8 results about "Delayed bone maturation" patented technology

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.

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 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.

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.

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

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.

FIELD: pediatrics; endocrinology.SUBSTANCE: invention can be used to predict the development of idiopathic precocious development (IPD) in obese girls under 9 years of age. The method includes determining the patient's height and BMI. Then, bioimpedance analysis is performed and the basal metabolic rate (BM), percentage of adipose tissue, active cellular mass (ACM) and skeletal muscle mass (SMM), and the proportion of SMM are determined. In addition, X-rays of the hands are performed and bone age is determined using the Greulich atlas. Furthermore, with normal values of BM, percentage of body fat and ACM, elevated values of BMI, SMM, proportion of SMM, the presence of tall stature and bone age ahead of biological age by 1-3 years, the development of IPD is predicted in girls with obesity up to 9 years of age.EFFECT: method is simple, accessible, and allows for the effective prediction of the development of IPD in obese girls.1 cl, 2 ex

The invention provides a bone age prediction method based on multi-modal feature fusion, a storage medium and electronic equipment. The method comprises the following steps: predicting a plurality of key anatomical points in a wrist X-ray image based on a pre-trained key point detection network, and segmenting a plurality of regions of interest; extracting global feature vectors of the regions of interest based on a pre-trained backbone network, extracting local depth feature vectors of a plurality of regions of interest based on two pre-trained lightweight networks sharing weights, and splicing and fusing the local depth feature vectors and gender feature vectors, obtaining significant feature vectors of the local features; calculating an attention weight based on the splicing of the global feature vector and the local feature apparent feature vector, and obtaining a fusion feature vector based on the attention weight; and inputting the fusion feature vector into a preset regression device, and outputting a bone age prediction value. According to the method, high-precision prediction can be achieved, high interpretability is achieved, and bone age prediction adapting to individualized differences is achieved.