15834results about "Surgical robots" patented technology

A surgical visualization system for use with a robotic surgical system that includes a surgical tool movable with respect to a tissue of a patient in response to an instrument motion control signal is disclosed. The surgical visualization system includes a camera and a control circuit coupled to the camera. The control circuit is configured to determine a distance between the surgical tool and the tissue and adjust a magnification of the camera based on the distance between the surgical tool and the tissue.

A federated distributed computational system enables secure biological data analysis and genomic medicine with enhanced oncological therapy capabilities. The system implements patient-specific tumor-on-a-chip analysis through microfluidic control systems and cellular heterogeneity preservation, while integrating fluorescence-enhanced diagnostics using CRISPR-LNP targeting and robotic surgical navigation. The architecture coordinates spatiotemporal analysis of gene therapy delivery through molecular imaging and immune response tracking, and implements bridge RNA integration with multi-target synchronization. Treatment selection is optimized through multi-criteria scoring and patient-specific simulation modeling. Each federated node contains a local processing unit for biological data analysis, privacy preservation protocols, and a hierarchical knowledge graph structure. The system implements cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration, enabling research institutions to collaborate on complex, large-scale biological analyses while maintaining strict data privacy controls.

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

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.

One or more data streams may be used for controlling multi-system interaction. For example, a data stream may be received, and a surgical option associated with a surgical instrument may be selected based on the data stream. A control signal associated with the surgical instrument may be generated based on the selected surgical option. A surgical device may receive an external data stream from a source external to the surgical system. The device may derive, based at least on the external data stream, decision contextual information. The device may select a surgical option associated with a surgical instrument based on the decision context information. The device may generate a visual indication of the decision context information associated with selecting the surgical option. The device may generate a control signal associated with the surgical instrument based on the selected surgical option.

A modular surgical system for use in a surgical procedure is disclosed. The modular surgical system includes a control module including a power supply and surgical modules including a first surgical module and a second surgical module. The first surgical module is arranged in a stack configuration with the control module and the second surgical module. The power supply is configured to output power to the first surgical module and the second surgical module. A power backplane is configured to deliver the power to the first surgical module and the second surgical module through the first surgical module. A control circuit is configured to adaptively adjust power allocations to the first surgical module and the second surgical module.

Systems, methods, and instrumentalities are disclosed for displaying a magnitude and location of a resulting tissue tension from an applied force to an instrument and a resulting tissue reaction. A surgical stapling device for monitoring and adapting to tissue tension during a surgical stapling procedure may measure strain on at least one component of the surgical stapling device resultant from one or more of an opposed force applied by a user and a force resisted by tissue associated with the tissue tension. The device may determine a location of the tissue tension based on historical surgical data, the opposed force, and the resisted force. The device may highlight the location of the tissue tension to aid in real-time surgical decision-making.

Surgical systems and related computer-implemented methods are provided, including during performance of a surgical procedure on a patient, receiving, at a first surgical system, a first dataflow from a second surgical system, the first dataflow including first data regarding a measured patient parameter that the first surgical system is configured to use in performing a function during the performance of the surgical procedure. The computer-implemented methods including determining that a trigger event occurred during the performance of the surgical procedure such that a sum of a first bandwidth of the first dataflow and a second bandwidth of a second dataflow exceeds an available bandwidth, and in response to determining that the trigger event occurred, and during the performance of the surgical procedure, adjusting at least one of the first and second dataflows such that the sum of the first and second bandwidths does not exceed the available bandwidth.

The invention discloses a surgical robot resection navigation method based on multi-modal image registration. The method comprises the following steps: acquiring a preoperative multi-modal medical image of a patient and reconstructing a three-dimensional model containing a focus and a dangerous structure; acquiring an intraoperative real-time image; extracting cross-modal invariant features from the pre-operative and intra-operative images by using a first deep learning network, and establishing an initial corresponding relation; predicting an initial non-rigid deformation field by using a second deep learning network, based on the pre-operative / intra-operative image and the initial corresponding relation and in combination with physical model constraints; tracking a surgical tool in real time, and updating a non-rigid deformation field in real time by adopting an incremental calculation strategy; applying the updated deformation field to the preoperative three-dimensional model to generate a deformed model; and mapping the tool position to the deformed model, and generating a navigation instruction. According to the method, soft tissue deformation can be accurately compensated, multi-modal image information is effectively fused, navigation information is dynamically updated in real time, and the precision and safety of robot surgery are remarkably improved.

The teachings generally provide for a hand-held medical robotic system for use with a surgical tool, the system comprising an instrument comprising a hand-held portion, a tool support coupled to the hand-held portion, and an actuator assembly operatively interconnecting to move the tool support in a plurality of degrees of freedom to place the surgical tool and a localizer. The system additionally comprises a control system configured to determine a state of the surgical tool and determine a position and / or orientation of a reference coordinate system relative to a known coordinate system, activate a guided mode based on a first relationship criteria between the state of the surgical tool and the reference coordinate system, and deactivate the guided mode based on a second relationship criteria between the state of the surgical tool and the reference coordinate system, wherein the first relationship criteria and the second relationship criteria are different.

The invention belongs to the technical field of medical robots, and particularly relates to a mammary gland puncture surgical robot navigation system and device based on multi-modal image fusion. The multi-mode image data are registered and fused, high-precision three-dimensional reconstruction of the mammary gland tissue is achieved, more visual and accurate surgical navigation information is provided for doctors, the position of the puncture needle is tracked in real time, space registration is conducted on the puncture needle and the image data, and therefore the precision and safety of the surgery are improved, and the operation efficiency is improved. Meanwhile, puncture force feedback information of a puncture needle in the needle moving process is collected in real time, the implementation effectiveness of a puncture navigation path is evaluated, when it is found that the puncture path has risks, dynamic adjustment is conducted immediately, a new puncture path bypassing an obstacle area and reaching a focus is planned again, and the risk of the puncture path is avoided. And if the safe path cannot be obtained, the system automatically stops needle insertion and gives an acousto-optic early warning prompt, so that the surgical risk caused by manual misoperation in the traditional surgery is avoided.

A federated distributed computational system enables secure biological data analysis and genomic medicine through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates classical numerical simulations with machine learning models for biological system analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for biological data analysis and privacy preservation protocols. The system implements cellular machinery assembly analysis, real-time patient data integration, and multi-modal image integration with spatiotemporal health data annotation. Through a distributed graph architecture, the system enables cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration with adaptive dimensionality control. The system implements real-time therapeutic response prediction through multi-modal data analysis, enabling research institutions to collaborate on complex biological analyses while maintaining strict data privacy controls.

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. 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. The feedback loop receives data from one or more sensors at the robotic surgical system. The data is analyzed by the robotic surgery control system, and in response operation of the robotic surgical system is adjusted as needed. The surgical control system or the AI system uses intraoperative data describing a surgical procedure being performed. The feedback loop receives data from one or more sensors of the robotic surgical system regarding performance of at least a portion of the robotic surgical system. The data is analyzed by the robotic surgical control system. In response to analysis, operation of the robotic surgical system is adjusted as needed. The robotic surgical control system or AI system extracts one or more programmed steps used by the surgeon for positioning at least one of the surgical instruments during the surgical procedure.

A hub assembly includes a mount being axially movable along a drive surface, a catheter hub having a catheter, the catheter hub removably couplable with the mount, and a fluidics connector in fluid communication with the mount and the catheter hub to provide fluid and vacuum through the connector to the catheter hub.

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.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a strategy generation method and device based on hierarchical reinforcement learning, equipment and a medium. And processing environment state information to generate a sub-target and a specific action, generating a strategy reward signal in combination with state change, carrying out joint training and updating on the dynamic causal graph and the hierarchical reinforcement learning model based on the strategy reward signal, and generating an optimized action strategy. The state evolution relation is modeled by constructing the dynamic causal graph, so that the reinforcement learning can obtain causal understanding of the state change trend, decomposition and optimization of sub-targets and actions are realized in combination with a layered reinforcement learning architecture, the response precision and generalization ability of the action strategy in a complex environment are improved, and the method is suitable for application and popularization. Therefore, the task completion stability and the convergence efficiency of strategy training are improved.

A histotripsy therapy system configured for the treatment of tissue is provided, which may include any number of features. Provided herein are systems and methods that provide efficacious non-invasive and minimally invasive therapeutic, diagnostic and research procedures. In particular, provided herein are optimized systems and methods that provide targeted, efficacious histotripsy in a variety of different regions and under a variety of different conditions without causing undesired tissue damage to intervening / non-target tissues or structures.

An apparatus for treating or preventing a valvular disease, said apparatus comprising : - a ultrasound probe (2) located externally to a heart (H) of patient ( P), able to produce ultrasound waves focused inside said heart and suitable to generate, at a focal spot, a pressure sufficient to result in cavitation, - an imaging device (4) for mapping in real time a treatment region (C) of a cardiac valve of the patient, said treatment region comprising at least one leaflet of the cardiac valve, - a controller (5, 7, 8 ) configured for driving the ultrasound probe to emit a sequence of focused ultrasound waves, the controller being further configured for steering the focused ultrasound waves so as to scan the entire treatment region to soften the tissues of the treatment region. A method for treating or preventing valvular disease, carried out using said apparatus.

The invention relates to the technical field of surgical robot motion control, and discloses a surgical robot motion planning method based on a diffusion model, and the method comprises the steps: obtaining obstacle point cloud data, an initial posture and a target posture in a surgical environment; encoding the obstacle point cloud data into a potential space, and combining the encoded information with the initial attitude and the target attitude into a condition code; a Transform structure of an encoder is adopted to replace a U-Net structure in a traditional diffusion model, and the diffusion model is trained through forward diffusion and reverse denoising processes; in training, using a comprehensive loss function to optimize model performance, combining configuration space loss, geometric task space loss and collision loss, and introducing physical constraints to ensure that the trajectory conforms to kinematic characteristics and avoid collision; and in the operation task, generating a motion track based on the trained diffusion model. According to the invention, the motion planning efficiency and safety of the surgical robot in a complex environment can be improved.

Co-manipulation robotic systems are described herein that may be used for assisting with laparoscopic surgical procedures. The co-manipulation robotic systems allow a surgeon to use commercially-available surgical instruments while providing benefits associated with surgical robotics. 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 or a desired force applied by the instrument using the robot arm. For example, the robot arm may be moved automatically to maintain a constant tension force on an anatomical structure via the instrument in a constant tension mode.

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.

The invention relates to the technical field of force feedback handles, in particular to a remote surgical robot control system based on a force feedback handle and a control method thereof. The method comprises the following steps: carrying out multi-dimensional force data acquisition on an end effector of the surgical robot, and carrying out contact force size, direction and time sequence feature extraction to obtain multi-dimensional force feature data; performing surgical operation safety baseline analysis according to the multi-dimensional force feature data to generate surgical operation safety baseline data; performing dynamic force feedback threshold calculation according to the surgical safety operation baseline data to obtain force feedback threshold range data; and performing adaptive compression processing on different force feedback signals according to the force feedback threshold range data to obtain dynamic force feedback control parameters. Through real-time control instruction optimization and a closed-loop feedback strategy, the system can quickly respond to an operation intention, early warning is carried out on potential risks, and it is ensured that the operation process is safer and more reliable.

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 methods for designing and implementing patient-specific surgical procedures and / or medical devices are disclosed. In some embodiments, a method includes receiving a patient data set of a patient. The patient data set is compared to a plurality of reference patient data sets, wherein each of the plurality of reference patient data sets is associated with a corresponding reference patient. A subset of the plurality of reference patient data sets is selected based, at least partly, on similarity to the patient data set and treatment outcome of the corresponding reference patient. Based on the selected subset, at least one surgical procedure or medical device design for treating the patient is generated.

The present invention relates to a flexible continuum joint, a flexible robotic arm, and a minimally invasive surgical robot for TEM. The flexible continuum joint comprises a double-helix structure formed by two helical springs, wherein a central channel is provided inside the double-helix structure, a hollow tube arranged coaxially with the double-helix structure is provided in the central channel, and a plurality of notches are formed on the hollow tube. The flexible robotic arm is composed of a linkage joint formed by an active section and a passive section, and an end joint, wherein the active section and the end joint are both driven by a drive wire, and the passive section is driven by a nickel-titanium alloy rod. The minimally invasive surgical robot comprises an endoscopic arm and two robotic arms, and has 19 degrees of freedom overall. In addition, the provision of the linkage joint in the flexible robotic arm and the application of a hybrid drive mode combining a drive wire and a nickel-titanium alloy rod not only reduces the number of drives, but also avoids the problem of motion coupling between joints, thus effectively overcoming the problems of insufficient degrees of freedom and poor motion accuracy in existing minimally invasive surgical robots for TEM.

The invention relates to the technical field of artificial intelligence and data processing, in particular to a surgical robot quality control and fault prediction method based on artificial intelligence, and the method specifically comprises the following steps: collecting the data of a surgical robot through a sensor, carrying out the manual marking of the collected data, and forming a sample data set; performing multi-scale normalization processing on the sample data set; constructing a fault prediction model of the surgical robot by adopting a convolutional neural network, and inputting normalized data into the model for training to obtain a trained fault prediction model; preprocessing the collected new data, inputting the preprocessed data into the trained fault prediction model, and outputting a fault category prediction result corresponding to the input data by the fault prediction model; and constructing a three-dimensional quality control matrix, wherein the three-dimensional quality control matrix triggers a corresponding automatic calibration program based on a category prediction result output by the fault prediction model. According to the method, the fault features of the surgical robot can be accurately captured, and the accuracy of fault prediction and the generalization ability of the model are improved.

A natural language request is received for surgical event information associated with one or more network-connected medical devices. Based on the natural language request, a generative artificial intelligence model is prompted to generate a database query. This database query is used to query a graph database which returns, data responsive to the database query. Such responsive information can be conveyed to a user initiating the request in a user interface (e.g., GUI, audio, etc.). In some variations, the results from the graph database are used to poll one or more other models to obtain further contextual information or to provide curation of the results in a more user-friendly and intuitive manner.

A system comprises an EMD and coupled to a robotic drive. The EMD includes an external member defining a lumen and an internal member disposed in the lumen and coupled to a distal portion of the external member, and a linearly and rotationally-actuatable element coupled to the external member. The robotic drive includes a plurality of device modules, each of the plurality of device modules being independently linearly movable by the robotic drive, a first cassette coupled to a first one of the plurality of device modules and to the linearly and rotationally-actuatable element of the external member, and a second cassette coupled to a second one of the plurality of device modules and to the internal member.