464 results about "Robotic surgery" patented technology

A robotic surgical system includes a surgeon consol coupled to a patient consol, and the patient consol coupled to surgical instruments. A surgeon computer is coupled to or at the surgeon consol that is coupled to to one or more surgical instruments. A robotic surgery control system includes an artificial intelligence (AI) system with one or more deep learning algorithms. A feedback loop monitors and collects data from the one or more sensors. One or more cameras provide feedback to the robotic surgical system, and are configured to provide images of an anatomical object in at least a two dimensional (2D) arrangements of pixels / Deep learning algorithms of the AI system distinguish different anatomical objects from the images.

A robotic surgical system. a surgeon console operatively coupled to a patient console and one or more surgical instruments. A surgeon computer is coupled to or integrated with the surgeon console, the surgeon computer further operatively connected to the one or more surgical instruments; A surgical robot is coupled to a robotic surgery control system and a feedback loop. The robotic surgery control system includes or is coupled to an artificial intelligence (AI) system. A feedback loop is further configured to receive performance-related data from the one or more sensors, the data analyzed by the robotic surgery control system or the AI system to dynamically adjust the robotic system's operation as needed. A data extraction module retrieves, from the robotic surgery control system or the AI system. one or more programmed steps executed by the surgeon for positioning at least one of the surgical instruments during the surgical procedure.

A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy. An artificial intelligence (AI module has a trained neural network and a deep learning model trained on multi-institutional annotated surgical datasets, The AI module is configured to execute one or more of: fuse acquired video and imaging data into temporally and spatially coherent anatomical visualizations; generate continuously updating overlays aligned with the surgical field, with segmented anatomical features; projected tissue boundaries, proximity indicators for instruments, and predictive deformation trends; provide dynamic predictive trend visualization indicating zones of future anatomical complexity or risk; register and align preoperative imaging data with intraoperative imaging data in real time; adapt overlay presentation in response to tissue deformation without actuating the robotic manipulate or; and passively augment visual feedback without initiating any autonomous actuation of surgical instruments.

A robotic surgical system includes one or more robotic actuators configured to interact with biological tissue during a surgical procedure. A plurality of sensors include at least one of fiber Bragg grating sensors, piezoelectric strain sensors, or magnetostrictive sensors to capture real-time mechanical, elasticity, or deformation data from biological tissues. deep learning engine trained on a dataset comprising tissue mechanical responses across multiple tissue types, pathological states, and patient demographics. Pre-contact predictive adjustment profiles are generated for anticipated tissue interactions using preoperative imaging data registered to intraoperative coordinates. Intraoperative deviations are detected from predicted mechanical behavior and autonomously recalibrate actuator forces. Upcoming surgical maneuvers are anticipated based on prior task sequences and adjust actuator stiffness or damping properties in preparation for anticipated contact. An emergency override of actuator forces is provided via an anomaly detection module when real-time sensor data deviates beyond a threshold from the predicted safe mechanical response range. A feedback loop iteratively refines the deep learning engine during the procedure using supervised learning updates, anomaly detection, and reinforcement learning strategies. The reinforcement learning model is optionally shared across procedures to optimize distributed actuator force patterns for minimizing localized and cumulative tissue stress.

Systems and subsystems for closing an end effector of a stapler are disclosed. More specifically, the present disclosure relates to systems, devices, and subsystems for attachments for robotic surgeries. The surgical instrument is a robotic attachment. The surgical instrument includes a closure subsystem that moves independently of other subsystems that are operable independently of each other.

A robotic surgical system. a surgeon console operatively coupled to a patient console and one or more surgical instruments. A surgeon computer is coupled to or integrated with the surgeon console, the surgeon computer further operatively connected to the one or more surgical instruments; A surgical robot is coupled to a robotic surgery control system and a feedback loop. The robotic surgery control system includes or is coupled to an artificial intelligence (AI) system. A feedback loop is further configured to receive performance-related data from the one or more sensors, the data analyzed by the robotic surgery control system or the AI system to dynamically adjust the robotic system's operation as needed. A data extraction module retrieves, from the robotic surgery control system or the AI system. one or more programmed steps executed by the surgeon for positioning at least one of the surgical instruments during the surgical procedure.

A robotic surgical system network includes a plurality of robotic surgical systems. Each system includes robotic arms, sensors, a surgeon console, and a control system with an integrated AI module. A network interface is associated with robotic surgical system and provides secure data communication. A central or distributed data repository securely stores surgical data aggregated from the robotic surgical systems. The surgical data includes at least one of procedural data, sensor readings, imaging data, AI decision logs, surgical outcomes, or user interaction data. A training module utilizes aggregated surgical data to train or update AI models for the robotic surgical systems using unsupervised learning, transfer learning, or federated learning techniques. A cybersecurity module implements security measures for data transmission and system access, the measures comprising at least one of encryption, multi-factor authentication, or real-time threat detection.

A dynamic reference base (DRB) and associated method are provided for rigidly affixing an array frame to bone for use in navigated or robotic surgery. The DRB includes a bridge element that includes a connecting rod having a distal end and a proximal end; a clamp coupled to the distal end of the connecting rod; a first and a second pin guide extending distally from the clamp; a locking nut disposed on the clamp; and a clutch coupled to the proximal end of the connecting rod, the clutch being adapted to couple to the array frame. The DRB also includes the array frame, which is couplable to the clutch, and is adapted to include a plurality of tracking markers disposed thereon.

The invention involves a system and method for increasing positional accuracy of surgical systems that utilize magnetic or electromagnetic sensors to provide positional awareness to a surgeon or robot performing the surgery. The system takes advantage of electromagnetic tracking through sensors. These sensors are very accurate and repeatable, while being compact enough to not inhibit surgical procedures. The accuracy and repeatability of the sensors is <1 mm within a predetermined 6 inch×6 inch performance motion box. The system is constructed and arranged to map the distortion patterns of the sensor and, in real time, correct the distortion pattern to provide accurate location of anatomical structures for performance of a surgery.

Systems and methods for eye tracking and content tagging in minimally invasive surgical videos are described herein. Minimally invasive surgical videos may be captured while performing robotic surgeries. Robotic surgical systems described herein include robotic arms with interchangeable surgical tools. An endoscope at the end of one of the robotic arms captures video of the surgical procedure. The video is displayed on a display of the surgical system and an eye tracking device captures data corresponding to a gaze direction of the user on the display. Content tags are automatically generated in the image data based on areas of focus of the user. Information within the content tag may be generated based on surgical procedure steps derived from the image data, a log of current surgical procedures, and image data of previous surgical procedures.

Robotic surgical systems configured to control the movement and actuation of a single robotic arm, and the movement and actuation of first and second tools carried in a tool holder at a distal end of the robotic arm.

Robot calibration is crucial in multi-robot cooperative systems where the inaccuracy of robots can add up and cause large errors in the final trajectory of handled parts or process tools. In this work, a two-step calibration approach is proposed based on artificial neural networks (ANNs) and definition of compensated pose for a master-slave cooperative robot system. Measuring the pose of master and slave robots at different locations in their shared workspace is required to create pairs of joint angles and output pose errors as training data. The generated data is used to train two ANN models for compensating the master-slave relative error and the master robot errors. The master-slave relative error is corrected by introducing a compensated pose for the slave robot with respect to the master robot. A neural network is then trained to predict the error parameters of the compensated pose for the joint angles of both robots as the input. The master robot is then corrected individually using another ANN model to address the absolute accuracy of the cooperative system.Measurements and simulations have been performed on a dual-robot cooperative system before and after geometric calibration. The process of cross validation is carried out to find the best network architecture for the optimal performance in correcting the robots'errors. It has been shown that even after pre-existing model-based calibration of each robot, both the absolute accuracy of the master robot and the relative tracking accuracy can be further improved by the proposed implementation of ANN calibration.

Systems and methods for surgical robotic collision detection in accordance with aspects of the present disclosure are disclosed. In various embodiments, a system for surgical robotic collision detection includes a robotic cart having a robotic arm, an imaging device supported by the robotic cart or the robotic arm, the imaging device captures images within a field of vision of the imaging device, and a controller in operable communication with the robotic arm and the imaging device. The controller includes a processor and a memory storing instructions which, when executed by the processor, causes the controller to: receive the images from the imaging device, generate a grid including a first plurality of spatial points from the images, and detect a potential collision within the field of vision based on the generated grid.

A method of controlling a surgical system includes obtaining a surgical plan containing a plurality of planned cuts, receiving a first cutting tool from a plurality of cutting tools at a surgical device, collecting a first checkpoint by tracking a probe as the probe contacts a first predefined point on the first cutting tool, collecting a second checkpoint by tracking the probe as the probe contacts a second predefined point at a first bone of a patient, automatically selecting a first planned cut from the plurality of planned cuts based on the first checkpoint and the second checkpoint, and, in response to automatically selecting the first planned cut, guiding execution of the first planned cut using the first cutting tool.

Surgical systems and methods for manipulation of an anatomy that includes cortical and cancellous bone. A surgical manipulator has a robotic arm to move an instrument with an energy applicator. A sensor senses forces / torques applied to the energy applicator. Controller(s) obtain a tool path for the energy applicator to traverse. A segment of the tool path transitions into, or between, cortical bone and cancellous bone. The controller(s) control the manipulator to advance the energy applicator along the tool path according to a first feed rate to manipulate the cortical or cancellous bone and detect, with the sensor, a change in the forces / torques being indicative of the transition. In response to detection of this change, the controller(s) control the manipulator to advance the energy applicator along the tool path according to a second feed rate, greater / less than the first feed rate.

A gearbox assembly and surgical instrument including the same. The gearbox assembly includes a drive gear including a round gear and a lead screw such that a rotational input to the round gear rotates the lead screw. A first hub is threadingly engaged about the lead screw such that rotation of the lead screw translates the first hub. A second hub is spaced-apart from the first hub and engaged with a drive rod. A compression spring is disposed between the hubs. When a force acting against the drive rod is below a threshold, the rotational input translates the first hub, compression spring, second hub, and drive rod. When the force is equal to or above the threshold, the rotational input translates the first hub and compresses the compression spring against the second hub to maintain the second hub and drive rod in position.

A method for improved robotic cutting, the method comprising: determining at least one of cutting force data and bone motion data during robotic cutting; determining when at least one of the cutting force data and bone motion data exceeds a predetermined parameter; and when it is determined that at least one of the cutting force data and the bone motion data exceeds the predetermined parameter, providing the user with an indication of the same and pausing the robotic cutting so as to enable the user to mitigate the cause of at least one of the cutting force data and the bone motion data exceeding the predetermined parameter.

A robotic surgical system integrates artificial intelligence (AI) to enable dynamic inference arbitration and risk-driven autonomy. The system includes a surgeon console, robotic arms, and a control system with memory and processors configured to execute real-time surgical workflows. AI modules analyze intraoperative data, such as imaging, sensor input, and instrument telemetry, and compute context alignment scores to guide module selection, forecasting, and fallback execution. Confidence metrics are monitored, with thresholds triggering surgeon alerts, handoff, or autonomous continuation. The system supports intraoperative adaptation, surgeon fatigue detection, and real-time annotation of AI outputs for traceability. It enables improved tissue recognition, predictive planning, and context-aware adjustments through training on historical surgical data. AI-assisted decision support, deviation handling, and performance monitoring enhance safety and personalization across diverse procedures. The architecture supports modular deployment, continuous learning, and integration of multimodal data sources for precision-guided robotic surgery.

A robotic system includes a plurality of input control devices configured to be operated by an operator, a display device, and a controller coupled to the plurality of input control devices and the display device. The controller is configured to: determine a dynamic mapping, the dynamic mapping identifying at least a first association, the first association being between a first input control device of the plurality of input control devices and a first function, the first function being of a plurality of functions of a first instrument, the robotic system configured to support the first instrument; cause the display device to display a first user interface element, the first user interface element indicative of the first association; receive input from the first input control device; and cause, based on the dynamic mapping, the first function to be performed by the first instrument.

Robotic surgical systems and methods for resection of an anatomy. The system includes a cutting tool, a manipulator configured to move the cutting tool, and a control system. The control system associates a target plane with the anatomy, the target plane delineating a portion of the anatomy to be resected from a portion of the anatomy to remain unresected. The control system controls the manipulator to align the cutting tool to the target plane. The control system controls the manipulator in an automated mode to perform at least one of the following actions: automatically resect along the target plane with the cutting tool, automatically retract the cutting tool along the target plane, and automatically change a pose of the cutting tool on the target plane. The user or control system can pre-assign whether to perform any of the actions in the manual or automated mode.

A user interface for a surgical robotic system includes a plurality of handles, each removably attachable to a user interface assembly by a quick release connector. The selection of handles can include handles of varying size, degree of complexity, handles adapted for laparoscopic motion, handles adapted for true cartesian motion or handles customized to surgeon anthropometric data, etc.

Systems and subsystems for cutting and stapling tissue are disclosed. More specifically, the present disclosure relates to systems, devices, and subsystems for attachments for robotic surgeries. The surgical instrument is a robotic attachment including a roll subsystem. The roll subsystem comprises a rotatable shaft having a longitudinal axis, a roll input puck engageable with a roll robotic output, a worm gear coupled to and rotatable by the roll input puck, and a worm follower coupled to the rotatable shaft. Rotation of the roll input puck causes the worm gear to rotate the worm follower and thereby roll the rotatable shaft about its longitudinal axis.

Surgical instruments, robotic surgery systems, software for the same, and associated methods are disclosed in which instrument data is collected during a clamping time period in which tissue is clamped between opposing jaws. During the clamping time period, the instrument data includes a predictive portion in which the instrument data decays exponentially and therefore can be characterized by a mathematical feature such as a time constant, initial force or torque, elapsed decay time, etc. End time for the clamping time period, operational parameters for the instrument following the clamping time period, tissue characteristics, operational parameters for the instrument in successive clamping attempts, end effector articulation, and other instrument functions can be set / controlled based at least in part on the mathematical feature. In some examples, the instrument data includes a motor parameter which includes motor torque, clamping force, and / or motor speed.

The invention provides a method, a device and equipment for identifying path deviation in a screw implanting process, and belongs to the field of data processing. The method comprises the steps that target stress information and pose information of the tail end of a target robot used for implanting a target screw are obtained, and the tail end of the target robot holds the target screw; determining a current implantation path of the target screw according to the target stress information and the pose information; obtaining a target implantation path, wherein the target implantation path is generated based on preoperative image data of a patient; and determining a path offset condition according to the current implantation path and the target implantation path, wherein the path offset condition is used for indicating whether path offset exists or not. The method and the device are used for recognizing path deviation in the nail implanting process based on a robot, and the path precision judgment capability and the operation safety of a robot operation system are improved. And meanwhile, additional visual navigation equipment is not needed, so that the system complexity and cost are reduced.

The invention relates to the technical field of remote single-hole robot operation guidance and demonstration, and discloses a system and a method for remote single-hole robot operation guidance and demonstration, which realize efficient, stable and secure transmission of remote force feedback signals and control instructions through a low-delay secure communication link based on SRv6 + TSN and a national password SM9 encryption technology. The problem that transparency and real-time performance are difficult to consider in remote operation is effectively solved, experts can seamlessly perceive interaction force of local instruments and tissues and carry out accurate remote intervention, and through dynamic modeling of a cloud twinning service module and Transformer and graph neural network algorithms, the interaction force between the local instruments and the tissues can be accurately and remotely intervened. Real-time prediction of risks such as bleeding and perforation in the operation and accurate matching of tissue deformation are achieved, a visual and fused AR guidance interface is provided for experts in combination with a mixed reality guidance engine, and the accuracy of remote guidance and operation safety are remarkably improved.

A robotic surgical system includes a surgeon consol coupled to a patient console and coupled to one or more surgical instruments. The surgeon consol is used by a surgeon to perform a surgical procedure. A surgeon computer is coupled to or at the surgeon console. The surgeon consol is coupled to the one or more surgical instruments (manipulators). A surgical robot is coupled to a robotic surgery control system and a feedback loop. The feedback loop monitors and collects data from one or more sensors used to provide feedback to the robotic surgical system. An AI system has an AI architecture that uses input data for producing an AI model. A surgical robot is coupled to a robotic surgical control system that is coupled to or includes: the feedback loop, and the artificial intelligence AI system. The control system is coupled to the surgeon consol, one or more of the feedback loop, the AI system and the control unit configured to determine a spatial configuration data of at least a portion of the one or more manipulators using image or location information of one or more of vascular or nervous structures and organs relative to the surgical intervention. One or more of the: feedback loop; AI system; and spatial configuration data used singularly or in combination to provide enhanced navigation; identification of vascular or nervous structures and organs, and manipulation of the manipulators.

A method includes displaying, on a graphical user interface, a planned position of a virtual implant model relative to a virtual bone model of a bone. The virtual implant model includes a plurality of virtual screw holes offset from a central axis of the virtual implant model. The method also includes determining a planned rotational orientation of the virtual implant model by rotating, on the graphical user interface, the virtual implant model about the central axis of the virtual implant model such that the plurality of virtual screw holes rotate about the central axis. The method also includes controlling a robotic device to guide preparation of the bone to receive a physical implant in the planned position. The method also includes providing computer-assisted navigation configured to guide the physical implant into physical rotational alignment with the planned rotational orientation of the virtual implant model.

A gearbox assembly for use with a surgical instrument includes a drive gear configured to receive a rotational input, a first output gear configured to receive a rotational input from the drive gear, and a second output gear configured to receive a rotational input from the first output gear. The first output gear includes a first lead screw coupled to the first output gear such that the rotational input received by the first output gear rotates the first lead screw, and thereby translates a first nut threadingly engaged about the first lead screw along the first lead screw. The second output gear includes a second lead screw coupled to the second output gear such that the rotational input received by the second output gear rotates the second lead screw, and thereby translates a second nut threadingly engaged about the second lead screw along the second lead screw.

A robotic surgical assembly includes an instrument drive unit and a sterile interface module for coupling an electromechanical robotic surgical instrument to the instrument drive unit. The sterile interface module is detachable from the instrument drive unit and is equipped with a mechanism that allows for a manual actuation of the surgical instrument.

An end effector including a yoke arms, a pivot pin extending between the yoke arms, a pulley mounted on the pivot pin and having a drive pin extending laterally outward from a first side surface thereof, a cam member adjacent the first side surface and including a proximal body having a drive arm, wherein a cam pin is provided at a distal end of the drive arm, and wherein the proximal body has a cam slot that includes a vertical cam path and a horizontal cam path, wherein the vertical cam path is associated with the drive pin and the horizontal cam path is associated with the pivot pin, a first jaw carrier having a cam slot for a first portion of the cam pin and a second jaw carrier having a cam slot for a second portion of the cam pin.