A hierarchical workflow is configured to associate examination information captured using an imaging platform with contextual metadata. The examination information may include ultrasound image data, which may be associated with annotations, measurements, pathology, body markers, and/or the like. The hierarchical workflow may comprise templates associated with respective anatomical regions, locations, volumes, and/or surfaces. A template may define configuration data to automatically adapt the imaging platform to capture imaging data in the corresponding anatomical region. The template may further include guidance information for the operator, including processing steps for capturing relevant examination information. Additional examination information may be captured and included in the hierarchical workflow.

Provided is a 3D bone density and bone age calculation apparatus using an artificial intelligence-based rotation manner. The 3D bone density and bone age calculation apparatus includes a main body, and the main body includes a rotary drum including a drum shaft gear, an X-ray generator, an intensifying screen, and an image data capturer, a drum driver including a motor shaft gear connected to the drum shaft gear so as to rotate the rotary drum, a motor, support rollers and one of an origin sensor and an encoder, an outer case and an inner case, a front case and a rear case, a capturing holder, and a controller configured to select an image-captured position of the rotary drum, and configured to input a current age, sex and nutritional status of a patient, etc. The controller includes a display configured to display captured images and a diagram indicating bone age.

A failed-image decision support apparatus includes a hardware processor and an outputter. The hardware processor performs, among multiple types of failed-image determination processes, at least one failed-image determination process on a medical image, thereby generating a determination result and determination basis information indicating a basis for the determination result. The outputter outputs the determination result and the determination basis information. Another failed-image decision support apparatus includes a hardware processor. The hardware processor performs, among multiple types of failed-image determination processes, at least one failed-image determination process on a medical image, thereby generating a determination result and determination basis information indicating a basis for the determination result, and controls output of the determination result and the determination basis information.

A medical scan assisted review system is operable to receive, via a network, a medical scan for review. Abnormality data is generated by identifying a plurality of abnormalities in the medical scan by utilizing a computer vision model that is trained on a plurality of training medical scans. The abnormality data includes location data and classification data for each of the plurality of abnormalities. Text describing each of the plurality of abnormalities is generated based on the abnormality data. The abnormality data and the text is transmitted to a client device. A display device associated with the client device displays the abnormality data in conjunction with the medical scan via an interactive interface, and the display device further displays the text via the interactive interface.

Systems and methods for determining an image type are disclosed. A user interface includes a visual representation of a breast and a visual representation of a medical imaging system. The visual representation of the medical imaging system includes a visual representation of a detector and either a source or a compression paddle. The visual representation of the medical imaging system may be rotatable and positionable relative to the visual representation of the breast. Based on the relative position and orientation of the visual representation of the medical imaging system relative to the visual representation of the breast, an image type is determined. The image type may be displayed at the user interface.

Provided is an X ray image processing method including the following. One of computing modules stored in an X ray device is activated, in which the one of the computing modules corresponds to a measurement area. The measurement area corresponding to the one of the computing modules is measured by an image measurement module, and a measurement signal is produced. The measurement signal is transmitted to a computing unit by the image measurement module. A measurement image is computed by the computing unit according to the measurement signal, and is stored in a first storage unit in the X ray device. The one of the computing modules is written to the computing unit. The measurement image is transmitted to the computing unit by the first storage unit. The measurement image is analyzed by the computing unit using the one of the computing modules, and an analysis image is generated.

A method for determining an abnormality in a medical device from a medical image is provided. The method for determining an abnormality in a medical device comprises receiving a medical image, and detecting information on at least a part of a target medical device included in the received medical image.

An automated process for data annotation of medical images includes obtaining image data from an imaging sensor, partitioning the image data, identifying an object of interest in the partitioned image data, generating an initial contour with one or more control points with respect to the object of interest, identifying a manual adjustment of one of the control points, automatically adjust a position of at least one other control point within a predetermined range of the manually adjusted control point to a new position, the new position of the at least one other control point and manually adjusted control point defining a new contour, and generating an updated image with the new contour and corresponding control points.

Methods and systems are provided for molecular breast imaging. In one embodiment, a method for nuclear medicine imaging comprises: during an acquisition of emission data from an anatomy of interest, calculating an average counts per pixel in non-target tissue; and responsive to the average counts per pixel reaching a threshold, automatically stopping the acquisition. In this way, an amount of time spent by a patient undergoing an MBI procedure is optimized for the patient.

The present invention relates to a method and apparatus of utilizing an eye detection apparatus in a medical application, which includes calibrating the eye detection apparatus to a user; performing a predetermined set of visual and cognitive steps using the eye detection apparatus; determining a visual profile of a workflow of the user; creating a user-specific database to create an automated visual display protocol of the workflow; storing eye-tracking commands for individual user navigation and computer interactions; storing context-specific medical application eye-tracking commands, in a database; performing the medical application using the eye-tracking commands; and storing eye-tracking data and results of an analysis of data from performance of the medical application, in the database. The method includes performing an analysis of the database for determining best practice guidelines based on clinical outcome measures.