Abstract: A cleaning wizard monitors and provides feedback for cleaning of medical equipment to ensure that cleaning is performed based on best practices. The cleaning wizard receives a video stream comprising an item of medical equipment and inputs a first set of video frames from the video stream into a first machine learning model. The first machine learning model is trained to output whether the first set of video frames corresponds to activity that initiates a cleaning protocol for the item of medical equipment. Responsive to the cleaning protocol being initiated, the cleaning wizard inputs a second set of video frames into a second machine learning model trained to output whether the second set of frames meets criteria of the cleaning protocol. Responsive to all criteria of the cleaning protocol being met, the cleaning wizard transmits a notification to an operator that the cleaning protocol is complete.

Abstract: A cleaning wizard monitors and provides feedback for cleaning of medical equipment to ensure that cleaning is performed based on best practices. The cleaning wizard receives a video stream comprising an item of medical equipment and inputs a first set of video frames from the video stream into a first machine learning model. The first machine learning model is trained to output whether the first set of video frames corresponds to activity that initiates a cleaning protocol for the item of medical equipment. Responsive to the cleaning protocol being initiated, the cleaning wizard inputs a second set of video frames into a second machine learning model trained to output whether the second set of frames meets criteria of the cleaning protocol. Responsive to all criteria of the cleaning protocol being met, the cleaning wizard transmits a notification to an operator that the cleaning protocol is complete.

Abstract: A diagnosis system trains a set of machine-learned diagnosis models that are configured to receive an image of a patient and generate predictions on whether the patient has one or more health conditions. In one embodiment, the set of machine-learned models are trained to generate predictions for images that contain two or more underlying health conditions of the patient. In one instance, the symptoms for the two or more health conditions are shown as two or more overlapping skin abnormalities on the patient. By using the architectures of the set of diagnosis models described herein, the diagnosis system can generate more accurate predictions for images that contain overlapping symptoms for two or more health conditions compared to existing systems.

Abstract: An orthopedic implant including an outer surface and at least one opening extending through the outer surface for receiving a fastener for coupling the implant to a patient's bone or bone portion/fragment. The opening including a plurality of fins circumferentially disposed about the opening for engaging threads formed on a head portion of the fastener to secure the fastener to the implant. The plurality of fins being arranged and configured in first and second vertically spaced rows of fins. At least one of the fins including a different configuration, property, etc. relative to at least one of the other plurality of fins. In one embodiment, each of the first and second fins in a vertically stacked relationship includes a different configuration from the other of the first and second fins in that stack. In one embodiment, the different configuration includes a different length, a different thickness, or a combination thereof.

Abstract: A single handheld multimeter device includes two or more independent measurement channels that are completely electrically separated and independent from one another. Each measurement channel has its point of reference, typically configured to be coupled to the ground. Measurements by the two or more independent measurement channels can be conducted concurrently, and the measurement results of the two channels can be processed and displayed together.

Abstract: Systems and methods are disclosed herein for determining a diagnosis based on a base skin tone of a patient. In an embodiment, the system receives a base skin tone image of a patient, generates a calibrated base skin tone image by calibrating the base skin tone image using a reference calibration profile, and determines a base skin tone of the patient based on the calibrated base skin tone image. The system receives a concern image of a portion of the patient's skin, and selects a set of machine learning diagnostic models from a plurality of sets of candidate machine learning diagnostic models based on the base skin tone of the patient, each of the sets of candidate machine learning diagnostic models trained to receive the concern image and output a diagnosis of a condition of the patient.

Abstract: An orthopedic implant including an outer surface and at least one opening extending through the outer surface for receiving a fastener for coupling the implant to a patients bone or bone portion/fragment. The opening including a plurality of fins circumferentially disposed about the opening for engaging threads formed on a head portion of the fastener to secure the fastener to the implant. The plurality of fins being arranged and configured in first and second vertically spaced rows of fins. At least one of the fins including a different configuration, property, etc. relative to at least one of the other plurality of fins. In one embodiment, each of the first and second fins in a vertically stacked relationship includes a different configuration from the other of the first and second fins in that stack. In one embodiment, the different configuration includes a different length, a different thickness, or a combination thereof.

Abstract: Systems and methods are disclosed herein for determining a diagnosis based on a base skin tone of a patient. In an embodiment, the system receives a base skin tone image of a patient, generates a calibrated base skin tone image by calibrating the base skin tone image using a reference calibration profile, and determines a base skin tone of the patient based on the calibrated base skin tone image. The system receives a concern image of a portion of the patient's skin, and selects a set of machine learning diagnostic models from a plurality of sets of candidate machine learning diagnostic models based on the base skin tone of the patient, each of the sets of candidate machine learning diagnostic models trained to receive the concern image and output a diagnosis of a condition of the patient.

Abstract: A strut measurement and feedback device and corresponding systems and methods for use with an external fixator are disclosed. The strut measurement and feedback device can be attached to a strut of an external fixator. The strut measurement and feedback device can confirm that the strut measurement and feedback device is attached to a proper strut to be adjusted. The strut measurement and feedback device can provide real-time feedback to an individual as the length of the strut is being adjusted to ensure the length adjustment complies with a prescription for the length of the strut. As a result, a patient can more effectively comply with the prescription as adjustments to the strut are made over time, thereby improving the likelihood of successful bone alignment.

Abstract: A diagnosis system trains a set of machine-learned diagnosis models that are configured to receive an image of a patient and generate predictions on whether the patient has one or more health conditions. In one embodiment, the set of machine-learned models are trained to generate predictions for images that contain two or more underlying health conditions of the patient. In one instance, the symptoms for the two or more health conditions are shown as two or more overlapping skin abnormalities on the patient. By using the architectures of the set of diagnosis models described herein, the diagnosis system can generate more accurate predictions for images that contain overlapping symptoms for two or more health conditions compared to existing systems.

Abstract: A cleaning wizard monitors and provides feedback for cleaning of medical equipment to ensure that cleaning is performed based on best practices. The cleaning wizard receives a video stream comprising an item of medical equipment and inputs a first set of video frames from the video stream into a first machine learning model. The first machine learning model is trained to output whether the first set of video frames corresponds to activity that initiates a cleaning protocol for the item of medical equipment. Responsive to the cleaning protocol being initiated, the cleaning wizard inputs a second set of video frames into a second machine learning model trained to output whether the second set of frames meets criteria of the cleaning protocol. Responsive to all criteria of the cleaning protocol being met, the cleaning wizard transmits a notification to an operator that the cleaning protocol is complete.

Abstract: Systems and methods are disclosed herein for determining a diagnosis based on a base skin tone of a patient. In an embodiment, the system receives a base skin tone image of a patient, generates a calibrated base skin tone image by calibrating the base skin tone image using a reference calibration profile, and determines a base skin tone of the patient based on the calibrated base skin tone image. The system receives a concern image of a portion of the patient's skin, and selects a set of machine learning diagnostic models from a plurality of sets of candidate machine learning diagnostic models based on the base skin tone of the patient, each of the sets of candidate machine learning diagnostic models trained to receive the concern image and output a diagnosis of a condition of the patient.

Abstract: An orthopedic implant including an outer surface and at least one opening extending through the outer surface for receiving a fastener for coupling the implant to a patients bone or bone portion/fragment. The opening including a plurality of fins circumferentially disposed about the opening for engaging threads formed on a head portion of the fastener to secure the fastener to the implant. The plurality of fins being arranged and configured in first and second vertically spaced rows of fins. At least one of the fins including a different configuration, property, etc. relative to at least one of the other plurality of fins. In one embodiment, each of the first and second fins in a vertically stacked relationship includes a different configuration from the other of the first and second fins in that stack. In one embodiment, the different configuration includes a different length, a different thickness, or a combination thereof.

Abstract: A medical implant device such as, for example, an intramedullary (IM) nail including an improved sensing device for monitoring the progression of fracture healing in a patient is disclosed. In one embodiment, a medical implant device for attachment to a portion of a bone may include at least one energy-harvesting strain sensor operative to generate a charge responsive to a strain force on the medical implant device, and at least one electronic element configured to receive at least a portion of the charge, the at least one electronic element comprising a telemetry device operably coupled to the at least one energy-harvesting sensor, the telemetry device powered, at least in part, by the at least a portion of the charge to transmit sensor information associated with the charge to a receiver device. Other embodiments are described.

Abstract: A diagnosis system trains a set of machine-learned diagnosis models that are configured to receive an image of a patient and generate predictions on whether the patient has one or more health conditions. In one embodiment, the set of machine-learned models are trained to generate predictions for images that contain two or more underlying health conditions of the patient. In one instance, the symptoms for the two or more health conditions are shown as two or more overlapping skin abnormalities on the patient. By using the architectures of the set of diagnosis models described herein, the diagnosis system can generate more accurate predictions for images that contain overlapping symptoms for two or more health conditions compared to existing systems.

Abstract: A strut measurement and feedback device and corresponding systems and methods for use with an external fixator are disclosed. The strut measurement and feedback device can be attached to a strut of an external fixator. The strut measurement and feedback device can confirm that the strut measurement and feedback device is attached to a proper strut to be adjusted. The strut measurement and feedback device can provide real-time feedback to an individual as the length of the strut is being adjusted to ensure the length adjustment complies with a prescription for the length of the strut. As a result, a patient can more effectively comply with the prescription as adjustments to the strut are made over time, thereby improving the likelihood of successful bone alignment.

Abstract: A goal for a soccer skills-enhancing game is configured for practicing a chip shot, and includes an upper frame defining an aperture for receiving a ball vertically downward therethrough, and a lower frame for resting on a playing surface. Support members are operatively connected between the upper and lower frames to form a vertical space therebetween. A see-through wrap of fabric or plastic is wrapped around an outer side of the support members, encircling the vertical space to form the vertically-disposed goal. The goal preferably includes a bag, or net, attached to and held in an open position around the upper frame for catching a ball received through the aperture. The upper and lower frames may be hexagonally shaped. A game apparatus includes at least one goal and a ball. A method for playing the game includes scoring points for chipping or deflecting the ball into the bag.

Abstract: A digital multimeter stores multiple sequential measurements of physical or electrical parameters. Each of the sequential measurements has a name including an automatically generated descriptor. The descriptor for each sequential measurement may indicate a relative position of the measurement within the sequence. For instance, the descriptor may indicate whether the measurement was obtained before or after other measurements in the sequence.