21 results about "Age prediction" patented technology

This application belongs to the field of virtual art restoration technology, specifically providing a deep learning-based virtual art restoration system. The system primarily analyzes historical images and environmental data of the artifact and restoration materials to train an aging prediction model and simulate long-term appearance changes. By quantitatively comparing the differences and stability of their aging trajectories, it calculates the long-term compatibility score for each restoration material and automatically selects the optimal material to generate the final virtual restoration image. This application effectively solves the problem in existing technologies where it is difficult to predict significant visual differences that may appear between restoration materials and the artifact itself after long-term natural aging, ultimately leading to insufficient durability of the restoration results. It achieves scientific prediction and optimized selection of the long-term visual compatibility of restoration schemes, significantly improving the reliability and stability of restoration results and reducing the risk of restoration failure due to material aging incompatibility.

This invention provides a method for predicting aging based on metabolite profiles using nonlocal variance enhancement, relating to the fields of medical data analysis and metabolomics. The method acquires LC-MS and / or GC-MS metabolite profile data and the actual age of the subject to be predicted. It performs missing data checks and intra-class normalization on the data, constructs a class-constrained nonlocal variance enhancement feature extraction model, and combines multi-kernel principal component analysis and linear multi-view fusion to obtain fused metabolite profile features. The fused features are then input into a support vector regression model to output predicted age values. The aging degree is determined based on the difference between the predicted and actual age values, which improves the feature expression ability and interpretability of metabolite profile age prediction.

The application discloses a kind of based on DNA methylation's construction method of biological age prediction model, the application is downloaded from GEO data website, derived from Chinese population, contains calendar age data, whole blood sample 450k methylation chip data, by the method of elastic network combined bootstrap, 31 methylation sites of modeling candidate are screened, multiple linear regression, support vector machine, random forest and gradient boosting regression tree are used to carry out preliminary construction and evaluation of model, then further filter methylation sites using full subset regression, obtain methylation age prediction model based on 18 methylation sites.Finally, using any provincial team natural population data, methylation age prediction model is optimized, and finally the biological age prediction model based on 18 methylation sites is obtained.The model is suitable for Chinese population, the number of methylation sites contained is less, is not affected by blood cell components, and the prediction accuracy is good.

The application discloses a method for predicting biological age based on enteroscopy pictures, and belongs to the field of digestive endoscopy image analysis. The method first screens enteroscopy pictures without abnormalities, and completes quality control and edge cutting; then constructs an image package with multiple pictures in a single report, extracts features and weighted aggregation by using a multi-instance learning model based on attention; and converts age prediction into a classification task and introduces a Gaussian soft label to realize high-precision biological age prediction. The application can mine aging-related features from enteroscopy images without obvious abnormalities, does not need additional equipment and invasive operation, has strong generalization, and can be used for risk stratification of enteroscopy-negative people and precise prevention and treatment of colorectal cancer.

The application discloses a planar transformer aging prediction parameter correlation analysis method, system and device, and relates to the technical field of planar transformers. The application receives change data of to-be-analyzed parameters of a planar transformer at different time points; takes data recorded at zero time as a standard value to normalize the rest of the data; processes the normalized data through curve fitting to take parameter data with a slope closest to 1 as a reference sequence; uses a grey correlation degree analysis method to calculate correlation coefficients of the rest of the parameters and the selected reference sequence, and normalizes the correlation coefficients. The application uses the grey correlation degree analysis method to comprehensively apply multiple parameters of the planar transformer, provides a theoretical basis for weight distribution when the influence of the multiple parameters on the prediction process is comprehensively considered, and finally constructs a planar transformer life prediction model according to a mathematical statistics method, thereby improving the accuracy and reliability of planar transformer aging prediction.

This application belongs to the field of fetal brain age prediction technology, and relates to a method and system for predicting fetal brain age of the cerebellar vermis based on multimodal feature fusion. It employs the MST-Mamba segmentation network, and achieves synergy between local detail capture and global semantic modeling by embedding local-global aggregators at each level of the encoder. Simultaneously, a dynamic channel fusion unit is deployed at the jump connection between the encoder and decoder to avoid problems such as boundary ambiguity, missed classification, and misclassification. Through three parallel branches of a multi-granularity morphology-texture collaborative perception architecture, it simultaneously extracts two types of explicit features (macro-geometric and topological morphology) and two types of implicit features (micro-texture), achieving a comprehensive representation of the developmental features of the cerebellar vermis. After standardizing and calibrating the explicit features, they are spliced ​​and fused with the implicit features along the channel dimension to solve the problems of insufficient multimodal feature fusion and lack of calibration. Finally, prediction is performed using a multilayer perceptron regression head, ensuring the accuracy and stability of the brain age prediction results from the source.

This invention relates to a multi-organ biological age and disease risk assessment system based on chest CT radiomics, belonging to the field of medical image computer-aided analysis technology. The invention aims to address the problem that a single global image age cannot characterize organ-specific aging and lacks a closed-loop clinical risk quantification mechanism. The technical solution includes: receiving chest CT radiomics features and demographic parameters of the subject through an input layer; performing spatial mapping using a feature layer; outputting a 10-dimensional biological age through a three-stage cascaded machine learning model in the age prediction layer; calculating the age acceleration rate through a bias correction layer; quantifying the disease risk multiple by coupling a Cox proportional hazards model through a risk assessment layer; and finally, generating clinical interpretation through a large language model driven by an intelligent reporting layer. This invention achieves low-cost and non-invasive organ-level aging assessment, accurately capturing allometric aging, and improving the efficiency of clinical risk stratification and the standardization of interpretation.

The present application relates to the technical field of physiological data processing, in particular to a method for predicting heart age from echocardiographic video. The method acquires multi-plane ultrasound video and preprocesses it, and calculates adjacent and interval frame difference sequences. Based on the difference energy at each time, weights are dynamically generated to adaptively fuse and construct motion saliency feature sequences. Then, the original video and motion sequences are input into a pre-trained encoder to extract equal-dimensional semantic and motion features, which are fused after cross-modulation by a bidirectional gate mechanism. A multi-layer perceptron is trained using the fused features and the true age to construct a prediction model for each plane. The final heart age is output by fusing the initial results of multiple planes. The present application effectively breaks through the small sample training bottleneck, deeply fuses structure and motion information, and significantly improves the accuracy and stability of heart age prediction.

The invention relates to a biological age prediction model training method, an age prediction method, an age prediction device and electronic equipment, and the method comprises the steps: calling a data reading interface, and loading biological sample data; the biological sample data comprises demographic data and protein data; the protein data comprises expression quantity data of various proteins; preprocessing the biological sample data to obtain a preprocessed data set; constructing a multi-layer perceptron model, and performing initial training on the multi-layer perceptron model based on the preprocessed data set to obtain a preliminary training model; based on weight parameters of the preliminary training model, screening high-contribution characteristic proteins related to biological age to generate a characteristic protein set; and performing feature optimization training on the preliminary training model based on the feature protein set, and outputting the model after the feature optimization training as a biological age prediction model. According to the method, the training accuracy and the model generalization ability of the biological age prediction model can be improved, and the technical application cost is reduced.

This invention relates to the field of biological aging assessment technology, specifically a method for assessing cellular aging based on proteomics data and machine learning. The method includes: constructing a cell type-specific candidate feature set based on single-cell transcriptomics data; obtaining a protein feature matrix based on plasma proteomics data; constructing independent machine learning-based cell type-specific aging prediction regression models for each cell type, using age as the response variable and the protein feature matrix as input; inputting the proteomics data of the sample to be predicted into the corresponding cell type-specific aging prediction model to obtain the predicted lifespan and the lifespan difference characterizing cellular aging for different cell types. This invention achieves quantitative assessment of the aging degree of different cell types in different organs under in vivo conditions by constructing a functional mapping bridge between single-cell transcriptomics and plasma proteomics.

The application discloses an intelligent display control method and system for liquid crystal panel image recognition driving, and relates to the technical field of liquid crystal panels. The method comprises the following steps: collecting user image data of a liquid crystal panel through a camera and preprocessing the user image data to obtain a user image of the liquid crystal panel; inputting the user image of the liquid crystal panel into an improved VGG-Face model to output a user age prediction value; collecting environment data of the liquid crystal panel, and based on the environment data of the liquid crystal panel and the user age prediction value, performing display adjustment on the liquid crystal panel through a fuzzy logic control method to obtain a first display adjustment result; collecting user feedback data, training a gradient boosting tree model by taking the user feedback data as a training set to obtain a trained gradient boosting tree model, inputting the first display adjustment result into the trained gradient boosting tree model, and outputting a second display adjustment result, so that intelligent display control of the liquid crystal panel is realized.

The application provides a catalpa tree age prediction model, and relates to the technical field of predicting the age of a catalpa tree with a grade of three or more. The prediction model is as follows: y=0.013e 6.17x +53.616, wherein y is the age of the catalpa tree, and x is the average telomerase activity value of the leaves in the south upper part of the tree. The prediction model provided by the application can be used for monitoring the age of a catalpa tree with a grade of three or more, and the maximum error is 10%.

A method for predicting growth on the basis of growth age and providing a solution by using an artificial intelligence model may include the steps of: receiving biometric data of a measurement target; extracting data regarding the predicted age of peak height velocity (APHV), at which the growth velocity is expected to reach the maximum value, by using the biometric data of the measurement target; classifying the growth step of the measurement target into one of multiple growth steps on the basis of the extracted data regarding the predicted APHV; predicting the final height by inputting the extracted data regarding the predicted APHV into a trained neural network; and providing a growth management solution on the basis of the classified growth step and the predicted final height.

The present application relates to the technical field of intelligent evaluation of road engineering, and particularly relates to a method for predicting the aging degree of pavement material under the action of environmental temperature and humidity cycles, which specifically comprises the following steps: obtaining time-series temperature and humidity data and aging performance indicators through laboratory accelerated aging test, converting the time-series temperature and humidity data and aging performance indicators into effective cumulative exposure through time decay weight and temperature and humidity synergistic strengthening function; constructing a label vector by using nonlinear normalization and adaptive correction, and training a neural network model in combination with a multi-resonance feature vector; and outputting the predicted value of the aging indicator in the original physical dimension by means of neural network model prediction and reverse normalization processing. The present application realizes the quantitative characterization of the nonlinear cumulative effect of temperature and humidity cycles, improves the accuracy of aging prediction, and is suitable for service life evaluation and maintenance decision of pavement material.

The application discloses a multi-task real-time face analysis method and system based on OpenCV and light DNN, and relates to the technical field of computer vision. Input data in the form of static images or real-time video streams is preprocessed through OpenCV; face detection is completed based on a light MobileNet-SSD network optimized through channel pruning and dynamic weight distillation, and effective face images are screened; the face images are input into a multi-task light DNN model, synchronous inference of expression recognition, age prediction and gender classification is realized through a cross-task feature interaction mechanism; real-time video streams are processed through multi-thread parallel computing and optional CUDA GPU acceleration, analysis results are stored in a database in association and visual display and query are supported. The system corresponds to data input, preprocessing, face detection, multi-task analysis, parallel acceleration, data storage and result display and interaction modules. The application balances model precision and computing efficiency, improves complex scene adaptability and real-time response capability, and is suitable for various intelligent interaction scenes.

This invention provides a method, system, medium, and electronic device for predicting brain age. The method includes the following steps: acquiring brain MRI data, gender, and true age of a healthy human body; reconstructing a quantitative magnetic susceptibility image based on the brain MRI data; constructing a brain age prediction model; training the brain age prediction model based on the quantitative magnetic susceptibility image, the gender, and the true age, and then using the trained brain age prediction model to achieve brain age prediction. The brain age prediction method, system, medium, and electronic device of this invention, based on quantitative magnetic susceptibility imaging, uses a deep learning algorithm to predict brain age, achieving excellent brain age prediction performance and possessing good generalization and clinical translational potential.

This invention discloses a method for constructing an age prediction model based on DNA methylation microhaplotypes, belonging to the field of bioinformatics. Through bioinformatics analysis of methylation data from multiple whole blood samples, DNA methylation microhaplotype sites for accurate age prediction are selected using the sparse group Lasso algorithm. Further, a convolutional neural network is used for age regression modeling, and the stability and accuracy of the model are verified in multiple independent cohorts. This model provides a novel and effective technical means for age prediction in epigenetics, with high scalability and application prospects.

A physiological age prediction device based on multimodal physiological signal fusion includes: a signal preprocessing unit that resamples, filters, standardizes, and segments the original ECG and EEG signals; a feature extraction unit that uses a dual-branch feature extraction network to extract features from the preprocessed signals to obtain corresponding temporal feature sequences; each branch includes: a channel mixing layer, a lightweight MobileNetV2 feature extraction module, and a BiLSTM feature extraction module; a feature fusion unit that uses a multi-head bidirectional cross-attention fusion module to perform bidirectional interaction between the temporal feature sequences of the two modal signals to obtain fused features; and a multi-level temporal modeling and age prediction unit that models the complex temporal relationship of the fused features through a multi-level BiLSTM network, uses an additive attention mechanism to weight and aggregate the temporal features to obtain a global feature vector, concatenates the global feature vector with a conditional label vector to obtain the fused features, and inputs them into a two-layer fully connected network to obtain the final prediction result.

The application belongs to the technical field of building engineering, and discloses a building aging prediction method and system based on big data modeling, which comprises the following steps: S1, acquiring multi-source heterogeneous building related data; S2, preprocessing the multi-source heterogeneous building related data to extract multi-dimensional features related to building aging; S3, constructing a building aging knowledge graph; S4, constructing a multi-modal deep learning model; S5, training the multi-modal deep learning model; S6, inputting the processed building related data to be predicted into the trained multi-modal deep learning model for prediction according to steps S1-S3. The application uses a deep learning model to perform deep learning and multi-scale time series analysis on the structure health monitoring data, environmental parameters, material properties and historical maintenance records of the whole life cycle of the building, accurately capturing the nonlinear dynamic evolution law and multi-factor coupling effect in the building aging process.

This invention discloses an aging prediction method, system, and device based on multimodal biosignal fusion. The invention constructs customized training paradigms for three modalities: ocular medical imaging, behavior, and gait. For the ocular modality, a self-masking-driven three-stage progressive training process is employed, sequentially achieving self-supervised pre-training, domain data fine-tuning, and classification task adaptation. For the behavior modality, unsupervised learning is used to extract behavioral syllable features, establishing a quantitative correlation between behavior and aging degree. For the gait modality, high-dimensional gait features are extracted based on spatiotemporal feature quantification and structured statistical aggregation. This invention proposes a hierarchical cross-modal fusion architecture, sequentially performing single-modal feature encoding, image modality mean fusion, and cross-modal feature enhancement, achieving collaborative reasoning between medical images and behavioral / gait representations. This significantly improves the accuracy, robustness, and clinical interpretability of aging prediction, providing a structured new paradigm for cross-modal fusion in biomedical multimodal data analysis.

This invention provides a physiological age prediction model that predicts the physiological age of a subject by detecting physical examination indicators. These indicators include measurements of blood cells, glucose metabolism, tumor markers, urine components, anthropometric measurements, lipids, blood components, and tissue function. Preferably, these indicators include one or more selected from the following: glomerular filtration rate (eGFR), cystatin C, serum creatinine (SCr), blood urea nitrogen (BUN), timed up and go, light reaction time, fasting blood glucose (FBG), insulin-like growth factor (IGF), superoxide dismutase (SOD), insulin (INS), albumin (Alb), alkaline phosphatase (ALP), folic acid, lactate dehydrogenase (LDH), aspartate aminotransferase (AST), and high-sensitivity C-reactive protein (hs-CRP). More preferably, all of the aforementioned physical examination indicators are included. This invention also provides a physiological age prediction device applying the above-mentioned physiological age prediction model and a computer storage medium.