189 results about "Bone mass density" patented technology

The invention relates to the technical field of image processing, in particular to an intelligent fracture diagnosis system based on image recognition, which comprises an image analysis module, a mode recognition module, a form analysis module, a risk assessment module and an auxiliary decision module. According to the method, skeleton gray level distribution and boundary consistency are analyzed through continuous frames of X-ray images, fracture feature extraction precision and time sequence coherence are improved, key point space distribution, symmetry standards and form proportions are fused, fracture area structured quantitative evaluation is achieved, the form change trend and abnormal offset point screening are combined, and the accuracy of fracture feature extraction is improved. The method enhances abnormal trajectory recognition precision, associates bone mineral density and form offset, calibrates high-risk time periods, improves risk assessment perspectiveness and individual adaptability, dynamically corrects output content according to an inter-frame suggestion change trend and extended feedback, enhances timeliness of diagnosis suggestions and closed-loop feedback quality, and improves risk assessment accuracy. Image evolution, structural geometry and physiological data are integrally fused, and multi-dimensional intelligent judgment of fracture recognition and evaluation is achieved.

The invention relates to the technical field of osteoporosis diagnosis, and discloses an osteoporosis diagnosis method based on image recognition. The method comprises the following steps: acquiring bone density image data of a target object and bone structure image data of a target area based on medical imaging equipment, and determining a bone trabecula distribution condition of the target area according to the bone structure image data; constructing a three-dimensional bone mineral density model of the target object according to the bone mineral density image data, and determining an image analysis strategy by combining the three-dimensional bone mineral density model and the bone trabecula distribution condition; and an image analysis module acquires bone structure feature data of the target area according to the strategy, and identifies the bone density grade and the osteoporosis degree of the target area according to the feature data to obtain diagnosis data. According to the method, related data of the bone mineral density and the bone structure are integrated, comprehensive analysis is carried out in combination with the image recognition technology, an efficient and reliable new mode is provided for clinical diagnosis, the accuracy and objectivity of osteoporosis diagnosis are improved, and misdiagnosis and missed diagnosis are reduced.

The invention provides an osteoporosis automatic diagnosis method and system based on position specific attention, and the method comprises the steps: carrying out the dual-channel enhancement preprocessing of a lumbar CT image, and generating an enhanced image taking the bone mineral density and the bone trabecula structure characteristics into consideration; inputting the enhanced image into a deep convolutional neural network for multi-scale feature extraction to obtain a feature map containing spatial semantic information; on the basis of the anatomical position index information, generating attention weighted features which highlight the key diagnosis area of the vertebral body; global pooling is carried out on the attention weighted features, and then comprehensive diagnosis features containing anatomical priori knowledge are constructed; and inputting the comprehensive diagnosis features into a classifier to realize centrum-level accurate diagnosis. According to the invention, the workload of radiologists can be effectively relieved, and the efficiency and coverage of osteoporosis screening can be improved. Especially under the condition that primary medical institutions are lack of experienced radiologists, the primary medical service level can be improved.

The invention relates to the technical field of model construction, in particular to a method for creating an image recognition model based on clinical disease data integration. The method comprises the following steps: acquiring a medical clinical orthopedic image set; the bone density and the bone form of the medical clinical orthopedic image set are extracted, bone structure division is carried out on the medical clinical orthopedic image set, bone structure division images are generated, and the bone structure division images comprise large bone structure images and small bone structure images; analyzing the bone layer thickness of the big bone structure image and performing physical layer thickness compensation reconstruction on the big bone structure image to generate big bone middle virtual slice data; and performing detail guide reconstruction on the small bone structure image to generate small bone fracture texture reconstruction data. According to the invention, through image resolution enhancement, multi-modal data integration, motion simulation and optimization model training, the accuracy of skeleton image dynamic identification is improved.

The invention provides a plant lactobacillus TH13, and the preservation number of the plant lactobacillus TH13 is CGMCC (China General Microbiological Culture Collection Center) NO.32112. The plant lactobacillus TH13 provided by the invention can be used for fermenting folium cortex eucommiae and enzymatically hydrolyzed bone meal, and the fermentation product of the plant lactobacillus TH13 has high content of chlorogenic acid, proline, glycine and lysine. The fermentation product can significantly improve the content of alkaline phosphatase (ALP) and osteocalcin (OCN) in mouse serum and reduce the content of tartrate-resistant acid phosphatase (TRACP). Therefore, the fermentation product can significantly improve the bone mineral density of the femur, increase the number of bone trabecula and significantly reduce the separation degree of the bone trabecula, thereby promoting bone formation of a mouse with senile osteoporosis, inhibiting bone resorption and improving senile bone loss and bone microstructure deterioration. The preparation method of the probiotic preparation containing the plant lactobacillus and the probiotic fermentation product is simple, and large-scale production can be realized.

The invention provides a preparation method of non-denatured type II collagen capable of increasing bone mineral density and improving bone elasticity, and belongs to the technical field of biologication.The preparation method comprises the steps that an EDTA-citric acid buffer solution containing beta-mercaptoethanol is adopted for decalcifying cartilage, a supercritical CO2 extraction technology is adopted for degreasing, and the collagen is obtained; alkaline protease and collagenase II are adopted for substrate directional enzymolysis and gradient digestion; then the non-denatured type II collagen is separated by adopting three-phase centrifugal separation, and the non-denatured type II collagen with high extraction rate, high purity and high structural integrity can be obtained by adopting bionic H column chromatography purification.

The invention provides a design method of a personalized gradient porous bone scaffold based on patient apparent bone mineral density. The design method comprises the following steps: constructing a variable density gradient porous structure of the bone scaffold; obtaining sample data through compression mechanical simulation and fluid analogue simulation; constructing a BP neural network model; the apparent bone mineral density of a patient is obtained through detection, the theoretical elastic modulus is calculated, and the mechanical-permeability coordinated gradient bone scaffold structure parameters are predicted through a BP neural network model; and an intelligent platform for designing a personalized gradient porous bone scaffold structure is built. The method has the beneficial effects that the gradient porous structure is constructed by controlling the offset in the TPMS function, and the bone scaffold gradient porous structure which is matched with the apparent bone density of a patient and is coordinated in mechanical and penetrating performance is realized through performance simulation and neural network data processing; and a bone scaffold parameter prediction platform is established to provide a personalized bone scaffold structure design scheme for convenient and efficient bone repair medical treatment.

The invention relates to the technical field of medical imaging, and discloses an image processing method and system for a dual-energy X-ray bone mineral densitometer, and the method comprises the steps: collecting a dual-energy X-ray image, and carrying out the light and dark field correction of original projection data; based on the corrected projection data, using an aluminum sheet to simulate bones and glass to simulate tissues, carrying out data fitting to obtain a bone mineral density value, and fitting a bone-aluminum conversion coefficient; preprocessing the image, wherein the preprocessing comprises Gaussian smooth filtering, threshold processing and corrosion operation; segmenting the image by using a region growing algorithm, and carrying out edge detection by using a Canny operator; extracting an ROI (Region of Interest), and determining the boundary of the ROI by searching coordinates meeting a preset threshold condition within a preset proportion range in the vertical direction of the image; and calculating the area of the ROI, the bone mineral density BMD and the bone mineral salt content. The accuracy of bone mineral density measurement is improved, the influence of noise and artifacts can be effectively reduced, and the target area is accurately extracted.

The invention provides a method for constructing a skeleton prediction model based on an ROI-HU database and application, image segmentation is performed based on skeleton CT image data of different age groups, and a corresponding ROI-HU database is constructed based on pixel data of a constructed ROI region and an extracted Hu value. Regression training is carried out based on data features in a database, and a skeleton prediction model is constructed and can be used for predicting skeleton attributes of skeletons of patients at different age groups. According to the method, the patient can know the bone mineral density value of the corresponding age group, and can predict the bone attribute of the next stage, so that the patient can conveniently track and know the own bone development state in time. The bone attribute prediction is carried out based on the two indexes, meanwhile, the patient can know whether the bone of the patient has lesions or not, treatment can be carried out in time, and the prediction accuracy is improved.

The invention provides a bone mineral density measurement method based on a segmented large model and a lightweight semi-supervised model, which comprises the following steps of: acquiring an original hip joint X-ray image, and preprocessing the original hip joint X-ray image to obtain a data set; performing image segmentation on the data set by using a preset segmentation large model to obtain a final femoral head mask pattern; cutting and identifying the original hip joint X-ray image by using the final femoral head mask pattern to obtain a mixed supervision sample set only containing the unilateral femoral head; training a preset semi-supervised lightweight neural network model by using the mixed supervised sample set to obtain a trained semi-supervised lightweight neural network model; and predicting the bone mineral density of a newly input hip joint X-ray image by using the trained semi-supervised lightweight neural network model. According to the method, the segmentation large model SAM and an interactive prompt mechanism are introduced, the femoral head segmentation precision and generalization are remarkably improved, and high-precision prediction of the bone mineral density under the small sample condition can be realized by using the lightweight semi-supervised regression model.

The invention provides a centrum bone mineral density automatic measurement method, device and equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of centrum CT data, and carrying out the analysis through a deep learning model, and obtaining the space coordinate information of centrum key points; determining an axial reference line according to the centrum key points and constructing a region of interest of a front region; based on the space coordinate information of the centrum key points, calculating spine morphological characteristics of the centrum, and adjusting the position and direction of the centrum region of interest according to the spine morphological characteristics; and extracting CT values of all voxels in the adjusted region of interest, and calculating an average value of the CT values of all voxels to obtain a vertebral bone mineral density measurement result. According to the method, automatic and accurate measurement of the vertebral bone mineral density is achieved, the technical problems that a traditional method is low in automation degree and cannot accurately adapt to spine morphological changes are solved, and the accuracy and consistency of bone mineral density measurement are improved.

The invention belongs to the field of functional composite microbial preparations, and particularly discloses a microbial agent and GABA compounded composition and application thereof in promoting growth and development of children. The composition is prepared from a freeze-dried bacterial agent and a GABA (gamma-aminobutyric acid) compound, wherein the freeze-dried bacterial agent comprises lactobacillus johnsonii YH1136 and lactobacillus helveticus Helveticus; the GABA compound is prepared by loading GABA with the purity of greater than or equal to 98% on a nano cellulose carrier, and the loading rate is 20-35%. The composition realizes high survival rate of the microbial inoculum and targeted slow release of GABA through a freeze-drying process and enteric coating. Experiments show that the composition can significantly improve the serum GH / IGF-1 level, the bone mineral density and the body growth rate, and has a synergistic effect when combined with the vitamin D3. According to the invention, the problems of single strain function, low GABA utilization rate and gastric acid damage in the prior art are solved, and an efficient and safe solution is provided for children growth and development intervention.

The invention relates to a spine bone structure and bone mineral density automatic measurement method based on a CT image. The method comprises the following steps: performing CT scanning on a phantom with known bone mineral density to obtain a real bone mineral density calculation formula; all CT images in the DICOM format are read, DICOM analysis is carried out, and three-dimensional CT value data and other main information of all the images are obtained; performing outer contour segmentation on the whole spine in the CT image by using a deep learning network to obtain an outer contour of each vertebral body in the image; the method comprises the following steps: carrying out preliminary segmentation on cortical bones and cancellous bones by adopting a dynamic threshold method aiming at each vertebral body, selecting a preliminarily segmented cancellous bone region, using a maximum connected domain algorithm in a three-dimensional direction, retaining a maximum connected region, and further measuring the bone mineral density, namely calculating overall topology parameters, the bone mineral density and the bone mineral content of the current vertebral body; calculating the rubbing parameters, the bone mineral density and the bone mineral content of the current vertebral cortical bone and cancellous bone; and measuring the content of the cross section of the cone.

The invention discloses an eggshell membrane peptide for enhancing bone mineral density as well as a preparation method and application thereof, belongs to the technical field of egg by-product processing, and particularly relates to a preparation method of the eggshell membrane peptide for enhancing bone mineral density, which comprises the following steps: carrying out vacuum freeze drying and crushing on an eggshell membrane to obtain eggshell membrane powder; mixing the eggshell membrane powder with deionized water to obtain an eggshell membrane solution; adding keratinase, performing enzymolysis, and taking supernate; adding collagenase, carrying out enzymolysis, and centrifuging to take supernate, so as to obtain primary enzymatic hydrolysate; adding alkaline phosphatase, performing enzymolysis and centrifugation, and taking supernate to obtain enzymatic hydrolysate; and performing ultrafiltration and drying to obtain the eggshell membrane peptide. According to the eggshell membrane peptide for enhancing the bone mineral density, keratin cross-linking is destroyed through enzymolysis of keratinase, active peptide capable of stimulating osteoblast proliferation is released through enzymolysis of collagenase, phosphopeptide calcium binding sites are further activated through enzymolysis of alkaline phosphatase, calcium salt deposition is promoted, and the eggshell membrane peptide for enhancing the bone mineral density is provided.

The invention is suitable for the field of meat processing and production, and provides an intelligent recognition method and system for the three-dimensional contour of boned meat based on image recognition, and the method comprises the steps: carrying out the multi-modal image collection of frozen boned meat, and capturing a surface crack pattern; analyzing the crack pattern to construct a topological network model; reconstructing a bone-meat three-dimensional holographic model based on the topological network model, and marking joint, marrow cavity and bone mineral density abnormal areas; identifying potential skeleton weak points; generating a double-path cutting guide line; cooperative operation and visual presentation of the guide lines are controlled through dynamic compensation. Precise recognition and intelligent cutting guide of the internal skeleton structure of the boned meat are achieved, the segmentation precision and efficiency are improved, and the method is suitable for the field of meat processing and the like.

The invention discloses a method and device for intelligently achieving implant anchorage position generation, and the method comprises the steps: carrying out the fusion of oral cavity CBCT data and oral scanning model data, obtaining a fusion three-dimensional model, and carrying out the registration of the oral cavity CBCT data and the oral scanning data based on a non-rigid I CP algorithm during the fusion; performing feature point extraction on the fused three-dimensional model to obtain a feature point heat map, performing risk region division on the fused three-dimensional model to obtain a risk region, determining a bone mineral density grading map based on the fused three-dimensional model, and dividing a tooth root three-dimensional safety boundary; determining at least one target feature point based on the feature point heat map, the risk area, the bone mineral density grading map and the tooth root three-dimensional safety boundary, and determining implant anchorage position information corresponding to the target feature point based on the target feature point; and screening out an optimal implant anchorage target position from the implant anchorage position information. The purpose of intelligently determining the position of the implant anchorage is achieved, and the defects in the related technology are overcome.

The invention discloses application of a Trim3 gene and / or an expression product thereof in preparation of medicines for treating osteoporosis. The over-expression of the Trim3 can improve the bone mineral density of osteoporosis model mice, inhibit bone resorption, inhibit osteoclast differentiation and inflammatory response in vitro, improve osteoporosis and provide a new target for diagnosis and treatment of osteoporosis.

The invention discloses a dual-energy ray bone mineral density evaluation method based on a flat panel detector, and relates to the technical field of ray evaluation. Through a direct conversion mode of the flat panel detector, high-precision acquisition of low-energy and high-energy ray images is realized, and noise and resolution loss caused by traditional indirect imaging are remarkably reduced; primary correction and secondary correction are completed in the edge calculation nodes, so that the problems of discontinuous gray scale and distorted energy ratio of the boundary area of the skeleton and the soft tissue are effectively solved, and the authenticity of the image boundary is improved; and dynamic smoothing processing of the energy ratio is further combined to obtain a stable ratio image, so that the stability and repeatability of bone mineral density measurement are remarkably enhanced, and more accurate bone mineral density evaluation is realized.

The invention belongs to the technical field of biotechnology and tissue engineering, and particularly relates to application of pilose antler bud cell secretions in preparation of anti-osteoporosis drugs. The pilose antler bud cell secreta is obtained by adopting gradient centrifugation, and cell experiments show that the pilose antler bud cell secreta provided by the invention has the effect of promoting osteogenic differentiation of cells; animal experiment results show that the cornu cervi pantotrichum bud cell secreta provided by the invention can significantly improve the bone mineral density, bone volume fraction, bone trabecula number, bone trabecula thickness, bone trabecula separation degree and cortical bone mineral density of mice, increase the bone trabecula area of primary osteogenesis and secondary osteogenesis centers of the mice, improve osteoporosis symptoms of the mice, and improve osteoporosis of the mice. The effect is obviously better than that of bone marrow mesenchymal stem cell secretions, part of indexes are equivalent to the treatment effect of diphosphates, and the antler bud cell secretions provided by the invention can be used for preparing medicines for preventing, relieving or treating osteoporosis.

The invention relates to an application of plant lactobacillus Hi188 in promotion of bone health. The invention provides the application of the plant lactobacillus Hi188 or the afterbiogen thereof or the composition thereof in increasing the bone mineral density, increasing the bone mineral density, promoting the skeletal development, increasing the bone mineral content and / or promoting the tibia growth. Or preparing food for increasing bone mineral density, increasing bone mineral density, promoting skeletal development, increasing bone mineral content and / or promoting tibia growth; wherein the composition comprises: (A) Phytobacterium plantarum Hi188 or a postbiotic thereof, and (B) Bifidobacterium lactis Ca360 or a postbiotic thereof.

The invention relates to the technical field of osteoporosis detection, in particular to an osteoporosis risk assessment system and method for lumbar magnetic resonance images. The system comprises an image acquisition module, a structure processing module, a feature extraction module, a bone mineral density prediction module and a reasoning control module, through MRI image preprocessing and image feature vector extraction, in combination with non-image information such as age, gender, height, weight and the like, a backstepping mapping model is constructed in a multi-modal mode, and continuous estimation of bone mineral density and bone related characterization values is achieved. According to the method, quantitative extraction of the iconography parameters of the lumbar vertebra bone tissue can be achieved under the non-radiation condition, and a safe and repeatable opportunity analysis means is provided for iconography monitoring and follow-up visit of high-risk people; meanwhile, the adaptability of the model in multi-center data is remarkably enhanced, overfitting is avoided, and the consistency and reliability of calculation results of bone characterization parameter values between different devices and samples are improved.

The present invention relates to a diagnosis assistance device and method based on artificial intelligence processing of a radiographic image, wherein a disease diagnosis assistance device according to an embodiment of the present invention may comprise an information providing unit that constructs a diagnosis assistance model by learning a training radiographic image and additional information through a first feature processing unit, a second feature processing unit, and a feature fusion processing unit, determines a bone mineral density value and disease classification information with respect to a radiographic image to be diagnosed on the basis of the diagnosis assistance model, and provides disease diagnosis assistance information including the bone mineral density value, the disease classification information, and diagnosis basis information.

The invention discloses a cistanche deserticola extract for improving bone mineral density, the total content of echinacoside and verbascoside in the cistanche deserticola extract is greater than 25wt%, and the cistanche deserticola extract is obtained by adding distilled water into cistanche tubulosa coarse powder for extraction, filtration, filtrate centrifugation, concentration, extraction, concentration and impurity removal, dilution, after-treatment, concentration and drying. The total content of echinacoside and verbascoside in the cistanche deserticola extract for improving the bone mineral density is 25-30 wt%, and the cistanche deserticola extract is rich in shikimic acid, phenylpropionic acid and alkaloid components, so that the cistanche deserticola extract can stably play a role in improving the bone mineral density.

The present application relates to the technical field of dental root image processing, and particularly relates to an intelligent detection method for bone density change caused by dental root absorption area of impacted wisdom tooth. The present application constructs a coordinate system of images under different angles, obtains a gray weighted value of each pixel point in the images under each angle according to actual source image distances of the images under different angles and position distribution of preset anatomical structures, obtains a bone density change rate of each position pixel point in combination with displacement change amounts and gradient features of registered pixel points between the images under different angles, obtains a high risk degree threshold value of bone density change according to bone density change rate distribution of all position pixel points, and obtains a high risk area of bone density change of each image according to the bone density change rate of each position pixel point on each image and the high risk degree threshold value of bone density change. The present application improves the accuracy of bone density change detection by accurately analyzing the bone density change area.

The utility model relates to a bone mineral density ultrasonic detection device which comprises an adjusting seat fixedly arranged on a bottom plate and a calcaneus bone mineral density instrument arranged on the bottom plate in a left-right sliding mode, a seat is arranged on the adjusting seat, and the seat can be adjusted in the inclination direction of a leg placing plate in the calcaneus bone mineral density instrument. The front end of the seat face of the seat extends towards the calcaneus bone densitometer to form a supporting plate, and the supporting plate and the leg placing plate are parallel to each other and are arranged in an aligned mode. The bone mineral density ultrasonic detection device is convenient to use, the shanks can be just naturally attached to the supporting plate by adjusting the front-back position of a detected person on the seat surface, and due to the fact that the supporting plate is just aligned with a leg placing plate on the calcaneus bone mineral density instrument, the soles of the detected person can be tightly attached to a foot placing plate by adjusting the height of the seat, and the detection accuracy is improved. Therefore, the feet of the detector can be stably placed, and the problem that the detection result is inaccurate due to the fact that the detector exerts improper force and the feet are placed incorrectly is solved.

The invention discloses a data processing method and device, electronic equipment, medical equipment and a storage medium, and the method comprises the steps: obtaining a first brain magnetic resonance image set of a first type of object and a second brain magnetic resonance image set of a second type of object, imaging parameters of the first brain magnetic resonance image and imaging parameters of the second brain magnetic resonance image are the same, and bone mineral density value change degrees of the first type object and the second type object are different; determining candidate brain partitions according to the volume parameter difference of the first brain magnetic resonance image set and the second brain magnetic resonance image set in the same brain partition; and for each candidate brain partition, performing correlation analysis on the basis of the grey matter volume parameter, the associated bone mineral density value and the scale data of the candidate brain partition, and determining the target brain partition associated with the bone mineral density change according to the correlation analysis result, so that the correlation analysis of the bone mineral density change and the brain partition is realized, and the correlation relationship between the bone mineral density change and the brain partition is accurately determined.

The invention discloses a machine learning model construction method for predicting lumbar vertebra bone mineral density based on a CT image. The method comprises the following steps: step 1, image acquisition: collecting images and data including lumbar vertebra CT of a patient; step 2, image segmentation: on the cross section of each CT image, using ITK-SNAP software to carry out ROI segmentation on the cancellous bone part of the vertebral body so as to ensure that the construction of the model can reflect a cancellous bone part result which can best represent the real bone mineral density; step 3, performing deep learning by using DenseNet: realizing training and testing of a Densenet model on the basis of a Pytorch framework; and 4, performing verification by using the trained model: after the training is completed, evaluating the performance of the trained model by using a verification data set. According to the method, the patients in the training set and the test set are strictly screened, influence factors possibly causing inaccurate bone mineral density results are eliminated, results more conforming to actual bone mineral density values of vertebral bodies can be given, and the method has more clinical significance on lumbar surgery and bone mineral density treatment.

The utility model discloses an arm positioning device of a bone mineral density detector, which belongs to the technical field of medical devices and comprises a detection bin, a supporting mechanism is arranged in the detection bin, a groove is arranged on the upper end face of the supporting mechanism, and an arm can be placed in the groove. A positioning bulge is also arranged in the groove; the supporting mechanism is in sliding connection with the detection bin, so that the supporting mechanism can be moved out of the detection bin. According to the arm positioning device of the bone mineral density detector, the positioning device can quickly position the position of the arm, so that the detection efficiency and the experience feeling of a detected person are improved.

To accurately predict a locomotorium disease in an estimation device, method, and program.SOLUTION: An estimation device includes at least one processor. The processor functions as a learned neural network for deriving an estimation result related to bone density of a bone part from a plain radiation image acquired by executing plain radiography of a subject including the bone part. The learned neural network is learned by using, as teacher data, (i) two radiation images acquired by imaging the subject including the bone part by radioactive rays with different energy distribution, (ii) the radiation image of the subject and a bone image showing the bone part of the subject, or (iii) the radiation image of the subject and a bone density image indicating the bone density of the bone part of the subject, and information related to the bone density of the bone part of the subject.SELECTED DRAWING: Figure 3

The present invention relates to a continuous identity authentication method based on sliding vibration signals, and belongs to the field of mobile computing application technology. When the touch screen detects the sliding action of the human body, the vibration motor generates a specific vibration signal, and the accelerometer receives a unique vibration response affected by the sliding of the finger. Since each person's fingers have unique characteristics such as shape, size, bone density and muscle distribution, a vibration response with unique individual differences will be generated. This method designs a new vibration signal generation mechanism, including a combination of two different types of vibration signals and the addition of random frequency components. The present invention extracts different user biometrics from the two received signals, and designs a neural network to eliminate the influence of behavioral characteristics in the user's biometrics. Finally, continuous user identity authentication is achieved by utilizing biometrics that are unrelated to behavior. The present invention has low cost, strong anti-interference ability, high security, and is suitable for most relevant scenarios.