65 results about "Imaging study" patented technology

This invention discloses a method and system for following up on magnetic resonance imaging (MRI) images of amyloid-related radiological abnormalities, belonging to the field of medical image processing. The method includes: acquiring multimodal MRI image data; performing bias field correction and intensity normalization based on a bias field fitting model to generate a first image set; performing global alignment and edge detection on the first image set to extract anatomical physical boundaries, generating brain region mask data; performing branch deformation field registration on the first image set within the brain parenchyma defined by the brain region mask data to generate a spatially aligned second image set; calculating the average intensity of normal brain parenchyma regions in the second image set to obtain a scaling factor, acquiring a three-dimensional residual image; performing anisotropic diffusion filtering denoising on the three-dimensional residual image, and performing differential denoising and morphological screening processing, outputting the detection results. This invention eliminates systematic artifacts caused by insufficient bias field correction.

This invention discloses an AR-assisted puncture navigation spatial registration method and system based on environmental reference features, relating to the field of medical image navigation. The system includes an augmented reality navigation device, a permanent ceiling marker, and a temporary marker board. The augmented reality navigation device includes a binocular augmented reality camera group, a global tracking camera, a global spatial registration module, and a display module. The binocular augmented reality camera group constructs a binocular overlapping field of view, acquiring the pose information of the binocular augmented reality camera group relative to the temporary marker board during the calibration phase, and acquiring near-field images of the surgical area in real time during the surgical phase and displaying them on the display module. The global tracking camera acquires the pose information of the global tracking camera relative to the global coordinate system of the ceiling. The global spatial registration module solves the extrinsic parameter matrix between cameras and the environmental relationship matrix based on the pose information. During the surgical phase, the path planned by the imaging equipment is converted into a real-time navigation path and superimposed on the display module. This solves the complex calibration problem under non-overlapping fields of view.

The application relates to the technical field of orthopedic puncture, in particular to an orthopedic puncture navigation system; through the cooperation of a puncture positioning module and a control module, image data and a mechanical adjusting mechanism are organically combined, high-precision planning and positioning of a target puncture point, a puncture angle and a puncture depth are realized, the dependence on the experience of an operator in traditional puncture operation is significantly reduced, the problem of inaccurate positioning caused by artificial judgment deviation is effectively solved, the operation time is greatly shortened, so as to reduce the pain and operation risk of a patient, the puncture positioning module adopts a mechanical structure of a multi-degree-of-freedom adjustable machine, through the cooperation of a horizontal distance adjusting rod, a vertical distance adjusting rod and a positioning cylinder, flexible and accurate adjustment of the position and angle of a target puncture needle in a three-dimensional space is realized, the structure is compact and the adjusting logic is clear, the adaptability of the system to different anatomical structures and operation scenes is enhanced, and the flexibility and controllability of operation are improved.

This application relates to the field of biotechnology, specifically disclosing the application of immune cell subsets in the preparation of detection products for assessing the efficacy of neoadjuvant therapy and the risk of recurrence and metastasis in breast cancer. The detection method described in this application is non-invasive and convenient, requiring only peripheral blood, and is easy to repeat sampling and dynamic monitoring. This application also provides a predictive model for the efficacy of neoadjuvant therapy in breast cancer, and a predictive model for the risk of recurrence and metastasis in breast cancer treated with neoadjuvant therapy. The constructed predictive model has a high AUC value (0.82-0.87), and good sensitivity and specificity. Furthermore, through regular postoperative monitoring, abnormal dynamic changes in immune cell subsets can be detected, potentially indicating signs of recurrence earlier than imaging and traditional tumor markers. This helps identify advantageous populations before treatment and high-risk populations after treatment, enabling optimization of treatment plans and individualized adjustment of follow-up density.

The application discloses a viscoelasticity parameter inversion method, and belongs to the field of medical image processing and computational imaging. The method obtains the time-space image sequence data of the measured soft tissue after excitation, extracts the shear wave group velocity by using the time-of-flight method, and equivalently takes the shear wave group velocity as the phase velocity anchor point under the zero-frequency limit condition to determine the static shear modulus; the two-dimensional fast Fourier transform is used to extract the middle-high frequency band phase velocity dispersion curve; the rheological model of the measured soft tissue is constrained based on the static shear modulus, and the single-parameter fitting of the viscosity coefficient is combined with the middle-high frequency band phase velocity dispersion curve to obtain the viscoelasticity parameter.

The application discloses a lung injury evaluation system based on vascular endothelial cell protection, which comprises a sample processing module configured to perform sample pretreatment and multi-dimensional biomarker collection operation; a feature calculation module connected to the sample processing module and configured to perform endothelial function core index quantization and feature extraction operation; a data fusion module connected to the feature calculation module and configured to perform lung injury degree and endothelial protection efficiency correlation mapping operation; and a strategy generation module connected to the data fusion module and configured to perform dynamic risk assessment and evaluation strategy generation stage. The application has the following advantages and effects: through systematic fusion and dynamic analysis of multi-source heterogeneous data, quantitative correlation mapping between biomarkers and imaging features is established, thereby significantly improving the comprehensiveness of state evaluation and prediction ability.

This invention discloses a method for constructing an early prediction model of hydrocephalus secondary to intraventricular tumors based on the dynamic changes of inflammatory factors after surgery, belonging to the field of medical data processing and model construction. The method includes: acquiring the concentration values ​​of inflammatory factors and the hydrocephalus outcome of biological samples at multiple postoperative time points; calculating quantitative characteristic parameters characterizing the dynamic evolution of the inflammatory response, such as the dynamic change rate, cumulative load, and acceleration, based on the concentration values; and training a deep learning model using the quantitative characteristic parameters and clinical baseline data as input features, with the hydrocephalus outcome as the label. This invention achieves early quantitative warning of hydrocephalus risk, significantly improving the accuracy, sensitivity, and specificity of prediction. It solves the technical problem of existing technologies relying on post-operative monitoring methods such as imaging and lacking early biochemical warning capabilities.

This invention discloses a method for fusing intracavitary ultrasound images and CT images based on electromagnetic positioning, comprising the following steps: scanning CT images of the lesion site and extracting a set of CT marker points S1; installing an electromagnetic positioning sensor on the intracavitary ultrasound probe; scanning multiple intracavitary ultrasound images of the lesion site and extracting a set of electromagnetic marker points S2; registering the CT image coordinate system with the electromagnetic positioning system coordinate system based on the CT marker point set S1 and the electromagnetic marker point set S2; and achieving real-time fusion of the CT image and the intracavitary ultrasound image based on the registration result. Using this invention, real-time fusion of intracavitary ultrasound images and CT images is achieved based on an electromagnetic positioning system, fully utilizing the advantages of multiple medical images to provide doctors with rich imaging information about the patient's lesion.

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.

This application relates to a method, apparatus, device, medium, and program product for quantifying CT features of idiopathic pulmonary fibrosis. The method includes: acquiring a medical image of the lung to be processed; segmenting the lung parenchyma in the medical image to obtain bilateral lung regions and total lung volume; segmenting the bilateral lung regions to obtain multiple imaging features related to idiopathic pulmonary fibrosis, and determining the volume percentage corresponding to each of the multiple imaging features related to idiopathic pulmonary fibrosis and the total lung volume; and determining the predicted functional impairment value corresponding to the volume percentage of the multiple imaging features related to idiopathic pulmonary fibrosis based on a pre-generated linear regression model, wherein the linear regression model represents the correlation between the volume percentage of the multiple imaging features related to idiopathic pulmonary fibrosis and various lung function indicators. This method can improve processing efficiency.

The present application relates to the technical field of medical detection, and discloses an angle measuring tool for spinal imaging evaluation, comprising a U-shaped piece one, a clamping block one, a clamping groove, a U-shaped piece two, a clamping block two, a sliding groove, a horizontal rod one, an angle adjusting mechanism one, a horizontal rod two, an angle adjusting mechanism two and a scale line. The present application is not only applied to preliminary screening, but also applied to the measurement of full-spine X-ray film. The present application utilizes human body surface marks to perform accurate positioning and measurement, and measures the balance of main human body parts such as spine, shoulder, scapula and patella. The present application can solve the problem of positioning ambiguity caused by the change of body position, reduce the cumbersome procedures of multiple line drawing and measurement of doctors, greatly improve the work efficiency of doctors and the compliance of patients, and reduce the work intensity of doctors.

The application discloses a non-small cell lung cancer auxiliary decision-making method based on multi-modal causal representation and belongs to the field of clinical auxiliary decision-making. The method comprises the following steps: S1, obtaining clinical texts of non-small cell lung cancer patients, inputting the texts into a text feature extraction network, and obtaining text features; S2, obtaining imaging examination data of the non-small cell lung cancer patients, inputting the data into an image feature extraction network, and obtaining image features; S3, obtaining genomic data of the non-small cell lung cancer patients, inputting the data into an omics feature extraction network, and obtaining gene features; S4, mapping and splicing the text features, the image features and the gene features to obtain mixed variable representation, inputting the mixed variable representation into a random causal relationship network, and combining a pessimistic estimation mechanism to generate an auxiliary decision. Through the introduction of long text analysis of rotary position coding, lesion image extraction of multi-order gating aggregation and gene pathway analysis of graph attention mechanism, efficient representation of multi-modal data is realized.

The application discloses a kind of Chinese lung cancer high-risk population risk prediction method based on multi-modal feature fusion, comprising: step 1, data acquisition;Step 2, data preprocessing;Step 3, multi-source risk feature evaluation and screening: verify and evaluate the prediction efficiency of lung cancer epidemiology risk score, tumor marker and lung cancer related multi-gene risk score, and screen out the optimal single dimension score of each dimension from it;Step 4, multi-modal score construction: according to the smoking status of participants, the population is divided, the optimal single dimension score of each dimension screened out is standardized, then the multi-source features are fused using Logistic regression model and the weighted coefficients are optimized, and the multi-modal lung cancer risk prediction score suitable for smokers and non-smokers is constructed respectively.The present application has superior discrimination performance, can accurately identify high-risk individuals, guide subsequent imaging examination and early intervention, and improve the early diagnosis rate and treatment effect of lung cancer.

The application relates to a multifunctional contrast agent for capturing plasma cell free DNA and a preparation method and application thereof, the preparation method of the multifunctional contrast agent comprises the following steps: S1, activating a contrast agent surface carboxyl; S2, modifying a DNA binding protein on the contrast agent surface; and S3, sealing unreacted sites on the contrast agent surface. Compared with the prior art, the multifunctional contrast agent prepared by the application has good biocompatibility, not only has an imaging examination function, but also can be applied to capturing cell free DNA in circulating blood, and is further applied to tumor detection and tumor prognosis monitoring.

The application belongs to the field of biomedical models and gene engineering technologies, and relates to a large vestibular aqueduct syndrome animal model construction method and application. SLC26A4 The application adopts CRISPR / Cas9 gene editing technology to mutate a gene c.707T>C, so as to construct a genotype-confirmed SLC26A4 Mutant animal model, and through inner ear high-resolution imaging evaluation, longitudinal hearing physiological function monitoring and inner ear tissue molecular pathology analysis, it is confirmed that the model can stably reproduce the core phenotypes of human large vestibular aqueduct syndrome, including significant vestibular aqueduct and endolymphatic sac expansion, progressive hearing threshold increase and secondary damage of cochlear hair cells and supporting cells.

This invention provides a method for testing the needle insertion range of external glaucoma drainage tube implantation based on UBM. The method includes acquiring sagittal images of the limbus and anterior chamber angle of the subject's eye using a UBM system during the examination; interpreting and measuring the images to obtain image measurement data for needle insertion range calibration; setting a puncture point to simulate the scleral puncture position during external glaucoma drainage tube implantation; defining safety angles A and B in the UBM sagittal images of the corresponding puncture point; retaining data for one eye for each subject; and calibrating the needle insertion range based on the image measurement data and statistical analysis of safety angles A and B; using μA+1SD_A as the lower limit angle for needle insertion calibration and μB-1SD_B as the upper limit angle; and taking the midpoint of the interval as the recommended starting angle for needle insertion. This method addresses the technical problem in existing technologies where there is a lack of image-based quantitative data calibration testing for the safe needle insertion angle range corresponding to different scleral puncture points.

This invention discloses a system for predicting the risk of recurrence after lumbar disc herniation surgery based on domain offset correction, relating to the fields of medical data processing and clinical disease risk prediction technology. The system acquires multimodal clinical and imaging feature vectors and follow-up outcome data from training and independent validation cohorts. It trains a center classifier to quantify and correct for differences in multi-center data distribution, calculates and normalizes the importance weights of each training case, trains a survival analysis base learner based on the processed weights, and performs stacking and ensemble integration. The final deployment model is determined by combining the independent validation case cohort. The final deployment model is then used to infer the risk of recurrence in target patients after lumbar disc herniation surgery, outputting individualized recurrence risk scores, risk stratification, and recurrence-free survival curves. This improves the robustness and reliability of the risk prediction system in cross-center scenarios and is suitable for follow-up management and clinical decision support after lumbar disc herniation surgery.

This invention discloses a method for constructing an interpretable predictive model of pathogenic bacteria in chest CT scans, belonging to the field of artificial intelligence technology. The method includes: Step S1: acquiring a chest CT image dataset and pathogenic bacteria type labels, and constructing a pathogenic bacteria imaging knowledge graph using a medical knowledge base; Step S2: constructing an initial model, which includes: a noise-invariant encoder, a counterfactual causal intervention module, a knowledge-guided feature compensation module, an interpretability analysis module, and a classifier; Step S3: feeding the spatial attention weight map back to the noise-invariant encoder to update and iterate the anatomical pathological features. Based on the final features obtained after internal iterative optimization, the initial model is trained according to preset training rules until overall convergence. This invention effectively distinguishes between real lesions and noise artifacts in low-dose CT images through causal intervention and attention map difference-driven feature updates, improving the model's recognition accuracy and robustness.

The application discloses a kind of self-supervision low-dose CT denoising methods based on decomposition convolutional neural network.First, the low-dose computed tomography (Low Dose Computed Tomography, LDCT) imaging DICOM data is obtained;Second, the value of the pixel point outside the CT scanning boundary in LDCT imaging data is set to 0, and normalized processing is carried out in the range of [-1024, 3072], and it is stored as NPY format;Then, the low-dose CT data set is input into the self-supervision denoising network based on image decomposition for training, and the training model is obtained;Finally, the obtained training model is used to predict new LDCT, and the denoised image is obtained.The application can separate high-quality CT images from LDCT images without labeled images by decomposing LDCT images into clean images, signal-dependent noise images and signal-independent noise images.The application can directly obtain denoised images through the trained deep learning denoising model, which provides technical support for the image diagnosis work of clinicians.

The application discloses an adnexal mass benign and malignant risk assessment method based on exosome OCS and O-RADS combination. The biomarker combination provided by the application comprises ovarian cancer OCS score and imaging grading score, and the ovarian cancer OCS score is obtained based on the concentration of CA125 protein, HE4 protein and C5a protein in exosomes. The application first uses the OCS score in combination with the O-RADS imaging score, and by comprehensively combining the biological information and the imaging characteristics, the accurate diagnosis capability for the O-RADS 3 / 4 gray area patients is significantly improved, and unnecessary excessive medical treatment and missed diagnosis are reduced. The application also includes various clinical variables such as age, menopausal status and ultrasonic characteristics, and constructs a multi-dimensional combined diagnosis model, and further improves the diagnosis performance.

This invention relates to the field of healthcare informatics technology, specifically to a dynamic prediction system for the risk of delayed chest closure after surgery in children with congenital heart disease. It collects high-frequency vital signs signals during surgery, imaging report text, and clinical structured data; extracts the maximum Lyapunov exponent, correlation dimension, and approximate entropy as chaotic features; quantifies the ambiguous semantics in the imaging report into membership values; uses Dempster-Shafer evidence theory to fuse multimodal features and encode uncertainty; constructs a dynamic ensemble learning model, detects concept drift using ADWIN and updates it online, dynamically weights it based on Shapley values; uses multi-objective reinforcement learning to dynamically optimize the risk threshold; and generates counterfactual explanations to provide individualized intervention suggestions. This invention achieves accurate and dynamic early warning of the need for delayed chest closure.

This invention relates to the field of intelligent diagnosis and treatment technology, and particularly to a precision diagnosis and treatment method and system for non-high-risk chest pain populations. The method includes: acquiring known clinical information, known coronary artery imaging information, and known long-term clinical events of non-high-risk chest pain populations to construct a dataset; training an XGBoost model using the dataset to obtain a risk prediction scoring model; acquiring clinical information of the non-high-risk chest pain patient to be tested, inputting it into the risk prediction scoring model to obtain a risk prediction score; determining the group to which the non-high-risk chest pain patient belongs based on the risk prediction score; and determining subsequent diagnosis and treatment strategies based on the group to which the non-high-risk chest pain patient belongs and the patient's clinical information. This invention can more accurately predict the risk of non-high-risk chest pain patients, and for low-risk non-high-risk chest pain patients, it can reduce unnecessary invasive examinations and achieve effective resource allocation.

The application discloses a pneumoconiosis staging recognition method and system based on multi-modal artificial imaging feature fusion, and relates to the technical field of medical image recognition. The method comprises the following steps: standardizing and pre-processing an input chest X-ray image and extracting a lung field region; in the lung field region, detecting and counting micro nodules based on clinical imaging prior knowledge, and extracting micro nodule quantity features reflecting the quantity and spatial distribution characteristics of nodular lesions; calculating the first-order entropy features of the lung field region image to quantify the complexity of lung parenchyma texture, and extracting high-dimensional features representing gray heterogeneity and structure statistical characteristics by using an imaging feature analysis method. The multi-class artificial imaging features are standardized and fused in a unified feature space, and the fused features are input into a learning classification model for pneumoconiosis period recognition. The application improves the stability and interpretability of pneumoconiosis staging recognition through multi-modal artificial imaging feature fusion modeling.

This invention discloses a submillimeter-level diffusion magnetic resonance imaging (DTI) method for extracting the radial index of cortical layered cognitive structures, relating to the field of medical image processing technology. This invention aims to address the problems of existing technologies failing to accurately quantify the directionality of cortical microstructures and neglecting the heterogeneity of cortical layering. The method includes: acquiring DTI data and performing high-fidelity preprocessing such as MP-PCA denoising, Gibbs artifact removal, and Gaussian process distortion correction; estimating the diffusion tensor and extracting the principal feature vector based on the preprocessed data; reconstructing the cortical white matter and pia mater surface using T1 images and extracting the normal vector; calculating the absolute value of the dot product of the diffusion principal feature vector and the surface normal vector to construct a radial index map; and segmenting the cortex into multiple intermediate layers based on the principle of equal volume and extracting layering parameters. Through refined high-resolution data processing and geometric modeling, this invention can accurately quantify the radial alignment integrity of cortical nerve fibers, providing highly sensitive imaging markers for cognitive disorders such as schizophrenia.

The application discloses a specific metabolite for dynamically monitoring the curative effect of gastric cancer NAIC, a screening method and a prediction method, and belongs to the technical field of tumor treatment prediction. The screening method is as follows: obtaining plasma samples of gastric cancer patients before treatment and at key treatment nodes, detecting metabolite abundance data; based on imaging or pathological evaluation, the patients are divided into a treatment response group and a non-response group; comparing the metabolite changes of the two groups before and after treatment, screening metabolites that only change significantly in the response group and have opposite change trends between the two groups, and constituting a key metabolite set; using the metabolite abundance data in the set as features and the treatment response as variables, a LASSO regression model is trained, and finally specific metabolites for distinguishing curative effects are screened out. The curative effect prediction model and the prediction method based on the specific metabolites can realize early and non-invasive dynamic monitoring and prediction of curative effects, and provide a reliable basis for clinical individualized treatment.

An aortic aneurysm carries increasing risk of rupture with growing aneurysm diameter. This condition is typically asymptomatic, so screening and surveillance are essential. Ultrasound and other imaging methods are employed for such monitoring at high accuracy. However, these methods require an expert and are expensive. Aortic aneurysms are considerably under-detected at present and may become even more under-detected in the future as the disease prevalence increases with societal aging. This disclosure present devices that are convenient in use and cost for aortic aneurysm screening and surveillance.

This invention discloses a metabolomics-based method for predicting the efficacy of non-invasive pretreatment intervention (NAIC) in gastric cancer and specific metabolites, belonging to the field of biodetection technology. The method includes: obtaining plasma samples from gastric cancer patients before treatment, detecting the abundance of their metabolites, and obtaining baseline plasma metabolomics data; classifying patients into a response group and a non-response group based on post-treatment imaging assessment results; comparing the baseline metabolomics data of the two groups to screen for differentially expressed metabolites; using these differentially expressed metabolites as features, training the model using LASSO regression, determining the optimal parameters through cross-validation, and constructing an efficacy prediction model composed of specific metabolites and their regression coefficients; for the sample to be predicted, detecting the abundance of its specific metabolites and inputting them into the model to obtain a risk score, and predicting the patient's response to treatment by comparing it with a risk threshold. This invention enables accurate efficacy prediction before treatment, providing an important basis for individualized clinical treatment decisions.

The application discloses peripheral blood CD8 + The application discloses an application of T cell absolute count as a biomarker in prevention or evaluation of pulmonary lymphangioleiomyomatosis. The application breaks the limitation of traditional methods mainly depending on imaging or single serological index, provides a novel diagnosis strategy based on peripheral blood immune cell absolute count, is convenient for sampling (venous blood can be drawn), has low detection cost, can be used for dynamic monitoring of patient's disease severity and prognosis stratification, and lays a solid foundation for subsequent development of immune regulation target drugs for S-LAM.

The application provides a detection system for genetic risk assessment of neural tube defects, and relates to the technical field of bioinformatics. The system comprises: obtaining genomic sequencing data of a to-be-detected individual, performing targeted filtering based on a preset NTD-related gene set, and screening out rare pathogenic variants located in the gene set, MAF satisfying a preset frequency threshold, and predicted to have biological pathogenicity; then, the total number of the rare pathogenic variants is counted to obtain a mutation load value, which is compared with a preset determination threshold, and a risk assessment result is output. The application effectively removes the whole genome background noise by limiting the gene set range and quantifying the cumulative effect of rare pathogenic variants, breaks through the limitations of narrow coverage of traditional single gene detection and lag of imaging diagnosis, significantly improves the specificity and sensitivity of neural tube defect diagnosis, and can realize precise risk early warning in early pregnancy or before embryo implantation.