112 results about "Delayed bone maturation" patented technology

The application relates to the technical field of bone maturity analysis, and in particular to a bone age recognition method and system and an electronic device. The bone age recognition method includes the a step a of inputting a bone image into a target bone position detection model, and detecting position coordinates of a target bone in the bone image through the target bone position detectionmodel; a step b of cutting out a target bone image according to the position coordinates of the target bone; and a step c of inputting the target bone image into a bone stage classification model forbone age recognition. The bone age recognition method and system and the electronic device in the application adopt deep learning to automatically classify and recognize ulna and radius images withoutmanual intervention, improve the effectiveness and efficiency of bone age recognition and have better performance than the prior art; and also eliminate the need for manual acquisition, save more time and efforts, and are highly efficient.

The invention provides a hand bone X-ray film bone age assessment method and device, computer equipment and a storage medium. The method comprises the steps that a hand bone X-ray film with the bone age to be predicted is processed to be a hand bone photo with the specified pixel requirements; the hand bone photo is input into a preset convolutional-neural-network-based bone age assessment model for conducting calculation; a calculation result output by the bone age assessment model is obtained, wherein the result is the bone age of the hand bone. According to the hand bone X-ray film bone ageassessment method and device, the computer equipment and the storage medium, by means of the convolutional-neural-network-based bone age assessment model, bone age assessment can be conducted automatically, and the assessment accuracy rate is high.

A method and an apparatus for estimating bone age by at least one acoustic signal in an ossification-actuated skeletal structure. The apparatus includes an acoustic transmitter and an acoustic receiver positioned facing each other so that the structure is positioned between them. The structure has at least two bones. The transmitter is adapted for transmitting a signal to cross the structure transversely. An electronic moveable gantry is provided for adjusting the position of the acoustic transmitter and the acoustic receiver in relation to the structure. A computer system is enabled to perform one or more functions to position the moveable gantry; transmit the signal by the transmitter; control the signal transmitted by the transmitter; receive the transmitted signal by the receiver; and estimate bone age responsive to the received signal by at least one bone age calculation formula.

The invention discloses a bone age assessment method, which is characterized by comprising the steps of A, acquiring X-ray images of a plurality of target bone parts, and grading the acquired X-ray images according to the age group; B, performing feature extraction on a plurality of X-ray images in each grade, converting each grade of X-ray images into data by using a directional gradient histogram feature method, and building a mathematical model; and C, classifying the X-ray images in which the bone age is required to be assessed into the above grades by using a support vector machine classification algorithm. According to the bone age assessment method disclosed by the invention, an image processing technology and a computer vision technology are combined, and judgment for the bone maturity is realized through an interactive or automatic method.

The invention discloses an automatic bone age identification system based on an improved residual network, and aims at solving the problem that an automatic deep learning processing means lacks in bone age detection via X ray photos domestically. The method comprises that data enhancement is carried out on a training sample, normalization is carried out, a bone age classification network based onthe improved residual error network is trained, 16 different slide windows are used during test, 16 classification results are predicted, and a classification of highest frequency is obtained.

The invention discloses a bone age prediction method based on deep learning. The bone age prediction method comprises the following steps: step 1, obtaining the size of a target bone self-adaptive shear frame according to the age, gender and present height; step 2, performing automatic shearing according to the size of the shear frame and the coordinates of the target bone center point to obtain atarget bone picture; step 3, inputting the picture obtained in the step 2 into a bone development grade classification model for recognition to obtain a predicted development grade of each bone; andstep 4, referring to the score table of each bone development stage of a CHN method and a CHN method bone development maturity score and bone age comparison table to obtain a final bone age predictionresult. The invention also provides a bone age prediction system based on deep learning. The method has better stability and accuracy.

The invention relates to the field of image recognition, and discloses a bone age prediction method and device, a medium and electronic equipment. The method comprises the steps: acquiring to-be-predicted hand bone data; determining the gender of the to-be-predicted hand bone data; according to the gender, inputting the to-be-predicted hand bone data into a convolutional neural network model corresponding to the gender to obtain a predicted bone age output by the convolutional neural network model, wherein the convolutional neural network model is obtained by training in advance based on a triple loss function and a regression loss function. According to the method, the bone age prediction is carried out by utilizing the convolutional neural network model obtained by training based on thetriple loss function and the regression loss function, so that the bone age prediction efficiency is improved, the bone age prediction cost is reduced, and the bone age prediction accuracy is improved. In addition, the hand bone data of the corresponding gender is predicted specially by using the convolutional neural network model of the specific gender, so that the bone age prediction accuracy isfurther improved.

The invention provides a remote online bone age interpretation system capable of realizing remote online interpretation for bone age films, and belongs to the field of health assessment. The bone age refers to bone development age, can reflect body development situations objectively and has great significance in the aspects of bone age judgment in athletic contests, evaluation of adolescents' growth and development, prediction of adult height, selection of athletes, auxiliary diagnosis of metabolic diseases and the like. However, the bone age interpretation is required to be carried out by professionals, besides, the popularizing rate of special bone age equipment is lower, the probability of time delays between X-ray film shooting and the interpretation is high, and the process from the X-ray film shooting at hospital to the interpretation of a bone age result by the professionals is tedious, time-consuming and labor-consuming. The remote online bone age interpretation system can realize multiple functions such as digitization of bone age film collection, automatic film uploading, online interpretation of the bone age films, immediate feedback of the interpretation result and the like, greatly facilitates bone age testing, and has substantial significance for general survey of growth and development level, rapid interpretation of the bone age result and the like.

The invention provides an image preprocessing method for reducing the interference features of the bone age evaluation model. The method comprises the following steps: 1) a hand region is detected; 2)a hand-type image is segmented; 3) palm angle is adjusted. The invention further provides an image preprocessing system for reducing interference features of the bone age evaluating model, comprisinga hand region detection module, a hand-shaped image segmentation module and a palm angle adjusting module, wherein the hand region detection module is used for positioning a hand bone, and removing interference features formed by different positions of the hand region in the image; the hand-shaped image segmentation module is used for the unification of segmentation and background of the hand bone, and the interference formed by different backgrounds is removed; the palm angle adjusting module is used for unifying the angle of the hand bone and removing interference formed by different angles. According to the invention, the position and the angle of the hand bone are adjusted through a preprocessing means, and the background is removed; the purpose of data amplification is achieved in amode of reducing interference feature, so that the model training is more effective, the classification capability and the robustness are good.

The invention discloses a bone age evaluation method based on a convolutional neural network and a multiple attention mechanism. The method comprises the following steps: in a training stage, taking ametacarpal bone image as the input of a backbone network, obtaining a feature map F through a feature extractor, and obtaining a bone age regression value; taking the input of a multi-attention module as a feature map F, obtaining M sub-attention maps through compression operation and attention map splitting operation, conducting point multiplication of each sub-attention map with the feature mapF, and then obtaining a corresponding bone age regression value; combining a backbone network with the bone age regression value obtained by the multiple attention module, and training a neural network by adopting a multi-task learning strategy; and in a test stage: inputting a to-be-tested metacarpal bone image into the trained neural network, and obtaining a bone age evaluation value through the backbone network. The model can be trained end to end; meanwhile, an attention distribution diagram can be automatically generated, and better generalization is achieved; and in addition, based on the 2D convolutional neural network, the speed is high, the precision is high, and an average evaluation error is within 4.1 months.

The invention discloses a bone age evaluation method for realizing image segmentation and classification based on deep learning. The method comprises the following steps of: 1, processing a data set by using a digital image processing method to obtain sample data with higher quality; 2, manually marking a part of hand bone images, training an image segmentation network U-Net by using the part of images, then segmenting the data set by using the trained U-Net to obtain a background-removed data set, and making a training set, a verification set and a test set according to a certain proportion;3, training an improved image classification network VGG16 by using the processed data set; and 4, testing the trained model by using the test set and evaluating a result. Compared with an original bone age evaluation method, the improvement method provided by the invention effectively improves the accuracy of evaluating the hand bone image by the model, and has higher efficiency at the same time.

The invention discloses a bone age prediction method. A preset training set is trained in advance to form a bone age prediction model, and the bone age prediction model is used for bone age prediction. According to the technical scheme, through a brand-new attention mechanism adding mode, tan attention mechanism is added in a targeted mode according to different convolution layer information coupling degrees in the bone age prediction model, and the problem that a traditional attention mechanism is not easy to converge is solved; gender information is directly added into the neural network model for end-to-end training, and the accuracy of bone age prediction is further improved while the training process is optimized; the bone age prediction model used in the bone age prediction method isvisually verified through the attention mechanism, it is reflected that the model more pays attention to the hand bone part where the development degree of a patient is presented in a centralized mode, and a good prediction effect is achieved.

The present disclosure provides a bone age assessment and height prediction system including an image capturing unit and a non-transitory machine readable medium. The image capturing unit is for obtaining a target x-ray image data of a subject. The non-transitory machine-readable medium is for storing a program for assessing the development of the bones of a hand and the bone age of the subject, and predicting the adult height of the subject when executed by a processing unit. Therefore, the bone age assessment and height prediction system of the present disclosure can effectively improve the accuracy and sensitivity of the bone age assessment and the height prediction, and the time for assessing the bone age and predicting the height can be further shorten.

The invention provides a bone age evaluation method and system based on a graph convolutional neural network, a terminal and a storage medium. The method comprises the steps: obtaining N target epiphysis of a wrist bone image; adopting a feature extraction network model to extract initial features X of N target epiphysis and nearby areas of the wrist bone image; adopting a context feature fusion network model based on a graph convolution network, adding the initial feature X and the context feature subjected to multilayer graph convolution to obtain a final fusion feature, inputting the fusionfeature into a prediction distribution network model, and predicting to obtain development level distribution of N target epiphysis, correspondingly calculating development grade expectation and variance, development score expectation and bone maturity total score; and performing calculating to obtain a final bone age prediction value according to a bone age standard by utilizing the predicted development grade and the calculated bone maturity total score. Context feature fusion is realized through the graph convolution network to promote information communication among different local bone regions, accumulated bone age errors are avoided through a bone maturity total score loss function, and bone age evaluation accuracy and robustness are realized.

The invention relates to a bone age assessment method and system. The method is applied to Raspberry Pi and comprises the following steps of: receiving a bone age assessment request; acquiring a hand bone image and a user gender corresponding to the hand bone image according to the bone age assessment request; and performing cutting and histogram matching on the hand bone image, performing numerical conversion on the user gender, transmitting the user gender to a nerve calculation rod, and performing bone age assessment according to the hand bone image and the user gender through a pre-trained bone age assessment model deployed in the nerve calculation rod. By adopting the method, the safety can be improved, and meanwhile, the processing speed is increased to improve the assessment efficiency.

The invention discloses a bone age prediction method and device based on deep learning, electronic equipment and a storage medium, and relates to the technical field of image processing. The method comprises the following steps of obtaining to-be-predicted X-ray bone maps of a male to-be-predicted object and a female to-be-predicted object; inputting a bone key region extraction model; obtaining aplurality of bone key region graphs of the to-be-predicted object, inputting the bone maturity prediction model to obtain a bone classification result corresponding to the plurality of bone key region graphs of the to-be-predicted object, and calculating the bone classification result through the male bone age prediction model or the female bone age prediction model to obtain a bone age prediction result. By combining an existing bone age scoring method, a plurality of deep learning models are used for predicting an X-ray bone diagram of a to-be-predicted object, a result obtained by each model can be used for clinical explanation, and a prediction result can be rapidly and accurately output.

The invention discloses an intelligent adjusting air conditioner. The intelligent adjusting air conditioner comprises a machine shell, a negative oxygen ion generator, a sleep monitoring module and acontrol module, wherein the machine shell is provided with an air inlet and an air outlet; the negative oxygen ion generator is arranged in the machine shell; the sleep monitoring module is used for detecting the sleep state of a user in real time; the control module is in communication connection with the negative oxygen ion generator and the sleep monitoring module; and a bone age detection module is arranged in the control module. According to the intelligent adjusting air conditioner, the bone age detection module is arranged in the control module, meanwhile, the identity information and the like of the user are stored, whether the bone age of the user is backward in development or not can be easily judged, so that whether the negative oxygen ion generator is started or not is judged,and the deep sleep time of the user is prolonged; and on the other hand, the sleep state of the user is monitored in real time, so that the negative influence on the growth of bones due to long-time continuous release of negative oxygen ions is avoided.

The invention discloses a method for automatically identifying a bone age image after digital processing, which comprises the steps of image collection, image preprocessing, target prediction and result reporting, and is characterized in that according to a bone development grade standard of a CHN method, 14 target bone areas of an image sample are respectively marked by using an image marking tool in the target prediction; a bone grade identification model trained through deep learning is adopted to position, segment and classify 14 target bone areas, then according to physiological data of a testee and according to a score table of bone development stages of a CHN method, bone grades of all the target bone areas are converted into different scores and accumulated, and the scores of all the target bone areas are obtained. And finally, the corresponding bone age is calculated according to the comparison table of the bone development maturing score and the bone age of the CHN method, so that the problems of long time consumption and low precision of bone age film reading are solved, and favorable support is provided for rapid evaluation of the bone age.

The invention discloses a hand epiphysis development grade determination method and related equipment. The method comprises the steps of obtaining a feature map of a first hand skeleton image; inputting the feature map into a preset hand epiphysis development grade determination model to obtain a position probability map of hand epiphysis points and a development grade probability map of the handepiphysis points output by the hand epiphysis development grade determination model; and determining the development level of the hand epiphysis point according to the position probability graph of the hand epiphysis point and the development level probability graph of the hand epiphysis point. According to the invention, the probability that each position in the hand bone image is an epiphysis point position and the probability that the development level of each position is determined are determined through the hand epiphysis development level determination model, and the development level ofeach hand epiphysis point in the hand skeleton image is determined; according to the technical means, the technical problem that an existing bone age prediction method is low in reliability is solved, and then the technical effect of scientifically determining the bone age is achieved.

The invention discloses a bone age calculation method based on probability. The method comprises the following steps: by obtaining classification grades and corresponding probabilities of a deep learning model to each bone of a left hand, selecting two grades with the highest possibility and corresponding probabilities, multiplying the normalized probability by the scores corresponding to the bonegrades to obtain respective scores, adding the scores to obtain a final score of the bone, and looking up a table according to a CHN method score table to obtain the bone age. According to the method, errors caused by judgment errors of the output result of the depth model classifier are reduced, the grade boundary between bones is blurred, more reasonable corresponding calculation scores can beobtained for bones with grades difficult to judge, the accuracy of the CHN method is improved, and bone age judgment is closer to a real value.

According to an embodiment of the present disclosure, a method of assessing bone age by using a neural network performed by a computing device is disclosed. The method includes receiving an analysis image which is a target of bone age assessment; and assessing bone age of the target by inputting the analysis image into a bone age analysis model comprising one or more neural networks. The bone age analysis model, which is trained by supervised learning based on an attention guide label, includes at least one attention module for intensively analyzing a main region of the analysis image.

The embodiment of the invention provides a bone age evaluation method and device, and solves the problems that an existing bone age evaluation method cannot automatically identify difficult bone / epiphysis and cannot effectively intervene in uncertainty of bone age evaluation. The bone age evaluation method comprises the following steps: inputting a bone X-ray image into a bone age evaluation network model to obtain probability distribution data, the probability distribution data comprising a plurality of matching probability values corresponding to a plurality of standard bone age maps, and the plurality of standard bone age maps corresponding to a plurality of bone age standard values; inputting the bone X-ray image into an uncertainty estimation network model to obtain an uncertainty prediction value used for representing the uncertainty of the bone age evaluation value of the bone X-ray image; and when the uncertainty prediction value is greater than a preset threshold, sending keyauditing prompt information. According to the invention, bone / epiphysis with high uncertainty (easy to diverge) can be evaluated according to automatic identification of bone age, and doctors are recommended to focus on auditing.

The invention discloses an X-ray-transmitting hand fixing device for bone age measurement of children. The X-ray-transmitting hand fixing device comprises a hand supporting plate used for supporting afar end of an arm, a wrist, a palm, a little finger, a ring finger, a middle finger and an index finger, and a supporting block used for supporting a thumb. The hand supporting plate and the supporting block are both made of X-ray transmitting materials. A palm containing groove is formed in the rear side of the upper end face of the hand supporting plate, and a little finger containing groove, aring finger containing groove, a middle finger containing groove and an index finger containing groove are formed in the front side of the upper end face. The supporting block is arranged on the right side of the hand supporting plate in a front-back moving mode, and a thumb containing groove is formed at the upper end face rightwards. The little finger, the ring finger, the middle finger, the index finger and the thumb of a child can be separated; and the device can be suitable for children of different ages as much as possible, so that fingers of the children of different ages can be flexibly separated to facilitate subsequent clear X-ray shooting, and the device is high in adaptability in use and good in use effect.

The invention provides a bone age detection method based on multi-modal adversarial training. The bone age detection method comprises the following steps: constructing a bone age prediction data set;constructing a bone age detection model based on multi-modal adversarial training for training; in the prediction stage, reserving only a discriminator of the bone age detection model, adding softmaxto the last layer, and loading and training the optimal discrimination model weight for result prediction. According to the bone age detection method based on multi-modal adversarial training, the method comprises the steps of: adopting the adversarial training mode to improve the accuracy of model prediction; carrying out multi-modal bone age detection by utilizing the medical images and the textmedical records; performing training by using the Chinese teenager X-ray data set to obtain a bone age recognition result conforming to Chinese teenagers; achieving bone age recognition of multi-modal data, wherein a recognition result is more accurate in combination with text information.

The invention provides a bone age prediction method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring a target skeleton image; performing key point detection on the target skeleton image through a first key point detection model to obtain a first target skeleton key point; performing first posture correction processing on the target skeleton image according to the first target skeleton key point to obtain a standard skeleton image; performing segmentation processing on the standard skeleton image through the image segmentation model to obtain a preliminary skeleton foreground image; performing key point detection on the standard skeleton image through a second key point detection model to obtain a second target skeleton key point; performing second posture correction processing on the preliminary skeleton foreground image according to the second target skeleton key point to obtain a standard skeleton foreground image; and inputting the standard skeleton foreground image into a bone age prediction model for bone age prediction to obtain bone age prediction data. The accuracy of bone age prediction can be improved.

The invention is applicable to the technical field of bone age, and discloses a multi-skeletal development level detection method and terminal equipment. The method comprises the following steps: acquiring a multi-skeletal image, wherein the multi-skeleton image is an image containing at least two to-be-detected skeletons; obtaining a trained multi-skeletal development level detection model, wherein the multi-skeletal development grade detection model comprises at least two output branches, and each output branch outputs a development grade of a to-be-detected skeleton; and inputting the multi-skeleton image into the trained multi-skeleton development level detection model to obtain the development level of each to-be-detected skeleton in the multi-skeleton image, wherein the number of output branches of the multi-skeletal development level detection model is greater than or equal to the number of to-be-detected skeletons in the multi-skeletal image. According to the invention, the development levels of a plurality of bones can be detected at the same time through one multi-bone development level detection model with a plurality of output branches, the plurality of detection modelsdo not need to be operated at the same time, resources can be saved, and the detection efficiency is improved.

The invention belongs to the technical field of bone age prediction, and particularly relates to a teenager bone age data management method and abnormity early warning method based on big data. The management method comprises the following steps: configuring a teenager bone age database, wherein the database comprises teenager identity information, teenager hand bone X-ray films and age information, regional feature information and bone age related information corresponding to the X-ray films; dividing teenager identity information and corresponding bone age related information according to the regional features; acquiring bone age information corresponding to an X-ray film through the hand bone X-ray film; performing feature extraction on the bone age related information to obtain a plurality of bone age related feature values; and respectively determining a correlation constant of each bone age correlation characteristic value to the bone age relative age deviation value K. According to the invention, early warning is carried out on the abnormal development of the bone age of the teenagers on the premise that X-ray photography is not needed through the daily acquired bone age related information of the teenagers, so that the attention of parents of the teenagers with abnormal bone age is aroused, and the abnormal bone age of the teenagers can be found as soon as possible.

The invention discloses an application of deferoxamine mesylate for injection in treating senile bone loss and bone marrow stem cell aging. It is shown in the invention that after deferoxamine mesylate is injected into an elderly rat, the phenomenon of bone aging (older bone loss) is reversed, and the aging of bone marrow stromal cells (stem cells) separated from a bone marrow cavity after medicine injection is relieved. It is further proved that intraperitoneal injection of a deferoxamine mesylate medicine can relieve bone mass loss related to rat age and recover aging phenotypes of rat bonemarrow stromal cells, such as promotion of cell growth (cell proliferation and colony formation), increase of osteogenesis, reduction of adipogenesis, reversion of the expression level of cell aging related markers, resistance to oxidative stress and the like.

The invention discloses a bone age measurement method and system, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring an ultrasonic image and preprocessing the ultrasonic image; performing quality screening on the ultrasonic image by using a trained quality control model based on deep learning; extracting an ossification center region and a cartilage region from the ultrasonic image qualified in quality screening by using a trained multi-target segmentation model based on deep learning; and performing multi-target feature fusion on the ossification center region, the cartilage region and the clinical information, and performing XGBoost regression analysis on the fused multi-target features to obtain a bone age measurement value. The bone age measuring method and system are noninvasive and non-radiative to the human body, high in safety, convenient to detect and accurate in detection result.