Abstract: Methods and systems for automatically triaging an image study of a patient generated as part of a medical imaging procedure. One system includes a computing device including an electronic processor. The electronic processor is configured to submit at least a portion of the image study to a cognitive system, the cognitive system configured to analyze the image study using a model developed using machine learning, receive, from the cognitive system, a BI-RADS classification assigned to the image study using the model, and automatically triage the image study based on the classification assigned to the image study by the cognitive system.

Abstract: Methods and systems for automatically triaging an image study of a patient generated as part of a medical imaging procedure. One system includes a computing device including an electronic processor. The electronic processor is configured to receive, from a cognitive system applying a model developed using computer vision and machine learning techniques based on deep learning methodology to classify image studies, a classification assigned to the image study using the model, and automatically generating a worklist based on the classification assigned to the image study using the model, the worklist prioritizing a plurality of tasks for treating the patient.

Abstract: Methods and systems for automatically triaging an image study of a patient generated as part of a medical imaging procedure. One system comprises a computing device including an electronic processor. The electronic processor is configured to receive, from a cognitive system applying a model developed using computer vision and machine learning techniques based on deep learning methodology to classify image studies, a classification assigned to the image study using the model, automatically generate a differential diagnosis for the patient based on the classification assigned by the model, and automatically adjust triaging of the image study based on the differential diagnosis.

Abstract: Methods and systems for automatically triaging an image study of a patient generated as part of a medical imaging procedure. One system includes a computing device including an electronic processor. The electronic processor is configured to submit at least a portion of the image study to a cognitive system, the cognitive system configured to analyze the image study using a model developed using machine learning, receive, from the cognitive system, a BI-RADS classification assigned to the image study using the model, and automatically triage the image study based on the classification assigned to the image study by the cognitive system.

Abstract: Methods and systems for automatically triaging an image study of a patient generated as part of a medical imaging procedure. One system includes a computing device including an electronic processor. The electronic processor is configured to receive, from a cognitive system applying a model developed using computer vision and machine learning techniques based on deep learning methodology to classify image studies, a classification assigned to the image study using the model, and automatically generate a structured report for the image study based on the classification assigned by the model, the structured report accessible by a radiologist via a structured reporting system.

Abstract: Methods and systems for automatically triaging an image study of a patient generated as part of a medical imaging procedure. One system includes a computing device including an electronic processor. The electronic processor is configured to submit at least a portion of the image study to a cognitive system, the cognitive system configured to analyze the image study using a model developed using machine learning, receive, from the cognitive system, a BI-RADS classification assigned to the image study using the model, and automatically triage the image study based on the classification assigned to the image study by the cognitive system.

Abstract: Methods and systems for verifying a manually-generated report for a medical image. One system comprises an electronic processor configured to receive a first report for the medical image generated by a first radiologist, receive a second report for the medical image generated by a cognitive system, and automatically compare the first report and the second report to detect a discrepancy between the first report and the second report. The electronic processor is also configured to, in response to not detecting a discrepancy between the first report and the second report, submitting the first report for the medical image. The electronic processor is also configured to, in response to detecting a discrepancy between the first report and the second report, assign the medical image to a second radiologist, receive a third report for the medical image generated by the second radiologist, and submit the third report for the medical image.

Abstract: Methods and systems for automatically triaging an image study of a patient generated as part of a medical imaging procedure. One system includes a computing device including an electronic processor. The electronic processor is configured to submit at least a portion of the image study to a cognitive system, the cognitive system configured to analyze the image study using a model developed using machine learning, receive, from the cognitive system, a BI-RADS classification assigned to the image study using the model, and automatically triage the image study based on the classification assigned to the image study by the cognitive system.

Abstract: Methods and systems for automatically triaging an image study of a patient generated as part of a medical imaging procedure. One system comprises a computing device including an electronic processor. The electronic processor is configured to receive, from a cognitive system applying a model developed using computer vision and machine learning techniques based on deep learning methodology to classify image studies, a classification assigned to the image study using the model, automatically generate a differential diagnosis for the patient based on the classification assigned by the model, and automatically adjust triaging of the image study based on the differential diagnosis.

Abstract: Methods and systems for verifying a manually-generated report for a medical image. One system comprises an electronic processor configured to receive a first report for the medical image generated by a first radiologist, receive a second report for the medical image generated by a cognitive system, and automatically compare the first report and the second report to detect a discrepancy between the first report and the second report. The electronic processor is also configured to, in response to not detecting a discrepancy between the first report and the second report, submitting the first report for the medical image. The electronic processor is also configured to, in response to detecting a discrepancy between the first report and the second report, assign the medical image to a second radiologist, receive a third report for the medical image generated by the second radiologist, and submit the third report for the medical image.

Abstract: Methods and systems for automatically triaging an image study of a patient generated as part of a medical imaging procedure. One system includes a computing device including an electronic processor. The electronic processor is configured to receive, from a cognitive system applying a model developed using computer vision and machine learning techniques based on deep learning methodology to classify image studies, a classification assigned to the image study using the model, and automatically communicate with a resource allocation system to reserve at least one medical resource for treating the patient based on the classification assigned by the model.

Abstract: Methods and systems for automatically triaging an image study of a patient generated as part of a medical imaging procedure. One system includes a computing device including an electronic processor. The electronic processor is configured to receive, from a cognitive system applying a model developed using computer vision and machine learning techniques based on deep learning methodology to classify image studies, a classification assigned to the image study using the model, and automatically generating a worklist based on the classification assigned to the image study using the model, the worklist prioritizing a plurality of tasks for treating the patient.

Abstract: Methods and systems for automatically triaging an image study of a patient generated as part of a medical imaging procedure. One system includes a computing device including an electronic processor. The electronic processor is configured to receive, from a cognitive system applying a model developed using computer vision and machine learning techniques based on deep learning methodology to classify image studies, a classification assigned to the image study using the model, and automatically generate a structured report for the image study based on the classification assigned by the model, the structured report accessible by a radiologist via a structured reporting system.

Abstract: A system and method for utilizing a spatial three-dimensional display unit with a medical diagnostic imaging system is disclosed. The medical diagnostic imaging system may be a picture archival communication system. The system may include at least one two-dimensional display unit and an additional spatial three-dimensional display unit. Accordingly a user may view two and three-dimensional images of similar anatomical objects. The two and three-dimensional images may be linked, providing the user with a consistent viewing angle. The system may be used to review a surgical path. The display parameters of a first three-dimensional data set may be mapped to a second three-dimensional data set. The surgical path of the first data set and the second data set may be displayed on spatial display units. Accordingly, the anatomical objects along the surgical path may be viewed at different points in time, for example prior to surgery and after surgery.

Abstract: Certain embodiments of the present invention provide a clinical information workflow profiling system including a workflow engine adapted to collect profiling data for a healthcare workflow task and a profiling processor adapted to analyze the profiling data to determine an alternative configuration of the healthcare workflow task.

Abstract: Certain embodiments of the present invention provide methods and systems for providing clinical display and search of electronic medical data from a variety of information systems. Certain embodiments provide a user interface system including a processor operating a plurality of electronic clinical functionalities; an input allowing a user to enter data and operate clinical functionalities; a learning module maintaining a record of user selected functionalities; and a display output providing a functionality interface and a selection interface to a user. Certain embodiments provide a method for navigating among a plurality of functionalities in an electronic clinical application by maintaining a record of a user's navigation history and generating an array of functionality links based on the record.

Abstract: A system and method for utilizing a spatial three-dimensional display unit with a medical diagnostic imaging system is disclosed. The medical diagnostic imaging system may be a picture archival communication system. The system may include at least one two-dimensional display unit and an additional spatial three-dimensional display unit. Accordingly a user may view two and three-dimensional images of similar anatomical objects. The two and three-dimensional images may be linked, providing the user with a consistent viewing angle. The system may be used to review a surgical path. The display parameters of a first three-dimensional data set may be mapped to a second three-dimensional data set. The surgical path of the first data set and the second data set may be displayed on spatial display units. Accordingly, the anatomical objects along the surgical path may be viewed at different points in time, for example prior to surgery and after surgery.

Abstract: Certain embodiments of the present invention provide a clinical workflow system including a user interface adapted to present a visualization of a workflow to a user. The workflow represents a clinical protocol including a plurality of tasks. The user interface is adapted to generate a state machine for the workflow based on the clinical protocol by converting a sequential model of the clinical protocol into a plurality of states. Each state is associated with a task in the clinical protocol. At least one state in the state machine is connected with a non-sequential state.

Abstract: A system and method for utilizing a spatial three-dimensional display unit with a medical diagnostic imaging system is disclosed. The medical diagnostic imaging system may be a picture archival communication system. The system may include at least one two-dimensional display unit and an additional spatial three-dimensional display unit. Accordingly a user may view two and three-dimensional images of similar anatomical objects. The two and three-dimensional images may be linked, providing the user with a consistent viewing angle. The system may be used to review a surgical path. The display parameters of a first three-dimensional data set may be mapped to a second three-dimensional data set. The surgical path of the first data set and the second data set may be displayed on spatial display units. Accordingly, the anatomical objects along the surgical path may be viewed at different points in time, for example prior to surgery and after surgery.

Abstract: A technique is provided for generating scout or navigational images based upon a series of related images. The related images are analyzed to compute pixels of scout images, such as by determining a characteristic of aligned pixels of each of the related images. The scout or navigational images are then projections of the related images and individual rows or columns of the scout images correspond to individual images of the related set. One or more of the related images may then be accessed by review of the scout image or images. The scout images may be referred to in their own right for an overview of features of interest and may be modified by a user. The technique provides for efficient time management by allowing specific images to be identified in large image series, and facilitates intelligent use of bandwidth for accessing specific images in a viewer workstation.