interest by performing a color space conversation operation; and

displaying, using a user interface at a display device, the digitized slide.

11. The method of claim 10 , wherein receiving the user dataset comprises receiving the user dataset for an electronic health record.

12. The method of claim 10 , further comprising:

generating, using the at least a processor, diagnostic data as a function of the user dataset; and

identifying, using the at least a processor, the one or more regions of interest as a function of the diagnostic data.

13. The method of claim 10 , further comprising:

generating, using the at least a processor, a plurality of slide data as a function of the digitized slide and the user dataset.

14. The method of claim 13 , further comprising:

attaching, using the at least a processor, the plurality of slide data to the digitized slide as metadata.

15. The method of claim 10 , wherein the user dataset comprises:

a plurality of textual data associated with the at least a pathology slide; and

a plurality of image data associated with the at least a pathology slide.

16. The method of claim 10 , wherein the at least one scan parameter comprises a magnification level.

17. The method of claim 10 , wherein the at least one scan parameter comprises a focus depth.

18. The method of claim 10 , wherein scanning the at least a pathology slide comprises scanning the at least a pathology slide in a single pass.

Assignments (1)

Continuity (2)

References Cited (13)