# US 11,309,074 B2 · Systems and methods for processing images to prepare slides for processed images for digital pathology > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,309,074 B2 - Document ID: US11309074B2 - Status: Granted - Kind: B2 - Application number: 17/346,923 - Filed: 2021-06-14 - Published: 2021-09-30 - Granted: 2022-04-19 - Earliest claimed: 2019-05-28 - Assignees: PAIGE,AI, Inc. - Applicants: PAIGE.AI, Inc. - Inventors: Rodrigo Ceballos Lentini; Christopher Kanan; Patricia Raciti; Leo Grady; Thomas Fuchs - Web page: https://patentgrep.com/patent/US11309074B2 ## CPC - G06T 7/0012: {Biomedical image inspection} - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G16H 30/40: for processing medical images, e.g. editing - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G06T 2207/10056: Microscopic image - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro - G06T 2207/30096: Tumor; Lesion - G06V 2201/03: Recognition of patterns in medical or anatomical images - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Systems and methods are disclosed for processing an electronic image corresponding to a specimen. One method for processing the electronic image includes: receiving a target electronic image of a slide corresponding to a target specimen, the target specimen including a tissue sample from a patient, applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies and/or predict a needed recut, the training images including images of human tissue and/or images that are algorithmically generated; and based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared. ## Claims ### Claim 1 A computer-implemented method for processing an electronic image corresponding to a specimen, the method comprising: receiving a target electronic image of a slide corresponding to a target specimen, the target specimen comprising a tissue sample from a patient; applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies, the training images comprising either or both of images of human tissue and images that are algorithmically generated; and based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared using immunohistochemistry. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein determining deficiencies comprises determining a likelihood that the additional slide is to be prepared based on specimen information of the target specimen, and in response to the likelihood being greater than or equal to a predetermined amount, automatically ordering the additional slide to be prepared. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the additional slide is automatically ordered in response to the machine learning system identifying a diagnosis that automatically initiates an additional test. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein the additional slide is automatically ordered in response to the machine learning system identifying a diagnosis that automatically initiates an additional test, and wherein the diagnosis that automatically initiates the additional test comprises additional test is automatically initiated in response to a diagnosis of any one or any combination of lung adenocarcinoma, breast carcinoma, endometrioid adenocarcinoma, colonic adenocarcinoma, adenocarcinoma in other tissues, sarcomas, prognostic biomarkers, suspicious lesions, amyloid presence, and/or fungal organisms. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the additional slide is automatically ordered in response to the machine learning system identifying a morphology that automatically triggers a genetic test. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein the ordering the additional slide comprises ordering a new stain to be prepared for the slide corresponding to the target specimen. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the ordering the additional slide comprises ordering a recut for the slide corresponding to the target specimen. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, further comprising outputting an alert on a display indicating that the additional slide is being prepared. ### Claim 9 A system for processing an electronic image corresponding to a specimen, 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 target electronic image of a slide corresponding to a target specimen, the target specimen comprising a tissue sample from a patient; applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies, the training images comprising either or both of images of human tissue and images that are algorithmically generated; and based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared using immunohistochemistry. ### Claim 10 (depends on 9) The system of claim 9, wherein determining deficiencies comprises determining a likelihood that the additional slide is to be prepared based on specimen information of the target specimen, and in response to the likelihood being greater than or equal to a predetermined amount, determining to automatically order the additional slide to be prepared. ### Claim 11 (depends on 9) The system of claim 9, wherein the additional slide is automatically ordered in response to the machine learning system identifying a diagnosis that automatically initiates an additional test. ### Claim 12 (depends on 9) The system of claim 9, wherein the additional slide is automatically ordered in response to the machine learning system identifying a diagnosis that automatically initiates an additional test, and wherein the diagnosis that automatically initiates the additional test comprises any one or any combination of lung adenocarcinoma, breast carcinoma, endometrioid adenocarcinoma, colonic adenocarcinoma, adenocarcinoma in other tissues, sarcomas, prognostic biomarkers, suspicious lesions, amyloid presence, and/or fungal organisms. ### Claim 13 (depends on 9) The system of claim 9, wherein the additional slide is automatically ordered in response to the machine learning system identifying a morphology that automatically triggers a genetic test. ### Claim 14 (depends on 9) The system of claim 9, wherein the ordering the additional slide comprises ordering a new stain to be prepared for the slide corresponding to the target specimen. ### Claim 15 (depends on 9) The system of claim 9, wherein the ordering the additional slide comprises ordering a recut for the slide corresponding to the target specimen. ### Claim 16 (depends on 9) The system of claim 9, further comprising outputting an alert on a display indicating that the additional slide is being prepared. ### Claim 17 A non-transitory computer-readable medium storing instructions that, when executed by processor, cause the processor to perform a method for processing an electronic image corresponding to a specimen, the method comprising: receiving a target electronic image of a slide corresponding to a target specimen, the target specimen comprising a tissue sample from a patient; applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies, the training images comprising either or both of images of human tissue and images that are algorithmically generated; and based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared using immunohistochemistry. ### Claim 18 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein determining deficiencies comprises determining a likelihood that the additional slide is to be prepared based on specimen information of the target specimen, and in response to the likelihood being greater than or equal to a predetermined amount, determining to automatically order the additional slide to be prepared. ### Claim 19 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein the additional slide is automatically ordered in response to the machine learning system identifying a diagnosis that automatically initiates an additional test. ### Claim 20 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein the additional slide is automatically ordered in response to the machine learning system identifying a diagnosis that automatically initiates an additional test, and wherein the diagnosis that automatically initiates the additional test comprises any one or any combination of lung adenocarcinoma, breast carcinoma, endometrioid adenocarcinoma, colonic adenocarcinoma, adenocarcinoma in other tissues, sarcomas, prognostic biomarkers, suspicious lesions, amyloid presence, and/or fungal organisms.