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 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 determining image characteristics serving as a basis for a diagnosis associated with an image study type. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive an image study from the at least one data source over the interface. The image study being of the image study type and including a plurality of images. The electronic processor is also configured to determine an image characteristic for each of the plurality of images and determine whether each of the plurality of images was used to establish a diagnosis. The electronic processor is also configured to store the image characteristic for each of the plurality of images and an indicator of whether each of the plurality of images was used to establish the diagnosis in a data structure.

Abstract: Methods and systems for automatically analyzing clinical images using models developed using machine learning. One system includes a server having an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive training information from the at least one data source over the interface. The training information includes a plurality of images and graphical reporting associated with each of the plurality of images. Each graphical reporting includes a graphical marker designating a portion of one of the plurality of images and diagnostic information associated with the portion of the one of the plurality of images. The electronic processor is also configured to perform machine learning to develop a model using the training information. The electronic processor is also configured to receive an image for analysis and automatically process the image using the model to generate a diagnosis for the image.

Abstract: Methods and systems for automatically selecting an implant for a patient. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive at least one image of the patient from the data source over the interface. The electronic processor is also configured to receive an intended location of the implant with reference to the at least one image. The electronic processor is also configured to automatically determine an anatomical structure based on the at least one image. The electronic processor is also configured to determine a preference. The electronic processor is also configured to automatically select one or more suggested implants based on the intended location, the anatomical structure, and the preference. The electronic processor is also configured to display the one or more suggested implants through a graphical user interface.

Abstract: Methods and systems for automatically determining diagnosis discrepancies for clinical images. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive a first diagnosis from the at least one data source over the interface. The first diagnosis is specified by a diagnosing physician for an anatomical structure represented in an image. The electronic processor is also configured to determine a second diagnosis for the anatomical structure. The second diagnosis is generated after the first diagnosis. The electronic processor is also configured to store the first diagnosis and the second diagnosis in a data structure. The electronic processor is also configured to automatically determine a discrepancy between the first diagnosis and the second diagnosis based on the data structure.

Abstract: Methods and systems for automatically scoring diagnoses associated with clinical images. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive a diagnosis associated with an image of a patient from the at least one data source over the interface. The diagnosis is for an anatomical structure represented in the image. The electronic processor is also configured to receive a pathology result for the patient for the anatomical structure generated after the diagnosis from the at least one pathology result source over the interface. The electronic processor is also configured to automatically generate a score based on a comparison of the diagnosis and the pathology result. The electronic processor is also configured to display the score within a graphical user interface.

Abstract: Methods and systems for automatically scoring diagnoses associated with clinical images. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive a diagnosis associated with an image of a patient from the at least one data source over the interface. The diagnosis is for an anatomical structure represented in the image. The electronic processor is also configured to receive a pathology result for the patient for the anatomical structure generated after the diagnosis from the at least one pathology result source over the interface. The electronic processor is also configured to automatically generate a score based on a comparison of the diagnosis and the pathology result. The electronic processor is also configured to display the score within a graphical user interface.

Abstract: Methods and systems for automatically determining a clinical image or portion thereof for display to a diagnosing physician. One system includes an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive training information from the at least one data source and determine a subset of images included in each of the plurality of image studies displayed to one or more diagnosing physicians. The electronic processor is also configured to perform machine learning to develop a model based on the training information and the subset of images included in each of the plurality of image studies and receive the image study. The electronic processor is also configured to process the image study using the model to determine a subset of the plurality of images and flag the subset of the plurality of images for manual review by the diagnosing physician.

Abstract: An illustrative embodiment of a computer-implemented process for non-leading computer aided detection of features of interest in a dataset, designates a particular formation using a computer recognizable gesture to identify a gestured location in an analyzed view of the dataset in response to a user identifying the particular formation in the analyzed view. The dataset is generated by a computer and representative of a portion of an object characterized by the dataset. Responsive to identifying the gestured location, the particular formation is displayed to the user, and a composition is revealed including additional structural imagery, functional imagery and findings resulting from machine learning and analysis. Responsive to revealing the composition to the user, the user is prompted to select performance of accept selection, reject selection or modify selection with regard to the particular formation displayed.

Abstract: Methods and systems for using contextual information to generate reports for image studies. One method includes determining contextual information associated with an image study wherein at least one image included in the image study loaded in a reporting application. The method also includes automatically selecting, with an electronic processor, a vocabulary for a natural language processing engine based on the contextual information. In addition, the method includes receiving, from a microphone, audio data and processing the audio data with the natural language processing engine using the vocabulary to generate data for a report for the image study generated using the reporting application.

Abstract: Methods and systems for using contextual information to generate reports for image studies. One method includes determining contextual information associated with an image study, at least one image included in the image study loaded within a reporting application and automatically selecting, with an electronic processor, at least one discrete data element for a structured report generated using the reporting application for the image study based on the contextual information. The method also includes receiving a value for the at least one discrete data element and adding the value for the at least one discrete data element to the structured report.

Abstract: Methods and systems for automatically determining a clinical image or portion thereof for display to a diagnosing physician. One system includes an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive training information from the at least one data source and determine a subset of images included in each of the plurality of image studies displayed to one or more diagnosing physicians. The electronic processor is also configured to perform machine learning to develop a model based on the training information and the subset of images included in each of the plurality of image studies and receive the image study. The electronic processor is also configured to process the image study using the model to determine a subset of the plurality of images and flag the subset of the plurality of images for manual review by the diagnosing physician.

Abstract: Methods and systems for automatically analyzing clinical images using models developed using machine learning. One system includes a server having an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive training information from the at least one data source over the interface. The training information includes a plurality of images and graphical reporting associated with each of the plurality of images. Each graphical reporting includes a graphical marker designating a portion of one of the plurality of images and diagnostic information associated with the portion of the one of the plurality of images. The electronic processor is also configured to perform machine learning to develop a model using the training information. The electronic processor is also configured to receive an image for analysis and automatically process the image using the model to generate a diagnosis for the image.

Abstract: Methods and systems for automatically selecting an implant for a patient. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive at least one image of the patient from the data source over the interface. The electronic processor is also configured to receive an intended location of the implant with reference to the at least one image. The electronic processor is also configured to automatically determine an anatomical structure based on the at least one image. The electronic processor is also configured to determine a preference. The electronic processor is also configured to automatically select one or more suggested implants based on the intended location, the anatomical structure, and the preference. The electronic processor is also configured to display the one or more suggested implants through a graphical user interface.

Abstract: Methods and systems for automatically determining diagnosis discrepancies for clinical images. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive a first diagnosis from the at least one data source over the interface. The first diagnosis is specified by a diagnosing physician for an anatomical structure represented in an image. The electronic processor is also configured to determine a second diagnosis for the anatomical structure. The second diagnosis is generated after the first diagnosis. The electronic processor is also configured to store the first diagnosis and the second diagnosis in a data structure. The electronic processor is also configured to automatically determine a discrepancy between the first diagnosis and the second diagnosis based on the data structure.

Abstract: Methods and systems for automatically determining image characteristics serving as a basis for a diagnosis associated with an image study type. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive an image study from the at least one data source over the interface. The image study being of the image study type and including a plurality of images. The electronic processor is also configured to determine an image characteristic for each of the plurality of images and determine whether each of the plurality of images was used to establish a diagnosis. The electronic processor is also configured to store the image characteristic for each of the plurality of images and an indicator of whether each of the plurality of images was used to establish the diagnosis in a data structure.

Abstract: Methods and systems for automatically analyzing clinical images and determining when additional imaging may aid a diagnosis. One system includes at least one data source and a server. The server includes an electronic processor and an interface for communicating with the at least one data source. The electronic processor is configured to receive an image of a patient from the at least one data source over the interface. The electronic processor is also configured to automatically process the image to generate a diagnosis for the image. The electronic processor is also configured to automatically determine, based on the diagnosis, at least one additional image for supplementing the diagnosis. The electronic processor is also configured to automatically locate the at least one additional image for the patient and when the at least one additional image is available, automatically update the diagnosis based on the at least one additional image.

Abstract: Methods and systems for automatically mapping biopsy locations to pathology results. One system includes a server including an electronic processor and an interface for communicating with at least one data source and at least one pathology result source. The electronic processor is configured to receive an image from the at least one data source over the interface. The electronic processor is also configured to receive a biopsy location. The biopsy location includes a three-dimensional position mapped to a position within the image. The electronic processor is also configured to automatically locate an electronic pathology result for the biopsy location within the at least one pathology result source over the interface. The electronic processor is also configured to generate an electronic correlation between the biopsy location and the electronic pathology result. The electronic processor is also configured to display the image with the biopsy location marked within the image.

Abstract: Methods and systems for automatically analyzing clinical images using rules and image analytics. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive training information from the at least one data source over the interface. The training information includes a plurality of images and graphical reporting associated with each of the plurality of images. The electronic processor is also configured to perform machine learning to develop a model using the training information and receive an image for analysis. The electronic processor is also configured to determine a set of rules for the image and automatically process the image using the model and the set of rules to generate a diagnosis for the image.