55 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 invention relates to the field of food materials, and particularly discloses a gamma-aminobutyric acid and hydrolyzed egg yolk powder composite beverage capable of promoting sleep and inhibiting bone age and a preparation method of the gamma-aminobutyric acid and hydrolyzed egg yolk powder composite beverage. A gamma-aminobutyric acid and hydrolyzed egg yolk powder composite beverage capable of promoting sleep and inhibiting bone age comprises the following raw materials in percentage: 3-5% of xylitol, 3-5% of fructo-oligosaccharide, 2-4% of gamma-aminobutyric acid, 3-9% of antioxidant composite concentrated juice, 0.16-1% of purple carrot juice beverage thick slurry, 0.1-0.19% of calcium aspartate, 0.02-0.05% of casein hydrolyzed peptide, 0.02-0.05% of hydrolyzed egg yolk powder, 0.02-0.05% of casein phosphopeptide and the balance of water. The formula comprises the following components in percentage by weight: 0.006 to 0.01 percent of zinc gluconate, 0.0005 to 0.00085 percent of zinc-rich edible yeast powder, 0.0005 to 0.00085 percent of tea theanine, 0.0005 to 0.00085 percent of N-acetylneuraminic acid, 0.0018 to 0.09 percent of a traditional Chinese medicine composition, 0.005 to 0.01 percent of L-calcium lactate, 0.0005 to 0.01 percent of collagen peptide, 0.1 to 0.3 percent of xanthan gum, 0.0005 to 0.00085 percent of melissa officinalis, 0.2 to 0.98 percent of a compound flavoring agent and the balance of purified water. Through the synergistic effect of all the components, the sleep quality is effectively improved, overactivity of bone cells is inhibited, and the bone age development is delayed.

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 invention relates to the technical field of image processing, in particular to a metacarpal and phalanx image processing method and device. The method comprises the following steps: acquiring an initial metacarpal and phalanx image, and performing brightness processing on the initial metacarpal and phalanx image to obtain a first metacarpal and phalanx image; performing contrast processing on the first metacarpal and phalanx image to obtain a second metacarpal and phalanx image; constructing a preset convolution kernel, and performing definition processing on the second metacarpal and phalanx image based on the preset convolution kernel to obtain a metacarpal and phalanx target image; and inputting the metacarpal and phalanx target image into the bone age recognition model, and outputting a bone age recognition result. According to the method, dynamic image enhancement optimization can be carried out on metacarpal and phalanx X-ray images of different devices and different qualities through brightness, contrast and definition processing, so that the processed metacarpal and phalanx target images can highlight morphological characteristics of epiphysis, the bone age recognition accuracy is improved, and the bone age recognition efficiency is improved. The problem of low bone age recognition accuracy caused by different image qualities in the prior art is solved.

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.

The present invention discloses a real-time monitoring system for teenagers' physical exercise, which relates to the field of intelligent monitoring technology. Through the spatiotemporal alignment processing of joint three-dimensional motion, electromyographic signals, and plantar pressure and the dynamic matching technology of bone age, an individualized health assessment model is constructed. Timing deviation is eliminated through unified clock marking and interpolation compensation, and parameters such as bone stress deviation, muscle synergy asymmetry, and plantar pressure imbalance are extracted, and weighted comprehensive health indicators are generated. Combined with a preset training path model, the health indicator trend is analyzed in real time and hierarchical adjustment instructions are triggered, the training intensity, interval and action plan are dynamically optimized, and the path model parameters are corrected through a closed-loop feedback mechanism to achieve adaptive updates. It solves the defects of large fusion deviation and strong lag of static evaluation of traditional teenagers' physical fitness monitoring data, realizes real-time and accurate monitoring of teenagers' physical exercise and personalized training optimization, and effectively reduces the risk of sports injuries.

The invention belongs to the field of medical image processing and artificial intelligence, and particularly relates to a construction method and application of a bone age evaluation model based on label distribution learning, and the method comprises the steps: constructing a bone age evaluation model which comprises a sub-model A, K sub-models B, a selection unit and a convolutional network C; k corresponds to different age groups; training the sub-model A by adopting the training sample set, and training the model B of the corresponding age group by adopting the training sample set of each age group; the model A obtains predicted bone age label probability distribution A according to each input image; the selection unit is used for substituting the distribution A into a normal probability density function with the real bone age of the input image as the center, calculating a normal distribution probability value, selecting a sub-model B of an age group corresponding to the age of the input image, and predicting again according to the input image to obtain predicted bone age label probability distribution B; and the convolutional network C is used for integrating the distribution A and the distribution B to generate final bone age label probability distribution. The accuracy of bone age evaluation can be 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 discloses a bone age detection device and method, and relates to the technical field of bone age detection.The device comprises a solid sound conducting pad bracelet, an ultrasonic function module and a forearm fixing and position adjusting module; the ultrasonic function module comprises an ultrasonic image processing system and two ultrasonic probes; the ultrasonic image processing system is used for obtaining the maximum value of the transverse diameter of the ossification center and the maximum value of the transverse diameter of the epiphysis according to the ultrasonic data; according to the maximum value of the transverse diameter of the ossification center and the maximum value of the transverse diameter of the epiphysis, the ultrasonic epiphysis ossification rate is calculated; and comparing the ultrasonic epiphyseal ossification rate with the standard characteristic data to obtain the age corresponding to the ultrasonic epiphyseal ossification rate. According to the method, the bone age is detected in an ultrasonic mode, so that radioactive X-rays are prevented from being used, meanwhile, the calculated ultrasonic epiphyseal ossification rate is compared with the standard characteristic data, the corresponding age is obtained, and the objectivity and accuracy of bone age detection are guaranteed.

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 present invention proposes a method and system for intelligent bone age assessment based on feature point detection. The method includes: preprocessing acquired hand X-ray images; inputting the preprocessed images into an epiphyseal detection model to detect bone key points in the images; the epiphyseal detection model includes: a local network and a global network; centered on the bone key points, the preprocessed images are cut with a fixed length and width to obtain epiphyseal ROI regions; the epiphyseal ROI regions are input into a classification model to obtain corresponding bone categories and developmental levels; and according to a curve chart, the final bone age assessment result is obtained based on the category and developmental level corresponding to each bone. The present invention utilizes a positioning network plus a classification network to replace the target detection network, greatly reducing the amount of data required for training. Furthermore, the data for training the positioning network does not require detailed annotation by professionals, saving annotation costs. The prediction accuracy and time required are basically the same as those of a target detection network trained with large amounts of data.

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 invention relates to a bone age prediction method and device, equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring skeleton input data, and preprocessing the skeleton input data to obtain preprocessed skeleton input data; detecting all joints in the preprocessed skeleton input data through a target detection algorithm to obtain a key skeleton region detection result; target joint screening is carried out according to the key skeleton area detection result, and key skeleton joint screening features are obtained; and according to the key bone joint screening features, performing bone age grade prediction through a pre-trained deep neural network model, and outputting a bone age prediction result. By adopting the method, the bone age evaluation efficiency and the bone age evaluation accuracy can be improved.

The invention relates to a bone age evaluation method by a bone age evaluation device. The bone age evaluation method comprises the following steps: (a) acquiring an X-ray image of the side surface of an elbow; (b) segmenting an olecranon region from the X-ray image; (c) analyzing the morphological characteristics of the olecranon ossification center in the segmented olecranon ossification area; and (d) determining the bone age based on the analyzed morphological characteristics.

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 utility model belongs to the technical field of medical instruments, and particularly relates to a digital X-ray bone age instrument for bone age detection, which comprises an outer protective box body, an X-ray generating component and a flat panel detector, an inner cavity of the outer protective box body is divided into a shooting space and an equipment mounting space, an extending opening for a hand to extend into is arranged on the outer protective box body, and the X-ray generating component is arranged on the outer protective box body. The outer side face of the outer protection box body is further provided with an extending opening, the extending opening is communicated with the shooting space in the inner cavity of the outer protection box body, the outer side face of the outer protection box body is further provided with an adjusting opening, the adjusting opening is communicated with the shooting space in the inner cavity of the outer protection box body, and the outer protection box body is further provided with an observation window. According to the utility model, the observation window and the adjusting port are arranged on the outer protective box body, so that a tested person can adjust the position and posture of a palm by himself / herself conveniently, other adults can stretch hands into a shooting space through the adjusting port conveniently, an infant whose palm is difficult to adjust to the correct position and posture by himself can be directly assisted to adjust, the use is more convenient, and the working efficiency is improved. And the use efficiency can be 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.