2040 results about "Surgical procedures" patented technology

A holographic augmented reality ultrasound needle guide system and method includes an augmented reality display such as a headset wearable by a user. The augmented reality display is configured to depict a virtual ultrasound image. The augmented reality display is further configured to allow a user to select a desired reference point on the virtual ultrasound image. The system is configured to depict a holographic needle guide based on the selection of the desired reference point. The system is also configured to adjust a trajectory of the holographic needle guide to avoid intersecting undesired anatomical structures. The augmented reality display is further configured to stamp the holographic needle guide into a selectively locked trajectory and position.

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.

A surgical device includes a plurality of cameras integrated therein. The view of each of the plurality of cameras can be integrated together to provide a composite image. A surgical tool that includes an integrated camera may be used in conjunction with the surgical device. The image produced by the camera integrated with the surgical tool may be associated with the composite image generated by the plurality of cameras integrated in the surgical device. The position and orientation of the cameras and / or the surgical tool can be tracked, and the surgical tool can be rendered as transparent on the composite image. A surgical device may be powered by a hydraulic system, thereby reducing electromagnetic interference with tracking devices.

Co-manipulation robotic systems are described herein that may be used for assisting with surgical procedures, including laparoscopic surgery. The co-manipulation robotic systems allow a surgeon to use commercially-available surgical tools while providing benefits associated with surgical robotics. Advantageously, the surgical tools may be seamlessly coupled to the robot arms using a disposable coupler while the reusable portions of the robot arm remain in a sterile drape. Further, the co-manipulation robotic system may operate in multiple modes to enhance usability and safety, while allowing the surgeon to position the instrument directly with the instrument handle and further maintain the desired position of the instrument using the robot arm.

Examples described herein provide a computer-implemented method that includes performing training of a self-knowledge distillation encoder and a self-knowledge distillation decoder for video frames of a surgical procedure. A trained version of the self-knowledge distillation encoder and the self-knowledge distillation decoder can be combined as a phase recognition model to predict surgical phases of the surgical procedure in one or more videos.

A surgical microscope lighting control system comprising a controller configured to: estimate or determine a lighting need of a surgeon during at least part of a surgical procedure based at least on a viewing status of a region of illumination, wherein the viewing status of the region of illumination is estimated or determined (a) based at least on whether or not the region of illumination is being displayed, or (b) based at least on tracking data of the surgeon or a head-mounted display; determine an adjustment to be made to an illumination provided by a lighting unit of a surgical microscope based on the lighting need; and in response to determining the adjustment, send a first signal to the lighting unit to change the illumination according to the adjustment.

The present disclosure relates to processing data streams from a surgical procedure using multiple interconnected data structures to generate and / or continuously update an electronic output. Each surgical data structure is used to determine a current node associated with a characteristic of a surgical procedure and present relevant metadata associated with the surgical procedure. Each surgical data structure includes at least one node interconnected to one or more nodes of another data structure. The interconnected nodes between one or more data structures includes relational metadata associated with the surgical procedure.

Systems and methods for providing assistance to a surgeon during an implant surgery are disclosed. A method includes defining areas of interest in diagnostic data of a patient and defining a screw bone type based on the surgeon's input. Post defining the areas of interest, salient points are determined for the areas of interest. Successively, an XZ angle, an XY angle, and a position entry point for a screw are determined based on the salient points of the areas of interest. Successively, a maximum screw diameter and a length of the screw are determined based on the salient points. Thereafter, the screw is identified and suggested to the surgeon for usage during the implant surgery.

The invention provides an automatic operation process management method and device based on multi-sensor fusion, and relates to the technical field of automatic processes, and the method comprises the steps: constructing an instrument pose flow and operation field geometric model under a unified coordinate system through unified timestamp alignment and spatial geometry solution of multi-source sensor data; semantic segmentation and three-dimensional reprojection are combined to generate a shielding probability graph, cross-channel compensation reconstruction is carried out, and the image integrity is improved. And forming a conflict risk set through voxel field discretization and short-time prediction, and performing combined solution with standardized operation process constraints to generate a scheduling instruction and a safety time window. And finally, mapping the image, the pose and the physiological signal to a knowledge graph, and outputting consistency judgment and deviation correction suggestions in combination with a time sequence attention recognition process node. According to the method, the problem that the operation process management is not accurate enough due to the problems of view overlapping and the like of multi-source sensor data can be solved.

The invention discloses a surgical operation system with a training or auxiliary function, particularly relates to the field of intelligent surgical operation systems, and cooperatively works through five modules including multi-modal sensing, layered AI decision making, dynamic feedback, closed-loop learning and an expansion interface. MEMS pressure sensing and biological signal fusion sensing are adopted, strategic layer three-dimensional navigation and execution layer magneto-rheological damping control are combined, and an operation whole-process optimization system is constructed; a multi-source data persistent evolution model is integrated through federal learning, and precise man-machine interaction is realized by matching eye movement tracking AR and a variable stiffness touch glove. According to the system, the intraoperative risk identification accuracy is improved, the operation efficiency is improved, third-party equipment extension and doctor skill quantitative evaluation are supported, and the operation safety and the training standardization level are remarkably improved.

A surgical planning system for use in surgical procedures to repair an anatomy of interest includes a preplanning system to generate a virtual surgical plan and a mixed reality system that includes a visualization device wearable by a user to view the virtual surgical plan projected in a real environment. The virtual surgical plan includes a 3D virtual model of the anatomy of interest. When wearing the visualization device, the user can align the 3D virtual model with the real anatomy of interest, thereby achieving a registration between details of the virtual surgical plan and the real anatomy of interest. The registration enables a surgeon to implement the virtual surgical plan on the real anatomy of interest without the use of tracking markers.

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 surgical system comprising a surgical instrument and a surgical hub is disclosed. The surgical instrument is configured to perform a surgical procedure and comprises a housing, a shaft assembly, an end effector, and a sensor configured to sense a parameter associated with a function of the surgical instrument. The surgical hub comprises a processor and a memory. The memory stores instructions executable by the processor to receive data from the sensor, wherein the data comprises the parameter associated with the function of the surgical instrument; infer a step of the surgical procedure being performed by the surgical instrument, based on the data from the sensor; receive data from a database, wherein the data from the database is associated with the step of the surgical procedure; compare a value of the parameter to the data received from the database; and control the surgical instrument based on the comparison.

An interactive and dynamic surgical simulation system may be used in the context of a computer-implemented interactive surgical system. The surgical simulation system may provide coordinated surgical imagining. A processor may be configured to execute a simulation of a surgical procedure. The surgical procedure may be simulated in a simulated surgical environment. The processor may generate a first visual representation and a second visual representation. The first visual representation may be of a first portion of the simulated surgical environment. The second visual representation may also be of the first portion of the simulated surgical environment.

The invention discloses a machine learning-based intra-operative bleeding risk prediction method and device for spinal surgery. The machine learning-based intraoperative bleeding risk prediction method for spinal surgery comprises the following steps: acquiring information of a patient to be predicted; obtaining a trained hemorrhage risk prediction model; and inputting the information of the patient to be predicted into the trained massive hemorrhage risk prediction model so as to obtain a prediction result. According to the method, the high-precision prediction model is trained through large-scale patient data (including basic information, operation parameters, blood indexes and the like), so that the accuracy and the stability of intraoperative SBL risk prediction are improved. And a Cell Saver use suggestion based on a risk threshold is provided, and blood resource allocation is optimized. Clinical decision-making efficiency is improved through an automatic tool, blood transfusion related complications (such as infection and immune response) are reduced, and patient prognosis is improved.

The subject matter described in this disclosure can be embodied in methods, systems, and program products for receiving user input that selects an arrangement of surgical instrument trays for use during a surgical procedure. A display device presents a graphical representation of the selected arrangement of surgical instrument trays. A computing system receives image data depicting multiple actual surgical instrument trays; and analyzes the image data to determine an extent to which the multiple actual surgical instrument trays and actual surgical instruments located on the trays match the selected arrangement of surgical instrument trays. The display device presents a graphical indication of the extent to which the multiple actual surgical instrument trays match the selected arrangement of surgical instrument trays.

The invention discloses a self-adaptive sensing anti-shake regulation and control method and system for a spinal surgery auxiliary mechanical arm, and relates to the technical field of spinal surgery, and the method comprises the steps: collecting multi-dimensional data of a mechanical arm tail end actuator through a multi-dimensional sensor group, and transmitting the multi-dimensional data to an AeroStead space-level stability calculation platform; the platform carries out filtering processing on the data, and vibration of the mechanical arm and disturbance components caused by external interference are separated; constructing a spine mechanical arm rigid-flexible coupling dynamic disturbance observation and compensation model by using the disturbance component to observe disturbance and generate a compensation amount; calling a spinal operation area three-dimensional reconstruction and dynamic obstacle avoidance mixed density network model, and fusing different images to generate an operation area three-dimensional dynamic model and an obstacle avoidance adjustment signal; a mechanical arm motion control instruction is generated through a force sense position cooperation self-adaptive control algorithm; and the mechanical arm is driven to move for closed-loop regulation and control. According to the method, through fusion of multiple models and algorithms, the disturbance compensation precision, the operation area modeling accuracy and the control collaboration are improved, and the operation safety is guaranteed.

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.

The invention relates to a mechanical arm configuration determining method and device, a surgical robot system and equipment. The method comprises the following steps: determining a positioning type based on an operation entry point coordinate and an operation target point coordinate of a target object under a mechanical arm base coordinate system; based on the positioning type, determining a candidate configuration recommendation range of the mechanical arm; and the target configuration of the mechanical arm is determined based on the candidate configuration recommendation range, the operation entry point coordinates and the operation target point coordinates. By adopting the method, the target configuration meeting the operation type and positioning requirements can be quickly and effectively recommended.

The invention relates to the technical field of intelligent medical information, in particular to a urinary surgery risk AI intelligent assessment method and system. The method comprises the steps that intraoperative data in the urinary surgery operation process are collected in real time; inputting the intraoperative data into a risk feature fusion AI model, performing context association analysis of different stages of the operation, and fusing each intraoperative multi-source modal feature to generate a risk assessment comprehensive feature set; analyzing the risk assessment comprehensive feature set by using a risk assessment prediction AI model, dynamically adjusting the priority and prediction weight of each risk index in combination with a doctor judgment priority weight adjustment mechanism, and predicting to obtain an intra-operative real-time comprehensive risk score; and visually displaying the intraoperative real-time comprehensive risk score and the risk prompt information in a terminal in real time. According to the AI intelligent assessment method, the causal relationship between the risks is constructed by associating the data of each stage of the operation with doctor judgment, and the AI intelligent assessment method capable of dynamically predicting the key risks in the operation process is realized.

The invention relates to the technical field of artificial intelligence, and discloses a spinal surgery mechanical arm precise regulation and control system integrating deep learning and dynamic calibration, and the system comprises a digital twinborn body construction module which is used for constructing a dynamic spinal digital twinborn body of a target object; the motion path analysis module is used for generating a planned motion path of the mechanical arm; the coarse regulation and control module is used for calculating a translation vector and a rotation vector of the mechanical arm and executing coarse regulation and control of the mechanical arm when the path error is not smaller than a preset first threshold value; the fine regulation and control module is used for calculating the rigidity coefficient of the mechanical arm and executing fine regulation and control of the mechanical arm when the path error is smaller than a first threshold value and not smaller than a preset second threshold value; and the micron-order regulation and control module is used for calculating pulse voltage required by the micro-motion platform and executing micron-order regulation and control of the mechanical arm when the path error is smaller than a second threshold value and not smaller than a preset safety threshold value. The control precision and safety of the spine surgery mechanical arm can be improved.

Novel tools and techniques are provided for implementing intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem for pulmonary procedures. In various embodiments, a computing system might analyze received one or more first layer input data (i.e., room content-based data) and received one or more second layer input data (i.e., patient and / or tool-based data), and might generate one or more recommendations for guiding a medical professional in guiding a surgical device(s) toward and within a lung of the patient to perform a pulmonary procedure, based at least in part on the analysis, the generated one or more recommendations comprising 3D or 4D mapped guides toward, in, and around the lung of the patient. The computing system might then generate one or more XR images, based at least in part on the generated one or more recommendations, and might present the generated one or more XR images using a UX device.

The present disclosure relates to surgical systems, devices and methods for planning and performing surgical procedures, including bone fracture repair. The systems and methods disclosed herein can be utilized to establish physical models of anatomical structures.

Methods of generating a surgical operative note for a surgical procedure, such as a spinal surgical procedure, and associated systems and devices are disclosed herein. In some embodiments, a representative method includes capturing surgical procedure data of the surgical procedure with a sensor array positioned to view the surgical procedure, and identifying features in the surgical procedure data relevant to the surgical procedure. The method can further comprise processing the identified features to provide contextual information about the surgical procedure, and utilizing an artificial intelligence (AI) application to generate the operative note based on the identified features and the contextual information. The operative note can include a natural language, structured, and coherent description of the surgical procedure that summarizes the surgical procedure, including the type of surgery performed, specific surgical techniques used, intraoperative findings, and / or postoperative care instructions.

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.

An anti-skive bone drill assembly is provided, and may be used for spinal surgical procedures. The anti-skive assembly can include a sheath, a drill bit disposed within the sheath, and a mechanism for maneuvering the drill bit within the sheath. The anti-skive assembly, and particularly the sheath, may be mounted in a surgical robot. The anti-skive assembly may be used to side cut a bone to create a flattened surface in an otherwise non-perpendicular bone surface.

A catheter system for controlled sympathectomy procedures is disclosed. A catheter system for controlled micro ablation procedures is disclosed. Methods for performing a controlled surgical procedure are disclosed. A system for performing controlled surgical procedures in a minimally invasive manner is disclosed.

The invention discloses a method for widening keratinized gingiva by using a plasma matrix membrane and a preparation method of the plasma matrix membrane, and the method comprises the following steps: determining an expected widening position of the keratinized gingiva based on preoperative CBCT and intraoral scanning data of a patient; root-direction valve reduction is carried out in the area to be widened, and local original keratinized gingiva with the width of 1-2 mm is reserved; preparing a solid plasma matrix and a liquid plasma matrix by adopting a horizontal centrifugal mode, and preparing the plasma matrix membrane by utilizing a plasma matrix pressurizer; and sewing and fixing the obtained plasma matrix membrane in the to-be-widened area. According to the novel operation mode for widening the keratinized gingiva through the plasma matrix, it can be avoided that a second operation area is opened up to obtain an autograft, the postoperative reaction of a patient is reduced, and the economic burden of the patient is relieved; compared with a finished collagen matrix material, the plasma matrix contains autologous bioactive cells and factors and has a better regeneration promoting function.