In general, devices, systems, and methods for fiducial identification and tracking are provided.

A non-transitory computer readable medium storing data and computer implementable instructions that, when executed by at least one processor, cause the at least one processor to perform operations for generating cross section views of a wound, the operations including receiving 3D information of a wound based on information captured using an image sensor associated with an image plane substantially parallel to the wound; generating a cross section view of the wound by analyzing the 3D information; and providing data configured to cause a presentation of the generated cross section view of the wound.

A method of recording a state of an apheresis machine or a dialysis machine having a disposable component disposed thereon. The disposable component has tubing and a blood component bag. The disposable component is configured to be installed on the apheresis device or dialysis machine by a human operator. The method includes acquiring with a camera an image of a forward-facing external surface portion of the apheresis machine or dialysis machine and the tubing and the blood component bag of the disposable component. The image is acquired automatically without requiring user input. The method includes recording medical device information for the apheresis machine or dialysis machine comprising the image. The method includes generating a message comprising the image and transmitting the message comprising the image to a remote computer.

A needle collection and counting tray for use in a clinical setting, such as an operating room, into which a user deposits used needles. The tray is then placed into a counting and identification machine the utilizes object recognition technology to identify and count the needles on the tray.

The present disclosure includes systems, methods and media for rendering objects translucent and for recovery of anatomical information blocked by the objects in medical images.

Embodiments of the present disclosure include a system for determining an error associated with an electrode disposed on a medical device. The system comprises a processor and a memory storing instructions on a non-transitory computer-readable medium. The instructions are executable by the processor to receive an electrode signal from the electrode disposed on the medical device. The instructions are further executable by the processor to receive a plurality of other electrode signals from a plurality of other electrodes disposed on the medical device. The instructions are further executable by the processor to determine that the electrode signal received from the electrode disposed on the medical device is an outlier in relation to the plurality of other electrode signals from the plurality of other electrodes disposed on the medical device, based on a comparison between the electrode signal and the plurality of other electrode signals.

A surgical system includes a detector that includes an array of pixels configured to detect light reflected by a surgical device and generate a first signal. The first signal includes a first dataset representative of a visible image of the surgical device. The surgical system also includes a processor configured to receive the first signal and a second signal representative of one or more operating parameters of the surgical device. The processor is also configured to generate a modified image of the surgical device that includes information related to one or more operating parameters.

The present disclosure relates generally to medical imaging, and more specifically to machine-learning techniques to generate intraoperative fluorescence images of a subject (e.g., to aid a surgery, to aid diagnosis and treatment of diseases). The system can receive an intraoperative white light image of the subject, input the intraoperative white light image of the subject into a generator of a trained generative adversarial network (GAN) model trained. In some examples, the GAN model is trained using a plurality of training image pairs, and each training image pair comprises an intraoperative white light training image and an intraoperative fluorescence training image of a same tissue. The system can obtain, from the generator, the generated intraoperative fluorescence image of the subject and display, on a display, the generated intraoperative fluorescence image of the subject.

A surgical robotic arm control system and a surgical robotic arm control method are provided. The surgical robotic arm control system includes a surgical robotic arm, a first image capturing unit, a second image capturing unit, and a processor. The first image capturing unit is used for obtaining a field image. The field image includes a first target image of a target object. The second image capturing unit is disposed at an end position of the surgical robotic arm and is used to obtain a second target image of the target object. The processor analyzes the field image to obtain robotic arm movement information, and controls the surgical robotic arm to move to approach the target object. The processor analyzes the target image to obtain robotic arm rotation information, and controls an angle and a posture of the surgical robotic arm.

One example method for detecting phases of a surgical procedure via video processing includes accessing a video of the surgical procedure and dividing the video into one or more blocks, each of the blocks containing one or more video frames. For each of the blocks, the method includes applying a prediction model on the video frames of the respective block to obtain a phase prediction for each of the video frames. The prediction model is configured to predict, for an input video frame, one of the plurality of phases of the surgical procedure. The method further includes generating an aggregated phase prediction for the respective block by aggregating the phase predictions of the video frames, and modifying the video of the surgical procedure to include an indication of a predicted phase of the respective block based on the aggregated phase prediction.