FIELD OF THE DISCLOSURE

Various embodiments of the present disclosure pertain generally to developing artificial intelligence (AI) technology to detect biomarkers, genomic features, treatment resistance and other relevant features necessary for additional testing of pathology specimens. More specifically, particular embodiments of the present disclosure relate to systems and methods for predicting, identifying or detecting biomarkers and genomic features of prepared tissue specimens. The present disclosure further provides systems and methods for creating a prediction model that predicts labels from unseen slides.

BACKGROUND

There may be multiple steps, incurred costs, and time required for a pathologist to receive results for a Biomarker or Genomics Panel. For a biomarker result, (a) a pathologist may note the appropriate or suspicious part of a patient; (b) a lab may receive the request for a slide stain; (c) the lab cuts the block or finds the appropriate unstained slide; (d) the part is stained; and (e) the test is logged electronically to the case and given to pathologist for final review. For a genomics panel, (a) the request for a molecular test may be given to a pathologist; (b) the pathologist may select a slide from which to sequence; (c) prompt recuts of tissue to be made; (d) prompt a tumor to be scraped based on a pathologist's outline from a previous biopsy cut; (e) genome in the scraped tumor tissue may be sequenced; and (f) a genetic report may be created. These processes can be expensive and time intensive.

The foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. The background description provided herein is for the purpose of generally presenting the context of the disclosure. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art, or suggestions of the prior art, by inclusion in this section.

SUMMARY

According to certain aspects of the present disclosure, systems and methods are disclosed for predicting a biomarker and/or at least one genomic feature in a digital image associated with a tissue specimen.

A computer-implemented method for processing an electronic image corresponding to a specimen includes: receiving one or more digital images associated with a tissue specimen, a related case, a patient, and/or a plurality of clinical information; determining one or more of a prediction, a recommendation, and/or a plurality of data for the one or more digital images using a machine learning system, the machine learning system having been trained using a plurality of training images, to predict a biomarker and a plurality of genomic panel elements; and determining, based on the prediction, the recommendation, and/or the plurality of data, whether to log an output and at least one visualization region as part of a case history within a clinical reporting system.

A system for processing an electronic image corresponding to a specimen includes a memory storing instructions; and at least one processor executing the instructions to perform a process including receiving one or more digital images associated with a tissue specimen, a related case, a patient, and/or a plurality of clinical information; determining one or more of a prediction, a recommendation, and/or a plurality of data for the one or more digital images using a machine learning system, the machine learning system having been trained using a plurality of training images, to predict a biomarker and a plurality of genomic panel elements; and determining, based on the prediction, the recommendation, and/or the plurality of data, whether to log an output and at least one visualization region as part of a case history within a clinical reporting system.

A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform a method for processing an electronic image corresponding to a specimen includes: receiving one or more digital images associated with a tissue specimen, a related case, a patient, and/or a plurality of clinical information, determining one or more of a prediction, a recommendation, and/or a plurality of data for the one or more digital images using a machine learning system, the machine learning system having been trained using a plurality of training images, to predict a biomarker and a plurality of genomic panel elements, and determining, based on the prediction, the recommendation, and/or the plurality of data, whether to log an output and at least one visualization region as part of a case history within a clinical reporting system.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosed embodiments, as claimed.

