Abstract: Data is received that is generated by at least one sensor forming part of a surgical instrument. The sensor(s) on the surgical instrument can characterize use of the surgical instrument in relation to a patient. A first machine learning model can construct a force profile using the received data. The force profile includes a plurality of force patterns. In addition, a plurality of features are extracted from the received data. Thereafter, one or more attributes characterizing use of the surgical instrument are determined by a second machine learning model using the constructed force profile and the extracted features. Data characterizing the determination can be provided (e.g., displayed to a surgeon, etc.).

Abstract: Data is received that is generated by at least one sensor forming part of a surgical instrument. The sensor(s) on the surgical instrument can characterize use of the surgical instrument in relation to a patient. A first machine learning model can construct a force profile using the received data. The force profile includes a plurality of force patterns. In addition, a plurality of features are extracted from the received data. Thereafter, one or more attributes characterizing use of the surgical instrument are determined by a second machine learning model using the constructed force profile and the extracted features. Data characterizing the determination can be provided (e.g., displayed to a surgeon, etc.).

Abstract: Each of a plurality of edge computing devices are configured to receive data streams generated by at least one sensor forming part of a respective medical device (e.g., a sensor-equipped surgical tool, etc.) which, in turn, characterizes use of the respective medical device in relation to a particular patient. Each of the edge computing devices can execute at least one machine learning model which generates or from which model attributes are derived. The generated model attributes are anonymized using an anonymization technique such as k-anonymity. The anonymized generated model attributes are homomorphically encrypted and transmitted to a central server. Encrypted model attribute updates to at least one of the machine learning models are later received from the central server which results in the machine learning models executing on one or more of the edge computing devices to be updated based on the received encrypted model attribute updates.

Abstract: Data is received that is generated by at least one sensor forming part of a surgical instrument. The sensor(s) on the surgical instrument can characterize use of the surgical instrument in relation to a patient. A force profile segmentation model can construct a force profile using the received data. The force profile includes a plurality of force patterns. The force profile segmentation model includes at least one first machine learning trained using historical surgical instrument usage data. In addition, a plurality of features are extracted from the received data. Thereafter, one or more attributes characterizing use of the surgical instrument are determined by a force profile pattern recognition model using the constructed force profile and the extracted features. The force profile pattern recognition model includes at least one second machine learning model. Data characterizing the determination can be provided (e.g., displayed to a surgeon, etc.).

Abstract: A cordless wireless and mobile display system having an integrated system for delivering uninterrupted power to a display is described. The cordless wireless and mobile display includes wireless capabilities that allows it to receive and transmit information from associated medical devices and to send data to other designated devices. A battery assembly includes a primary and secondary battery that supplies uninterrupted power to a display. Sensors periodically sample the power output from the battery in current use to ensure that enough power is available to the display. When sensors detect power output levels have fallen below a preset value, indicators alert users that the system is switching to a backup power supply and that the primary battery supply needs recharging.

Abstract: Systems and methods for transforming and displaying a video signal on a display are provided with any number of features. In some embodiments, system is configured to receive a first set of input signals in a controller. The system can determine in the controller a first set of luminances and colors that would be produced on a first display with the first set of input signals. The system can then determine in the controller a second set of luminances that would produce the first set of colors on a second display. The system can then generate in the controller a second set of input signals that would produce the second set of luminances on the second display, and can output the second set of input signals to the second display.

Abstract: Systems and methods for transforming and displaying a video signal on a display are provided with any number of features. In some embodiments, system is configured to receive a first set of input signals in a controller. The system can determine in the controller a first set of luminances and colors that would be produced on a first display with the first set of input signals. The system can then determine in the controller a second set of luminances that would produce the first set of colors on a second display. The system can then generate in the controller a second set of input signals that would produce the second set of luminances on the second display, and can output the second set of input signals to the second display.

Abstract: This invention related to a clinical connectivity device coupled to a display. The clinical connectivity device comprises a user interface configured to select a display format; a controller configured to receive commands from the user interface and for controlling operation of the clinical connectivity device; and a selector coupled to the controller and an adjuster. The selector is configured to receive input signals of graphics, image, video, audio or data from devices in a clinical setting and is further configured to select a signal to be transmitted to an adjuster based on the input signal format. The adjuster is coupled to the controller and the display for matching the received signal format to the display format.