94 results about "Delayed bone maturation" patented technology

The invention discloses a teenager physical exercise real-time monitoring system, and relates to the technical field of intelligent monitoring. An individualized health assessment model is constructed through space-time alignment processing of joint three-dimensional movement, electromyographic signals and plantar pressure and a bone age dynamic matching technology. Time sequence deviation is eliminated through unified clock marking and interpolation compensation, parameters such as bone stress deviation degree, muscle synergy asymmetry degree and plantar pressure unbalance degree are extracted, and comprehensive health indexes are generated through weighting. And in combination with a preset training path model, analyzing a health index trend in real time and triggering a grading adjustment instruction, dynamically optimizing training intensity, interval and action schemes, and correcting path model parameters through a closed-loop feedback mechanism to realize adaptive updating. The defects that traditional teenager physique monitoring data fusion is large in deviation and static evaluation hysteresis is high are overcome, real-time accurate monitoring and personalized training optimization of teenager physique exercise are achieved, and the sport injury risk is effectively reduced.

The invention provides a cervical vertebra segmentation and key point detection method based on a diffusion model, and relates to the technical field of medical image processing, and the method carries out the denoising processing of a cervical vertebra image through the diffusion model, improves the image quality, and lays a foundation for the subsequent segmentation and key point detection. A cervical vertebra region and key point positions can be accurately extracted by combining a model of a deep learning technology, and reliable data support is provided for bone age staging; the bone age staging based on the results can objectively and quantitatively evaluate the growth and development degree of a patient and assist a doctor in making a personalized orthodontic treatment scheme, and in addition, in combination with a stress distribution diagram generated by a biomechanical model and a self-adaptive segmentation model of a preprocessed image, a segmentation strategy can be adjusted according to individual differences, so that the accuracy of bone age staging is improved. The precision and the robustness are improved; meanwhile, parameters such as blood flow velocity and temperature distribution are incorporated into a machine learning model, new indexes are provided for cervical vertebra bone age staging, and the individuation and accuracy of evaluation are enhanced.

The invention relates to the technical field of medical image processing, artificial intelligence and the like, and provides a bone age assessment method and system based on an AI model and a bone age instrument. A detected left wrist X-ray image is preprocessed through gray scale standardization, noise filtering and a self-supervised learning model, the ratio of the width of the distal radius epiphysis to the width of the metaphysis, the length of the metaphysis of the third palm bone and graded skeleton features of the maturity of auxiliary ossification points are extracted through a Vision Transform, and multi-modal data fusion is conducted on the extracted skeleton features and demographic information. And a multi-layer perceptron model is adopted to predict the fused multi-dimensional bone age related data, and a bone age evaluation report is output, so that errors in bone age prediction of children and teenagers are effectively reduced, and the detection efficiency is improved.

The invention relates to a cervical vertebra bone age staging identification method. The method comprises the following steps: S1, acquiring a skull side film of a cervical vertebra bone age staging object; s2, inputting the skull side position film into a cervical vertebra region target detection model to remove irrelevant backgrounds, and only retaining target regions containing C2, C3 and C4 joints; s3, inputting the area detected in the S2 into an instance segmentation model to segment C2, C3 and C4 joints, and labeling category labels; s4, inputting the area detected in the S2 into a mark point detection model to extract mark points with detail information at the edges of the bone joints C2, C3 and C4; and S5, inputting the features extracted in S3 and S4 into a cervical vertebra staging estimation module to obtain a final staging result. Compared with the prior art, the method has the advantages that the traditional manual skull side position film evaluation is converted into automatic detection, segmentation and staging tasks by using deep learning, so that the diagnosis efficiency is greatly improved, the staging accuracy is improved, and a doctor is effectively assisted in making an orthodontic plan.

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 discloses a bone age evaluation method based on phalanx and carpal bone adaptive relation learning, and belongs to the technical field of computer vision and image processing, and the method comprises the steps: S1, constructing a bone age regression model which comprises a target detection module, a feature extraction module and a regression module; s2, extracting a middle finger region and a carpal region of a target skeleton through the trained target detection module; s3, respectively extracting bone age features of the target bones through a feature extraction module, and splicing the bone age features into a bone feature map according to channels; and S4, adaptively learning the relationship among the skeleton features in the skeleton feature map through a regression module, and evaluating the bone age. According to the model architecture provided by the invention, development characteristics of bones such as phalanges and carpal bones in different age groups are adaptively learned by applying multiple attention mechanisms, the interpretability of the model is greatly improved, a more accurate bone age evaluation result is obtained, and the bone age evaluation result is closer to clinical medical experience and is more suitable for clinical application. The performance of the model is superior to that of a model using global images for learning and regression.

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 present application provides a bone age prediction method, device, electronic device and storage medium, which belongs to the field of artificial intelligence technology. The method includes: obtaining a target bone image; performing key point detection on the target bone image through a first key point detection model to obtain a first target bone key point; performing a first posture correction process on the target bone image according to the first target bone key point to obtain a standard bone image; performing a segmentation process on the standard bone image through an image segmentation model to obtain a preliminary bone foreground map; performing key point detection on the standard bone image through a second key point detection model to obtain a second target bone key point; performing a second posture correction process on the preliminary bone foreground map according to the second target bone key point to obtain a standard bone foreground map; inputting the standard bone foreground map into the bone age prediction model to perform bone age prediction, and obtaining bone age prediction data. The present application can improve the accuracy of bone age prediction.

The utility model discloses an accurate bone age ruler, which comprises a film viewer panel, the outer wall of the film viewer panel is slidably sleeved with a U-shaped slide fastener, the outer wall of the U-shaped slide fastener is provided with a circular groove, the inner wall of the circular groove is fixedly connected with a threaded column, and the outer wall of the threaded column is rotatably connected with a circular block. According to the utility model, the U-shaped slide fastener, the threaded column, the round block, the rubber pad, the rectangular block, the first ruler, the T-shaped long groove, the first scale line, the T-shaped column, the second ruler, the second scale line, the locking cap, the fastening cap and the limiting block are arranged, so that the first ruler and the second ruler are placed on the film viewer panel, and the first ruler and the second ruler cannot rotate when not subjected to external force through friction force; the first ruler is used for measuring the lengths of the phalanx, the radialis and the ulna, the second ruler is used for measuring the widths of the phalanx, the radialis and the ulna, multiple times of taking and calibration are not needed under the condition that the measuring precision is not affected, and standard reference and recording are facilitated.

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 invention discloses a method for constructing a bone age detection model, which comprises the following steps of: inputting an X-ray film image in a training set into a bone age detection model to obtain a corresponding predicted bone age, and training the bone age detection model by minimizing the difference loss between the predicted bone age and a corresponding bone age label, wherein the bone age detection model comprises a global model, local models in one-to-one correspondence with k bone age segments and a fusion module, and when an X-ray film image is input into the bone age detection model, an X-ray film obtains first probability distribution and second probability distribution of the bone age through the global model and the corresponding local models, the first probability distribution and the second probability distribution are input into a fusion module, third probability distribution is obtained, the corresponding predicted bone age is obtained according to the third probability distribution, the X-ray image in the training set is an image containing bone age information, and the corresponding label is the bone age. According to the method, the bone age prediction precision is improved by considering the global features of the bone age and the specific features of the bone age.

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 embodiment of the present invention provides a bone age assessment method, comprising: obtaining a skeletal map and a voiceprint sequence of a target person; extracting features from the skeletal map through a preset image feature extraction network to obtain skeletal features of the skeletal map; extracting features from the voiceprint sequence through a preset voiceprint feature extraction network to obtain voiceprint features of the voiceprint sequence; fusing skeletal features with voiceprint features through a preset first feature fusion network to obtain fusion features of skeletal features and voiceprint features; classifying the fused features for bone age through a preset classification network, and assessing the target person's bone age based on the classification results of the bone age classification to obtain a bone age assessment result for the target person. The method can detect the age corresponding to the bones in the skeletal map, and because voiceprints are added as auxiliary detection, the accuracy of bone age assessment is improved, and there is no need for doctors to manually observe the skeletal map and interpret the skeletal image, thereby improving the efficiency of bone age assessment.

The present invention relates to a method for predicting bone age of children, comprising the following steps: randomly selecting a part of hand X-rays from a public data set of hand X-rays with bone age labels to form a data set, and adjusting all pictures to a specified size; establishing a TENet model and constructing a training set at the same time; using the training set as the input of the TENet model, using the Adam optimizer to train the TENet model, and obtaining a trained TENet model when the maximum number of iterations is reached. The model of the present invention can accurately predict the bone age of children.

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.

The invention belongs to the technical field of medicines, and particularly relates to application of lycium barbarum glycopeptide in preparation of a medicine for preventing or treating senile ossification. The experimental results show that the wolfberry glycopeptide can improve the posterior limb movement ability and the bone trabecula condition of old mice by increasing the femoral hardness, relieving the bone tissue calcium loss condition, improving the bone formation rate, promoting osteoblasts and inhibiting osteoclasts, so that the effect of treating bone aging is achieved. As a traditional Chinese medicinal material extraction product, the lycium barbarum glycopeptide can be effectively used for preventing and treating senile ossification, has the prospect of being developed into the medicine for preventing and treating senile ossification, provides more treatment choices for clinically preventing or treating senile ossification, and has important social benefits and economic values.

The invention provides bone age detection equipment and a bone age measurement method. The bone age detection equipment comprises a host, a detection base, an ultrasonic image transducer and a deformable water bag, the deformable water bag is arranged at an acoustic window opening of the ultrasonic image transducer, degassing water is arranged in the deformable water bag, and the ultrasonic image transducer is immersed in the degassing water; the host receives an operation instruction of an operator, controls the ultrasonic image transducer to acquire an ultrasonic image of a to-be-detected part according to the operation instruction, sequentially performs coarse positioning and fine positioning on the acquired ultrasonic image of the to-be-detected part, screens out a standard section ultrasonic image of the to-be-detected part, and transmits the standard section ultrasonic image to the ultrasonic image transducer; the bone maturity degree is calculated according to the standard section ultrasonic image of the to-be-measured part, the standard section ultrasonic image of the to-be-measured part can be rapidly and accurately positioned, dependence of manual operation is reduced, errors of the manual operation are avoided, and the speed and precision of ultrasonic bone age evaluation are improved.

The present invention discloses a positioning device and a detection method for collecting bone age images of children, belonging to the technical field of bone age detection, specifically including: setting a standard inspection period, collecting bone age images of children every period and obtaining inspection records written by medical staff; these records are then input into a preset BERT model to extract bone age feature keywords; an auxiliary detection model is constructed using a convolutional neural network and a recurrent neural network and trained through a labeled historical data set; the child's age and keywords are converted into vectors and input into the trained auxiliary inspection model, and the bone age difference between the child's actual age and bone age is output; every n inspection periods, the system will statistically analyze the bone age difference sequence of the child, calculate the inspection period adjustment coefficient according to the index and through a specific formula, and then adjust the number of days of the next n inspection periods; the present invention improves the popularization rate and effectiveness of bone age detection.

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 utility model provides a bone age instrument detection structure, which comprises a support frame used for providing fixed support; the supporting plate is arranged at the upper end of the supporting frame; the bottom plate is arranged at the lower end of the supporting frame; the ray generator is arranged on the upper surface of the supporting plate and emits detection rays towards the bottom plate; the detector is arranged on the upper surface of the bottom plate, and the detector and the ray generator are oppositely arranged; and the channel assembly is arranged above the detector, the channel assembly is provided with a channel window for an object to be detected to enter and exit, and a positioning assembly for assisting in positioning the detection position is arranged in the channel assembly. The bone age instrument detection structure has the advantages of being convenient to position and detect and good in shielding protection.

The present invention provides a bone age assessment method, device and computer-readable storage medium based on a deep convolutional neural network. The bone age assessment method includes: collecting wrist bone X-ray films marked with bone age information, constructing a first deep convolutional neural network based on the UNet algorithm to perform image segmentation on the wrist bone X-ray films to obtain a preprocessed image set, and selecting some images as training set images, and the remaining images as test set images; constructing a second deep convolutional neural network based on the DenseNet algorithm, and inputting the training set images to train the parameters of the second deep convolutional neural network, thereby obtaining a trained second deep convolutional neural network, and the trained second deep convolutional neural network has a learned network weight parameter; inputting the test set image into the trained second deep convolutional neural network, and outputting the bone age assessment result after the trained second deep convolutional neural network is iterated. After adopting the above technical solution, the bone age assessment result can meet the requirements of clinical application.

The invention provides a forensic medicine pelvic adult bone age estimation method based on enhanced OfficientNetV2. The forensic medicine pelvic adult bone age estimation method comprises the following steps: firstly, acquiring MSCT image data of a pelvis; performing 3D reconstruction on the MSCT image data to obtain 3D image data; and finally, determining a bone age estimation result of the forensic pelvic adult according to the preprocessed and data enhanced MSCT image data, the 3D image data and the enhanced OfficientNetV2 model. According to the enhanced OfficientNetV2 model, a linear layer and a nonlinear activation function are added on the basis of an OfficientNetV2 model, so that an output value of the model is mapped into a predicted value, and the predicted value is controlled within a specified range. According to the pelvic adult bone age estimation method based on the deep learning, an original OfficientNetV2 model is improved, a linear layer and a nonlinear activation function are added to map an output value of the model into a predicted value and control the predicted value in a specified range, exploratory application of deep learning in the aspect of forensic pelvic adult bone age estimation is achieved, the influence of subjective factors is effectively avoided, and the accuracy of an automatic recognition result is improved.

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.

The present invention discloses a method and system for evaluating children's bone age with autonomous learning ability. First, the YOLOv5 model is used for coarse-grained classification to locate the positions and categories of 13 bone regions from the original image, and the 13 bone regions are cropped out from the original image. Then, the CAP model is used for fine-grained classification to predict the bone age grade of each bone region. Finally, the bone age is calculated according to the Chinese 05 bone age scoring standard. The system includes two deep neural networks, namely the object detection model YOLOv5 for coarse-grained classification and the context-aware attention pooling model CAP for fine-grained classification, which perform data preprocessing, feature extraction, bone age grade classification and calculation on the input image. The model update module is used to automatically update the CAP model and perform incremental training on the CAP model by using the incremental learning method based on knowledge distillation. It has the ability of autonomous learning. It has high prediction accuracy and low prediction cost.

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.