15 results about "Vertebral compression fracture" patented technology

The invention provides a spine centrum compression fracture recognition method and system based on artificial intelligence, and the method comprises the steps: receiving original spine CT sequence data, and outputting a preprocessed standardized image; inputting the preprocessed image into a three-dimensional U-Net + + network to obtain a centrum segmentation probability graph; outputting the segmented centrum set and the position code thereof; for each segmented centrum, extracting a corresponding area based on a centrum mask, and splicing all features to form a comprehensive feature vector of each centrum; inputting the three-dimensional image block of each vertebral body into an image flow network to extract visual semantic features; and automatically generating a structured PDF report. According to the spine centrum compression fracture recognition method and system based on artificial intelligence, the problem of image quality difference caused by different CT devices and scanning parameters is effectively solved through the multi-scale residual enhancement technology and self-adaptive window level adjustment, and standardized processing of images is achieved.

The present invention relates to a system and a method for planning veterinary orthopedic surgery, for restoring volume and / or geometry of a bone, by expansion of at least one expandable bone implant (1) between a folded configuration and a deployed configuration, the implant comprising a body extending along a longitudinal axis (L) between a proximal end (11) connectable to an implantation instrument (A) for holding the implant and a distal end (12) intended to be inserted first into the bone, wherein the system comprises a plurality of expandable bone implants (1) and at least one implantation instrument (A) corresponding to each of the implants (1) and at least one planning support presenting the implants (1) and instrument (A), with reference to at least one standardized classification of the various types of fractures identified, in particular vertebral compression fractures, the planning support being capable of being implemented in the form of a human-machine interface.

The present application relates to the technical field of image analysis, in particular to an MRI image edema recognition method for osteoporotic vertebral compression fractures, which comprises: acquiring a target MRI image corresponding to an osteoporotic vertebral compression fracture of a target patient, and dividing the target MRI image; determining a characteristic performance index; determining a merging possibility index between each two fragment regions according to the characteristic performance index difference, the gray difference and the position difference between each two fragment regions; adaptively merging the fragment regions in the target MRI image; based on all the seed points screened out, performing region segmentation on the target MRI image through a watershed algorithm; and screening out an edema region. The present application adaptively performs region segmentation on the image, comprehensively considers the gray distribution, gradient distribution and shape rule condition when screening the edema region, realizes edema region recognition, and improves the accuracy of edema region recognition.

The invention relates to the technical field of early screening of osteoporotic compression fracture, and provides an osteoporotic compression fracture screening method based on a multi-modal large language model. A multi-modal large language model is introduced to carry out quantitative evaluation on key functions such as posture alignment, motion coordination and pain-related reactions of a patient from images and videos, and the key functions are output in a standardized scoring form, so that structural features with clear clinical significance are automatically extracted. And constructing a machine learning model based on the above structured features, evaluating the OVCF occurrence risk, and determining key discriminant factors and the action direction thereof in combination with an SHAP feature contribution analysis and decision tree visualization method, so as to realize interpretable expression of the prediction process. The technical scheme which is safe, low in cost, explainable and easy to popularize is provided for early screening of the OVCF, and the method is suitable for various application scenes such as community and home screening.

The invention discloses an umbrella-shaped vertebral body forming spreader which comprises a first supporting part, a sliding block, a spreading block and a driving piece. The sliding block is connected with the first supporting part, and a deformation cavity is formed between the sliding block and the first supporting part, so that the sliding block can contract in the direction close to the first supporting part; the distraction blocks are arranged on the two sides of the sliding block and connected with the sliding block and the end face of the first supporting part through deformation strips, and when the sliding block moves in the direction close to the first supporting part, the deformation strips deform, so that the distraction blocks expand in the direction away from the sliding block; when the opening block expands in the direction away from the sliding block, the opening block is in an inclined state. The driving piece is rotationally installed in the first supporting part, connected with the sliding block and used for controlling the sliding block to move in the direction close to the first supporting part. By means of the arrangement, the vertebral compression fracture distraction reduction and fixation functions can be achieved without depending on bone cement, and the application range is widened.

The present invention relates to a system and a method for planning veterinary orthopaedic surgery, for restoring the volume and / or geometry of a bone, by the expansion, between a folded configuration and a deployed configuration, of at least one expandable bone implant (1) comprising a body extending along a longitudinal axis (L) between a proximal end (11) that can be connected to an implantation instrument (A) for holding the implant, and a distal end (12) intended to be inserted first into the bone, wherein the system comprises a plurality of expandable bone implants (1) and at least one implantation instrument (A) corresponding to each of said implants (1), and at least one planning support having said implants (1) and instrument (A), with reference to at least one standardised classification of the various types of fractures identified, in particular vertebral compression fractures, it being possible for said planning support to be implemented in the form of a human-machine interface.

The invention provides a red-exhausted bone pain prescription for old-age spine compression fracture and a preparation method of the red-exhausted bone pain prescription. Comprising the following raw materials: 50-60 parts of safflower carthamus, 50-60 parts of dragon's blood, 30-40 parts of frankincense, 30-40 parts of myrrh, 20-25 parts of radix aucklandiae, 20-25 parts of ligusticum wallichii, 20-25 parts of achyranthes bidentata, 20-25 parts of radix angelicae pubescentis, 12-18 parts of Chinese starjasmine stem, 12-18 parts of philippine flemingia root, 12-18 parts of radix clematidis, 12-18 parts of artemisia anomala, 12-18 parts of adhatoda ventricosa, 12-18 parts of common gendarussa herb, 12-18 parts of asiatic toddalia root and 4-6 parts of honey-fried licorice root. The preparation method comprises the steps of raw material preparation, mixing and stirring, degassing treatment, canning and cooling. Skin irritation is remarkably reduced while the strong effects of activating blood, setting bones, dredging collaterals and relieving pain are guaranteed.

The invention relates to the technical field of fracture rehabilitation instruments, in particular to an osteoporosis vertebral compression fracture repositor which comprises a chest supporting plate and a back supporting assembly, shoulder straps are symmetrically arranged between the back supporting assembly and the chest supporting plate, the back supporting assembly comprises a plurality of back supporting plates, and the adjacent back supporting plates are hinged to each other; one back supporting plate is fixedly connected with a driving box; a pulley piece is arranged on the side wall of the driving box, a pulley piece is also arranged on one back supporting plate, a steel wire rope is arranged in the driving box, the pulley pieces are used for guiding the steel wire rope, and the end, away from the driving box, of the steel wire rope is fixedly connected with the side wall of the adjacent back supporting plate; a driving assembly is arranged in the driving box and used for pulling the ends, away from the back supporting plates, of the steel wire ropes so that the back supporting plates can be bent and straightened. An adjusting assembly used for adjusting the distance between the chest supporting plate and one back supporting plate is arranged between the chest supporting plate and the back supporting plate. According to the invention, adaptive longitudinal supporting force is provided for the back during restoration, and the restoration effect of the vertebral body is improved.

Computational tools for automated and quantitative assessment of metastatic spinal stability. Computational image analysis methods, which can include deep learning, are used to automatically quantify spinal geometry / quality and disease burden from medical images (CT and MR), which can be used to predict patient outcomes for vertebral stability / fracture risk. Two tools are provided: 1) an interpretable, improved, automated SINS (Spinal Instability Oncological Scoring) tool that calculates improved quantitative individual image-based parameters used in SINS and calculates stability via a similar scoring system; and 2) a tool that directly estimates spinal stability / fracture risk based on 3D imaging (with or without non-imaging patient-specific clinical data). The automated tool can be used to improve clinical workflow (through automation) as well as the accuracy, sensitivity, and specificity of spinal stability prediction, which can aid clinicians in better directing treatment to optimize patient outcomes. This tool is particularly useful in patients undergoing planned stereotactic body radiation therapy (SBRT) due to the accessibility of clinical images and the high likelihood (14%) of vertebral compression fractures (VCFs) and associated mechanical instability following this procedure.

The invention relates to a vertebral stability assessment method and system for senile osteoporosis vertebral compression fracture. The method comprises the following steps: collecting dynamic stress data and cone motion data of a target cone in real time, and calculating a stress concentration coefficient, an energy dissipation rate and a motion coupling degree; performing weighted calculation processing on the stress concentration coefficient, the motion coupling degree and the energy dissipation rate to obtain an initial stability index, and performing neural network verification processing on the initial stability index and the bone mineral density value to obtain a dynamic stability index; and according to a preset grading standard, carrying out grading and strategy matching processing on the dynamic stability index to obtain a vertebral stability grade and a corresponding treatment strategy. According to the method, dynamic stress and motion data are collected in real time, multi-parameter calculation is fused, a dynamic stability index is obtained through neural network verification, precise evaluation and personalized treatment strategy matching of the vertebral stability are achieved, and evaluation comprehensiveness and treatment pertinence are improved.

The invention provides a vertebral compression fracture preoperative diagnosis and treatment method and device based on Sudoku division. The method comprises the steps that a spine preoperative prone position thin-scanning CT image is obtained; inputting the spine preoperative prone position thin-scanning CT image containing the fractured centrum into the trained osteoporotic centrum compression fracture recognition model to obtain the recognized fractured centrum; planning vertebral pedicle bony puncture surface path parameters and 3D guide plate parameters of the fractured vertebral body based on Sudoku division; and printing and generating the 3D guide plate based on the planned 3D guide plate parameters. According to the application, the osteoporotic centrum compression fracture recognition model is used for recognition, and the puncture approach path is planned through the pedicle bony puncture surface through sudoku division, so that the angle and the direction of the puncture needle are planned in the pedicle puncture safety area, and related complications of operation puncture in an operation are avoided.

The present application relates to the technical field of early screening of osteoporotic vertebral compression fractures, and proposes an osteoporotic compression fracture screening method based on a multi-modal large language model, which takes patient posture images and action videos and other unstructured visual data as input, introduces a multi-modal large language model to quantitatively evaluate key functions such as patient posture alignment, motion coordination and pain-related responses from images and videos, and outputs in the form of standardized scores, automatically extracting structured features with clear clinical significance. Based on the above structured features, a machine learning model is constructed to assess the risk of OVCF, and combined with SHAP feature contribution analysis and decision tree visualization methods, the key discriminant factors and their action directions are clarified, realizing the interpretable expression of the prediction process. A safe, low-cost, interpretable and easy-to-promote technical solution is provided for the early screening of OVCF, which is suitable for various application scenarios such as community and home screening.

The application provides an osteoporosis and osteoporotic vertebral compression fracture identification model, a training and prediction method, and a technical scheme for predicting and identifying osteoporosis and osteoporotic vertebral compression fracture based on deep learning technology and repeated use of chest CT scan images of patients for lung nodule screening. The first input data of the model includes 2D image information data cut out from a chest CT image slice and 2D segmentation mask information data cut out from a vertebral mask slice; the second input data includes patient age and gender clinical information; a multi-layer feature extraction layer of the model is used for processing the first input data, and an ultimate feature extraction graph is obtained after the multi-layer feature extraction layer; a prediction layer is used for splicing the processed ultimate feature extraction graph and the second input data, and finally, two full connection layers arranged in parallel are used for osteoporosis category prediction and vertebral compression fracture category prediction, respectively.

This invention relates to the field of fracture risk prediction technology, and more particularly to a risk prediction system for osteoporotic vertebral compression fractures based on DES-DXA-MRI fusion. The data acquisition module acquires the patient's DES image data, DXA detection data, MRI image data, and clinical risk factor data. The data fusion module generates a multimodal fusion feature set containing both image modality features and clinical modality features. The risk prediction module calls a pre-trained multimodal fracture risk prediction model to predict the fracture risk probability, risk contribution factors, and development trends of each vertebral segment, generating a risk assessment result set. The network construction module constructs a vertebral fracture risk assessment network based on the assessment results, including the vertebral segment topology, risk factor association weights, and time-series evolution paths. The risk warning module generates a fracture risk warning instruction set and sends it to a clinical decision support platform to achieve accurate prediction and personalized intervention.

A system and a method for planning human orthopaedic surgery for restoring the volume and / or the geometry of a bone, by expanding at least one expandable bone implant (1) between a folded configuration and a deployed configuration, the implant including a body extending along a longitudinal axis (L) between a proximal end (11) connectable with an implantation instrument (A) for holding the implant and a distal end (12) intended to be firstly inserted into the bone, wherein the system includes a plurality of expandable bone implants (1) and at least one planning support presenting the implants (1) and instrument (A), with reference to at least one standardized classification of the various types of fractures identified, in particular vertebral compression fractures, the planning aid being implementable in the form of a human-machine interface.