Abstract: After selecting two or more image series for comparison, images of the image series are interleaved so that they are alternatively displayed in a comparison pane on a display device. In one embodiment, after one or more image series are selected for comparison, an interleaved image series is created containing each of the images of the one or more selected image series, or, alternatively, the interleaved image series comprises links to the images arranged in the interleaved pattern. If differences exist in the images of the multiple image series, these differences may be more easily detectable as the display device cycles between the images. Comparison of images in an interleaved image series may be more advantageous if the images of each selected image series are of a common anatomical area, common image size, and the images are in the same order.

Abstract: A conversation management system provides a conversational computing interface that manages verbal exchanges between a computer (e.g., artificial intelligence) and a human operator. In a medical embodiment, conversational input from the computing system is generated based on medical knowledge, uses deep learning algorithms, and/or intelligently tracks the state of a conversation so as to the most relevant data to the user.

Abstract: A method, computer system, and a computer program product for dynamically altering at least one image is provided. The present invention may include receiving a plurality of data, wherein the received plurality of data includes at least one existing medical image. The present invention may also include determining that one or more user instructions for the received existing image were received. The present invention may then include implementing the one or more user instructions on the received existing medical image. The present invention may also include altering the received existing medical image based on the one or more implemented user instructions and a medical knowledge base.

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.

Abstract: Disclosed are systems and methods for the use of mobile computing devices, such as tablets and/or smartphones, as user-customizable input devices that interface with more sophisticated computer systems (or “parent computing systems”), such as a picture archiving and communication system (PACS) and/or an electronic medical record system (EMR). Further described are systems and methods of mobile device user input and interface optimization and customization based on a user's identity, a parent computing system's characteristics, and/or a tilt angle of a display.

Abstract: Methods and systems for generating an electronic structured report associated with a displayed electronic medical image. One method includes receiving an annotation for the electronic medical image, automatically determining, with an electronic processor, an anatomical location within the electronic medical image associated with the annotation, and automatically determining, with the electronic processor, a location within the electronic structured report associated with the anatomical location based on a predetermined mapping. The method also includes automatically populating the location of the electronic structured report based on the annotation.

Abstract: Systems and methods for providing user interfaces are disclosed. In certain embodiments, a menu having a number of icons can be provided on a display device such that the icons are arranged around an initial cursor position, or an area that is touched by a user's finger or stylus, for example. Due to the icons being arranged around the initial cursor position, any one of the icons from the menu can be chosen with relatively small cursor movement. In certain embodiments, the menu can be divided into regions that overlap with the icons, such that cursor movement from the initial cursor position into a given region has a similar effect as movement into the corresponding icon itself (without actually moving the cursor onto the desired icon).

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 generating and displaying a simulated anatomical photograph. One system includes an electronic processor. The electronic processor is configured to receive a first selection from a user, the first selection designating a body part of a patient, receive a second selection from the user, the second selection designating a time period, automatically access imaging information for the patient associated with the first selection and the second selection, automatically generate the simulated anatomical photograph for the body part for the time period based on the imaging information, and display the simulated anatomical photograph to the user within a graphical user interface.

Abstract: Methods and systems for generating a simulated anatomical photograph based on a medical image generated by an imaging modality. One method includes receiving, with an electronic processor, the medical image, anatomically segmenting the medical image, with the electronic processor, to determine a plurality of anatomical structures represented in the medical image, and determining, with the electronic processor, how each of the plurality of anatomical structures is photographically depicted by accessing at least one knowledge base.

Abstract: Methods and systems for generating and displaying a simulated anatomical photograph based on a medical image generated by an imaging modality. The system comprises an electronic processor configured to receive the medical image, determine an anatomical structure in the medical image, and automatically generate the simulated anatomical photograph based on the anatomical structure, wherein the pixels of the simulated anatomical photograph represent a simulated cross-sectional anatomical photograph of the anatomical structure. The electronic processor is also configured to determine a degree of confidence of a portion of the simulated anatomical photograph, compare the degree of confidence to a threshold, and, in response to the degree of confidence of the portion of the simulated anatomical photograph failing to satisfy the threshold, display the portion of the simulated anatomical photograph differently from another portion of the simulated anatomical photograph.

Abstract: The systems and methods of the present disclosure may provide, among other features, easy-to-learn, efficient, and/or unambiguous methods for controlling rotation and/or other manipulation of multi-dimensional (for example, 2D and/or 3D) images and/or objects. The systems and methods may be used for any type of image display/manipulation on a wide variety of computer systems and coupled displays including personal computers with monitors, phones, tablets, and televisions. In general, a user may select a particular rotation plane (for example, rotation only in x axis) by placement of a cursor, or touch of a finger, over a certain portion of the image such that subsequent movements of the mouse result in only rotations in the particular plane, and unwanted rotations and/or other manipulations in other planes do not occur. In this way, the user can more precisely control rotations of the 3D image and/or object.

Abstract: Medical imaging device- and display-invariant segmentation and measurement is provided. In various embodiments, a plurality of medical images is read from a data store. Metadata of each of the plurality of medical images is read. The metadata identifies an image acquisition device associated with each of the plurality of medical images. Based on the plurality of medical images and the metadata of each of the plurality of images, a learning system is trained to determine one or more image correction parameters. The one or more image correction parameters optimize segmentation of the plurality of medical images.

Abstract: A method and system is provided for automatically determining a key image for display to a user as part of analyzing an image study generated as part of a medical imaging procedure. The system includes a memory storing a plurality of image studies, a display device for displaying images and an electronic processor interacting with the memory and the display device. The electronic processor is configured to determine a first key image within a plurality of images included in a first image study and to automatically determine, by executing one or more rules associated with one or more of the first key image, a user, a type of the first image study, a modality generating the first image study, an anatomy, a location of the modality, and patient demographics, a second key image included in at least one second image study. The system displays the second key image with the first key image to aid a user.

Abstract: Methods and systems for displaying a medical image captured as part of a medical imaging procedure. One system includes an electronic processor configured to receive a plurality of images from an image database, the plurality of images included in a medical exam associated with the procedure. The electronic processor is also configured to automatically determine a classification for each of the plurality of images using a classification model analyzing content of each of the plurality of images, the classification including one of a textual image and an anatomical image. The electronic processor is also configured to determine a set of rules displaying the plurality of images and display a subset of the plurality of images based on the classification determined for each of the plurality of images and the set of rules.

Abstract: A system for automatically processing an image included in an image study generated as part of a medical imaging procedure includes at least one display device; at least one memory for storing images from one or more image studies; and an electronic processor. The electronic processor is configured to: display a set of medical images corresponding to an image study on the at least one display device, determine a key image included in the set of medical images, display the key image within a montage template, automatically annotate the key image, and display the annotated key image within the montage template.

Abstract: Systems and techniques are disclosed for improvement of machine learning systems based on enhanced training data. An example method includes providing a visual concurrent display of a set of images of features, the features requiring classification by a reviewing user. The user interface is provided to enable the reviewing user to assign classifications to the images, the user interface being configured to create, read, update, and/or delete classifications. The user interface is responsive to the user, with the user response indicating at least two images with a single classification. The user interface is updated to represent the single classification.

Abstract: Systems and techniques are disclosed for improvement of machine learning systems based on enhanced training data. An example method includes accessing a database storing associations between objects included in medical images and classifications of the objects. A risk assessment model adapted to determine a risk condition for an object is accessed, the assessment based on features of the object. Risk conditions associated with respective objects are determined based on the risk assessment model. A group of objects associated with a first risk condition is identified. An interactive user interface is generated for display, the user interface concurrently displaying images of the group of objects. The interactive user interface enables a user to select subsets of images to be concurrently assigned a user-selected classification. User selected classifications are provided to a machine learning system adapted to update the risk assessment model based on the classifications to increase accuracy of the model.

Abstract: Systems and techniques are disclosed for improvement of machine learning systems based on enhanced training data. An example method includes generating an interactive classification user interface concurrently displaying a first group of medical images and a second group of medical images, each group depicting objects associated with a respective classification. User input indicating movement of medical images from the first group to the second group is detected. The moved medical images are classified according to the second group. The re-classified medical images are provided to a machine learning system, with the machine learning system updating based on analysis of object characteristics of the re-classified medical images to increase accuracies associated with automated assignment of classifications.