Abstract: An approach for a computer program to receive image data of a subject including at least a portion of a spine of the subject and a chronological age of the subject. The approach includes the computer program pre-processing the image data including at least a portion of a spine. The approach includes determining an apparent age of the spine or a portion of the spine of the subject using a trained artificial intelligence deep learning algorithm.

Abstract: Methods and systems of summarizing medical data. One system includes an electronic processor configured to analyze medical data to extract a medical concept and a plurality of additional attributes of the medical concept and store the medical concept and the plurality of additional attributes. The electronic processor is configured to generate a first medical summary associated with the patient, where the first medical summary is based on the stored medical concept and at least a first additional attribute included in the stored plurality of additional attributes. The electronic processor is configured to receive a user interaction with the first medical summary. The electronic processor is configured to generate a second medical summary associated with the patient based on the user interaction, the second medical summary is based on the stored medical concept and at least a second additional attribute included in the stored plurality of additional attributes.

Abstract: Systems and methods for selectively processing image studies with an artificial intelligence system. One system includes an electronic processor configured to select an image study awaiting review and update a workflow status of the image study to a first status indicating that the image study has been claimed for review by the artificial intelligence system. The electronic processor is also configured to apply at least one of the plurality of rules to the image study to determine whether the image study is applicable for processing by the artificial intelligence system, and, in response to determining the image study is not applicable for processing by the artificial intelligence system based on the at least one of the plurality of rules, update the workflow status associated with the image study to a second status to make the image study available for claiming by a manual reviewer or another artificial intelligence system.

Abstract: Systems and methods for selecting a prior comparison study. One system includes an electronic processor configured to, for a medical image study associated with a patient, select a prior comparison image study. The electronic processor is also configured to automatically determine, based on monitored user interaction with the selected prior comparison image study, a usefulness of the selected prior comparison image study. The electronic processor is also configured to automatically update a selection model based on the usefulness of the prior comparison image study to a user.

Abstract: Systems and methods for selecting a prior comparison study. One system includes an electronic processor configured to, for a medical image study associated with a patient, select a prior comparison image study. The electronic processor is also configured to automatically determine, based on monitored user interaction with the selected prior comparison image study, a usefulness of the selected prior comparison image study. The electronic processor is also configured to automatically update a selection model based on the usefulness of the prior comparison image study to a user.

Abstract: Systems and methods for selectively processing image studies with an artificial intelligence system. One system includes an electronic processor configured to select an image study awaiting review and update a workflow status of the image study to a first status indicating that the image study has been claimed for review by the artificial intelligence system. The electronic processor is also configured to apply at least one of the plurality of rules to the image study to determine whether the image study is applicable for processing by the artificial intelligence system, and, in response to determining the image study is not applicable for processing by the artificial intelligence system based on the at least one of the plurality of rules, update the workflow status associated with the image study to a second status to make the image study available for claiming by a manual reviewer or another artificial intelligence system.

Abstract: A method, a computer system, and a computer program product for marketplace creation and model training in a blockchain network is provided. Embodiments of the present invention may include uploading images to a marketplace. Embodiments of the present invention may include recording references to the images on a blockchain ledger. Embodiments of the present invention may include measuring local and global annotations. Embodiments of the present invention may include determining an image quality threshold has been met. Embodiments of the present invention may include providing an annotation quality metric to the marketplace. Embodiments of the present invention may include pre-processing and standardizing the images. Embodiments of the present invention may include training a model. Embodiments of the present invention may include recording the model in the blockchain ledger. Embodiments of the present invention may include providing the model to the marketplace.

Abstract: A method, system, and computer program product provide disease simulation in synthetic projection imagery. The method obtains first medical imaging data of a first imaging type as source imaging data. A second imaging type to be generated from the source imaging data is identified. The method identifies a parameter set for the second imaging type. Second medical imaging data is modeled from the first medical imaging data based on the parameter set. A set of synthetic images is generated from the first medical imaging data based on the modeled second medical imaging data.

Abstract: An embodiment of the invention may include a method, computer program product and system for analyzing cardiac function of a patient. An embodiment may include receiving a plurality of digital image representations of cardiac function at rest. An embodiment may include receiving a plurality of digital image representations of cardiac function at stress. An embodiment may include selecting a digital image representation of cardiac function at rest and a corresponding digital image representation of cardiac function at stress. An embodiment may include aligning the selected representation of cardiac function at rest with the selected corresponding representation of cardiac function at stress. An embodiment may include identifying a difference between the selected representation of cardiac function at rest and the selected corresponding representation of cardiac function at stress based on a displayed overlay of the aligned representations.

Abstract: One or more processors may identify a missing image in the set of multi-view images. Each of the images is associated with a particular view type. The one or more processors may generate, utilizing a replacement AI model, a replacement image for the missing image in the set of multi-view images. The replacement image is generated utilizing an AI model trained to generate a replacement image using training images from two or more time-adjacent sets of images. The one or more processors may identify a duplicate image in the set of multi-view images. The one or more processors may generate, utilizing a quality generative AI model, a characteristic improved image based on the duplicate image for the set of multi-view images. The one or more processors may output the replacement image and the characteristic improved image.

Abstract: An approach for a computer program to receive image data of a subject including at least a portion of a spine of the subject and a chronological age of the subject. The approach includes the computer program pre-processing the image data including at least a portion of a spine. The approach includes determining an apparent age of the spine or a portion of the spine of the subject using a trained artificial intelligence deep learning algorithm.

Abstract: Aspects of the present invention disclose a method for processing bulk historical data. The method includes one or more processors identifying one or more features of messages of incoming data queries of a computing device, wherein the one or more features include structured and unstructured data. The method further includes aggregating one or more segments of bulk historic data for a plurality of individuals based at least in part on the one or more features of the messages of the incoming data queries. The method further includes determining a classification of each individual of the plurality of individuals based at least in part on the aggregated one or more segments of the bulk historic data. The method further includes prioritizing processing of the aggregated one or more segments of the bulk historic data based at least in part on the classification of each individual of the plurality of individuals.

Abstract: One or more processors may identify a missing image in the set of multi-view images. Each of the images is associated with a particular view type. The one or more processors may generate, utilizing a replacement AI model, a replacement image for the missing image in the set of multi-view images. The replacement image is generated utilizing an AI model trained to generate a replacement image using training images from two or more time-adjacent sets of images. The one or more processors may identify a duplicate image in the set of multi-view images. The one or more processors may generate, utilizing a quality generative AI model, a characteristic improved image based on the duplicate image for the set of multi-view images. The one or more processors may output the replacement image and the characteristic improved image.

Abstract: Methods and systems for detecting a dissection in surface of an elongated structure in a three dimensional medical image. One system includes an electronic processor configured to receive the three dimensional medical image and determine a periphery of the elongated structure included in the three dimensional medical image. The electronic processor is also configured to generate a non-contrast image representing the periphery of the elongated structure and superimpose a contrast image associated with the three dimensional image on top of the non-contrast image to generate a superimposed image. The electronic processor is also configured to detect at least one dissection in the elongated structure using the superimposed image and output a medical report identifying the at least one dissection detected in the elongated structure.

Abstract: Systems and methods for generating electronic reports for medical images. One system includes an electronic processor configured to access a plurality of prior reports associated with a user and automatically generate a mapping using machine learning based on the plurality of prior reports. The mapping associates language included in the plurality of prior reports with at least one section of a report. The system stores the mapping to a memory, receives input from the user for an electronic report associated with a medical image, accesses the mapping from the memory, automatically determines a section in the electronic report associated with the input based on the mapping, and automatically inserts text into the section in the electronic report based on the input.

Abstract: In an approach to automatic detection of vertebral dislocations, training data is received, where the training data includes an ordered sequence of radiographic image patches of vertebrae and intervertebral spaces, and location data for each vertebra and each intervertebral space. Location deep learning models are trained to detect a location of each vertebra and each intervertebral space from the training data. Classification deep learning models are trained to classify an ordered sequence of image patches to identify vertebral anomalies of the vertebrae and the intervertebral spaces from the training data. Responsive to receiving radiographic image files, the location deep learning models and the classification deep learning models are applied to the radiographic image files to create a condition assessment.

Abstract: Methods and systems for detecting a dissection of an elongated structure in a three dimensional medical image. One system includes an electronic processor that receives the three dimensional medical image. The electronic processor determines a first periphery and a second periphery of the elongated structure, the first periphery and the second periphery associated with an enhancing part and a non-enhancing part, respectively, of the elongated structure. The electronic processor determines whether the first periphery or the second periphery best illustrates an outermost periphery of the elongated structure and generate a base image based on whether the first periphery or the second periphery best illustrates an outermost periphery of the elongated structure.

Abstract: Methods and systems for detecting a dissection of an elongated structure in a three dimensional medical image. One system includes an electronic processor configured to receive the medical image and detect a centerline of the elongated structure. The electronic processor is configured to determine a plurality of two dimensional cross sections of the medical image based on the centerline. For each of the two dimensional cross sections, the electronic processor is configured to determine a radial density profile and determine a density gradient based on the radial density profile. The electronic processor is configured to analyze one or more of a plurality of density gradients determined for each of the two dimensional cross sections, detect a dissection in the elongated structure based on the analysis of the density gradient for each of the two dimensional cross sections, and output a medical report identifying the dissection.

Abstract: A method, system, and computer program product provide disease simulation in synthetic projection imagery. The method obtains first medical imaging data of a first imaging type as source imaging data. A second imaging type to be generated from the source imaging data is identified. The method identifies a parameter set for the second imaging type. Second medical imaging data is modeled from the first medical imaging data based on the parameter set. A set of synthetic images is generated from the first medical imaging data based on the modeled second medical imaging data.

Abstract: A method, system, and computer program product for disease simulation and identification in medical images. The method generates a set of synthetic projection images from first medical imaging data of a first imaging type. Second medical imaging data of a second imaging type is projected onto the set of synthetic projection images. One or more synthetic images of the set of synthetic projection images are modified to generate a set of modified projection images based on the second medical imaging data. The method generates an imaging model based on the set of modified projection images. The method obtains a patient medical image of the first imaging type and identifies an attribute of interest on the patient medical image based on the imaging model.