11 results about "Fetal brain" patented technology

The invention belongs to the technical field of fetal brain age prediction, and relates to an earthworm cerebellar fetal brain age prediction method and system based on multi-modal feature fusion, an MST-Mamba segmentation network is adopted, and local-global aggregators are embedded in each level of an encoder, so that the cooperation of local detail capture and global semantic modeling is realized; meanwhile, a dynamic channel fusion device is deployed at the jump connection part of the encoder and the decoder, so that the problems of fuzzy boundary, missed division, wrong division and the like are avoided; through three parallel branches of a multi-granularity form-texture collaborative perception architecture, two types of explicit features of macroscopic geometry and topological form and implicit features of microscopic texture are synchronously extracted, and comprehensive characterization of the development features of the earthworm cerebellar part is realized; the explicit features are subjected to standardized calibration and then spliced and fused with the implicit features in the channel dimension, the problems that multi-modal feature fusion is insufficient and calibration lacks are solved, finally prediction is conducted through a multi-layer perceptron regression head, and the accuracy and stability of the brain age prediction result are guaranteed from the source.

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.

The invention discloses a fetal brain development assessment method and a computer program product, and belongs to the field of medical image analysis. The method comprises the following steps: firstly, acquiring a brain image of a fetus, and then segmenting the brain image by using a segmentation model trained based on a multi-gestational-age sample and a loss function adjusted along with gestational ages to obtain a brain segmentation result; calculating a brain development index of the fetus based on the segmentation result; estimating the gestational age of the fetus by using a gestational age estimation model, wherein the gestational age estimation model comprises a segmentation model serving as a generator and a discriminator obtained through adversarial training; acquiring a normal brain development index range of the corresponding gestational age from a reference database based on the estimated gestational age; and finally, comparing the calculated brain development index with a normal range to obtain a fetal brain development evaluation result.

The application provides a dynamic modeling method and system for multi-field skill AI automatic semantic labeling, and relates to information retrieval, medical health and cross-field skill fusion scene. Through nonlinear dynamic modeling and biomedical stability control, the semantic label automatic generation and real-time matching of talent skill and health intervention effectiveness are realized. The application innovatively integrates the sound wave frequency of fetal brain promoting music method, maternal action instruction and blood type nutrition scheme into the "skill-time" frequency domain space, combines the Gevrey smoothing operator and fractional derivative evolution equation, accurately separates the temporary fetal movement fluctuation and low-frequency core characteristics, and breaks through the characterization limitation of traditional models on nonlinear biological coupling effect. By introducing the "intervention effect-time" frequency domain component and local dependency constraint, the short-term effectiveness and long-term value of music intervention on fetal neurodevelopment are quantified, and the model distortion problem caused by extreme data or cross-field parameter resonance in traditional methods is solved.

The present application relates to a kind of fetal brain brain age estimation method and device based on deep imbalance regression, the method includes: introducing label to fetal brain magnetic resonance image training set, using label distribution smoothing strategy to obtain effective label density distribution;Establish multi-scale hierarchical segmentation fetal brain feature extraction regression network;Regression focus mean square error loss function is constructed, and effective label density is used to reweighting regression focus mean square error loss function;Grade ordering similarity regularizer is constructed;With the reweighted focus mean square error regression loss function as main function, grade ordering similarity regularizer is used as balance regularization term, and the total loss function of fetal brain feature extraction regression network is constructed.The method can significantly improve the performance of regression model, can let model better learn the continuous information of regression age label, obtain lower mean absolute error, help to identify the abnormality of brain development and reduce the risk of adverse development.

The invention provides for the detection of a perturbed gene network, which includes highly expressed genes during fetal brain development, which is dysregulated in neuron models of autism spectrum disorder (ASD). High-confidence ASD risk genes are upstream regulators of the network modulating RAS / ERK, PI3K / AKT, and WNT / / β-catenin signaling pathways. The invention demonstrates how the heterogeneous genetics of ASD can dysregulate a core network to influence brain development at prenatal and very early postnatal ages and, thereby, the severity of later ASD symptoms. The invention provides a model for diagnosis, prognosis determination, and optionally treatment and monitoring, for any disease by comparing molecular marker patterns in non-affected tissues in a subject with healthy controls to determine a dysregulated network in the subject based on a co-expression pattern of interacting genes.

The invention discloses a method for generating a fetal brain nuclear magnetic volume image based on a diffusion model. The method comprises the following steps of: performing brain tissue extraction on an image data set of a fetal brain three-dimensional volume to obtain a real three-dimensional fetal brain volume and a corresponding image mask; constructing a diffusion model; gaussian noise is added to the real three-dimensional fetal brain volume according to the time step to obtain a noisy volume, the time step is embedded through linear layer processing to obtain a time step code, and the diffusion model is trained based on the noisy volume, the time step code and the image mask; initializing a noisy volume, performing iterative denoising based on the time step, outputting predicted noise based on the trained diffusion model, and calculating the noisy volume of the next time step; adding a fidelity optimization process in a set time step, and replacing the volume corresponding to the original time step with the volume of a fidelity optimization result; and outputting the completely denoised generation volume after iterative denoising. According to the invention, the reconstruction quality of the volume of the three-dimensional fetal brain nuclear magnetic image is improved.

The application provides a dynamic training optimization system and method based on multi-dimensional biological feedback, and relates to the technical field of intelligent sensing and behavior optimization.The application realizes accurate optimization of the mother-fetus interaction environment by constructing a joint evaluation matrix of physiological data of pregnant women and fetal development indicators, and introduces a regulation technology based on body dynamics and beta wave music cooperation, which significantly reduces the matching deviation of maternal physiological parameters and fetal needs.Meanwhile, by using dynamic nutrition intervention and amniotic fluid dynamics monitoring based on biological rhythm synchronization, the dynamic balance of the intervention during pregnancy and the efficient development of fetal synapses are realized, and combined with multi-dimensional sensory stimulation and neural plasticity enhancement scheme, not only the synaptic density and neural network efficiency of the fetal brain functional area are greatly improved, but also the key problems such as single stimulation, non-systematic intervention and development evaluation lag in the existing fetal education method are effectively solved.

The invention relates to an application of TLR < 4 > siRNA (TLR < 4 > siRNA (at) LNP-CSA-BP) coated with TLR < 4 > siRNA (TLR < 4 > siRNA) connected with placental Chondroitin sulfate A binding peptide (Placental Chondroitin sulfate A binding peptide, CSA-BP) injected in a maternal pregnancy period, in improvement of pregnancy complications and prevention of progeny neurodevelopment disorder diseases, in particular to an application of TLR < 4 > siRNA coated with LNP-CSA-BP coated with TLR < 4 > siRNA coated with LNP-CSA-BP. According to the invention, TLR4siRNA (at) LNP-CSA-BP is used for carrying out caudal vein injection on a pregnant mouse, so that a placenta and fetal brain inflammation microenvironment (induced by toxoplasma gondii STAg) maternal immune activation can be inhibited. Behavior experiments further prove that under a maternal immune activation model, TLR4siRNA (at) LNP-CSA-BP caudal vein injection pregnant mice can significantly improve phenotypes of neurodevelopmental disorder diseases of filial generation mice, such as core symptoms (social ability and repeated engraving behaviors) of autism behaviors, and the promising transformation prospect is proved.

The invention discloses a fetal brain age prediction method and system based on self-supervised structure perception fusion. The method comprises the steps of obtaining a fetal brain MRI image, and performing preprocessing and image segmentation to obtain a plurality of image blocks; calculating a structural complexity score for each image block, and generating a corresponding structural mask based on the structural complexity score; shielding the fetal brain MRI image through a structural mask to obtain a shielding image; inputting the occlusion image into a pre-trained structure perception encoder to obtain a structure perception feature map; inputting the fetal brain MRI image into a supervision encoder to obtain a high-level semantic feature map; and fusing the structure perception feature map and the high-level semantic feature map to obtain fused features, and inputting the fused features into a global average pooling layer and a full connection layer to obtain a gestational week prediction value. According to the invention, the precision and robustness of gestational week prediction are improved in a small sample and weak labeling scene.

The invention relates to a fetal brain development state assessment method and system, belongs to the technical field of brain development assessment, and solves the problems of high subjectivity, low efficiency and single analysis dimension when fetal brain development is assessed in the prior art. Comprising the steps of obtaining a to-be-evaluated fetal MRI image, and performing preprocessing to obtain an initial fetal MRI image; obtaining a fetal brain region image based on the initial fetal MRI image and a trained brain region recognition model; obtaining a gestational week stage of the fetal brain based on the fetal brain region image and a trained gestational week discriminator; based on the fetus brain region image, the gestational week stage of the fetus brain and a trained sectional type brain region segmentation model, obtaining a fetus brain region segmentation image, and further extracting features of each brain region of the fetus; based on the features of each brain region of the fetus, the equivalent gestational week of the fetal brain development is obtained, then the development offset is obtained according to the actual gestational week of the fetus, and then the evaluation result of the fetal brain development is obtained.