211 results about "Radiomics" patented technology

The invention relates to the technical field of medical diagnosis, and discloses a DCM early noninvasive analysis method based on multi-radiomics and serum markers. Collecting image data through a multi-modal medical imaging device, and collecting serum marker data through a blood detection device; respectively generating a radiomics feature set and a serum marker time sequence feature set by using a multi-scale feature extraction algorithm and a time sequence analysis model; fusing the features by adopting a dynamic weighted fusion strategy to generate a joint feature matrix; inputting the model into a pre-trained multi-task deep learning model to predict a DCM risk probability; and finally, based on a genetic algorithm, optimizing the diagnosis decision tree and outputting an early DCM diagnosis result. The method is noninvasive and accurate, and can effectively improve the early diagnosis accuracy of DCM.

The invention relates to the technical field of image recognition, and discloses a carotid plaque stability and stroke risk prediction system fusing multiple modes, and the system comprises a data collection and fusion module, a feature extraction and association module, a risk assessment and layering module, an intervention decision module, and a report generation and feedback module. When carotid plaque stability and cerebral apoplexy risk prediction is carried out, plasma proteomics, radiomics and clinical data are synergistically integrated by establishing a multi-modal data fusion analysis framework, so that the limitation that a traditional method depends on a single data source is overcome; the plaque risk can be evaluated from multiple dimensions of biological activity and morphological features, the comprehensiveness and accuracy of risk prediction are improved, a more reliable diagnosis basis is provided for clinic, interaction and sensitivity influence among different modal features can be adaptively quantified by introducing dynamic feature correlation modeling and a real-time weight calibration mechanism, and the risk prediction accuracy is improved. And objectivity and consistency of risk assessment results are ensured.

The invention discloses a system and a method for predicting postoperative recurrence risk after triple negative breast cancer neoadjuvant therapy based on multi-modal time sequence medical image data, and belongs to the field of medical image analysis. The system comprises a data processing module used for constructing a multi-modal data set; the multi-modal feature extraction and screening module is used for extracting deep learning, radiomics and tumor habitat features from the region and carrying out feature screening; the model training module is used for constructing a time sequence model based on a Transform architecture and carrying out training through a multi-task learning strategy integrated with time consistency constraint and gene association auxiliary loss; and the recurrence risk prediction module is used for loading the trained model and outputting a recurrence probability and a risk level. According to the method, the multi-modal time sequence image and gene information are fused, so that the recurrence risk of the triple negative breast cancer patient is dynamically and accurately quantified, and support is provided for clinical individualized treatment decision.

The invention discloses a tumor prognosis prediction method and a tumor prognosis prediction system, which are used for constructing a multi-modal fusion model based on image-pathology to improve the prognosis prediction efficiency of tumors, especially pancreatic cancer, and providing reference information for clinical decision-making. According to the technical scheme, the method comprises the following steps: S1, preprocessing an original tumor enhanced CT image, segmenting a tumor region, extracting radiomics features and depth image features of the tumor region, and establishing a CT image feature set; s2, after feature preprocessing is carried out on the tumor clinical data, clinical features with statistical significance are screened out, and a clinical feature set is established; s3, carrying out Hamp; e, preprocessing the pathological image, segmenting a tissue region, extracting spatial relation features, and generating a pathological spatial feature set; and S4, based on a feature interaction method, carrying out multi-modal fusion on the CT image features, the clinical features and the pathological spatial features, inputting a full-connection neural network, constructing a tumor survival risk prediction model, and outputting a tumor survival risk probability through the tumor survival risk prediction model.

The invention belongs to the technical field of medical image processing, and particularly relates to an esophageal cancer treatment effect and survival combined prediction method and system based on multiple modalities, and the method comprises the steps: obtaining a preoperative CT image and Hamp of an esophageal squamous cell carcinoma patient; e, the dyed digital pathological image, transcriptome data and clinical diagnosis and treatment information are preprocessed and subjected to feature extraction, and radiomics embedding representation, pathomics embedding representation and pipeline branch embedding representation are obtained respectively; the radiomics embedded representation, the pathomics embedded representation and the pipeline branch embedded representation are aligned and input into a multi-modal fusion module based on a multi-head self-attention mechanism for feature fusion, and fusion feature representation is generated; and based on the fusion feature representation, synchronously outputting a curative effect prediction result and a survival prediction result by using a multi-task output module, and evaluating model prediction performance by using a curative effect evaluation index and a survival analysis index.

The invention provides a breast cancer recurrence risk prediction method, system and device based on an ultrasonic image, and relates to the field of intelligent medical treatment, the method uses a deep convolutional neural network to perform deep network feature extraction on a breast ultrasonic image, and uses a deep learning semantic segmentation algorithm to perform accurate positioning and automatic segmentation on a breast tumor region of interest, thereby improving the accuracy of breast cancer recurrence risk prediction. Meanwhile, habitat analysis is carried out on the ultrasonic images to extract tumor heterogeneity features, multi-level and multi-mode features such as deep learning features, radiomics features and habitat analysis features are fused, a breast cancer recurrence risk prediction model is constructed, breast cancer recurrence risk prediction is carried out, and a breast cancer recurrence risk assessment result is output. And a quantitative basis is provided for clinical treatment decisions. The accuracy and robustness of recurrence risk prediction are remarkably improved through multi-feature fusion, and standardization and objectification of breast cancer prognosis evaluation are achieved. In addition, the method further has the advantages of being easy and convenient to operate, low in cost, noninvasive, nonradiative, good in repeatability and the like.

The invention discloses a meniscus injury prediction method and system based on a neural network, and the method comprises the steps: obtaining a knee MRI image, carrying out the automatic segmentation of the knee MRI image based on a convolutional neural network, extracting a plurality of image features based on the segmented image, and obtaining a meniscus injury prediction result. And constructing a meniscus damage prediction model based on the SNN network model, and carrying out model training and verification. The meniscus injury diagnosis accuracy and diagnosis efficiency are improved, future injury risk prediction is achieved, valuable prediction information is provided for clinicians, prevention and treatment schemes are helped to be formulated, meanwhile, radiomics feature and load structure feature heat maps and a mixed attention mechanism are introduced, the clinical interpretability is enhanced, and the diagnosis accuracy and efficiency of meniscus injury are improved. And a full-automatic diagnosis process is realized.

The invention discloses a method for constructing a machine learning model based on cerebellar subregion multi-modal radiomics. According to the method, [18F] FDG PET metabolic features and 3DT1 W MRI structural features of a cerebellar subregion are extracted, a random forest classification model is constructed after feature selection, and high-precision identification of the Alzheimer's disease (AD) and a cognitive normal (CN) is realized in combination with SHAP analysis. According to the method, the multi-modal radiomics characteristics of the cerebellar subregion are integrated for the first time, the accuracy and interpretability of AD early diagnosis are improved, and a non-invasive and efficient diagnosis tool is provided for clinic.

The invention discloses a postoperative portal vein pressure prediction system, which relates to the technical field of portal vein pressure prediction, and is characterized in that three-dimensional structure regions of interest of the liver and the spleen are segmented based on preoperative and postoperative abdomen enhanced CT vein phase image data of a patient; image omics features including first-order statistical features, texture features and shape features are extracted from the segmented regions of interest of the three-dimensional structures of the liver and the spleen, and core feature screening is carried out on the extracted features; integrating the screened core features with clinical hemodynamic parameters and surgical parameters to construct a multi-modal prediction model of the portal vein pressure gradient; and predicting and outputting a portal vein pressure gradient predicted value by using the multi-modal prediction model of the portal vein pressure gradient, and determining a postoperative portal vein pressure gradient risk grade of the patient. The portal vein pressure gradient prediction method solves the problems of insufficient dynamic evaluation capability, multi-modal information integration and risk layering application in the prior art, and realizes non-invasive and accurate prediction of the portal vein pressure gradient.

The invention discloses a Crohn disease focus automatic segmentation and activity evaluation system based on deep learning, which belongs to the field of medical artificial intelligence and comprises a data preprocessing unit, a focus automatic segmentation unit, a radiomics feature extraction unit, a feature screening and dimension reduction unit and an activity classification unit. According to the method, an nnU-Net deep learning segmentation model is combined with image omics feature extraction, multi-stage feature screening and machine learning classification technologies, so that full-process automation from CTE image preprocessing, focus automatic segmentation, feature extraction and screening to activity classification is realized. The system can efficiently and accurately segment the focus of Crohn's disease, automatically assesses the disease activity based on the screened key radiomics characteristics, significantly improves the consistency, objectivity and efficiency of diagnosis, and is suitable for clinical auxiliary diagnosis and scientific research analysis.

An ultrasound-metabolic synergistic system for diagnosing SLN metastasis of breast cancer. A deep learning model based on grayscale ultrasound is constructed to predict SLN metastasis of breast cancer, and 9 grayscale deep features and 15 grayscale manual features associated with SLN metastasis are obtained. A matrix material based on two-dimensional material MXene / multi-walled carbon nanotubes improves the detection of small metabolic molecules in blood, and metabolomics analysis results show that this has certain potential in detecting SLN metastasis of breast cancer, while revealing metabolic pathways closely associated with metastasis, such as the glucose-alanine cycle and glycolysis, suggesting that alterations in relevant molecular pathways may underlie heterogeneous features in ultrasound radiomics. The combination of ultrasound omics and metabolomics further improves the diagnostic performance for SLN metastasis of breast cancer. Therefore, organic integration of the two serves the diagnosis of SLN metastasis of breast cancer, providing a new strategy and theoretical basis for the accurate diagnosis of SLN metastasis of breast cancer, and possesses practical clinical application values.

A method for determining a recurrence of a disease in a patient includes generating a medical image of an organ of the patient and then extracting an invasive edge around an area of interest in the medical image. A plurality of radiomics features is obtained from the invasive edge and the recurrence of the disease is determined based on the plurality of radiomics features.

A computer-implemented method is for providing radiomics-related information. In an embodiment, the computer-implemented method includes receiving radiomics-related data; determining, based on the radiomics-related data and an assistance algorithm, a function for processing the radiomics-related data; calculating, based on the radiomics-related data and the function for processing the radiomics-related data, the radiomics-related information; and providing the radiomics-related information.

The invention relates to the technical field of medical data analysis, in particular to a urinary system tumor big data analysis system which comprises a target kernel generation module, a kernel matrix calculation module, a parameter optimization module and a risk division module. According to the method, a target kernel matrix reflecting clinical prognosis differences is constructed and serves as an optimization reference, a radiomics and genomics feature kernel matrix is generated through hardware acceleration parallel computing, kernel function width parameters are dynamically iteratively updated based on an alignment degree numerical value so as to ensure that multi-modal feature distribution is highly matched with a prognosis label, and the accuracy of the multi-modal feature distribution is improved. A multi-dimensional feature space containing rich pathological information is constructed by combining a weighted fusion mechanism after centralization processing, so that a support vector machine is trained to determine a high-robustness decision boundary, and precise division of tumor risk levels is realized while high-dimensional data calculation delay is greatly reduced; and the reliability and timeliness of auxiliary diagnosis and treatment results in a complex pathological environment are effectively improved.

The invention discloses a bladder cancer level evaluation method and device based on radiomics and a storage medium. The bladder cancer level evaluation method and device are used for improving bladder cancer level evaluation accuracy and reliability. Obtaining a multi-parameter MRI image of the bladder cancer, wherein the multi-parameter MRI image comprises a T2 weighted image, a diffusion weighted image and a dynamic contrast enhanced image; performing tumor focus part sketching on the multi-parameter MRI image; respectively carrying out feature extraction on the delineated T2 weighted image, diffusion weighted image and dynamic contrast enhanced image by using a radiomics package to generate a bladder cancer feature set; performing feature difference screening on the bladder cancer feature set according to the clinical diagnosis tag; carrying out dimension reduction processing on the screened bladder cancer feature set; constructing a single-parameter MRI classification model and a multi-parameter joint classification model according to the bladder cancer feature set after dimension reduction; and using the single-parameter MRI classification model and the multi-parameter joint classification model to jointly evaluate the bladder cancer level of the to-be-detected data.

The invention discloses a cerebral apoplexy patient postoperative neural function real-time monitoring method. The method comprises the steps that brain MRI image data and clinical information of a to-be-monitored patient and post-operation electroencephalogram data collected in real time are collected and preprocessed; respectively extracting neurophysiological features and radiomics features of the patient to be monitored based on the preprocessed data; performing significant feature screening on the clinical information, the neurophysiological features and the radiomics features, performing preprocessing on the screened data, and combining minimum absolute contraction and selection operator regression analysis to obtain quantitative electroencephalogram data feature indexes and radiomics scores; inputting the screened clinical information, the quantitative electroencephalogram data characteristic index and the radiomics score of the to-be-monitored patient into a trained prediction model, and predicting a risk index of early neurological deterioration of the to-be-monitored patient; the problem that the evaluation result is inaccurate due to the fact that a single clinical feature is adopted to evaluate the neural function in a traditional method is solved.

The invention discloses a benign and malignant identification and growth prediction system based on pulmonary nodule radiomics, and relates to the technical field of medical image processing. Comprising an acquisition module used for acquiring a pulmonary nodule segmentation mask, a CT value and surrounding tissue segmentation data; the calculation module is used for synchronously calculating a first score representing internal CT value distribution heterogeneity, a second score representing surface complexity and a third score representing blood vessel interaction according to the data; the judgment module is used for triggering a high-risk alarm when the scores of the three items all exceed corresponding threshold values; the prediction module is used for correcting the basic growth model according to the three scores and generating growth prediction data; and the output module outputs an alarm and a prediction result. The system also comprises optimization modules of weight fusion, time sequence processing, growth partitioning and the like. According to the scheme, through multi-dimensional feature fusion and dynamic modeling, more accurate identification of benign and malignant pulmonary nodules and more reliable growth trend prediction are realized, and key support is provided for clinical decision making.

Provided is an intelligent grading method and system for pulmonary nodules based on multi-modal feature fusion, including: obtaining ROI and VOI of pulmonary nodules based on chest CT examination images and examination reports by utilizing clinical multi-modal data from physical examination population, designing a multi-task feature extraction network based on attention mechanism, to obtain radiomics features and deep image features from the ROI and VOI; designing a cross-modal feature fusion method based on graph representation learning, designing a multi-modal information extraction method, obtaining specific feature representations and graph structures of modalities, and then fusing the feature representations and the graph structures; and proposing an optimization and clinical verification method of pulmonary nodule grading GCN model based on self-supervised learning, to realize fine grading of pulmonary nodule malignancy with slight differences, thereby providing a new approach to design of fine-grained classification algorithms.

The invention discloses a rectal cancer postoperative recurrence risk prediction system and method based on multi-modal time sequence data, and relates to the technical field of medical artificial intelligence. The system comprises a data acquisition and preprocessing module, a feature extraction module and a multi-modal feature fusion and modeling module. The method comprises the following steps: constructing a cross-modal data set containing time sequence clinical data, a time sequence MR image and a biopsy digital pathological image; respectively extracting clinical features, radiomics and deep learning features of the MR image, and nucleus morphology and spatial distribution features of the pathological image; and fusing all the features by using a Transform network, and constructing a prediction model. According to the method, macroscopic images, micropathology and dynamic time sequence information are integrated, tumor heterogeneity is comprehensively quantified, the problem that prediction of a single-mode static model is not accurate is solved, the postoperative recurrence risk of the stage III rectal cancer patient can be evaluated more accurately, and clinical treatment decision making is assisted.

The invention provides an osteoporosis fracture risk prediction method and system based on multi-modal imageomics. The method comprises the following steps: S1, acquiring multi-modal image data of the lumbar vertebra of a user; s2, performing feature correction according to the acquired multi-modal image data to obtain a time-space aligned multi-modal feature tensor; s3, according to the obtained multi-modal feature tensor, the integrity of the bone microstructure at each position is evolved based on the trained bone microstructure evolution model, and a bone microstructure integrity evolution result is obtained; and S4, according to the obtained bone microstructure integrity evolution result, extracting a fracture risk characteristic part, and further performing fracture risk prediction on the fracture risk characteristic part to obtain a fracture risk prediction result. According to the invention, the accuracy and reliability of osteoporotic fracture risk prediction can be improved.

The invention belongs to the technical field of medical image processing, and particularly relates to a non-functional pancreatic neuroendocrine tumor preoperative index prediction method and system. According to the method, on the basis of abdomen CT image lesion segmentation, pathological grading (low risk / high risk) and lymph node metastasis (LNM) risks are jointly predicted through a multi-task deep learning framework; according to the method, tumor morphological features, radiomics features and clinical parameters (such as tumor size and position) extracted by a segmentation network are combined, and high-precision prediction of preoperative key indexes is realized by using a feature fusion module and a lightweight classification head; through a cross-modal attention mechanism and multi-center data verification, the model achieves the pathologic classification AUC of 0.75 on an internal verification set, achieves the LNM prediction AUC of 0.78, and is significantly superior to traditional clinical experience judgment. According to the method, reliable decision support is provided for preoperative precise operation planning and personalized treatment.

The invention discloses a chronic disease prognosis method, a chronic disease prognosis model, a chronic disease prognosis device, computer equipment and a storage medium. The chronic disease prognosis method comprises the following steps: acquiring a medical image and medical data of a to-be-prognosed person; in the medical image, deep learning features and radiomics features are extracted; determining shared features and unique features of the deep learning features and the radiomics features in the deep learning features and the radiomics features; performing fusion processing based on the shared features and the unique features to obtain target fusion features; based on the target fusion features and the medical data, constructing a multi-omics graph; and performing prognosis treatment based on the multiple omics graphs to obtain a prognosis result of the to-be-prognosed person. Through the target fusion features and data of different modalities such as medical data, the constructed multi-omics graph can enhance the modeling ability of the model for complex pathological information on the basis of improving expression complementarity among different modalities, so that the accuracy and robustness of a prognosis prediction result are remarkably improved.

The invention relates to the technical field of gene and immune molecule detection, in particular to a method for predicting lung cancer EGFR genotype and immune molecule expression level, and the method specifically comprises the following steps: S1, obtaining a CT image: obtaining the CT image of a non-small cell lung cancer patient, the CT image comprising a tumor area and a peritumor area; s2, image omics characteristics are extracted, wherein the image omics characteristics are extracted from the tumor area and the peritumor area respectively; s3, extracting deep network features: extracting the deep network features from the CT image by using a deep neural network based on an attention mechanism; and S4, constructing a prediction model: inputting the radiomics characteristics and the deep network characteristics into the prediction model, and outputting prediction results of EGFR genotypes and immune molecule expression levels. The method for predicting the lung cancer EGFR genotype and immune molecule expression level has the advantages of non-invasiveness, high efficiency, accurate prediction, clinical practicability, model interpretability and technical compatibility and flexibility.

The invention relates to the technical field of medical imaging omics analysis, in particular to a pancreatic perivascular fat image feature-based operation area inflammation degree grading method, which comprises the following steps of: determining clinical risk factors for pancreatic operation area inflammation degree grading through a statistical method; respectively segmenting ROI (Region of Interest) 1-6 in the preprocessed CT vein phase image and the preprocessed vein phase image through the combination of a TotalSegmentor segmentation model, an nnUNet segmentation framework and a region growing algorithm, and extracting cross-region image omics characteristics; and constructing an inflammation degree grading model based on the cross-regional radiomics characteristics and the clinical risk factors through a plurality of machine learning algorithms. According to the method, in the fusion model constructed by combining the risk factors and the radiomics characteristics, the clinical risk factors are found by using retrospective research, and meanwhile, the clinical risk factors and the radiomics characteristics are spliced by adopting an attention mechanism, so that the grading precision and efficiency of the inflammatory degree of the fusion model are ensured.

The invention discloses a method for constructing an osimertinib curative effect prediction model based on an elastic score and T790M mutation, and relates to the technical field of biological medicines, and the method is technically characterized in that the osimertinib curative effect prediction model based on the elastic score and the T790M mutation is constructed; the interaction mechanism between the radiomics characteristics and the EGFR mutation state is explored, so that a more accurate decision basis is provided for personalized clinical treatment, and the treatment effect is improved.

The invention discloses a curative effect evaluation system for treating liver cancer by combining HDRBT with thermal ablation in multi-modal radiomics, and relates to the field of liver cancer treatment, and the system comprises a multi-modal image data acquisition module which is used for collecting multi-modal image data; the three-dimensional visual model construction module is used for reconstructing a three-dimensional structure of liver tumors, peripheral blood vessels and bile ducts through an image segmentation algorithm, and generating a visual model by fusing radiomics characteristics; the combined treatment plan generation module is used for integrating the dosimetry parameters of the HDRBT and the process parameters of the thermal ablation, optimizing a catheter implanting path, the position of a thermal ablation electrode and a treatment sequence, and generating a combined treatment scheme; and the curative effect evaluation module is used for quantitatively analyzing tumor volume change, ablation boundary integrity and residual tumor activity through accurate registration of multi-modal images before and after treatment. According to the scheme, the problems of incomplete thermal ablation and high recurrence rate of hepatocellular carcinoma can be solved, and full-chain technical support is provided for clinical precise application.

The invention relates to a kidney lump benign and malignant analysis method and device based on a laparoscopic ultrasound image, and belongs to the technical field of medical image processing.The method comprises the steps that radiomics characteristics of the laparoscopic ultrasound image are obtained, and radiomics scores of lump malignant risks are calculated according to the radiomics characteristics; determining an independent risk factor corresponding to the patient clinical variable, and constructing a clinical prediction model according to a mapping relationship between the patient clinical variable and the independent risk factor; and according to the radiomics score and the clinical prediction model, constructing a kidney lump benign and malignant analysis model. The technical problem that in the prior art, massive quantitative image features cannot be efficiently and accurately mined from medical images, and the most valuable iconography features cannot be screened out for analyzing clinical information is solved.

The invention relates to the technical field of federated learning, and discloses an image omics-oriented cross-domain federated machine learning training method, which comprises the following steps that: a server distributes a global orthogonal semantic base to establish a shared gradient space recessive semantic coordinate system, and when the stability of a local training process meets a preset condition, a client sends the global orthogonal semantic base to the client; a local gradient is projected to a coordinate system and uploaded together with a digital fingerprint representing the physical characteristics of imaging equipment, and a server groups clients according to the digital fingerprint and performs weighted aggregation based on the information concentration degree on received projection vectors in each group so as to reconstruct a global gradient to update a model. According to the method, through an aggregation mode of firstly performing grouping according to physical causes and then performing weighting in groups according to information values, systematic deviation from equipment differences and real pathological semantics from data contents can be effectively stripped, and sensitivity to key clinical features is kept.

The invention provides a breast cancer adjuvant therapy pathology complete remission prediction method and electronic device.The breast cancer adjuvant therapy pathology complete remission prediction method comprises the steps that at the first stage, based on multi-time-point and multi-parameter MRI images of a patient before and after adjuvant therapy, the breast cancer adjuvant therapy pathology complete remission prediction result is obtained; training an image feature encoder with a tumor dynamic change identification capability through a dynamic supervision pre-training mode; and a second stage of constructing a multi-modal deep fusion network based on the image feature encoder to integrate the image features, radiomics features and clinical pathological features of the patient, and outputting a prediction result of complete pathology remission, the radiomics features being extracted from the MRI image. And the purposes of high precision and strong generalization capability are achieved.

The invention provides computer equipment for executing a sub-solid pulmonary nodule growth prediction method based on CT imageomics, and the computer equipment comprises a memory, a processor and a computer program, and when the processor executes the program, density value extraction is carried out on a sub-solid pulmonary nodule region through multi-stage CT images, the internal structure of a nodule is segmented by adopting a convolutional neural network, and the internal structure of the nodule is obtained; obtaining nodule internal density distribution data from the segmentation result; calculating a cavity expansion rate and an edge density increase rate in unit time by combining the density amplitude data according to a focus characteristic influence degree evaluation result; according to the lesion cavity expansion rate and the edge density growth rate, the malignant progress risk level is judged, and malignant progress time window estimation is obtained; according to the malignant progress time window estimation, the matching degree between the density distribution data and the development speed index is calibrated through clinical feedback data, and sub-solid pulmonary nodule growth prediction output is obtained.