3467 results about "Spinal column" patented technology

The invention discloses a covariance self-adaptive control method for a quadruped robot for spine joint adjustment. The method comprises the following steps that 1, a spine quadruped robot kinematics model with spine joints, a robot trunk dynamics model with the spine joints and a robot leg multi-rigid-body dynamics model are established; 2, based on the energy stability margin theorem and the spine joint coupling effect, a posture control method of the spine joint quadruped robot is provided by combining a covariance self-adaptive evolutionary strategy, and the angle, rigidity and damping coefficient of spine joints are estimated and adjusted according to the terrain gradient; 3, extracting the spatial relationship between the trunk mass center and the foot ends of the robot as an expected reference, updating the expected mass center position in real time, and constructing a model prediction control method to control the posture of the spinal joint quadruped robot; 4, the position of the foot falling point of the swing leg is designed in advance in combination with the additional freedom degree provided by the spine joint and MPC prediction state information. The motion stability of the robot in the complex terrain is improved.

The invention provides a seat comfort optimization calculation method based on a sensor and AI modeling, and the method comprises the steps: collecting the pressure distribution data of the sitting posture of a passenger and a spine contact area in real time through an attitude sensor and a pressure sensor which are disposed on a seat; a seat supporting area is divided into a cervical vertebra area, a thoracic vertebra area, a lumbar vertebra area and a sacrococcygeal area, and a pressure distribution prediction model of each area is established; constructing an input vector based on the average pressure, the maximum pressure, the pressure gravity center, the body type parameter and the pathological weight factor, and outputting a target support pressure value, a pressure avoiding curve and a neighbor cell migration proportion of each partition by using an artificial intelligence model; dynamic adjustment is achieved through the partitioned air bag and the driving unit, and secondary fine adjustment and protection are achieved in combination with feedback monitoring. The system comprises a posture sensing module, a pressure sensing module, a data processing module, a model building module, a regulation and control execution module and a feedback monitoring module, and self-adaptive regulation and control of different areas of the spine of a passenger can be achieved.

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.

The invention discloses a spine three-dimensional evaluation method and system based on RGB images and anatomical constraints, and belongs to the field of medical image analysis. The method comprises the following steps: carrying out preprocessing and anatomical symmetry correction on an upright position back RGB image shot by the intelligent mobile equipment for a single time; extracting regions of interest of shoulder blade and iliac crest mark points by using a lightweight segmentation network; generating an initial depth map through a depth estimation model fusing a vertebra attention mechanism and a multi-constraint loss function; carrying out physiological scale calibration on the initial depth map based on height and weight parameters; a cascade GAN is adopted to eliminate clothes interference and reconstruct a back three-dimensional surface; based on biomechanical characteristics, NURBS parameters are adjusted in a self-adaptive mode to fit a spine three-dimensional curve; and outputting a coronal plane Cobb angle, a trunk rotation angle and a sagittal plane curvature parameter. Through monocular depth estimation and dynamic evaluation of anatomical constraint, the problems of radiation risk, subjective error and clothes interference of a traditional method are solved.

The invention discloses a scoliosis orthosis registration method based on deep learning and biomechanical constraint, and relates to the technical field of scoliosis orthosis design, and the method comprises the steps: respectively obtaining first feature information of a three-dimensional spine model and second feature information of a three-dimensional body surface model, the second feature information comprises a geometric feature, a pressure feature and a motion feature; the first feature information and the second feature information are simultaneously input to a pre-constructed hybrid configuration network for rigid registration and non-rigid registration, the rigid registration outputs an initial alignment model, and the non-rigid registration outputs high-precision local adaptation data with reasonable dissection; and performing joint optimization on the initial alignment model and the high-precision local adaptation data with reasonable dissection to obtain a final registration result. Geometric and motion features of a spine and a body surface are extracted through double-flow features, so that a registration network of coarse rigidity and then fine non-rigidity is carried out, and transition from geometric registration to function optimization is realized.

The invention discloses an in-vitro bionic mechanical testing system and method for spinal skeletal muscles, and particularly relates to the field of mechanical testing, and the system comprises a bionic construction module, a finite element modeling module, a multi-axis mechanical loading module, an iterative calculation module and a correction module; through modular design, the system comprises a vertebral body unit, an intervertebral disc simulation assembly and a skeletal muscle simulation unit, each assembly is customized according to anatomical features of a human body, a finite element modeling module is utilized to generate a three-dimensional model, a multi-directional mechanical load is applied, and response data is collected; experimental data are input into the finite element model for iterative calculation, so that stress distribution, a strain field and kinematic deviation are compared; if the error exceeds a preset threshold value, the model parameters and the physical parameters are corrected until the experiment and simulation results converge; and finally, based on the optimized finite element model, predicting a failure mechanism of the spinal skeletal muscle under a complex load, and evaluating the risks of fracture, hairline fracture, nucleus pulposus protrusion and muscle strain.

The present disclosure provides for spinal implants configured for lateral insertion techniques deployable between a contracted position and an expanded position. The spinal implant may include a first endplate and a second endplate, each having a plurality of guide walls and inclined ramps. The spinal implant may further include a moving mechanism having first and second trolleys configured to act against the first and second plurality of ramps. The moving mechanism may further include a first set screw and a second set screw opposite the first set screw. The moving mechanism may be configured to operably adjust a spacing between the first and second endplates upon simultaneous rotation of the first and second set screws along a rotation axis, and may also operably adjust an angle of inclination between the first and second endplates upon rotating the first set screw or second set screw along the rotation axis.

The invention relates to the technical field of spine rehabilitation training, and discloses a computer vision recognition and guidance system for spine rehabilitation training actions. The system comprises a data acquisition module for acquiring user spine motion three-dimensional dynamic image data by using a multi-camera array; the action recognition module tracks bone joint points based on an improved algorithm to generate a real-time action sequence; the posture evaluation module is used for extracting spine curvature and joint angle features through a multi-scale convolutional neural network; the guidance instruction generation module inputs the features into a reinforcement learning model to generate a personalized instruction; and the feedback execution module constructs a fuzzy self-adaptive control model to correct the motion trail of the spine. According to the system, training actions can be accurately recognized, personalized guidance is provided, real-time correction is achieved, the spine rehabilitation training effect is effectively improved, subjectivity and errors of manual evaluation are reduced, and rehabilitation requirements of different patients are met.

The embodiment of the invention discloses a spinal metastatic tumor treatment scheme generation system. According to one specific embodiment, the system comprises a data processing server, an information fusion server and a scheme generation server which are in communication connection with one another, and the data processing server is used for preprocessing multi-source patient data to obtain standard multi-source patient data; the information fusion server is used for executing the following steps: performing feature code fusion on standard multi-source patient data to obtain a multi-source patient feature vector; performing feature enhancement on the multi-source patient feature vector to obtain a joint patient characterization vector; generating an initial therapeutic schedule result based on the joint patient characterization vector; and the scheme generation server is used for performing feature decision processing on the initial treatment scheme result to obtain a final treatment report. According to the embodiment, waste of computing resources can be reduced, and system response time can be shortened.

Embodiments include example systems, methods, and computer-accessible mediums for analysis of anatomical images for assessment of stenosis and disc degeneration. In some embodiments, systems, devices, and methods described herein include selecting a region of interest (ROI) in a three-dimensional (3D) volume of image data of a plurality of vertebra of a spine, identifying one or more anatomical parts in the ROI, determining one or more parameters of the one or more anatomical parts, and assessing a severity of a spinal deformity based on the one or more parameters.

The invention discloses a spine load monitoring system for dynamically adjusting a rehabilitation plan after an operation, and relates to the technical field of spine load monitoring, a monitoring data acquisition module collects preoperative and postoperative multi-modal data, and provides a comprehensive basis for the rehabilitation plan; a rehabilitation training initial parameter setting module determines expected recovery effect data and initial parameters of each stage, so that rehabilitation training is more targeted; in the rehabilitation process, the recovery state evaluation module evaluates training and recovery effects in real time and discovers problems in time; the rehabilitation training initial parameter adjusting module adjusts parameters when the recovery effect does not reach the standard, it is ensured that the rehabilitation plan always conforms to the condition of the patient, and the problem that in the prior art, a personalized rehabilitation plan cannot be made accurately according to preoperative and postoperative multi-modal monitoring data of the patient is solved. The training effect is difficult to dynamically evaluate in the rehabilitation process, and rehabilitation training initial parameters are difficult to adjust in time to guarantee the effectiveness and safety of the rehabilitation process.

The invention discloses a multi-axis dynamic scoliosis direction correcting device, which belongs to the technical field of medical instruments and comprises an upper flitch plate, a lower flitch plate, a spine plate, a movable connecting piece, a tension sensor, a hydraulic adjusting mechanism, a positioning and supporting mechanism and a control system, an upper arc-shaped plate surrounding the armpit is arranged on the outer side of the upper flitch, a lower arc-shaped plate surrounding the lower portion of the spine is arranged on the outer side of the lower flitch, and fixing mechanisms are arranged between the upper flitch and the upper arc-shaped plate and between the lower flitch and the lower arc-shaped plate. The movable connecting piece composed of the first ball seat and the first ball bead between the spine plates can achieve accurate attachment and flexible adaptation according to the natural trend of the spine, it is ensured that the device can accurately lock the deformity segment of scoliosis, a foundation is laid for subsequent targeted correction, and meanwhile the spine plates can be used for accurately locking the deformity segment of scoliosis. The tension sensors on the two sides of the spine plate can continuously monitor the pressure of the malformation section, and real-time data support is provided for correction force regulation and control.

The invention relates to the technical field of artificial intelligence, and discloses a teenager scoliosis intelligent quantitative evaluation system fused with multi-modal imaging. The system comprises a multi-modal data management module, a cross-modal registration fusion module, a dynamic quantitative analysis module, a risk prediction module, a three-dimensional reconstruction module, a report generation module and a system management module, and by setting a multi-modal registration verification mechanism, accurate matching of a bony structure and soft tissue is ensured. Key parameter calculation errors caused by registration failure are reduced, the three-dimensional reconstruction precision is greatly improved compared with traditional single-mode evaluation, a dynamic respiration compensation module is introduced, in the spine deformation parameter quantification stage, spine physiologic changes in a respiration movement period are automatically captured, measured values are corrected based on an individualized dynamic model, and the accuracy of three-dimensional reconstruction is improved. Pathological progression and physiological fluctuation are distinguished, the misjudgment risk caused by neglecting breathing phase change in traditional static measurement is avoided, and the reliability of scoliosis progression risk assessment is improved.

The embodiment of the invention provides a spine image recognition method and related equipment. The spine image recognition comprises the following steps: acquiring a spine image to be detected, and preprocessing the spine image to be detected to obtain a target spine image; the target spine image is input into a key point recognition model, position information output by the key point recognition model for each section of vertebral body in the target spine image is obtained, and the position information is marked on the target spine image and at least comprises positioning frame information, a vertebral body name and angular point coordinates; the key point identification model comprises a CBAM module; correcting and adjusting the position information to obtain corrected position information; and performing intervertebral disc positioning and vertebra identification according to the corrected position information. According to the technical scheme, the improved key point recognition model is adopted, the positioning frame, the cone name and the angular point coordinates are directly output without depending on a segmentation model, the process is simplified, and the detection speed and robustness are improved.

The invention relates to the technical field of motion capture and data processing, in particular to a method for realizing real-time dynamic capture of a three-dimensional space of a human spine by applying an IMU (Inertial Measurement Unit). According to the method, a personalized three-dimensional dynamic spine model is constructed based on CT image data of a user, and a mathematical model of the spine is established by fusing a physiological anatomical structure of the spine and a robot mechanism motion theory so as to accurately describe motion characteristics of the spine. An IMU (inertial measurement unit) sensor is adhered to a key part of the back of a user, an individualized spine model is driven by acquiring and processing IMU data in real time, real-time reconstruction of dynamic movement of the spine is realized, and space angles among vertebral sections of the spine are accurately calculated. The dynamic change of the spine in the movement process can be accurately captured, the movement posture of the spine can be deeply analyzed, and the movement effect of the spine can be accurately and visually tracked and quantified in real time.

Systems and methods of providing an expandable and conforming interbody implant are described. According to some implementations, an interbody implant includes a first surface configured to be disposed adjacent to a first bony endplate, and a second surface configured to be disposed adjacent to a second body endplate, with a height of the interbody implant extending between the first surface and the second surface. According to some implementations, the interbody implant is configured to be selectively disposed in an insertion configuration and a deployed configuration. In some cases, the height of the interbody implant is greater in the deployed configuration than in the insertion configuration. Moreover, in some cases, the first surface is configured to conform to a topology of the first bony endplate, and the second surface is configured to conform to a topology of the second bony endplate.

The utility model discloses a novel channel expanding device for a single-side double-channel spine endoscopic surgery, and relates to the technical field of medical equipment. The pressurizing plates are mounted on the two sides of the holding part and are symmetrically distributed; the beneficial effects of the utility model are that: be equipped with holding piece, pressurizing plate, delivery ball bin, delivery box and elastic bandage, when in use, the pressurizing plate is used for the pressure operation, the delivery box supplements the required dilation auxiliary solution, the limit bandage carries out the limit installation of first dilation plate, second dilation plate and holding piece, and the use is convenient. Double buffering protection treatment is carried out when the holding piece is pressurized through the damping shock absorber and the spring, the service life of the equipment is prolonged, the equipment is convenient to carry through the elastic binding band, the overall structure is simple and easy to operate, the use requirement is met, and the practicability is high. And normal expansion, convenient carrying and multi-stage buffer protection treatment functions are integrated when the equipment is used, so that the overall use flexibility is improved.

Methods and systems for spinal cord stimulation (SCS) are disclosed. The methods and systems involve the use of electrode leads implanted within the spine of a patient to record nerve responses induced by a stimulus. The disclosed neural responses differ in various aspects from the electrical responses previously measured in the SCS context, such as stimulation artifacts and evoked compound action potentials (ECAPs). The disclosed neural responses typically occur at a later time after inducing the stimulation pulse. Another significant feature is that when the frequency of evoked stimulation is ultra-low, such as about 10 Hz or less, the disclosed neural response is most significantly observed at a consistent, relatively constant amplitude as a whole. The disclosed methods and systems may use these neural responses as indications of pain, treatment, and / or another clinically relevant dimension to guide / confirm stimulation placement, as well as feedback control for stimulation parameters.

Bone anchor assemblies are disclosed herein that provide for a single bone anchor assembly that can be utilized across a range of spinal surgical procedures, reduce manufacturing burden and cost, and provide for greater flexibility during a surgical procedure. The bone anchor assemblies disclosed herein include an implantable shank and a receiver member having two spaced apart arms which form a U-shaped seat to receive a rod, among other components. The bone anchor assemblies disclosed herein also provide a number of features to enhance capability and usability. Examples include features to facilitate better implantation of the shank, better coupling of instrumentation to the anchor, better performance in reducing a spinal fixation element, such as a rod, into the receiver member seat, and others.

The invention discloses a scoliosis rapid screening method and device, and the method comprises the steps: S1, obtaining frame-by-frame images of a human spine through a visual sensor, and forming a human standing video sequence; s2, acquiring three-dimensional coordinate information of main skeleton key points of a human body; s3, analyzing the three-dimensional coordinate information of the main skeleton key points of the human body; s4, collecting human body front and back double-view-angle stoop motion videos through a color vision sensor; s5, carrying out frame-by-frame processing segmentation on the motion video sequence of human body stooping; s6, splicing the two-dimensional back contours extracted frame by frame to generate a complete back three-dimensional curved surface model; and S7, judging whether the scoliosis risk exists in the testee or not by combining the dynamic change indexes of the back contour features in the motion state, combining the back contour features of each frame extracted frame by frame and a plurality of indexes calculated according to the skeleton key points and inputting the indexes into the joint learning model.

The invention relates to the technical field of tumble prediction, in particular to a tumor patient tumble prediction system based on machine learning, which comprises a stride frequency recognition module, a physiological fluctuation monitoring module, a gait coupling judgment module, a posture analysis module and a risk output regulation and control module. According to the method, by accurately positioning the wave crests and the wave troughs in the acceleration signal period and analyzing the frequency change direction of the adjacent sections, accurate marking of the mutation sections is achieved, the sensitive capture capacity of behavior state changes is effectively improved, and the synchronous extraction mode of the mutation sections and the physiological signals is improved. Through consistency comparison of attitude angle change and gravity center track direction, accuracy of motion structure abnormity and imbalance state identification is enhanced, pressure distribution and spine compression direction are extracted, corresponding pairing between structure pressure and physiological load is realized, optimization of time sequence integrity is realized, and accuracy of motion structure abnormity and imbalance state identification is improved. And multi-dimensional data fusion analysis has high pertinence and stability.

The invention provides a spinal medical image registration method and system based on semantic reconstruction. Constructing spinal column 3D-CT image data and corresponding 2D image data of the spinal column 3D-CT image data; providing a 2D-3D reconstruction model, and reconstructing the 2D image data to obtain a 3D feature map b; providing a 3D-3D feature extraction model, and taking 3D-CT image data as input to obtain a feature map s with the same dimension as the 3D feature map; providing a parallel double-U-shaped network model based on cross attention, and fusing and registering the feature map b and the feature map s; a multi-weight loss function based on pixel and semantic information is adopted for optimization; and training to obtain a registration model, and performing spine medical image registration by using the registration model. According to the method, the information and precision loss caused by dimension reduction is avoided, the 2D / 3D information fusion capability is improved, the attention weight of the key semantic region is improved, and accurate spine image registration can be realized while the image shooting times can be reduced.

The invention relates to the technical field of rehabilitation medical treatment, and discloses a muscle group stress analysis method and device, a terminal and a storage medium, and the method comprises the steps: respectively obtaining motion data collected by inertial measurement units arranged on the body of a user, and recognizing gait events based on the motion data corresponding to the inertial measurement units of the lower limbs, dividing the continuous motion data into a plurality of gait cycles according to the gait event; fusing the motion data acquired by each inertial measurement unit to obtain a three-dimensional posture of each preset segment of the body, and constructing a three-dimensional motion track of the spine area of the user in combination with a human kinematics chain model; processing the three-dimensional motion track and the known muscle group stress data by adopting a singular value decomposition embedding regression method to obtain a preliminary target muscle group stress curve of each gait cycle; and correcting the initial target muscle group stress curve based on a human body multi-rigid-body dynamic model in combination with a control cost function to obtain a final target muscle group stress curve.

The invention discloses an angle-adjustable spine blood coagulation electrode which comprises a tool bit assembly, a fixed body and a movable wire clamp. The tool bit assembly comprises an insulating rod, an inner tube and an outer tube, two electrodes which are insulated and separated are arranged at one end of the insulating rod, each electrode is connected with a conductive wire, and each conductive wire penetrates through the other end of the insulating rod and is connected with a cable; a first cavity and a second cavity which are communicated are formed in the fixing body, a steel pipe chuck and a screw cap are fixed to the other end of the first cavity, and a first handle and a second handle are arranged outside the fixing body. A reset spring is arranged in the first cavity; the movable wire clamp comprises a small-diameter section and a large-diameter section, and a third handle is arranged outside the movable wire clamp. The scalpel head can axially stretch out and draw back and circumferentially rotate to adjust the angle of the scalpel head through a single hand, the operation range is small, the scalpel head rotates more gently, use is more convenient, and the requirement for multi-angle blood coagulation in a spinal operation can be met.

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 invention provides a cortical bone screw automatic planning method and device based on a statistical morphology model. The method comprises the following steps: acquiring CT image data of any object; based on the CT image data, segmenting point cloud information of a vertebral body to be subjected to screw placement; acquiring a statistical shape model and cortical bone screw planning information corresponding to the serial number; and registering the statistical shape model to the point cloud information, and determining a cortical bone screw plan of the vertebral body to be subjected to screw placement. In the application, the preset screw placement planning of the cortical bone screw is performed through the statistical shape model, and the preset screw placement planning is mapped to the current spinal column three-dimensional model through registration, so that the preset screw placement planning only needs to be performed on the statistical shape model, and a screw placement model is not needed, thereby solving the problem that a large number of cases need to be collected to realize the screw placement model; therefore, the problem of nail placement of the cortical bone can be solved.

The invention provides a massage robot personalized physiotherapy method and system based on a cloud platform, and relates to the technical field of massage robots, and the method comprises the steps: carrying out the static posture scanning and dynamic posture capturing of a user; carrying out posture structure modeling based on a multi-modal fusion algorithm; calculating a security boundary, and marking a risk area; a massage path is planned through an improved RRT algorithm, and a personalized massage track is output; executing user massage, and monitoring massage pressure and spine displacement data in real time; abnormal values of massage pressure and spine displacement data are judged in real time, and personalized massage tracks are adjusted according to abnormal conditions. According to the method, an exclusive biomechanical digital twinborn model is established by adopting static and dynamic posture capture starting from individualization and fusing multi-dimensional data, and a safety constraint posture model is constructed through precisely quantified safety boundary calculation and risk area labeling, so that a unique massage path is planned; the individual difference requirements from the structure to the physiological tolerance are met.

The invention relates to the technical field of spine correction, in particular to a spine correction traction force control method and system, and the safety, effectiveness and intelligent level of spine correction treatment are improved. The method comprises the following steps: dividing a spine into at least three sub-regions according to an anatomical structure of the spine; the at least three sub-regions comprise a cervical vertebra region, a thoracic vertebra region and a lumbar vertebra region; performing biomechanical data acquisition on each sub-region to obtain a spine state feature set corresponding to each sub-region; the spine state feature set at least comprises a pressure distribution feature, a bending angle feature and a muscle tension feature; for different sub-regions, inputting the spine state feature set into a corresponding spine correction state evaluation model to obtain a spine correction state score of each sub-region; the anatomical structure and functional importance of the spine are considered, and the comprehensive influence weight of each sub-region is obtained from a preset weight database;

A device for performing intervertebral body fusion between at a vertebral joint, and associated systems and methods for manufacturing the device are disclosed herein. In some embodiments, the device includes an expandable main body configured to be locked in a desired configuration between a superior and an inferior vertebrae at the vertebral joint to facilitate the intervertebral body fusion at the vertebral joint. A first endplate is connected to the main body. The first endplate can include a superior surface having one or more patient-specific features configured to engage and mate with an inferior surface of the superior vertebra. A second endplate is connected to the main body. The second endplate can include an inferior surface having one or more patient-specific features configured to engage and mate with a superior surface of the inferior vertebra.

The invention discloses a spine image key point detection algorithm based on multi-task learning. The algorithm comprises the following steps: constructing a multi-task deep learning network model comprising a segmentation branch and a key point positioning branch; a feature aggregation module based on multi-scale cavity convolution is embedded in the segmented branches, and the multi-scale cone feature extraction capability of the model is enhanced through splicing fusion of multiple cavity rate convolution branches and global pooling branches; a cross-task attention fusion module is introduced between the two branches, and bidirectional dynamic interaction and complementation between segmentation features and key point features are realized by generating and fusing first-order and second-order context attention maps; semantic alignment loss is designed in a training stage, collaborative optimization of two tasks is promoted by constraining the consistency of segmentation masks and key point heat maps in a high-level feature space, global information of segmentation and local information of key point detection are fully utilized, and the accuracy and stability of spine centrum key point positioning and the accuracy of a segmentation result are improved.