Cardiac image workflow interpretation time prediction
A method predicts an interpretation time for a medical image examination of a subject comprising one or more medical images. A plurality of data inputs is obtained, where the data inputs are associated with the medical image examination or the subject of the medical image examination, and the data points represent parameters affecting the interpretation time. The plurality of data inputs are input to a trained artificial intelligence algorithm, wherein the algorithm automatically provides a predicted interpretation time based on said plurality of data inputs. The predicted interpretation time is output to a clinical management system. A clinical management system incorporating the aforementioned method and a computer program product encoded with the aforementioned method are also provided.
1 . A method implemented by a processor for optimizing prediction of interpretation times for a plurality of medical image examinations of subjects, each medical image examination comprising one or more medical images, the method comprising:
training a regression-based, artificial intelligence algorithm by obtaining training data inputs and corresponding actual interpretation times for a plurality of examinations, training the artificial intelligence algorithm to map states defined by the training data inputs to corresponding predicted interpretation times using a reinforcement learning approach, and minimizing a reward function to optimize the artificial intelligence algorithm to improve the predicted interpretation times over time, wherein the reward function comprises an absolute value of a difference between the predicted interpretation time and a corresponding actual interpretation time for each examination;
obtaining a plurality of data inputs associated with each medical image examination and/or each subject of said medical image examination, wherein data points of the plurality of data inputs represent examination variables affecting interpretation time;
inputting the plurality of data inputs associated with the medical image examinations and/or the subjects, respectively, to the trained artificial intelligence algorithm;
estimating corresponding optimized predicted interpretation times using the trained artificial intelligence algorithm based on said plurality of data inputs;
providing said predicted interpretation times to a user of a clinical management system performing interpretations of the medical image examinations;
obtaining available time of the user for image interpretation;
selecting a combination of medical image examinations waiting for interpretation having a cumulative predicted interpretation time less than the user's available time; and
presenting a list of the selected combination of medical image examinations to the user for the user to perform the interpretations of the medical image examinations.
2 . The method of claim 1 , wherein the artificial intelligence algorithm further provides a confidence level for each of the predicted interpretation times.
3 . The method of claim 1 , wherein the artificial intelligence algorithm ranks the predicted interpretation times for the medical image examinations and highlights a longest predicted interpretation time.
4 . The method of claim 1 , wherein the plurality of data inputs comprises: at least one of body mass index of the subject of the medical image examination, patient age, or patient gender, at least one of type of medical image study, previous image modalities available, disease type, history of diastolic dysfunction, presence of atrial fibrillation, or presence of coronary artery disease, and at least one of type of imaging modality, number of archived images or loops, or exam type.
5 . The method of claim 1 , wherein the plurality of data inputs comprises each of: body mass index of the subject of the medical image examination, type of medical image study, patient age, patient gender, previous image modalities available, disease type, history of diastolic dysfunction, presence of atrial fibrillation, presence of coronary artery disease, type of imaging modality, number of archived images or loops, sonographer's notes, and exam type.
6 . The method of claim 1 , wherein the reward function is updated using a Bellman's equation for reinforcement learning.
7 . The method of claim 1 , further comprising:
obtaining actual interpretation time for each medical image examination, wherein the artificial intelligence algorithm uses the actual interpretation time and the plurality of data inputs for reinforcement learning by further minimizing the reward function to further optimize the artificial intelligence algorithm.
8 . The method of claim 1 , further comprising:
providing a search function to the user that triggers the artificial intelligence algorithm to predict an interpretation time for two or more selected imaging types using a mapping function and state variables X.
9 . A clinical management system configured to optimize prediction of interpretation times for medical image examinations of subjects, the clinical management system comprising a processor operably connected to a non-transitory memory, the memory having encoded thereon machine-readable program code that, when executed by the processor, causes the processor to:
train a regression-based, artificial intelligence algorithm by obtaining training data inputs and corresponding actual interpretation times for a plurality of examinations, training the artificial intelligence algorithm to map states defined by the training data inputs to corresponding predicted interpretation times using a reinforcement learning approach, and minimizing a reward function to optimize the artificial intelligence algorithm to improve the predicted interpretation times over time, wherein the reward function comprises an absolute value of a difference between the predicted interpretation time and a corresponding actual interpretation time for each examination;
obtain a plurality of data inputs associated with each medical image examination and/or each subject of said medical image examination;
input the plurality of data inputs associated with the medical image examinations and/or the subjects, respectively, to the trained artificial intelligence algorithm;
estimate optimized predicted interpretation times using the trained artificial intelligence algorithm based on said plurality of data inputs;
display said predicted interpretation times to a user of the clinical management system performing interpretations of the medical image examinations;
obtain available time of the user for image interpretation;
select a combination of medical image examinations waiting for interpretation having a cumulative predicted interpretation time less than the user's available time; and
present a list of the selected combination of medical image examinations to the user for the user to perform the interpretations of the medical image examinations.
10 . The clinical management system of claim 9 , wherein the predicted interpretation times are presented in a table of medical imaging examinations awaiting interpretation.
11 . The clinical management system of claim 9 , wherein the machine-readable program code, when executed by the processor, further causes the processor to:
provide a search function to the user that triggers the artificial intelligence algorithm to predict an interpretation time for two or more selected imaging types using a mapping function and state variables X.
12 . The clinical management system of claim 9 , wherein the artificial intelligence algorithm further automatically provides confidence levels for the predicted interpretation times, respectively, and the machine-readable program code, when executed by the processor, further causes the processor to:
provide said confidence levels to the user of the clinical management system performing interpretations of the medical image examinations.
13 . A machine-readable storage media having encoded thereon program code for optimizing prediction of medical imaging interpretation time that, when executed by a processor, causes the processor to:
train a regression-based, artificial intelligence algorithm by obtaining training data inputs and corresponding actual interpretation times for a plurality of examinations, training the artificial intelligence algorithm to map states defined by the training data inputs to corresponding predicted interpretation times using a reinforcement learning approach, and minimizing a reward function to optimize the artificial intelligence algorithm to improve the predicted interpretation times over time, wherein the reward function comprises an absolute value of a difference between the predicted interpretation time and a corresponding actual interpretation time for each examination;
obtain a plurality of data inputs associated with each medical image examination and/or each subject of said medical image examination, wherein data points of the plurality of data inputs represent parameters affecting interpretation time;
input the plurality of data inputs associated with the medical image examinations and/or the subjects, respectively, to the trained artificial intelligence algorithm;
estimate corresponding optimized predicted interpretation times using the trained artificial intelligence algorithm based on said plurality of data inputs;
cause said predicted interpretation times to be displayed to a user of a clinical management system performing interpretations of the medical image examinations;
obtain available time of the user for image interpretation;
select a combination of medical image examinations waiting for interpretation having a cumulative predicted interpretation time less than the user's available time; and
present a list of the selected combination of medical image examinations to the user for the user to perform the interpretations of the medical image examinations.
14 . The machine-readable storage media of claim 13 , wherein the artificial intelligence algorithm further provides confidence levels for the predicted interpretation times, respectively.
15 . The machine-readable storage media of claim 13 , wherein the artificial intelligence algorithm ranks the predicted interpretation times for medical image examinations and highlights a longest predicted interpretation time.
16 . The machine-readable storage media of claim 13 , wherein the plurality of data inputs comprises: at least one of body mass index of the subject of the medical image examination, patient age, or patient gender, at least one of type of medical image study, previous image modalities available, disease type, history of diastolic dysfunction, presence of atrial fibrillation, or presence of coronary artery disease, and at least one of type of imaging modality, number of archived images or loops, or exam type.
17 . The machine-readable storage media of claim 13 , wherein the plurality of data inputs comprises each of: body mass index of the subject of the medical image examination, type of medical image study, patient age, patient gender, previous image modalities available, disease type, history of diastolic dysfunction, presence of atrial fibrillation, presence of coronary artery disease, type of imaging modality, number of archived images or loops, sonographer's notes, and exam type.