17 results about "Bone age assessment" patented technology

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

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

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

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

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

The 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 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 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 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.

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

The invention 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; extracting bone age prediction key features from the preprocessed bone input data through a deep convolutional neural network model combined with an attention mechanism; and according to the bone age prediction key features, performing bone age prediction through a pre-trained feedforward artificial 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 the technical field of artificial intelligence and medical image interdiscipline, in particular to a metacarpal and phalangeal epiphysis classification-oriented multi-modal feature fusion lightweight system. The system comprises a construction module used for constructing a preset classification model which is a lightweight classification model; and the training module is used for taking the acquired metacarpal and phalanx images as input, taking the preset classification categories corresponding to the metacarpal and phalanx images as output, and training a preset classification model based on the improved loss function to obtain a target classification model. The lightweight classification model for classifying the metacarpal and phalanx images is small in parameter quantity and high in calculation speed. In addition, the lightweight classification model is trained by adopting an improved loss function to obtain a target classification model, so that the classification is more accurate, and the problems of low efficiency and high recognition error rate during bone age evaluation in the prior art can be solved.

This invention discloses a remote medical system for children's bone age assessment and growth monitoring, relating to the field of medical diagnostic technology. The system includes: user-end data acquisition compatible with home / hospital equipment and sensors, automatic collection reminders, and transmission identification; image preprocessing with filtering and enhancement; AI-based bone age assessment based on an age-specific model, switching between dual standards, outputting results and anomaly markers; growth analysis comparing with standard curves; a remote interactive encrypted platform, pushing results for annotation and diagnosis; data storage with double encryption, sorted by timeline, supporting retrieval and authentication; anomaly warning with three threshold levels, triggering multi-channel alerts and automatic appointment scheduling; human-computer interaction via touchscreen or APP, providing an entry point, with key operations requiring verification. This invention achieves convenient remote data acquisition and accurate bone age assessment, scientifically monitors growth trends and warns of anomalies, strengthens cross-institutional collaboration and personalized intervention, ensures data security, improves management efficiency and intervention effectiveness, and adapts to the needs of children throughout their growth cycle.

The application discloses an intelligent bone age evaluation method based on an attention mechanism, which comprises the following steps: performing data preprocessing and enhancement on an original image, detecting a region of interest of the image after processing and dividing the image into 14 bone blocks; performing network training on the 14 bone blocks; and obtaining a final bone age value by performing grade recognition on each bone of a to-be-tested picture and comparing a score table. The application firstly performs filtering technology, Gaussian noise reduction and binary processing, then performs region of interest detection and segmentation by using a YoloV5 network, accurately divides and segments 14 reference bone regions, then recognizes the grade by using a recognition network after adding an attention mechanism, and finally obtains a final bone age value by comparing a bone development staging score table and a CHN bone development maturity and bone age comparison table.

The present application belongs to the technical field of deep learning bone age detection, and particularly relates to a child bone evaluation method based on multi-scale features and differentiable fuzzy sets, comprising the following steps: S1: image preprocessing and multi-channel fusion construction; S2: multi-scale semantic layered feature extraction; S3: feature channel alignment and spatial scale unification; S4: multi-scale adaptive attention weighted fusion; S5: visual feature and gender feature joint embedding fusion; S6: differentiable fuzzy set regression bone age prediction; S7: joint loss function construction and optimization; S8: conflict-free sample balancing training strategy; S9: model training smoothing and reasoning enhancement. The present application can realize high-precision, high-robustness and clinically close automatic bone age evaluation through three-scale feature layered extraction, cross-scale attention fusion, gender feature embedding and differentiable fuzzy set regression, in combination with exclusive sample balancing and training optimization strategies.