42 results about "Delayed bone maturation" patented technology

The invention belongs to the technical field of medical image processing, and particularly relates to a left-hand X-ray image double-stage bone age evaluation system based on cross-modal learning. The system comprises a data preparation module, a coarse granularity classification module, a fine granularity regression module and an output module. The method comprises the following steps: firstly, preprocessing a hand X-ray image, and constructing cross-modal feature input in combination with structured text description of a standard bone age map; through an SBA-CLIP + cross-modal learning model, alignment and fusion of image features and text features are realized, and coarse-grained classification of bone age intervals is completed; and according to a classification result, dynamically calling a fine classification sub-model of a corresponding interval, and realizing fine regression prediction of the bone age. In the process, a bimodal attention alignment module is introduced to enhance internal feature representation of images and texts, and cross-modal alignment and classification are improved in combination with a comparison loss function guided by cyclic consistency. According to the method, the prediction precision and interpretability are remarkably improved, and the method has wide application prospects and clinical value.

The application discloses a bone age prediction method and system based on a multi-modal large model and a computer readable storage medium, belongs to the technical field of artificial intelligence and medical image processing, and comprises the following steps: fusing a left-hand DR image and clinical indexes through an end-to-end mode, constructing a multi-modal large model fine-tuned according to a bone age field, and outputting image-observed description of each bone in a left-hand wrist and bone-by-bone structured results of maturity levels; calculating a total bone age value through a scoring rule of a bone age evaluation standard, and generating and outputting a bone age evaluation report containing the bone-by-bone image-observed description, the maturity levels and the total bone age value. The application solves the problems of insufficient utilization of clinical indexes, heavy report writing burden, easy hallucination of model output and insufficient compliance of bone age calculation in the prior art, improves the accuracy of bone age prediction, clinical compliance and evaluation efficiency, and can be widely applied to hospitals, primary medical institutions and children growth and development screening scenes.

The application relates to the technical fields of medical image processing and artificial intelligence, and provides a bone age evaluation method and system based on an AI model and a bone age instrument. The left wrist X-ray image of a subject detected in the bone age instrument is acquired, and the demographic information of the subject is recorded. The detected left wrist X-ray image is pretreated through gray standardization, noise filtering and a self-supervised learning model. The Vision Transformer is used to extract the distal radius epiphysis and metaphysis width ratio, the third metacarpal metaphysis length and the auxiliary ossification point maturity grading skeletal features, and the multi-modal data fusion is carried out with the demographic information. The multi-layer perception model is used to predict the fused multi-dimensional bone age related data, and a bone age evaluation report is output, so that the error in the bone age prediction of children and adolescents is effectively reduced, and the detection efficiency is improved.

The application provides a bone age evaluation method and device based on global and local feature cooperation, equipment and medium, relates to the field of image processing, and includes: establishing an initial evaluation model and training to obtain a target evaluation model; obtaining a bone image to be evaluated, and inputting the preprocessed bone image to the target evaluation model; performing feature extraction by using a first convolutional network to obtain global features; performing identification and cutting by using a pre-trained target detection model to obtain a plurality of sub-images containing ROI regions of preset categories; performing feature extraction on each sub-image by using a second convolutional network to obtain a plurality of local features; performing convolution and normalization on the global features and the local features to obtain global context local features; after fusing each local feature with the global context local features, connecting the global features and the local features, and processing through a fully connected layer, a bone age evaluation result is obtained, and the problem that there is no full-automatic bone age evaluation method for sufficiently mining data features is solved.

The present invention is a bone age assessment method executed by a bone age assessment device, which includes: (a) acquiring an X-ray image of the lateral side of the elbow; (b) dividing the olecranon region from the X-ray image; (c) analyzing the morphological characteristics of the olecranon ossification center in the divided olecranon region; and (d) determining bone age based on the analyzed morphological characteristics.

The utility model relates to the technical field of medical instruments and the like, and provides a portable bone age instrument which is provided with a machine body which comprises an X-ray detection box module, a machine body shell and a machine body upper cover. The machine body upper cover is assembled at the upper end of the machine body shell, and the X-ray detection box module is assembled in the machine body shell; a shell inlet is formed in the side wall of the upper end of the machine body shell, a detection opening and a detection channel are formed in the upper end of the X-ray detection box module, and the shell inlet, the detection opening and the detection channel are communicated; a main control module is arranged to control the X-ray detection box module to detect the bone age of the hand in the detection channel and obtain detection data for analysis and processing; and a main support frame is arranged to support the X-ray detection box module and the machine body shell, so that measurement by a user is facilitated, the size of the bone age instrument is reduced, the structure of the bone age instrument is simplified, the assembly difficulty of the bone age instrument is reduced, and the carrying convenience of the bone age instrument is improved.

This invention discloses a bone age-assisted diagnostic system and method based on multimodal clinical data fusion and retrieval enhancement. The system includes acquiring X-ray images of the subject's hand and multimodal clinical data containing endocrine indicators; extracting visual feature vectors from the images using a deep learning model and encoding the clinical data into clinical semantic feature vectors; deeply fusing visual and clinical features through a multimodal cross-attention mechanism to generate diagnostic fusion features; predicting bone age values ​​based on the fusion features; retrieving standard atlas descriptions from a medical knowledge base using visual features and retrieving disease risk warnings using the fusion features; and finally inputting the predicted values, atlas descriptions, and risk warnings into a generative large language model to generate a natural language diagnostic report containing imaging evidence and clinical recommendations. This invention solves the problem of existing technologies relying solely on images and being unable to interpret the basis for judgment.

The invention relates to a deep learning and logic correction segmentation combined bone age assessment method, which aims at the problems of high joint extraction false drop rate, difficulty in epiphysis maturity grade classification and the like, through a logic correction segmentation mechanism of confidence ranking, a semi-supervised pseudo-label generation strategy and a global attention enhancement module based on normalization, the bone age is assessed. And the accuracy and clinical robustness of bone age evaluation are remarkably improved. The application scenarios of the method include but are not limited to the precise medical fields of children endocrine dysplasia screening, bone age lag or advance pathological diagnosis, orthopedic treatment scheme making, growth hormone intervention curative effect evaluation and the like.

A method for calculating a predicted adult height according to one embodiment of the present application includes generating a growth prediction model; and calculating a predicted adult height using the generated growth prediction model, wherein the generating the growth prediction model further includes acquiring input data and growth statistical data, including height information at the time of measurement, chronological age information at the time of measurement, bone age information at the time of measurement, and gender information; calculating an adult height based on the input data and the growth statistical data; and generating the growth prediction model based on the input data and the calculated adult height.

1. The name of the design product: micro-aging bone age instrument detection device. 2. The use of the design product: for human bone age determination. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view. 5. The bottom of the design product is not easy to see or not visible during use, omit the top view.

The invention provides a bone age AI prediction system based on MRI, and belongs to the technical field of medical image processing. The system comprises a data acquisition unit used for acquiring joint MRI image data; the data preprocessing unit is used for preprocessing the joint MRI image data; and the bone age prediction unit is used for performing bone age prediction according to the preprocessed joint MRI image data by utilizing the trained bone age prediction model and outputting a bone age differential diagnosis result. According to the system, MRI images are used for bone age prediction, radiation damage in the radiography process is avoided, meanwhile, MRI has the advantages that cartilage around epiphysis is accurately visualized, the contrast ratio is high, data are easy to store, the system is suitable for bone age prediction research, meanwhile, pure automatic prediction is adopted, human factor interference can be reduced, and accuracy is high.

The application provides a palate midline suture maturity prediction method based on deep learning, obtains a plurality of head lateral film image samples, respectively labels palate midline suture maturity information, and obtains a head lateral film image dataset; a palate midline suture maturity prediction model is constructed, the palate midline suture maturity prediction model comprises an image preprocessing module, a feature extraction module, a hybrid multi-scale attention convolutional neural network and a feature fusion module; the head lateral film image dataset is used to train the palate midline suture maturity prediction model, and a trained palate midline suture maturity prediction model is obtained; after a head lateral film image to be predicted is input into the trained palate midline suture maturity prediction model, a palate midline suture maturity prediction result is obtained; the application can reduce subjective differences of artificial interpretation, can improve the accuracy, consistency and reliability of bone age prediction, can realize non-invasive, objective and efficient palate midline suture maturity prediction, and provides a scientific basis for clinical expansion arch treatment decision-making.

The invention provides a training method of a bone age prediction model and a bone age prediction method and device. The invention relates to the technical field of computers, in particular to a bone age prediction model training method which comprises the steps that a plurality of first samples and a plurality of second samples are obtained, the first samples comprise hand bone image samples of human bodies in a first group, and the second samples comprise hand bone image samples of human bodies in a second group; the group scale of the first group is larger than that of the second group; and using the sequence knowledge of the relative ages contained in the plurality of first samples to assist the plurality of second samples to train the bone age prediction model to obtain a target bone age prediction model for the second group.

The invention relates to a method for three-dimensionally printing bone organs and application, belongs to the technical field of three-dimensional printing, and provides a material composition for three-dimensionally printing the bone organs, the material composition comprises bio-ink, osteoblasts and osteoclasts, and the number ratio of the osteoblasts to the osteoclasts is 1: (2-3.5). Meanwhile, the invention provides a method for carrying out three-dimensional printing on a bone organoid by utilizing the material composition and a method for preparing a bone aging organoid. The bone organoid provided by the invention provides an ideal experimental platform for pathogenesis research of degenerative bone diseases, can be used for bone tissue engineering research, can also provide an important experimental basis for drug screening, treatment strategy optimization and the like of degenerative diseases such as osteoarthritis and the like, and has remarkable scientific value and application potential.

This invention discloses a self-activated NO nanopump, its construction method, and its application. The self-activated NO nanopump comprises a chemiluminescent substrate CPPO and a NO donor BNN6 encapsulated in DSPE-PEG-Mal. The DSPE-PEG-Mal surface is conjugated with a LepR antibody. The self-activated NO nanopump of this invention can specifically target LepR... + It accumulates in cells and releases intracellular NO, thereby effectively reversing cellular aging and differentiation fate. It rescues bone aging by upregulating the glycolysis pathway. It possesses strong H2O2 scavenging capacity, NO production capacity, and anti-aging ability. In vivo administration significantly reverses osteoporosis in OVX mice. The self-activated NO nanopump provided by this invention offers a novel strategy for the treatment of bone aging.

The embodiment of the present application provides a bone maturity prediction method and device, the method comprises the following steps: obtaining target finger bone series data and target carpal bone series data corresponding to target hand bone image data respectively; inputting the target hand bone image data and the target finger bone series data into a preset finger bone maturity prediction model, so that the finger bone maturity prediction model outputs a finger bone maturity prediction value for bone age evaluation; and inputting the target hand bone image data and the target carpal bone series data into a preset carpal bone maturity prediction model, so that the carpal bone maturity prediction model outputs a carpal bone maturity prediction value for bone age evaluation. The present application can effectively improve the efficiency, automation and intelligence of the bone maturity prediction process, reduce the labor and time cost, and effectively improve the comprehensive coverage and accuracy of the bone maturity prediction result.

This invention belongs to the field of bone age assessment technology and discloses a bone age assessment method and system based on MRI and deep neural networks. The MRI- and deep neural network-based bone age assessment method includes: using deep learning algorithms to comprehensively analyze three weighted MRI images, constructing an automated, radiation-free bone age assessment model with multi-feature fusion, and using the bone age assessment model to automatically identify, extract, and fuse effective features from MRI three-dimensional images to obtain the bone age assessment result. This invention combines artificial intelligence with multimodal MRI three-dimensional information, fully leveraging the advantages of MRI—non-ionizing radiation, high resolution, and multi-layered structure—and maximizing the synergistic effects between different modalities. It is expected to discover effective features that are difficult for experts to notice, improve network feature learning, and enhance the accuracy and precision of bone age assessment. This will help realize an integrated configuration of green health examination, accurate bone age inference, and efficient automatic assessment in in vivo bone age identification practice, providing strong scientific evidence for court trials.

The invention discloses a bone age prediction method, device and equipment based on a deep learning model, and belongs to the technical field of medical image analysis, and the bone age prediction method comprises the steps: carrying out the preprocessing of an original hand bone image of a current user, and obtaining a target hand bone image; inputting the target hand bone image into a trained region cutting network to obtain a target segmentation region; inputting the target hand bone image marking the target segmentation region into a trained deep learning model, so as to enable the deep learning model to extract hand bone features of the current user from the target segmentation region; and predicting the bone features of the hand bones by using a regression model to obtain bone age prediction data of the current user. According to the method, accurate skeleton region segmentation is performed by using the segmentation network, and the bone age prediction is performed in combination with the deep learning model, so that the interference of background noise can be avoided while effective skeleton features are extracted, and the prediction accuracy and reliability are remarkably improved.

The invention provides a lifting type detection device based on bone age ultrasonic detection, and relates to the technical field of medical instruments. A group of moving frames are mounted on the base in a threaded manner, a group of moving arms are slidably mounted at the outer ends of the moving frames, a group of host machines are fixedly mounted at the tops of the outer ends of the moving frames, and limiting blocks at the outer ends of the bottoms of mounting seats are mounted on limiting grooves in the side ends of the ultrasonic detectors in an inserted manner; the device can realize height adjustment and angle fine adjustment, and an arc-shaped friction block is matched with a threaded rod to adjust the rotation tightness; the ultrasonic detector is quickly disassembled and assembled by utilizing a limiting block, a supporting rod and a second elastic piece; the movable arm is matched with the positioning groove through the guide block and the wedge-shaped block to realize position adjustment, so that the adjustability and the operation convenience of the device are integrally improved; the problems that the reliability of detection signals and results is influenced by inflexible adjustment, the accuracy comprehensiveness is reduced due to difficulty in adapting to various skeleton detection, the cost and time are increased due to tedious disassembly and maintenance of an ultrasonic detector, and the working continuity is influenced are solved.

The application discloses a coarse-to-fine bone age regression method based on multi-scale high-order self-attention, first extracts multi-scale features and multi-scale global self-attention response maps of the original hand bone image, fuses the latter and the down-sampling features of the original hand bone image to obtain fusion features, inputs the fusion features into a regression module, calculates an adaptive triad loss in the regression module to obtain a bone age regression value and an MSE loss for calculating the bone age, adjusts model parameters, trains the model on an original data set, then inputs the original data set into a backbone network, extracts high-dimensional representations of the image and inputs the high-dimensional representations into a K-means clustering algorithm, clusters the data set into multiple age stages, finally inputs the data in each age stage into multiple original backbone networks, trains multiple regression models, and obtains bone age prediction values in each age stage. The method can effectively perform left-hand bone regression detection of teenagers, and provides a general and efficient regression model and regression framework for a clinical computer bone age regression application scenario.

The invention relates to the technical field of medical imaging, in particular to an auxiliary positioning method and system for a dual-energy X-ray bone mineral density and bone age instrument. The method comprises the following steps: acquiring a real-time positioning image of a hand of a subject; dynamically adjusting the identification line template according to subject information to generate an individualized positioning identification line; image processing is conducted on the images collected in real time, whether the hand posture meets the preset standard or not is judged, the coincidence degree is judged, and if the coincidence degree is too low, an instruction is generated to conduct posture correction guiding; the positioning image and the individualized positioning identification line are displayed in a real-time overlapping mode; according to the generated prompt instruction, voice and character prompt is carried out, and the subject is guided to gradually adjust the hand position until the positioning requirement is met. According to the method, the individual hand structure difference is fully considered, the positioning mark line template is adaptively zoomed through the wrist width measurement result in the real-time image, and individualized matching of the template and the hand contour of the subject is achieved.

The invention discloses a children growth promoting preparation, which belongs to the technical field of traditional Chinese medicine preparations and comprises fructus alpiniae oxyphyllae, radix pseudostellariae, dried ginger, angelica sinensis, fructus amomi, radix inulae inula, pericarpium citri reticulatae, serissa serissoide, panax japonicus, rhizoma cibotii, radix scrophulariae and cattle bone powder. The preparation integrates the traditional Chinese medicine theory of tonifying spleen and kidney and regulating qi and blood, wherein the fructus alpiniae oxyphyllae, radix pseudostellariae, dried ginger and the like can tonify spleen and promote appetite, significantly improve the digestive function and improve the absorption rate of nutrient substances; the Tibet inula root and the fructus amomi regulate middle-jiao qi movement, relieve abdominal distension and qi stagnation and further optimize the absorption state; the panax japonicus, the rhizoma cibotii and the cattle bone powder directly act on a skeletal system to tonify kidney and strengthen bone, promote cartilage cell proliferation and bone longitudinal growth, and effectively improve height and growth speed. The formula gives consideration to intervention on growth disorder problems such as precocious puberty and the like, and through two-way regulation of nourishing yin and purging fire and tonifying kidney and replenishing essence, advanced bone age is delayed, and precious time is won for height increase.

The invention provides a children bone age comprehensive evaluation method based on deep learning. According to the technical scheme, the method comprises the steps that S1, a bone evaluation map is obtained according to the actual age of a target child; s2, obtaining a skeleton monitoring factor according to the skeleton image data of the target child; s3, inputting the skeleton monitoring factor of the target child into the skeleton evaluation map, and analyzing to obtain an abnormal monitoring factor and an abnormal factor parameter of the target child; s4, analyzing to obtain the abnormally influenced bone age of the target child and the evaluated bone age of the child; s5, transmitting the abnormal monitoring factor to an abnormal factor area, and analyzing to obtain a target feature factor, a target abnormal diagnosis factor and a bone age assessment symptom of the target child; and S6, transmitting the evaluated bone age of the child and the bone age evaluation symptom to an evaluation result area, and obtaining a comprehensive bone age evaluation result of the target child according to the evaluation result area. According to the invention, comprehensiveness and accuracy of children bone age evaluation results are improved.

The invention belongs to the field of bone age prediction, and particularly relates to an automatic bone age prediction method based on multi-modal feature matching. A hand bone X-ray image is obtained, and an enhanced hand region image is obtained through preprocessing; inputting the hand region into a segmentation network to obtain a plurality of local feature regions, and adding text tags according to the regions; corresponding image and text feature vectors are obtained through a feature matching network, and the weight of a feature extraction network is reserved; a targeted loss function is added in the training process; and finally performing fine tuning to obtain an automatic bone age prediction model. According to the method, the text labels are selected to carry out auxiliary feature matching, so that the feature positioning capability of the model on different local areas is improved. Through a cyclic feature matching mode, the generalization of the model on different data sets is improved, the effect of accurate prediction on different data sets through one model is achieved, the training overhead is saved, and the availability of the model is improved.

The application discloses a bone age prediction method, which comprises the following steps: pre-training a bone age prediction model through a preset training set, and using the bone age prediction model to predict the bone age. The technical scheme adds an attention mechanism according to the coupling degree of different convolution layer information in the bone age prediction model, improves the problem that the traditional attention mechanism is not easy to converge, and directly adds the gender information into the neural network model for end-to-end training, thereby optimizing the training process and further improving the accuracy of the bone age prediction. The bone age prediction model used in the bone age prediction method is verified through the attention mechanism, and the model pays more attention to the metacarpal part of the patient, thereby having a good prediction effect.

The invention discloses nutritional composite powder for promoting growth and bone age balance. The milk powder is prepared from the following raw materials in parts by weight: 30-50 parts of modified milk powder, 15-25 parts of whey protein concentrate, 5-10 parts of compound minerals, 0.3-0.7 part of gamma-aminobutyric acid, 0.1-0.3 part of L-lysine, 0.4-0.6 part of bone collagen peptide, 2-5 parts of hydrolyzed yolk powder, 1-3 parts of yolk globulin, 3-6 parts of milk minerals, 2-4 parts of yolk phospholipoprotein, 1-3 parts of compound vitamins and 1-2 parts of citrus fibers. The health food is prepared from 1-2 parts of poria cocos powder, 1-2 parts of Chinese yam powder, 0.5-1 part of endothelium corneum gigeriae galli powder, 0.3-0.5 part of taurine, 1-3 parts of lactic acid bacteria compound powder, 1-2 parts of inulin and 0.1-0.3 part of fructus momordicae powder. The invention belongs to the technical field of nutritional food, and particularly provides nutritional composite powder capable of promoting growth and bone age balance, which is comprehensive in nutritional ingredients, scientific in proportion, capable of effectively supplementing various nutrients required by growth and development of teenagers, capable of remarkably promoting skeletal development, capable of improving body immunity and capable of providing comprehensive nutritional support for healthy growth of the teenagers.

A method for calculating an expected adult height, according to an embodiment of the present application, comprises the steps of: generating a growth prediction model; and calculating an expected adult height by using the generated growth prediction model, wherein the step of generating the growth prediction model further comprises the steps of: acquiring growth statistics data and input data including height information at a point in time of measurement, chronological age information at the point in time of measurement, bone age information at the point in time of measurement, and gender information; calculating the adult height on the basis of the input data and the growth statistics data; and generating the growth prediction model on the basis of the input data and the calculated adult height.

The application discloses a kind of pediatric growth promoting preparation, belong to the technical field of traditional Chinese medicine preparation, including Fructus Schisandrae, radix pseudoginseng, dried ginger, angelica, amomum villosum, incense, dried tangerine or orange peel, June snow, Chir Chir, dog spine, and cow bone powder.The preparation of the present application combines the theory of traditional Chinese medicine for invigorating the spleen and kidney, regulating qi and blood, wherein Fructus Schisandrae, radix pseudoginseng, dried ginger and the like can invigorate the spleen and stimulate appetite, significantly improve the digestive function and improve the absorption rate of nutritional substances;Amomum villosum and amomum villosum regulate the middle-jiao qi movement, relieve abdominal distension and stagnation of qi, and further optimize the absorption state;Chir Chir, dog spine and cow bone powder directly act on the skeletal system, tonify the kidney and strengthen the bone, promote chondrocyte proliferation and longitudinal growth of the skeleton, and effectively improve the height growth speed.The formula takes into account the intervention of growth disorders such as precocious puberty, delays the bone age through the bidirectional regulation of nourishing yin and purging fire and tonifying the kidney and filling essence, and gains valuable time for height growth.

The utility model discloses a convenient bone age detector, and relates to the technical field of detection equipment, and the convenient bone age detector comprises a housing, a detector, an all-in-one computer and a printer. The shell is provided with a detection port; the detector is installed in the shell, and the detection port is connected with the detector and used for detecting the bone age. The all-in-one computer is installed on the side wall of the shell and used for controlling the detector to conduct bone age detection. And the printer is arranged in the shell, is positioned below the detector, is connected with the all-in-one computer and is used for printing bone age detection images. The bone age detector integrates the detector, the all-in-one computer and the printer, so that the whole detection process can be completed in one device. The integrated design greatly simplifies the operation process, and a user does not need to switch among a plurality of devices, so that the detection efficiency is improved. In addition, the instant detection function of the detector further improves the convenience of the detector, so that a user can quickly obtain a detection result.

A method and system for constructing a bone aging degree prediction model, a device and a medium. The method comprises: acquiring CT images of target groups; dividing each CT image into a background region and regions of interest; on the basis of grayscale values, performing region subdivision on the regions of interest on each CT image, so as to obtain a plurality of subdivided regions in the regions of interest, and obtain the area proportion of each subdivided region in the regions of interest; and, on the basis of the CT images of the target groups and the area proportion of each subdivided region in the regions of interest on each CT image, and by means of a deep learning model framework, training to obtain a bone aging degree prediction model. The method can intelligently and more accurately predict the degrees of osteoporosis of people under test and whether they suffer from osteoporosis.