54 results about "Disease progress" patented technology

The invention provides a contrast image analysis method and device for digital diagnosis and treatment equipment. The method comprises the following steps: predicting a disease progress condition based on a focus volume characteristic parameter, a density statistical parameter and a quantitative index of a focus surrounding tissue density difference; judging whether the prediction result of the disease progress condition reaches a preset risk threshold range or not, if the prediction result is within the preset risk threshold range, judging that the disease progress condition is in a risk state, and generating high-risk early warning information; and judging the severity of the illness state according to the judgment result of the illness state progress condition prediction and in combination with the fitting data of the focus volume and density changing along with time, performing stage classification on the focus progress process, and generating a quantitative illness state diagnosis report.

The invention discloses a clinical aid decision-making system based on big data, and relates to the technical field of intelligent medical treatment, and the system extracts structured and unstructured data from electronic medical records and medical images, employs a bidirectional LSTM deep learning framework based on a self-attention mechanism, carries out the alignment of the cross-modal features of medical record texts and image data, and carries out the recognition of the cross-modal features of the medical record texts and the image data. The method comprises the following steps: establishing a medical knowledge graph based on an RDF triple, modeling a high-order interaction relationship through a cross-modal interaction attention mechanism CMA, enabling disease expression to be more accurate and interpretable, constructing the medical knowledge graph based on the RDF triple, dynamically expanding knowledge in combination with a graph neural network GNN, and matching the disease expression of a patient through a semantic similarity calculation model; a multi-layer similarity calculation framework is adopted to perform similar case screening, coarse screening is performed through surface feature matching, deep semantic matching is performed in combination with GNN optimized disease semantic vectors, cases with similar disease progress paths are inferred and matched through a knowledge graph, and comprehensive and multi-level similar case screening is realized.

The invention relates to a data fusion processing method and system for an intelligent sensor based on MEMS, and the method comprises the following steps: carrying out the non-stationary signal decomposition of a multi-modal physiological parameter time sequence data stream, and obtaining a key vital sign feature sequence; performing phase coupling degree calculation on the key vital sign feature sequence to obtain an inter-organ function correlation intensity matrix; performing high-dimensional feature space projection on the critical patient based on the inter-organ function association intensity matrix to obtain a pathological state feature map of the patient; performing nonlinear kinetic analysis on the pathological state characteristic spectrum of the patient to obtain a disease progress dynamic trajectory; and performing clinical decision support reasoning based on the disease progress dynamic trajectory to obtain personalized treatment suggestions, thereby solving the technical problem that the existing analysis method is often limited to observation of a single physiological parameter or simple statistical analysis and lacks the ability to understand the disease mechanism from the overall perspective.

The invention provides an osteoporosis prediction method and system based on a centrum CT image, and the method comprises the steps: obtaining a patient centrum CT image, and carrying out the preprocessing of the image, and obtaining standardized three-dimensional voxel data; performing spatial resampling on the original data to a uniform resolution; constructing a hierarchical feature extraction network for centrum bone structure perception, and designing a non-uniform sampling mechanism for centrum density distribution; constructing an intervertebral biomechanical conduction diagram network; designing an osteoporosis specific loss function; and outputting a grading prediction result containing confidence, generating probability distribution of each grade through a softmax function, and generating a visual thermodynamic diagram of the lesion area. Through time sequence consistency constraint, the system can analyze image changes of the same patient at different time points and evaluate the treatment effect and the disease progress. The dynamic monitoring ability provides a powerful tool for long-term management of chronic osteoporosis, and is helpful for timely adjustment of treatment schemes and improvement of prognosis of patients.

The invention relates to the field of ulcerative colitis, and discloses a multi-parameter monitoring system for evaluating the intestinal barrier function of ulcerative colitis, which is used for realizing the goal of automatically adjusting a treatment scheme according to a monitoring result by constructing a closed-loop feedback treatment system. According to the invention, by designing the nanoprobe of which the surface is modified with a specific binding element and combining with a swallowing capsule endoscopy, real-time imaging and molecular level dynamic tracking of the intestinal barrier are realized, genetically engineered bacteria are constructed to quantify intestinal permeability abnormality and local inflammation level, and an intestinal barrier function damage score and a disease progress prediction result are output; individual monitoring is achieved through transfer learning, ultrasonic elastography, optical coherence tomography, wearable equipment for measuring intestinal electric field impedance spectroscopy and other technologies are adopted, an intestinal wall mechanics-electrophysiology composite parameter set is obtained, and the evaluation accuracy is improved.

The invention relates to the technical field of rectal cancer prognosis prediction, in particular to a rectal cancer prognosis prediction method and device based on a multi-modal image and electronic equipment. The method comprises the following steps: firstly, acquiring an MRI image and a pathological image of a patient as original input data; respectively preprocessing the MRI image and the pathological image by adopting a double-flow heterogeneous feature extractor; then, by establishing a dynamic association mechanism, the change of the mapping relation between the two kinds of modal feature data is tracked in real time, the dynamic association mechanism can adjust the feature weight in a self-adaptive mode, and key changes in the disease progress process are effectively captured; and finally, based on the obtained dynamic association feature data, applying a pre-trained prediction model to obtain a prognosis prediction result. The MRI image and the pathological image are effectively integrated, and the accuracy of predicting the disease progress and the prediction repeatability are improved.

The invention relates to the technical field of data information processing, in particular to a multi-modal medical database construction method and system, and the system comprises a data collection module, a data preprocessing module, a data storage module, and a multi-modal model construction module. According to the method, the deep learning diagnosis model based on multi-modal data fusion is constructed, so that accurate early warning of disease progress key nodes is realized; the model integrates multi-modal data such as clinical indexes, laboratory examination and iconography data, processing is performed through methods such as feature extraction, standardization and normalization, key information in each modal data is extracted, disease development rules and patient individual differences are captured, and high-risk patients are identified; particularly, the deep learning model is utilized to process and predict multi-time-point data, so that the timeliness and accuracy of the model are improved, early recognition and timely intervention are realized, and a better treatment opportunity is provided for a patient.

The invention relates to a training method of a respiratory tract infection disease progress and prognosis prediction model. The training method comprises the following steps: extracting mRNA from peripheral blood of a target patient, and carrying out transcriptome sequencing to obtain a sequencing result; based on the ferroptosis related gene set, comparing ferroptosis score differences of two groups of patients with community-acquired pneumonia and sepsis, and screening corresponding ferroptosis related genes with statistical significance from a sequencing result; screening out genes meeting preset conditions from the ferroptosis related genes based on LASSO regression; and establishing an RTI clinical outcome prediction model through logistic regression by taking whether the patient is sepsis or not as an outcome dichotomy variable and taking the screened gene expression quantity as a prediction variable. According to the invention, after the prediction model is subjected to machine learning screening such as LASSO and the like, the core feature with the highest prediction value is reserved, so that the risk of over-fitting of the model on training data is reduced.

A method may include applying a cell classification model to identify, based at least on an image of a biological sample, one or more cell types present in the biological sample. The cell classification model may be trained to differentiate between a plurality of cell types including a first cell type whose likelihood of being a macrophage satisfies a threshold and a second cell type whose likelihood of being the macrophage fails to satisfy the threshold. A composition profile for the biological sample may be generated based on the one or more cell types identified in the biological sample. At least one of a disease diagnosis, a disease progress, a disease burden, and a treatment response for a patient associated with the biological sample may be determined based on the composition profile of the biological sample. Related systems and computer program products are also provided.

The invention relates to the technical field of prediction and evaluation of lung cancer samples, and discloses a prediction and evaluation method of a lung cancer sample, which comprises the following steps: collecting clinical information of a lung cancer patient, acquiring biological data of the lung cancer sample, sorting medical image data, extracting key genes, proteins or pathway characteristics from the biological data, important features related to the lung cancer are screened by using a statistical method or a machine learning algorithm, a proper machine learning or deep learning model is selected according to data characteristics and a prediction target, and the interpretability, stability and calculation complexity of the model are considered. According to the method, tumors with high-risk characteristics, such as tumors with high invasiveness and high metastasis potential, can be found and intervened in time through prediction and evaluation of lung cancer samples, so that the disease progress is delayed, the treatment effect is improved, the treatment effect and disease change can be monitored through regular prediction and evaluation of the lung cancer samples in the treatment process, and the treatment effect is improved. The treatment scheme is adjusted in time, and the maximization of the treatment effect is ensured.

The invention discloses an Alzheimer's disease diagnosis and prediction method based on multi-source knowledge guidance, and belongs to the technical field of medical artificial intelligence assistance. Comprising the following steps: acquiring a nuclear magnetic resonance image, brain statistical data and clinical data of a participant; processing the nuclear magnetic resonance image by using a dual-path feature extraction strategy, obtaining global brain features and local brain features, carrying out collaborative learning, and generating fused visual features; the brain statistical data and the fused visual features are fused and then input into a trained diagnosis module for processing, and a diagnosis result at the current moment is obtained; and taking the clinical data as guidance, combining the diagnosis result at the current moment, and utilizing the trained prediction module to obtain the disease progress at the future moment. The multi-modal data can be integrated, the accuracy and comprehensiveness of Alzheimer's disease diagnosis and prediction are improved, and the problems that the accuracy of existing Alzheimer's disease diagnosis is not high, and clinical requirements are difficult to meet are solved.

The invention discloses a radiotherapy medical information comprehensive management system, and relates to the technical field of dose management. The first proportion and the second proportion of the patient are introduced, the two threshold values are combined, and the comprehensive index, the lesion volume and the treatment cycle length of the patient are comprehensively considered, so that more accurate and personalized initial dose calculation is realized. The first proportion and the second proportion are introduced, so that the system can more comprehensively evaluate the disease progress and treatment requirements of the patient, and the problem of inaccurate dosage caused by dependence on a single parameter in a traditional method is avoided. The comprehensive indexes are constructed, multi-dimensional data such as pathological features, physiological indexes and historical reactions of the patients are fused, and the treatment scheme can be adjusted according to the specific situation of each patient. And the safety and the effectiveness of the dosage are further improved. A weight factor based on a physiological characteristic value and a physiological characteristic reference value is introduced, and the dose is adjusted in real time through a closed-loop feedback mechanism, so that the flexibility and safety in the treatment process are ensured.

The invention provides a serum tumor marker detection array optimization method and system.The method is applied to a control module of an array device.The method comprises the steps that when a multi-needle detection platform conducts serum tumor marker detection on a preset sample, preliminary expression information sent by the multi-needle detection platform is obtained; acquiring patient medical history information, iconography information and pathology information corresponding to the preset sample; inputting the preliminary expression information, the patient medical history information, the iconography information and the pathology information into a pre-constructed Bayesian analysis model, and outputting a prediction weight, a prediction disease progress direction and a correlation marker corresponding to each tumor marker to generate target information; wherein the target spot information comprises a plurality of target markers; and controlling the multi-needle detection platform to perform serum tumor marker detection on the preset sample again according to the target spot information to complete array optimization of serum tumor marker detection. Therefore, an efficient and innovative solution is provided for early diagnosis and monitoring.

The invention belongs to the technical field of osteoarthritis management, and particularly relates to an osteoarthritis management system based on artificial intelligence and a computer storage medium. The invention provides an osteoarthritis management system based on artificial intelligence. The system integrates an LLM-DL hybrid architecture, a multi-specialist module cooperation framework and an RAG knowledge base system, and can cover a complete clinical process from patient preliminary diagnosis to treatment scheme formulation. The system can comprehensively and accurately evaluate the disease state of a patient, provides personalized disease progress prediction, not only predicts 2-year and 4-year functional results and iconography progress, but also can identify specific risk factors of the patient, and provides a basis for accurate intervention. Through the design of the whole system, the clinical working efficiency and the prediction accuracy are remarkably improved, the dependence on specialist doctors and high-end equipment is reduced, high-quality KOA management can also be implemented in resource limited areas, and therefore the overall medical resource requirement is effectively reduced.

The invention discloses an intelligent health early warning system based on deep learning, and relates to the technical field of medical health, and the system employs a causal convolutional network and a gating circulation unit double branch to extract long and short term features, generates a dynamic threshold value through adaptive weight fusion in combination with a dynamically updated personalized health baseline and a disease knowledge graph, and carries out the early warning of the health. The method has the advantages that long-scale health trend features are mined through the long-period feature extraction module, short-scale sudden abnormal features are captured through the short-period feature extraction module, and two types of feature weights are dynamically distributed and fused based on a medical risk level judgment rule in combination with the self-adaptive weight fusion module; and a personalized dynamic early warning threshold adaptive to the health state of the user is generated by combining a dynamic threshold generation module with a disease progress pathology knowledge graph, so that the defects that an existing system cannot separate long and short-term health signals and depends on a fixed threshold, so that an early hidden risk signal is covered, early warning is lagged and the false alarm and missing report rate is high are effectively overcome.

The invention discloses a Crohn disease postoperative bad outcome prediction method combining knowledge base construction and an experience-driven self-asking mechanism, and solves the problems of insufficient interpretability, insufficient clinical data utilization, weak generalization ability and the like of an existing prediction method. The method comprises the following steps: constructing a Crohn disease special medical knowledge base, extracting disease progress related factors from medical literatures and real medical records, and carrying out expert score weighting processing to form searchable knowledge entries; retrieving related knowledge injection context from a knowledge base based on a medical record input by a user, and generating preliminary prediction by using a large language model; structured reasoning is guided through a multi-layer prompt engine, diagnosis tasks are decomposed through the thinking chain technology, and self-check before reasoning is achieved by driving a self-asking mechanism through experience. And generating an introspection problem chain according to historical error cases, iteratively optimizing the reasoning process, and finally outputting a bad outcome prediction result and a detailed analysis report. According to the method, both traceable interpretability and prediction accuracy are considered, and reliable assistance is provided for postoperative clinical management of Crohn's disease.

The embodiment of the invention discloses a Crohn disease longitudinal queue course prediction system based on time perception Transform, and relates to the technical field of medical data processing, the system comprises a data preprocessing module, an input embedding layer, a Transform encoder and a multi-task multi-time window prediction header, the system breaks through the limitation that only baseline data is used for prediction of a traditional model, and the prediction efficiency is improved. According to the method, the full-cycle longitudinal data (such as inflammation indexes and medication records of each treatment) of the patient from the morbidity to the prediction can be integrated, and the disease progress, operation and medication risks of 1 year / 3 years / 5 years can be dynamically output. And reference is provided for clinicians.

The present invention relates to a disease prediction and disease analysis method for disease management, and a device for performing the method. The disease prediction and disease analysis method for disease management may comprise the steps in which a disease management device: receives user data; and generates user disease management data on the basis of the user data.

The invention relates to the technical field of biology, in particular to a construction method of an Elmod3 gene mutation mouse model. Based on clinical data of hereditary deafness family patients, ELMOD3c.512Agt is found; g abnormal mutation can cause delayed progressive sensorineural deafness of a patient. According to the invention, a molecular marker with Elmod3c.512Agt is constructed; the G point mutation mouse model is consistent with the mutation site of a patient, and after the mouse is born, the disease progresses under the normal feeding environment and the natural growth rule, so that the experiment conditions of the disease research of the model mouse are closer to the real disease development of family patients, and a researcher can deeply research how the ELMOD3 gene mutation causes deafness. And a foundation is laid for functional research and subsequent gene therapy research of the ELMOD3 in an inner ear auditory system.

Provided are multi-layer bioreactors for growing, maintaining, stimulating, monitoring and testing organoids and tissues derived from or representing hollow organs in organoid chambers. Also provided are uses of those bioreactors in modeling a disease process for monitoring disease progress and / or for assessing a biological effect, such as therapeutic efficacy and / or toxicity, e.g., organotoxicity. Also disclosed are bioreactors comprising organoid chambers that are useful as systems for measuring the volume, pressure, contractility, pump function, or electrophysiology of an organoid chamber as well as systems for controlling the pressure experienced by an organoid or tissue in an organoid chamber.

The invention discloses an AIDS infection course prediction model and an establishment method thereof, and relates to the technical field of AIDS diagnosis, the prediction model comprises an AIDS combined fungal infection prediction model and an AIDS combined multi-infection prediction model; the formula of the AIDS combined fungal infection prediction model is as follows: logistic (p) =-5.838 to 0.348 * CD4 + T + 0.128 * CD8 + T + 7.329 * CD4 / CD8 + 8.236 * CD64; the formula of the AIDS combined fungal infection prediction model is as follows: logistic (p) =-0.824 to 0.182 * CD4 + T + 0.054 * CD8 + T-0. 065 * L / CD45. The established prediction model for the disease progress of the AIDS patient is helpful for further improving the definite diagnosis rate of the AIDS combined fungal infection patient and the AIDS combined multi-infection patient so as to promote the early treatment of the AIDS combined fungal infection patient and the AIDS combined multi-infection patient; meanwhile, the casualty rate of the AIDS patient caused by complications such as opportunity infection is reduced as much as possible, and a theoretical basis and a reference basis are provided for early diagnosis and precise treatment of the AIDS patient clinically.

The invention relates to the technical field of plant disease control, and particularly discloses a screening method of a cruciferous crop clubroot control agent, which comprises the following steps: after accelerating germination of cruciferous crop seeds, transplanting the cruciferous crop seeds into a 1 / 2 MS solution, and culturing the cruciferous crop seeds to a two-leaf one-core stage to obtain seedlings; soaking the roots of the seedlings in a mixed system containing plasmodiophora brassicae spores and a medicament to be detected, and inoculating; and transplanting the inoculated plants into a newly prepared 1 / 2 MS solution for continuous culture, and regularly investigating morbidity and prevention and control effects to screen out the medicament with prevention and control effects on the clubroot of the cruciferae crops. According to the method provided by the invention, continuous and non-destructive dynamic observation on the same plant can be allowed, the disease progress is accurately monitored, and the medicament screening cost is greatly reduced.

The utility model relates to an auxiliary observation device for an abdominal cavity drainage bag, relates to the technical field of medical equipment, and is used for solving the problem that in the prior art, the color and the amount of liquid in the drainage bag cannot be clearly and accurately checked in a dark environment, so that the disease progress and the treatment effect of a patient cannot be accurately judged. Comprising a transparent placing box, an induction lighting piece, a weighing piece and a mounting frame used for being fixedly mounted on a wall, the weighing piece is fixedly mounted on the mounting frame, and the transparent placing box is detachably hung on the weighing piece; the transparent placing box is used for placing a drainage bag, the induction lighting part used for lighting is arranged below the transparent placing box, a light beam of the induction lighting part is arranged upwards, and a limiting part used for fixing the drainage bag is arranged on the transparent placing box.

The invention discloses an endometriosis detection system and a therapeutic agent, and belongs to the technical field of biomedical engineering.The detection system comprises an acquisition module and a prediction module, the acquisition module is used for acquiring clinical indexes, and the clinical indexes comprise CCL21; and the prediction module is used for predicting the probability of endometriosis through the prediction model and the clinical indexes. The therapeutic agent includes an inhibitor of CCL21. By detecting the expression level of CCL21 in the body of a patient and combining related biomarkers, the disease progress and the treatment effect can be more accurately predicted, so that an individualized treatment scheme is formulated; the CCL21 inhibitor provides accurate and effective targeted therapy, and provides possibility for individualized therapy and early intervention.

The invention relates to an interpretable Alzheimer's disease deep learning prediction method and system. The method comprises the following steps: acquiring multi-modal data of a candidate AD patient, wherein the multi-modal data comprises sMRI images and clinical and lifestyle data; processing the multi-modal data by using a pre-trained multi-modal deep fusion prediction model to obtain an AD risk level and a disease progress probability; wherein multi-modal features are extracted through the sub-modal feature extraction layer, deep fusion is carried out on the multi-modal features through the fusion modeling layer to obtain a global fusion feature vector, the global fusion feature vector is processed through the result output layer, and a risk level classification result and a disease progress probability in future preset time are obtained. By adopting the method, multi-modal heterogeneous data can be effectively fused, qualitative risk grading and quantitative disease course prediction are provided, and the diagnosis comprehensiveness is improved.

The invention relates to the technical field of medical images, in particular to an analysis method, system and device for a nuclear medical image radiation report and a medium. According to the method, the comprehensive correlation coefficient of the disease of the patient and the historical medical image of the patient is evaluated, the individual trajectory of the disease development of the patient can be fully considered, and the group deviation degree of the clinical data of the patient is calculated by associating the extracted clinical data feature value of the patient with the clinical data feature value of the same type of patient group; the model is constructed based on the comprehensive correlation coefficient of the patient's disease and the historical medical image and the group deviation degree of the patient's clinical data, the disease progress category of the patient is predicted, the patient's disease can be analyzed more comprehensively, and the disease progress of the patient can be managed better.

The invention discloses an apple leaf ring spot early diagnosis and chlorophyll content prediction method based on hyperspectral imaging and a CNN-LSTM mixed model. According to the method, hyperspectral images of apple leaves within the range of 400-1000 nm are collected through a hyperspectral imaging system, characteristic wave bands related to the disease progress and chlorophyll change are screened through a competitive adaptive reweighted sampling (CARS) and a continuous projection algorithm (SPA), and a convolutional neural network and long-short term memory network (CNN-LSTM) mixed model is input; early recognition of diseases and synchronous prediction of chlorophyll content are realized. And the chlorophyll prediction model is used for pixel-by-pixel prediction of the leaf spectral image, so that a spatial distribution visual image of the chlorophyll content of the apple leaf is realized, and lossless, rapid and quantitative evaluation of the health state of the apple leaf is realized.

The invention relates to the field of biochemical materials and organic photoelectric information materials, and discloses a near-infrared two-window fluorescent contrast agent which has remarkable advantages in the field of near-infrared two-window fluorescent imaging, can monitor dynamic change of molecular level in a living body in real time, and has a good application prospect in the field of near-infrared two-window fluorescent imaging. The characteristic is helpful for early detection and timely treatment of diseases. In the aspect of drug research, the contrast agent can be used for tracking distribution, metabolic pathways and action targets of drugs in organisms, so that the curative effect and potential toxic and side effects of the drugs are accurately evaluated. In a clinical treatment link, the system can monitor the disease progress and the treatment effect in real time, provides a key basis for scientific formulation of a personalized treatment scheme, and assists in improving the accuracy and effectiveness of medical services.

The invention discloses a predictive marker as well as a related product and application thereof, and relates to the field of tumor diagnosis. The invention finds that WNT5A can be used as a marker of any one or more of disease risk, prognosis risk, disease progress, treatment effect, residual focus and metastatic focus of squamous-cell carcinoma, is beneficial to accurate diagnosis and treatment of squamous-cell carcinoma, and accelerates the research and development of new drugs.

The invention discloses a microbial agent based on the synergistic effect of bifidobacterium pseudolongum and bifidobacterium animalis and application, and belongs to the technical field of biological medicine. The active ingredient of the microbial agent is a viable bacterium mixture of bifidobacterium pseudolongum and bifidobacterium animalis, or a metabolite co-cultured by the bifidobacterium pseudolongum and the bifidobacterium animalis. According to the microbial agent provided by the invention, an intestinal flora-butyric acid-peripheral / central immune axis is adjusted through a metabolic synergistic effect of the two strains, A beta deposition and neuroinflammation can be intervened from a pathologic source so as to intervene the progress of the Alzheimer's disease, and the safety is good. The synergistic effect of the bifidobacterium pseudolongum and the bifidobacterium animalis is applied to regulation of intestinal flora and intervention or auxiliary improvement of memory of the Alzheimer's disease, and a novel biological preparation and a technical path are provided for prevention and treatment of neurodegenerative diseases such as the Alzheimer's disease.