Abstract: Methods and systems for performing image analytics using graphical reporting associated with clinical images. One system includes at least one data source and a server. The server includes an electronic processor and an interface for communicating with the 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 model is used to automatically analyze an image.

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 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: Systems and methods for remote diagnostic imaging. The systems include a diagnostic imaging kiosk. The kiosk includes, for example, a first housing module and a second housing module. The first housing module is configured to house, among other things, a diagnostic imaging system (e.g., an X-ray system). The second housing module is configured to house electronics associated with the operation, control, and networking of the kiosk. The electronics include, for example, a primary controller, an X-ray controller, an X-ray generator, an internal display controller, an external display controller, one or more routers, and a digital radiology module. The kiosk is configured to communicatively connect to a remote technician's workstation, and a remote technician at the workstation is able to remotely control the kiosk through a packet-switched network.

Abstract: Systems and methods for remote diagnostic imaging. The systems include a diagnostic imaging kiosk. The kiosk includes, for example, a first housing module and a second housing module. The first housing module is configured to house, among other things, a diagnostic imaging system (e.g., an X-ray system). The second housing module is configured to house electronics associated with the operation, control, and networking of the kiosk. The electronics include, for example, a primary controller, an X-ray controller, an X-ray generator, an internal display controller, an external display controller, one or more routers, and a digital radiology module. The kiosk is configured to communicatively connect to a remote technician's workstation, and a remote technician at the workstation is able to remotely control the kiosk through a packet-switched network.

Abstract: Systems and methods for remote diagnostic imaging. The systems include a diagnostic imaging kiosk. The kiosk includes, for example, a first housing module and a second housing module. The first housing module is configured to house, among other things, a diagnostic imaging system (e.g., an X-ray system). The second housing module is configured to house electronics associated with the operation, control, and networking of the kiosk. The electronics include, for example, a primary controller, an X-ray controller, an X-ray generator, an internal display controller, an external display controller, one or more routers, and a digital radiology module. The kiosk is configured to communicatively connect to a remote technician's workstation, and a remote technician at the workstation is able to remotely control the kiosk through a packet-switched network.

Abstract: Systems and methods for remote diagnostic imaging. The systems include a diagnostic imaging kiosk. The kiosk includes, for example, a first housing module and a second housing module. The first housing module is configured to house, among other things, a diagnostic imaging system (e.g., an X-ray system). The second housing module is configured to house electronics associated with the operation, control, and networking of the kiosk. The electronics include, for example, a primary controller, an X-ray controller, an X-ray generator, an internal display controller, an external display controller, one or more routers, and a digital radiology module. The kiosk is configured to communicatively connect to a remote technician's workstation, and a remote technician at the workstation is able to remotely control the kiosk through a packet-switched network.