Abstract: Apparatus, systems, and methods to improve automated identification, monitoring, processing, and control of a condition impacting a patient using image data and artificial intelligence classification are disclosed. An example image processing apparatus includes an artificial intelligence classifier to: process first image data for a patient from a first time to determine a first classification result indicating a first severity of a condition for the patient; and process second image data for the patient from a second time to determine a second classification result indicating a second severity of the condition for the patient. The example image processing apparatus includes a comparator to compare the first classification result and the second classification result to determine a change and a progression of the condition associated with the change. The example image processing apparatus includes an output generator to trigger an action when the progression corresponds to a worsening of the condition.

Abstract: Apparatus, systems, and methods to capture and combine patient vitals and image data are disclosed. An example apparatus includes a video capturing device or an audio receiving device, a vitals data manager, and a vitals aggregator. The video capturing device captures visual vital information of a patient from a vital monitor during an imaging procedure. The audio receiving device captures audible vital information of the patient during the imaging procedure. The vitals data manager receives the captured visual vital information or the captured audible vital information, the captured visual vital information or the captured audible vital information to be tagged with an identifier of the patient to form tagged vitals information. The vitals aggregator receives the tagged vitals information and the image associated with the patient, the vitals aggregator to organize the tagged vitals information with the image associated with the patient to form a composite image.

Abstract: Apparatus, systems, and methods to improve imaging quality control, image processing, identification of findings, and generation of notification at or near a point of care are disclosed and described. An example imaging apparatus includes a processor to at least: evaluate first image data with respect to an image quality measure; when the first image data satisfies the image quality measure, process the first image data using a trained learning network to generate a first analysis of the first image data; identify a clinical finding in the first image data based on the first analysis; compare the first analysis to a second analysis, the second analysis generated from second image data obtained in a second image acquisition; and, when comparing identifies a change between the first analysis and the second analysis, trigger a notification at the imaging apparatus regarding the clinical finding to prompt a responsive action.

Abstract: Apparatus, systems, and methods to improve imaging quality control, image processing, identification of findings in image data, and generation of notification at or near a point of care for a patient are disclosed and described. An example imaging apparatus includes a memory including chest image data and instructions and a processor. The example processor is to execute the instructions to at least: process the chest image data using a trained learning network in real time after acquisition of the chest image data to identify a pneumothorax in the chest image data; receive feedback regarding the identification of the pneumothorax; and, when the feedback confirms the identification of the pneumothorax, trigger a notification at the imaging apparatus to notify a healthcare practitioner regarding the pneumothorax and prompt a responsive action with respect to a patient associated with the chest image data.

Abstract: Apparatus, systems, and methods to deliver point of care alerts for radiological findings are disclosed. An example imaging apparatus includes an image data store, an image quality checker, and a training learning network. The example image data store is to store image data acquired using the imaging apparatus. The example image quality checker is to evaluate image data from the image data store in comparison to an image quality measure. The example trained learning network is to process the image data to identify a clinical finding in the image data, the identification of a clinical finding to trigger a notification at the imaging apparatus to notify a healthcare practitioner regarding the clinical finding and prompt a responsive action with respect to a patient associated with the image data.

Abstract: A system for imaging a subject is provided. The system includes a radiation source, a radiation detector, and a controller. The radiation source is operative to transmit electromagnetic rays through the subject. The radiation detector is operative to receive the electromagnetic rays after having passed through the subject so as to generate a plurality of projections of the subject. The controller is operative to display one or more selectively adjustable markers that define an image stack. The controller is further operative to reconstruct one or more images within the image stack based at least in part on the plurality of projections. Reconstruction of the one or more images is on demand.

Abstract: A system for imaging a subject is provided. The system includes a radiation source, a radiation detector, and a controller. The radiation source is operative to transmit electromagnetic rays through the subject while the radiation source travels along a path defined by a sweep angle that is less than 365 degrees. The radiation detector is operative to receive the electromagnetic rays after having passed through the subject. The controller is operative to: acquire preliminary data regarding the subject from a sensor; determine an imaging parameter from the preliminary data; and acquire one or more images of the subject via the radiation source and the radiation detector based at least in part on the imaging parameter.

Abstract: The present disclosure is directed towards a method of changing wireless communication channels in a connected host and client system. For example, in one embodiment, the link quality of a connection is monitored by the host or the client. If the connection has a link quality below a predetermined threshold but remains intact, a channel switch request is sent, synchronization packages are exchanged between the host and client on the current channel, the channel of the system is changed to a new channel, and the system resumes communications on the new channel.

Abstract: The present disclosure is directed towards a method of changing wireless communication channels in a connected host and client system. For example, in one embodiment, the link quality of a connection is monitored by the host or the client. If the connection has a link quality below a predetermined threshold but remains intact, a channel switch request is sent, synchronization packages are exchanged between the host and client on the current channel, the channel of the system is changed to a new channel, and the system resumes communications on the new channel.

Abstract: The present technique provides a system and method for dynamic configuration of medical diagnostic systems using distributable multi-component configuration files having extractable component-specific configuration data. The component-specific configuration data is extractable and processable at each component receiving a broadcast of the distributable multi-component configuration file. If a configuration change is desired in the system or in a particular component, then the change is made via the distributable multi-component configuration file. For example, the foregoing distribution and extraction techniques may be performed during operation of the distributed medical diagnostic system in response to a global or application change, such as different medical diagnostic applications. Accordingly, the present technique provides a flexible and architecture-independent mechanism for configuring components of a distributed medical diagnostic system.

Abstract: The present technique provides a system and method for dynamic configuration of medical diagnostic systems using distributable multi-component configuration files having extractable component-specific configuration data. The component-specific configuration data is extractable and processable at each component receiving a broadcast of the distributable multi-component configuration file. If a configuration change is desired in the system or in a particular component, then the change is made via the distributable multi-component configuration file. For example, the foregoing distribution and extraction techniques may be performed during operation of the distributed medical diagnostic system in response to a global or application change, such as different medical diagnostic applications. Accordingly, the present technique provides a flexible and architecture-independent mechanism for configuring components of a distributed medical diagnostic system.