A computer assisted system is disclosed that includes an optical tracking system and one or more computing devices. The optical tracking system includes an RGB sensor and is configured to capture color images of an environment in the visible light spectrum and tracking images of fiducials in the environment in a near-infrared spectrum. The computer assisted system is configured to generate a color image of the environment using the color images, identify fiducial locations using the tracking images, generate depth maps from the color images, reconstruct three-dimensional surfaces of structures based on the depth maps, and output a display comprising the reconstructed three-dimensional surface and one or more surgical objects that are associated with the tracked fiducials. The computer assisted system can further include a monitor or a head-mounted display (HMD) configured to present augmented reality (AR) images during a procedure.

In an embodiment, an electrogram (EGM) processing system provides, for display by a head-mounted display (HMD) worn by a user, a holographic rendering of intracardiac geometry. The HMD also displays an electrogram waveform. The EGM processing system determines a gaze direction of the user by processing sensor data from the HMD. The HMD displays a marker overlaid on the electrogram waveform at a location based on an intersection point between the gaze direction and the electrogram waveform. The EGM processing system determines a measurement of the electrogram waveform using the location of the marker. The HMD displays the measurement of the electrogram waveform.

The present disclosure provides a system for enabling autonomous or semi-autonomous surgical operations. The system comprises: one or more processors that are individually or collectively configured to: process an image data stream comprising one or more images of a surgical site; fit a parametric model to a tissue surface identified in the one or more images; determine a direction for aligning a tool based in part on the parametric model; determine an optimal path for automatically moving the tool to perform a surgical procedure at the surgical site; and generate one or more control signals for controlling i) a movement of the tool based on the optimal path and ii) a tension force applied to the tissue by the tool during the surgical procedure.

A dental restoration device includes a sleeve having an exterior surface and an interior pocket that is at least partially filled with a dental cement, and a crown having an exterior surface and an interior surface that is mated to the exterior surface of the crown and bonded thereto by an adhesive disposed between the exterior surface of the sleeve and the interior surface of the crown.

A number of improvements are provided relating to computer aided surgery. The improvement relates to both the methods used during computer aided surgery and the devices used during such procedures. Some of the improvement relate to controlling the selection of which data to display during a procedure and/or how the data is displayed to aid the surgeon. Other improvements relate to the structure of the tools used during a procedure and how the tools can be controlled automatically to improve the efficiency of the procedure. Still other improvements relate to methods of providing feedback during a procedure to improve either the efficiency or quality, or both, for a procedure.

A method for adjusting the operation of a surgical instrument using machine learning in a surgical suite is disclosed. The method comprises the steps of gathering data during surgical procedures, wherein the surgical procedures include the use of a surgical instrument, analyzing the gathered data to determine an appropriate operational adjustment of the surgical instrument, and adjusting the operation of the surgical instrument to improve the operation of the surgical instrument.

Examples of systems and methods described herein may utilize augmented reality devices and pointers in generating intra-operative plans and providing guidance for knee surgery. A pointer may indicate one or more anatomical points. An augmented reality device may detect positions of knee anatomical features based on a position of the pointer and positions of fiducials associated with a marker affixed to body part proximate to a knee, such as a femur or a tibia. The augmented reality device may generate a planned resection plane based on the positions of the knee anatomical features, and determine an actual resection plane based on a view of a resection guide having a marker inserted in the guide. The augmented reality device may provide guidance to position the guide to align the actual resection plane with the planned resection plane.

A state assessment system, a diagnosis and treatment system and a method for operating the diagnosis and treatment system are disclosed. An oscillator model converts a physiological signal of a subject into a defined feature image. A classification model analyzes state information of the subject based on the feature image. An analysis model outputs a treatment suggestion for the subject based on the state information of the subject. An AR projection device projects acupoint positions of a human body onto the subject, for the subject to be treated based on the treatment suggestion.

A spinal disorder diagnosis and treatment planning system is provided. The diagnosis and treatment planning system includes a mixed reality holographic display including at least one processor, at least one camera, at least one sensor, and being configured to acquire data points corresponding to a surface of a body adjacent to vertebral tissue. A computer database is configured to transmit imaging of the body including the vertebral tissue to the mixed reality holographic display. The mixed reality holographic display is configured to display a first holographic image of the vertebral tissue superimposed with a body image including the surface. Methods are also disclosed.

The disclosure describes examples of machine-learned model based techniques. A computing system may obtain patient characteristics of a patient and implant characteristics of an implant. The computing system may determine information indicative of an operational duration of the implant based on the patient characteristics and the implant characteristics and output the information indicative of the operational duration of the implant. In some examples, one or more processors may be configured to receive, with a machine-learned model of the computing system, implant characteristics of an implant to be manufactured, apply model parameters of the machine-learned model to the implant characteristics, determine information indicative of dimensions of the implant to be manufactured based on the applying of the model parameters of the machine-learned model, and output the information indicative of the dimensions of the implant to be manufactured.