Systems and methods for processing electronic medical images to optimize a review order of pathology cases
Systems and methods are described herein for processing electronic medical images to optimize a review order of pathology cases. For example, a plurality of variables and one or more constraints may be received along with a plurality of pathology cases. Each case of the plurality of pathology cases may include one or more medical images of at least one pathology specimen associated with a patient. The medical images from each case, the plurality of variables, and the one or more constraints may be provided as input to a trained system. A sequential order for user review of the plurality of cases to optimize one or more of the plurality of variables based on the one or more constraints may be received as output of the trained system. Each case of the plurality of cases may be automatically provided to a user for review according to the sequential order.
1 . A computer-implemented method for processing electronic medical images to optimize a review order of pathology cases, comprising:
receiving a plurality of variables and one or more constraints, wherein:
the plurality of variables including at least one of a pathologist preference, a number of cases to be reviewed per day, pathologist expertise, a diagnosis quality, a time of day, a day of week, pathologist behavior, a client expectation, and a laboratory consideration, and
the one or more constraints including at least one of pathologist availability, pathologist qualifications, a case deadline, a time available, and a department goal;
receiving a plurality of pathology cases, each case of the plurality of pathology cases including one or more medical images of at least one pathology specimen associated with a patient;
providing the one or more medical images from each case, the plurality of variables, and the one or more constraints as inputs to a trained machine learning system, wherein:
the plurality of variables are bounded by the one or more constraints, and
the trained machine learning system having been trained to predict a sequential order for user review of the plurality of pathology cases based on a plurality of medical images, a plurality of training variables, and a plurality of training constraints;
receiving, as an output of the trained machine learning system, the sequential order for user review of the plurality of pathology cases; and
automatically providing each case of the plurality of pathology cases to a user for review according to the sequential order.
2 . The computer-implemented method of claim 1 , wherein the trained machine learning system determines a plurality of potential sequential orders and a score for each of the plurality of potential sequential orders indicating a level of optimization of the plurality of variables, and wherein the sequential order provided as the output is one of the plurality of potential sequential orders having a highest score.
3 . The computer-implemented method of claim 2 , wherein when at least a first variable and a second variable of the plurality of variables are to be optimized, the trained machine learning system determines a first score for the first variable and a second score for the second variable for each of the plurality of potential sequential orders, and the sequential order provided as the output is one of the plurality of potential sequential orders having a maximized overall score based on the first score and the second score.
4 . The computer-implemented method of claim 1 , wherein the plurality of variables to be optimized are user-selected variables.
5 . The computer-implemented method of claim 1 , wherein the trained machine learning system is a trained rules-based system.
6 . The computer-implemented method of claim 1 , further comprising:
receiving, as a further output from the trained machine learning system, one or more determined characteristics of the one or more medical images, the trained machine learning system having been further trained to process the one or more medical images from each case of the plurality of pathology cases to determine one or more characteristics of the one or more medical images; and
providing the one or more determined characteristics of the one or more medical images as a further input to the trained machine learning system, the one or more determined characteristics including a case complexity, a case type, a number of areas of interest per medical image or per case, an amount of tissue per medical image, or an image quality.
7 . The computer-implemented method of claim 1 , further comprising:
receiving one or more additional pathology cases, each case of the one or more additional pathology cases including the one or more medical images of the at least one pathology specimen associated with the patient;
providing the one or more medical images from each of the one or more additional pathology cases as a further input to the trained machine learning system; and
receiving, as the output of the trained machine learning system, an updated sequential order.
8 . The computer-implemented method of claim 1 , wherein the trained machine learning system is further configured to assign a subset of the plurality of pathology cases to each of a plurality of users.
9 . The computer-implemented method of claim 1 , further comprising:
generating a notification to prompt the user to take one or more breaks to increase optimization of the plurality of variables or the one or more constraints based on information received from the trained machine learning system.
10 . The computer-implemented method of claim 1 , wherein automatically providing each case of the plurality of pathology cases to the user for review according to the sequential order comprises:
automatically navigating from an initial case to a subsequent case according to the sequential order based on an indication that a review of the initial case is completed.
11 . The computer-implemented method of claim 10 , wherein the indication is an input received from the user or an event associated with case review completion that is automatically detected.
12 . The computer-implemented method of claim 1 , further comprising:
monitoring for values associated with the plurality of variables as the user is reviewing the plurality of pathology cases; and
providing the values to the trained machine learning system, wherein the trained machine learning system is re-trained based on the values for future optimizations.
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 a plurality of variables and one or more constraints, wherein the plurality of variables are bounded by the one or more constraints;
receiving a plurality of pathology cases, each case of the plurality of pathology cases including one or more medical images of at least one pathology specimen associated with a patient;
providing the one or more medical images from each case, the plurality of variables, and the one or more constraints as input to a trained machine learning system, the trained machine learning system having been trained to predict a sequential order for user review of the plurality of pathology cases based on a plurality of medical images, a plurality of training variables, and a plurality of training constraints;
receiving, as an output of the trained machine learning system, the sequential order for user review of the plurality of pathology cases; and
automatically providing each case of the plurality of pathology cases to a user for review according to the sequential order.
14 . The system of claim 13 , wherein the trained machine learning system determines a plurality of potential sequential orders and a score for each of the plurality of potential sequential orders indicating a level of optimization of the plurality of variables, and wherein the sequential order provided as the output is one of the plurality of potential sequential orders having a highest score.
15 . The system of claim 14 , wherein when at least a first variable and a second variable of the plurality of variables are to be optimized, the trained machine learning system determines a first score for the first variable and a second score for the second variable for each of the plurality of potential sequential orders, and the sequential order provided as the output is one of the plurality of potential sequential orders having a maximized overall score based on the first score and the second score.
16 . The system of claim 13 , wherein the plurality of variables to be optimized are user-selected variables.
17 . The system of claim 13 , wherein the trained machine learning system is a trained rules-based system.
18 . The system of claim 13 , further comprising:
receiving, as a further output from the trained machine learning system, one or more determined characteristics of the one or more medical images, the trained machine learning system having been further trained to process the one or more medical images from each case of the plurality of pathology cases to determine one or more characteristics of the one or more medical images; and
providing the one or more determined characteristics of the one or more medical images as a further input to the trained machine learning system, the one or more determined characteristics including a case complexity, a case type, a number of areas of interest per medical image or per case, an amount of tissue per medical image, or an image quality.
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 a plurality of variables and one or more constraints, wherein:
the plurality of variables including at least one of a pathologist preference, a number of cases to be reviewed per day, pathologist expertise, a diagnosis quality, a time of day, a day of week, pathologist behavior, a client expectation, and a laboratory consideration, and
the one or more constraints including at least one of pathologist availability, pathologist qualifications, a case deadline, a time available, and a department goal;
receiving a plurality of pathology cases, each case of the plurality of pathology cases including one or more medical images of at least one pathology specimen associated with a patient;
providing the one or more medical images from each case, the plurality of variables, and the one or more constraints as inputs to a trained machine learning system, wherein:
the plurality of variables are bounded by the one or more constraints, and
the trained machine learning system having been trained to predict a sequential order for user review of the plurality of pathology cases based on a plurality of medical images, a plurality of training variables, and a plurality of training constraints;
receiving, as an output of the trained machine learning system, the sequential order for user review of the plurality of pathology cases to optimize one or more of the plurality of variables based on the one or more constraints; and
automatically providing each case of the plurality of pathology cases to a user for review according to the sequential order.
20 . The computer-readable medium of claim 19 , wherein the trained machine learning system determines a plurality of potential sequential orders and a score for each of the plurality of potential sequential orders indicating a level of optimization of the plurality of variables, and wherein the sequential order provided as the output is one of the plurality of potential sequential orders having a highest score.