18586 results about "Reoperative surgery" patented technology

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.

The invention relates to the technical field of artificial intelligence, can be applied to the field of medical health and the field of financial transaction, discloses an action generation method and device based on multi-modal pre-training, a robot and a medium, and is applied to a high-frequency action generation scene of an intelligent surgical robot or an intelligent customer service and wealth management scene. The method comprises the following steps: acquiring a language instruction, a visual image and robot body sensing data; performing multi-modal feature alignment and cross-modal feature fusion on the language instruction, the visual image and the robot body sensing data through a pre-trained visual language model to generate a fused joint feature vector; generating a target continuous control instruction based on the fused joint features through a pre-trained target action model by adopting a flow matching technology; and generating continuous actions of the robot based on the target continuous control instruction. According to the method, the robot action generation efficiency and the cross-platform adaptability are improved.

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 medical information, in particular to an intelligent prediction model and method for postoperative complications of anesthetized patients, and the method comprises the steps: collecting preoperative to postoperative complete-cycle clinical data of a patient through a medical data interface; analyzing operation codes to generate risk features, extracting vital sign dynamic features, and establishing a complication probability mapping relation through a multi-modal fusion network; combining the complication probability and pharmacokinetic parameters to construct an optimization model, and solving an individualized anesthetic dosage interval by using a gradient descent algorithm; vital signs are dynamically monitored in the operation, a dose re-optimization mechanism is triggered, the infusion rate is adjusted, and a closed-loop control link is formed; and generating a visual decision report. According to the method, through deep integration of complete-cycle clinical data and multi-modal feature modeling, preoperative physiological parameters, operation coding semantic information and intraoperative vital sign dynamic modes are subjected to fusion analysis, a nonlinear mapping relation between dosage and complication probability is constructed, and the risk prediction precision and individualized adaptability are remarkably improved.

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.

The invention relates to the technical field of medical image processing, in particular to an artificial intelligence assisted intraoperative imaging method, which comprises the following steps: S1, preprocessing a multi-modal medical image, segmenting and recognizing an anatomical structure by a deep learning model according to the preprocessed image, measuring anatomical parameters based on a segmentation and recognition result, and generating an operation planning path by artificial intelligence according to the anatomical parameters; s2, collecting a C-shaped arm perspective image stream in real time, dynamically tracking space coordinates of a surgical instrument, comparing the position of the instrument with a surgical planned path, calculating offset, and when the offset is greater than an offset threshold, outputting correction guidance through an AR superposition layer; and S3, monitoring an image quality index in real time, dynamically adjusting exposure parameters through a reinforcement learning model, and when a metal implant is detected, switching a dual-energy-spectrum mode and executing an artifact suppression algorithm. According to the method, preoperative precise planning and intraoperative assistance are realized through artificial intelligence, the problems of poor image quality and high radiation risk are solved through technical optimization, and the method has important clinical application value.

A clinical vertebra image segmentation method for assisting pedicle screw placement surgery, said method comprising: constructing a VerseDiff-UNet end-to-end framework, the framework being integrated with a denoising diffusion probabilistic model (DDPM); combining a noise-added image with a marked mask by using the VerseDiff-UNet framework, and guiding a diffusion direction toward a target region; and introducing a shape priors module on the basis of the DDPM, and extracting structural semantic information from an input spine image. In order to capture specific anatomical prior information in a medical image, the shape priors module is combined and the module effectively extracts the structural semantic information from the input spine image, thereby enabling more accurate anatomical structure segmentation, and facilitating accurate diagnosis and treatment of spinal disorders.

A system for computer assisted navigation during surgery includes a computer platform that operates to register a target surgical area of a patient. In certain cases, a process includes: obtaining a pre-op CT image of a pelvic region of a patient and intra-operatively obtaining a point cloud data about the pelvic region with a navigated instrument, generating a 3D bone model which excludes non-targeted area such as a femur, and then merging the 3D bone model to the point cloud to register the target surgical area.

The invention discloses a preoperative and postoperative full-process optimization nursing system for the thoracic surgery department, relates to the technical field of medical nursing, and aims to solve the problem that physical execution nursing and information management decision are separated in the prior art; and the nursing intervention is lack of real-time response and closed-loop regulation and control capability to the highly dynamic physiological state of the thoracic surgery patient in the perioperative period. The nursing system is characterized by comprising a multi-mode physiological and environmental perception layer, a dynamic nursing decision engine and a closed-loop physical intervention execution layer. Wherein the sensing layer is responsible for data acquisition, state model construction, risk prediction and optimal intervention planning by a decision engine, and the execution layer accurately implements intervention and feeds back. By the adoption of the technical scheme, individualized, dynamic and prospective management of perioperative nursing of the patient in the thoracic surgery department can be achieved, a refined, individualized and intelligent management means is provided, and the clinical value is remarkable.

A system for computer assisted navigation during surgery includes a computer platform that operates to register a target surgical area of a patient. In certain cases, a process includes: obtaining a pre-op CT image of a pelvic region of a patient and intra-operatively obtaining a point cloud data about the pelvic region with a navigated instrument, generating a 3D bone model which excludes non-targeted area such as a femur, and then merging the 3D bone model to the point cloud to register the target surgical area.

The invention provides an oral surgery positioning and navigation method and system and a storage medium, and the method comprises the steps: calibrating an implanted mobile phone to obtain a unit vector of a rotating shaft of a drill bit and a three-dimensional coordinate value of the tip of the drill bit under an optical three-dimensional coordinate system, scanning to obtain a CT three-dimensional model of a patient, and segmenting to obtain a tooth CT model and an alveolar bone CT model; generating a tooth three-dimensional point cloud according to the real-time pattern of the speckle structured light on the teeth of the patient, acquiring a matrix transformation relation between a CT three-dimensional coordinate system and an optical three-dimensional coordinate system, and performing real-time navigation on the implanted mobile phone according to the real-time point position deviation and the real-time angle deviation of the drill bit. According to the technical scheme, the matrix transformation relation between the CT three-dimensional coordinate system and the optical three-dimensional coordinate system is calculated through the tooth CT feature points and the tooth point cloud feature points, and auxiliary navigation is carried out by comparing the target position of the implant with the real-time pose of the tip of the drill bit, the direction vector of the planting path and the unit vector of the rotating shaft of the drill bit. Operation is convenient and positioning precision is high.

The invention discloses a multi-modal data fusion-based intelligent management system for a thoracic surgery monitoring unit, belongs to the technical field of medical information and artificial intelligence, and aims to solve the limitation of an existing thoracic surgery monitoring system in the aspects of multi-modal data fusion, heterogeneous data semantic alignment and intelligent deep analysis and prediction decision. The system is characterized by comprising a multi-modal data acquisition unit, a heterogeneous data fusion and semantic alignment module, an intelligent analysis and prediction decision module, a man-machine interaction and visual presentation module and a secure storage and management module. By the adoption of the technical scheme, comprehensive multi-modal data fusion, high real-time performance, deep intelligent analysis and prospective prediction can be achieved, intelligent decision support, resource optimization, continuous learning and self-adaptive optimization are provided, and the intelligent level and patient management efficiency of the thoracic surgery intensive care unit are remarkably improved.

The invention discloses an anorectal postoperative intelligent follow-up visit management method and system based on a mobile terminal, and relates to the technical field of communication data. Comprising the steps that a dynamic follow-up survey matrix is constructed according to clinical indexes and preset weights of patients before and after an operation, and the follow-up survey matrix aims at determining the interval duration of all follow-up stages; the mobile terminal establishes a timeliness doctor-patient communication channel through the follow-up survey matrix, and the mobile terminal generates a personalized rehabilitation guidance scheme based on follow-up data. According to the method, the dynamic follow-up survey matrix is constructed, the comprehensive risk value is calculated according to the quantitative assignment of the clinical indexes and the weighting of the preset weight, and the follow-up interval is iteratively optimized in combination with the real-time recovery data, so that the precision of the follow-up period is realized, the dynamic adjustment reduces unnecessary follow-up disturbance to low-risk patients, and the follow-up efficiency is improved. Tracking frequency is enhanced for middle-high risk patients, and the defect that a traditional fixed follow-up visit mode is cut at a time is thoroughly overcome.

A system and method for using augmented reality device for use during a surgical procedure are described. A system may include an augmented reality device to present a virtual indication, such as a virtual indication of a surgical instrument, a force vector, a direction, or the like. The augmented reality device may present a virtual aspect of a procedure, such as a virtual animation of a step of a procedure. The augmented reality device may present a virtual object, indication, aspect, etc., within a surgical field while permitting the surgical field or aspects of the surgical field to be viewed through the augmented reality display (e.g., presenting virtual objects mixed with real objects).

A robotic drive system for driving one or more elongated medical devices, the robotic drive can include a linear member and at least four device modules coupled to the linear member. Each device module may be independently controllable. The plurality of device modules may be switched between a first configuration where each device module is populated with an elongated medical device and a second configuration where a subset of the at least four device modules is populated with an elongated medical device.

The invention discloses a motion error dynamic compensation method applied to a surgical mechanical arm, and belongs to the technical field of surgical mechanical arm control and medical automation. The method comprises the following steps: firstly, acquiring position data of each joint encoder of the surgical mechanical arm, a motor current signal, a position feedback signal of an end effector, load change information and accumulative operation duration in real time; processing the data, fusing the data through adaptive Kalman filtering to estimate a joint dynamic error, inputting a load change rate and an accumulated operation time length into a pre-training error prediction model to output a compensation basic quantity, and fusing the load change rate and the accumulated operation time length to generate a real-time error compensation quantity; and then the compensation amount is superposed to a joint original control instruction to correct motion parameters, and finally the compensated tail end position deviation is analyzed, and a precision identifier or an adjustment prompt is generated and fed back to a control system. According to the method, real-time error compensation is achieved, the compensation precision and working condition adaptability are improved, it is guaranteed that the mechanical arm is stably positioned, and the surgical operation risk is reduced.

A system for computer assisted navigation during surgery includes a computer platform that operates to register a target surgical area of a patient. In certain cases, a process includes: obtaining a pre-op CT image of a pelvic region of a patient and intra-operatively obtaining a point cloud data about the pelvic region with a navigated instrument, generating a 3D bone model which excludes non-targeted area such as a femur, and then merging the 3D bone model to the point cloud to register the target surgical area.

A system for computer assisted navigation during surgery includes a computer platform that operates to register a target surgical area of a patient. In certain cases, a process includes: obtaining a pre-op CT image of a pelvic region of a patient and intra-operatively obtaining a point cloud data about the pelvic region with a navigated instrument, generating a 3D bone model which excludes non-targeted area such as a femur, and then merging the 3D bone model to the point cloud to register the target surgical area.

Milling device for prosthetic surgery comprising a milling tool rotating about a milling axis, and a handling body. The handling body is provided with a drive rotating rod which develops along a longitudinal axis of linear rotation and is connected to the milling tool in order to make the milling tool rotate about the milling axis.

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.

The invention discloses a surgical robot dynamic compensation method based on multi-mode real-time 4D digital twinning and a related device. The method comprises the following steps: acquiring preoperative three-dimensional and four-dimensional image data of a patient, synchronously acquiring optical body surface movement data and in-vivo ultrasonic data during an operation, inputting the data into an end-to-end modular network in combination with current state data of a surgical robot for feature extraction and fusion, generating a 4D twinborn space containing spatio-temporal information, and generating a compensation instruction based on the space to control the surgical robot. According to the method, real-time fusion and dynamic modeling of multi-modal data in an operation can be realized, the self-adaption and compensation capability of a surgical robot in a complex surgical environment is improved, and the surgical precision and safety are enhanced.

The invention discloses a surgical instrument segmentation method based on a double prior guidance network, and the method specifically comprises the steps: constructing a hybrid encoder composed of a visual basic model and a state space model, and carrying out the coding processing of an input surgical endoscope image; the visual basic model is an SAM2-Hira model; the state space model is a MambaVision model; an adapter layer composed of a standard adapter and a Mama enhancement adapter is arranged between the coding layer and the decoding layer, and coding features are processed; setting a multi-context guide decoder, and performing multi-scale feature recovery and mask segmentation; a hybrid encoder, a Mama enhancement adapter and a multi-context guidance decoder form a double-priori guidance network DPG-Net, and high-precision surgical instrument segmentation is realized by using the DPG-Net. Accurate visual support is provided for the surgical robot, and the accuracy and safety of surgical operation are greatly improved.

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.

The invention relates to the field of intelligent control, and particularly discloses a shadowless lamp control method based on behavior recognition and prediction, which comprises the following steps: S1, establishing a three-dimensional rectangular coordinate system by taking an initial mounting position of a shadowless lamp as an original point, and determining coordinates as follows by adopting image data acquired by at least two groups of directional image acquisition equipment with different visual angles; s2, capturing the real-time position of the scalpel in real time through an image acquisition device, outputting coordinates, acquiring data of a plurality of continuous durations to form a historical trajectory data set, calling an operation scene template library preset with a plurality of types of typical operation trajectory features by the control terminal for the acquisition times, matching a trajectory feature template corresponding to the current operation type, and outputting the historical trajectory data set; performing feature alignment processing on the data and the input data by a fusion module to obtain adaptive trajectory data; according to the technical scheme, pre-judgment can be carried out in advance, the scene adaptability is high, and sterile fine adjustment is facilitated.

The invention is applicable to the technical field of medical image processing and mixed reality, and provides a laparoscopic surgery mixed reality navigation method based on deep learning and dynamic point tracking, which comprises the following steps: dynamically registering a three-dimensional model containing kidney, tumor and vessel with an initial frame of a laparoscope through a mixed reality alignment technology; the method comprises the following steps: constructing an operating forceps motion sensing model based on a time sequence deep neural network, realizing real-time control and parameter locking of a three-dimensional model pose, dynamically updating a two-dimensional feature point set by adopting a multi-feature-point combined tracker, constructing a candidate feature combination through a cross-quadrant sampling strategy, and constructing an operating forceps motion sensing model; a candidate feature combination is generated through a four-quadrant division and cross-regional sampling strategy, an optimal camera pose parameter is generated in combination with a re-projection error and pose continuity constraint, and an operation video is dynamically covered with a semitransparent three-dimensional model. The method can significantly enhance the spatial perception capability of the kidney anatomical structure, reduce the registration error of the kidney in the three-dimensional integrated kidney structure model and the laparoscope video, and improve the navigation precision.

The invention relates to the technical field of tumor interventional operations, and discloses a visual navigation method based on a tumor interventional operation robot. The method comprises the following steps: acquiring real-time medical image data of a tumor area containing multi-modal imaging information so as to comprehensively present anatomical details; and performing three-dimensional reconstruction on the image data to generate a tumor area three-dimensional anatomical structure model capable of visually displaying a space structure. Key anatomical feature points are extracted based on the model, space coordinates are calculated, a surgical robot intervention path is planned according to the coordinates, and an initial navigation track is generated; and continuously collecting real-time pose data of the robot in an operation, dynamically matching the real-time pose data with the initial navigation trajectory, adjusting motion parameters according to a matching result, and generating a corrected navigation instruction. The method can reflect the intraoperative anatomy condition in real time, dynamically optimize the path, solve the problems that traditional navigation depends on preoperative static images and lacks real-time adjustment, reduce operative complications and improve the treatment effect of patients.

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.