Systems and methods to process electronic images for histological morphology trajectory prediction
Systems and methods are described herein for processing electronic medical images to predict one or more histological morphologies. For example, one or more digital medical images may be received, the one or more digital medical images being of at least one pathology specimen associated with a patient. Patient clinical data for the patient may be received. A trained machine learning system may be determined. The patient clinic data and one or more digital medical images may be provided to the trained machine learning system. A histological morphology prediction of the patient may be determined, using the trained machine learning system. The histological morphology prediction may be output to a user and/or storage.
1 . A computer-implemented method for processing electronic medical images to predict one or more histological morphologies, comprising:
receiving one or more digital medical images, the one or more digital medical images being of at least one pathology specimen of a subject;
receiving subject clinical data for the subject;
determining a trained machine learning system;
providing the subject clinical data and one or more digital medical images to the trained machine learning system;
determining, using the trained machine learning system, a histological morphology prediction of the subject;
determining, using the trained machine learning system, a histological morphology trajectory;
determining one or more notifications to indicate a follow-up appointment, screening, and/or procedure to be scheduled based on the determined histological morphology trajectory; and
outputting the histological morphology prediction to a user and/or storage.
2 . The method of claim 1 , wherein the subject clinical data includes at least one of heart rate, blood measurements, activity, and diet-related measurements.
3 . The method of claim 1 , further including receiving subject environmental data for the subject, the subject environmental data including temperature, ultraviolet (UV) index, air quality index (AQI), nitrogen dioxide levels, particulate matter levels, water quality levels, and/or population density.
4 . The method of claim 1 , further including:
receiving location data of the subject.
5 . The method of claim 1 , further including:
identifying a saliency of each region within the one or more digital medical images; and
excluding non-salient image regions from subsequent processing.
6 . The method of claim 1 , further including:
determining a risk setting for the subject;
inputting the risk setting in the trained machine learning system; and
utilizing the risk setting to help determine the a histological morphology prediction of the subject.
7 . The method of claim 1 , further including:
determining a confidence for the histological morphology prediction, the confidence being the confidence that the morphology prediction is present within a tissue of the subject at a given time point.
8 . The method of claim 1 , further including:
determining a second histological morphology prediction for a future time point, the future time point being based on a type of cancer and cancer characteristics.
9 . The method of claim 1 , further including:
determining an image representing a histological morphology prediction trajectories.
10 . The method of claim 1 , wherein the histological morphology prediction is of prostate tissue morphologies and the histological morphology predictions includes prostatic intraepithelial neoplasia, necrosis, and/or atypical small acinar proliferation predictions.
11 . The method of claim 1 , wherein the clinical data is obtained from a wearable device on the subject.
12 . The method of claim 1 , further including:
reviewing the predicted histological morphology prediction;
determining whether one or more of the histological morphologies predictions meets a threshold value; and
upon determining a morphologies prediction breaks a threshold value, generating an alert for a healthcare professionals and/or the subject.
13 . A system for processing electronic medical images, the system comprising:
at least one memory storing instructions; and
at least one processor configured to execute the instructions to perform operations comprising:
receiving one or more digital medical images, the one or more digital medical images being of at least one pathology specimen of a subject;
receiving subject clinical data for the subject;
determining a trained machine learning system;
providing the subject clinical data and one or more digital medical images to the trained machine learning system;
determining, using the trained machine learning system, a histological morphology prediction of the subject;
determining, using the trained machine learning system, a histological morphology trajectory;
determining one or more notifications to indicate a follow-up appointment, screening, and/or procedure to be scheduled based on the determined histological morphology trajectory; and
outputting the histological morphology prediction to a user and/or storage.
14 . The system of claim 13 , wherein the subject clinical data includes at least one of heart rate, blood measurements, activity, and diet-related measurements.
15 . The system of claim 13 , further including receiving subject environmental data for the subject, the subject environmental data including temperature, ultraviolet (UV) index, air quality index (AQI), nitrogen dioxide levels, particulate matter levels, water quality levels, and/or population density.
16 . The system of claim 13 , further including:
receiving location data of the subject.
17 . The system of claim 13 , further including:
identifying a saliency of each region within the one or more digital medical images; and
excluding non-salient image regions from subsequent processing.
18 . The system of claim 13 , further including:
determining a risk setting for the subject;
inputting the risk setting in the trained machine learning system; and
utilizing the risk setting to help determine the a histological morphology prediction of the subject.
19 . A non-transitory computer-readable medium storing instructions that, when executed by a processor, perform operations processing electronic medical images, the operations comprising:
receiving one or more digital medical images, the one or more digital medical images being of at least one pathology specimen of a subject;
receiving subject clinical data for the subject;
determining a trained machine learning system;
providing the subject clinical data and one or more digital medical images to the trained machine learning system;
determining, using the trained machine learning system, a histological morphology prediction of the subject;
determining, using the trained machine learning system, a histological morphology trajectory;
determining one or more notifications to indicate a follow-up appointment, screening, and/or procedure to be scheduled based on the determined histological morphology trajectory; and
outputting the histological morphology prediction to a user and/or storage.