72 results about "Diagnosis early" patented technology

The invention provides a cancer early-stage dynamic risk prediction method and device, equipment and a storage medium, and relates to the technical field of data management and risk assessment. The method comprises the following steps: acquiring health medical data, key gene data and behavioral habit data of a target user; configuring a dynamic risk prediction model based on the key gene data and the behavior habit data to obtain a dynamic risk prediction model corresponding to the target user; and inputting the health medical data into a dynamic risk prediction model corresponding to the target user to obtain predicted health medical data of the target user, and performing cancer early-stage dynamic risk prediction on the target user based on the predicted health medical data. According to the method, the health condition of an individual can be comprehensively analyzed from multiple dimensions, the change of the physical condition of a patient can be better adapted, and a more reliable basis is provided for early diagnosis and treatment of cancers.

A multi-modal AI model for assisting knee joint tuberculosis diagnosis belongs to the field of assisting knee joint tuberculosis diagnosis and comprises a 3D image modal input module, a text modal input module, a test result modal input module, a multi-modal feature fusion module and a large language module. Aiming at the problems that the early diagnosis time of knee joint tuberculosis is too long and subjective interpretation is different, the diagnosis accuracy can be improved while the diagnosis time is shortened; according to the invention, an M3D model framework is optimized, CT / MRI images, laboratory indexes and text data are fused, and a multi-modal feature interaction auxiliary diagnosis system is constructed; according to the invention, accurate identification of early lesions of knee joint tuberculosis is realized, the misdiagnosis rate is reduced, and efficient and objective quantitative decision support is provided for clinic; according to the invention, the problems of high misdiagnosis rate and long flow caused by insufficient specificity in early diagnosis and examination of knee joint tuberculosis are solved through technical innovation.

This invention proposes a random forest-based diagnostic method for Pneumocystis jirovecii pneumonia (PCP), comprising the following steps: collecting case data to form a raw dataset; cleaning and normalizing the feature encoding of the raw dataset; calculating the importance score of each feature based on three indices: permutation importance, Gini importance, and mutual information, converting it into a feature sampling probability distribution, and selecting features with sampling probabilities higher than a preset threshold; constructing a random forest model and training and optimizing it; validating the model performance using a test set and outputting the trained PCP diagnostic model; inputting standardized test samples into the diagnostic model and outputting diagnostic results. This invention solves the industry problems of intolerance to traditional invasive diagnoses, high false positive / false negative rates in routine serological tests, and insufficient generalization ability of existing machine learning models, providing non-invasive, efficient, and universal technical support for early PCP diagnosis, and is suitable for clinical application in medical institutions at all levels.

The invention discloses an early diagnosis and intervention curative effect evaluation method for children with autism, and belongs to the technical field of medical diagnosis. The method specifically comprises the following steps: S1, obtaining historical case big data: collecting the historical case big data of an autistic truly diagnosed child and a normally developed child and corresponding diagnosis results and intervention diagnosis and treatment records, and associating to form a historical database; s2, historical data preprocessing: performing cleaning, standardization and missing value processing on historical case big data, and associating and integrating the historical case big data into a structured sample set; by collecting behavior, physiology, environment and family multi-dimensional data, mining an internal mode of the data in combination with depth features, and then utilizing a multi-modal fusion diagnosis model for analysis, dependence on subjective experience of doctors and a single scale is reduced; the classification accuracy of the model is optimized through cross validation, a diagnosis report contains core abnormal features and a judgment basis, manual recheck is supported, and the risks of missed diagnosis and misdiagnosis are further reduced.

This invention relates to the field of molecular diagnostics, specifically providing a diagnostic kit for the combined detection of DLBCL based on plasma exosomal miRNAs. This invention provides two or more peripheral blood exosomes used as diagnostic biomarkers for diffuse large B-cell lymphoma (DLBCL). Based on these DLBCL diagnostic biomarkers, this invention also provides a non-invasive, highly reproducible, and highly specific plasma exosomal miRNA diagnostic kit for the early diagnosis and follow-up monitoring of DLBCL. The plasma exosomal miRNA diagnostic kit provided by this invention is of great significance in the early diagnosis, genotyping, relapse monitoring, and efficacy evaluation of DLBCL.

The application discloses application of a marker in preparation of a product for diagnosing or assisting in diagnosing ischemic cerebral stroke, the marker being CLDN7 and / or PPM1B, and it is found through verification of clinical samples that the marker has high diagnostic efficiency on ischemic cerebral stroke, can be used as a biomarker for diagnosing and differentiating ischemic cerebral stroke, and has the advantages of high accuracy, good specificity, high sensitivity and the like, provides a brand-new thought and strategy for early diagnosis of ischemic cerebral stroke in the field, and has good clinical application value.

The present application relates to a kind of cardiac magnetic resonance longitudinal relaxation time measurement method, medium and equipment.The measurement method includes the following steps: S1 uses MOLLI sequence respectively before and after scanning contrast;Specifically, in the before and after scanning contrast, by setting different initial inversion time TI, setting 3 groups of LL experiment LL1, LL2, LL3 triggered by continuous ECG, enough relaxation curve sampling points are obtained after fusion;And data is collected at the end of diastole of cardiac cycle, and 1 image is obtained per heartbeat;S2 image processing.This measurement method can realize single-pixel high-resolution T1 mapping under the breath holding of selection data acquisition and multi-LL experiment data fusion, give consideration to high accuracy, high resolution and high efficiency, and have good compatibility and practicality, provide new scheme for early diagnosis of heart disease, lesion quantification and prognosis evaluation, have wide clinical application prospect.

The invention discloses a cardiopulmonary disease risk prediction method and system based on self-supervised diffusion enhancement. The method comprises the following steps: firstly, carrying out data acquisition and preprocessing; compressing the original audio data into a potential space through a DCVAE, and adding noise at a specified time step to obtain a noise-added potential representation; then, carrying out self-supervised feature learning by utilizing a DiT model of a diffusion converter, and collecting middle layer features through DiT model forward propagation; and finally, carrying out pyramid pooling on the intermediate features for risk prediction. The advanced signal processing technology and the deep learning model are combined, so that efficient and accurate disease risk prediction can be realized, and important help is provided for early diagnosis and personalized treatment of diseases; the method not only has important significance in the medical field, but also lays a foundation for the development of intelligent health monitoring equipment.

According to the pancreatic cancer PNI evaluation and curative effect prediction method and device, the early diagnosis accuracy and the curative effect prediction capability can be remarkably improved, the low-dose CT imaging omics analysis technical process is optimized, the high sensitivity and high specificity targets of pancreatic cancer PNI diagnosis are achieved, the application of an artificial intelligence auxiliary diagnosis technology in pancreatic cancer clinical diagnosis and treatment is promoted, and the application prospect is wide. The diagnosis and treatment efficiency is effectively improved; the medical cost is reduced. The method comprises the following steps: (1) carrying out standardization processing, segmentation and feature extraction on collected image data; (2) constructing a PNI evaluation model; (3) constructing a curative effect prediction model; and (4) model training and verification.

The invention relates to the technical field of veterinary diagnosis, discloses a down producing goat pneumonia early diagnosis system and an application method thereof, and aims to solve the problems of insufficient accuracy, timeliness, convenience and continuity in existing down producing goat pneumonia early diagnosis. The system comprises a physiological parameter acquisition unit, an environmental parameter acquisition unit, a data preprocessing unit, a feature extraction unit, a pneumonia diagnosis model unit, a decision early warning unit and a central processing unit. The method comprises the steps of collecting multi-modal data in real time, performing early diagnosis by a deep learning model after preprocessing and feature extraction, and generating early warning according to a result. According to the technical scheme, the physiological and environmental parameters of the down producing goats are comprehensively and continuously monitored in real time, the accuracy and reliability of early diagnosis are improved through deep learning, automatic early warning is achieved, the diagnosis period is shortened, and the morbidity and economic loss are reduced.

The invention discloses an abdominal aortic aneurysm serum protein fingerprint detection and analysis method and system based on an LASSO regression algorithm. The method comprises the following steps: by optimizing a serum sample pretreatment process and combining an MALDI-TOFMS (Matrix-Assisted Laser Desorption / Ionization Time of Flight Mass Spectrometry) mass spectrometry technology and high-throughput proteomics data analysis, screening differentially expressed proteins related to occurrence, development and rupture of abdominal aortic aneurysm in serum; and further constructing a risk assessment model based on the key protein combination by using an LASSO machine learning algorithm. The method provided by the invention has the advantages of high sensitivity and good specificity, can effectively distinguish abdominal aortic aneurysm patients from normal people, can accurately predict the rupture risk of abdominal aortic aneurysm, especially early small aneurysm, and provides important tools and bases for clinical early diagnosis, risk stratification and personalized treatment decision.

The invention discloses a colorectal cancer molecular marker and application thereof, and belongs to the technical field of biological medicine. The invention finds that ATP5MK has significant differential expression in colorectal cancer patients for the first time, can be used as a biomarker for colorectal cancer diagnosis, has the advantages of high accuracy, good specificity, high sensitivity and the like, provides a brand new idea and strategy for early diagnosis of colorectal cancer, and has good clinical application value.

The application provides a lung disease acoustic recognition method and device based on artificial intelligence. The lung disease acoustic recognition method based on artificial intelligence provided by the application comprises the following steps: acquiring a lung disease acoustic signal, framing the acoustic signal to obtain a plurality of acoustic signal segments; screening effective acoustic segments from the plurality of acoustic signal segments based on an adaptive zero-crossing rate algorithm; and identifying the health risk level corresponding to the effective acoustic segments based on an artificial intelligence model. The lung disease acoustic recognition method and device based on artificial intelligence provided by the application not only assist doctors in clinical diagnosis, but also realize automatic analysis and intelligent recognition of lung disease acoustic signals of different groups of people, and have high efficiency and universality. In addition, the recognition method is non-contact, non-invasive, low-cost, simple to operate, and supports detection and health management anytime and anywhere, realizes early screening and early warning of potential patients, reduces the rate of missed diagnosis, improves the detection rate of diseases, and realizes early diagnosis and prevention of lung diseases.

An apparatus for detecting limb ischemia in a subject includes a property sensor designed to obtain intramuscular property readings when implanted into the limb; the apparatus features an introducer for sensor placement and a controller that is configured to: receive the property readings from the property sensor; and at least one of: display the property readings on a display; and analyze the property readings to identify a change in an intramuscular property when compared to a reference temperature value, and following the detection, generates a signal or alert indicative of the change in the intramuscular property, whereby the change in the intramuscular property is indicative of a presence of the limb ischemia within the limb; method of use thereof; the apparatus provides a minimally invasive solution for continuous ischemia monitoring, improving early diagnosis, treatment decisions, and patient outcomes.

The present application relates to the technical field of biological medicine, in particular to a lipid metabolite combination for early diagnosis of VKH and application thereof, by obtaining plasma samples of patients with initial acute stage (early stage) VKH syndrome and healthy controls, and constructing sample-full lipid quantitative expression matrix, screening differential lipids through PCA unsupervised analysis and OPLS-DA supervised model, further screening lipids with high diagnostic performance by using elastic net logistic regression model, finally obtaining a diagnostic marker combination composed of 25 lipid metabolites, solving the technical problems of existing VKH diagnosis technology, such as invasiveness, insufficient sensitivity and stability of protein marker detection, limited diagnostic efficiency, lack of systematicness and standardized process in lipidomics research, achieving the effect of non-invasive, efficient, high sensitivity and high specificity of early auxiliary diagnosis of VKH syndrome, and providing objective and reliable technical means for precise identification of atypical cases and large-scale clinical screening.

The invention belongs to the technical field of medical detection and biological medicine, and discloses a serum protein marker combination for glioma diagnosis and prognosis evaluation and a processing system of the serum protein marker combination. Eight algorithms including KNN, SVM, random forest, XGBoost, AdaBoost, LGBM, Gaussian naive Bayes and decision tree cover traditional machine learning and integrated learning, and model robustness is improved; according to the method, interpretable feature screening is adopted, SHAP and LIME tools are combined, the contribution degree of each marker to a diagnosis result is clarified, and the problem of'black box 'of machine learning is solved; the invention provides a machine learning algorithm-based mass spectrometry system for glioma detection, which is good in detection performance, high in speed, convenient to operate and low in cost, so as to meet the requirements of clinical early diagnosis and non-invasive examination of glioma.

This invention relates to the field of medical diagnostic technology and discloses a non-invasive early DCM analysis method based on multi-radiomics and serum biomarkers. The method involves acquiring image data using multimodal medical imaging equipment and serum biomarker data using blood testing equipment; generating radiomics feature sets and serum biomarker time-series feature sets using multi-scale feature extraction algorithms and temporal analysis models, respectively; fusing these features using a dynamic weighted fusion strategy to generate a joint feature matrix; inputting this matrix into a pre-trained multi-task deep learning model to predict the probability of DCM risk; and finally, optimizing the diagnostic decision tree based on a genetic algorithm to output the early DCM diagnostic result. This method is non-invasive, accurate, and can effectively improve the accuracy of early DCM diagnosis.

The invention discloses a multi-modal data fusion-based rheumatoid arthritis early-stage AI intelligent diagnosis system, belongs to the technical field of medical artificial intelligence, and aims to solve the problems that in the prior art, RA early-stage diagnosis lags behind, the rate of missed diagnosis of serum negative patients is high, and risk assessment lacks quantitative standards. Comprising a multi-source data acquisition module, a data preprocessing module, a cross-modal fusion module, a dynamic time sequence model and a risk assessment module which are connected in sequence, according to the system provided by the invention, a dynamic tracking model of serological change-synovial inflammation-bone structure change is innovatively constructed, and an RA diagnosis window is advanced compared with that of a traditional method (depending on clinical symptoms) by capturing time sequence correlation of the serological change, the synovial inflammation and the bone structure change; aiming at the diagnosis difficulty of serum-negative RA, the system realizes great improvement of diagnosis performance through deep fusion of multi-modal features, serological core indexes, ultrasonic synovial dynamic features and X-ray bone microscopic features.

The invention discloses an application of CDH3 in diagnosis of recurrent abortion. The invention discovers that the expression quantity of the CDH3 is closely related to the recurrent abortion for the first time, and the reduction of the expression quantity can promote the occurrence of the recurrent abortion, which indicates the potential possibility of the CDH3 as an RSA early diagnosis marker. The invention is helpful for deeply knowing pathogenesis of RSA, provides a potential marker and a therapeutic target for clinical diagnosis, prevention and treatment of recurrent abortion, and has a good application prospect.

The application belongs to the technical field of biomedical diagnosis, and provides application of neutrophil extracellular traps in preparation of a lymphangitic inflammation diagnosis kit, wherein the application first discloses high expression of neutrophil extracellular traps in lymphangitic inflammation lesion tissues, and confirms that the expression level is closely related to disease occurrence and development, so that the neutrophil extracellular traps are used as an early diagnosis index to fill the technical blank of lack of early non-invasive screening means for lymphangitic inflammation in the prior art, precise diagnosis and disease monitoring of subclinical stage occult lymphangitic inflammation can be realized, misdiagnosis and missed diagnosis can be effectively avoided, reliable technical support and theoretical basis are provided for early prevention and timely intervention of serious complications such as secondary lymphedema, and thus the incidence of complications is significantly reduced and the prognosis of patients is improved.

The present application belongs to the field of intelligent medical treatment, and particularly relates to a method, device, medium and program product for predicting RMPP based on lung microvessel changes. The method comprises the following steps: S101: acquiring CT images of a pediatric MPP patient; S102: extracting the CT images to obtain quantitative PBV parameters, wherein the quantitative PBV parameters comprise BV10%, wherein BV10% refers to the percentage of blood volume in blood vessels with a cross-sectional area greater than 10 mm2 in total lung blood volume; and S103: if BV10% is higher than a first threshold value, determining that the pediatric MPP patient has a high risk of developing into RMPP, otherwise outputting a prediction result that the pediatric MPP patient has a low risk of developing into RMPP. The present application discovers the prediction effect of microvessel changes in CT on the occurrence and development of RMPP by quantitatively measuring the microvessel changes, realizes the clinical value of early diagnosis of RMPP, and thus drives early treatment in the clinic.

The invention relates to the field of diagnostic kits, in particular to application of an IGHV family molecule as a diagnostic marker in preparation of a diagnostic kit for psoriasis (PSO) and arthropathy type psoriasis (PsA), and the IGHV family molecule is selected from IGHV3-72, IGHV5-51, IGHG3, IGHV1-69, IGHV3-73 and / or IGHV3-9. Wherein the prediction efficiency of the IGHG3 is the highest, and the area under the curve (AUC) is 0.826 (95% CI: 0.693-0.959). IGHV family molecules are specifically expressed in psoriasis, the diagnosis specificity is high, and the cross reaction with other autoimmune diseases can be reduced; the invasive operation of skin biopsy can be avoided through nail sample detection; the kit is suitable for early screening of PSO, early diagnosis and prediction of PsA and dynamic monitoring of curative effect, and is widely applied.

The invention discloses a biomarker for aortic dissection detection and an application of the biomarker. The biomarker is one or more of MDK, NTN1, CCL21, LIPC, DNASE1L3, TFPI2, CTSG, HAMP, CXCL14, PLA2G2A, SCUBE1, GZMK, HP1BP3, SRP14 and TOP1. The biomarker is obtained by screening plasma of a patient with aortic dissection through plasma proteomics, and shows relatively high sensitivity and specificity. ELISA verification proves that the biomarker can be used as an early diagnostic biomarker of the aortic dissection, and can also be used as one of indexes for evaluating the disease severity of AD patients, so that a new direction is provided for clinical early diagnosis and long-term prognosis evaluation of the aortic dissection.

The invention provides a neutrophil transporter, a marker CD66b of the neutrophil transporter, and application of combination of the CD66b and other indexes to early diagnosis or auxiliary diagnosis of blood coagulation disorder. The invention provides CD66b, application of CD66b and other index combinations to preparation of reagents or kits for early diagnosis or auxiliary diagnosis of blood coagulation disorders, related reagents and kits, and a system for early diagnosis or auxiliary diagnosis of blood coagulation disorders. The CD66b, the combination of the CD66b and other indexes, the reagent or the kit and the system provided by the invention have the effect of early diagnosis or auxiliary diagnosis of the blood coagulation disorder.

The invention discloses a malignant tumor occurrence probability atlas acquisition and analysis system, and belongs to the technical field of malignant tumors, and the system comprises a data acquisition integration module which is used for collecting multi-source data of a patient; the data processing atlas acquisition module is used for processing the multi-source data of the patient, determining the characteristic data of the patient, performing pattern recognition on the characteristic data of the patient according to the malignant tumor occurrence probability prediction model, determining a malignant tumor occurrence probability prediction result and generating a malignant tumor occurrence probability atlas; and the map analysis visualization module is used for analyzing the malignant tumor occurrence probability map. The problems that the malignant tumor occurrence probability map cannot be effectively obtained and analyzed and the comprehensive development of malignant tumor prevention and control and treatment cannot be promoted in the prior art are solved. The malignant tumor occurrence probability map can be effectively obtained and analyzed, the early diagnosis rate of malignant tumors is increased, and comprehensive development of malignant tumor prevention and control and treatment can be promoted.

The invention belongs to the technical field of medical detection and diagnosis, and relates to application of urine IgA-C3 in IgA nephropathy diagnosis and treatment effect prediction markers, the urine IgA-C3 is applied to products for auxiliary diagnosis of the IgA nephropathy, and a new breakthrough is brought to diagnosis and treatment of the IgA nephropathy. The reliable and stable urine IgA-C3 compound ELISA detection method established by the invention can effectively detect the level of the compound in the urine of a patient, and provides a powerful tool for early diagnosis of diseases; through detection and analysis of a large number of patients and control people, the advantage of urine IgA-C3 in the aspect of differential diagnosis of IgA nephropathy and other nephropathy is defined, and the area under the ROC curve shows good diagnosis efficiency.

The invention discloses a cerebral arterial thrombosis marker miRNA-12128 and application thereof, and provides application of the marker miRNA-12128 in preparation of a cerebral arterial thrombosis diagnosis product, namely application of the marker miRNA-12128 in preparation of a detection chip and a detection kit. Researches find that the expression level of the miRNA-12128 in serum of a patient with cerebral arterial thrombosis is obviously higher than that of a healthy control group, and the expression level of the miRNA-12128 is positively correlated with the severity of cerebral arterial thrombosis. The expression level of miRNA-12128 in serum is detected through a real-time fluorescent quantitative PCR method, and the miRNA-12128 can be used for early diagnosis and prognosis evaluation of cerebral arterial thrombosis. The detection method provided by the invention is high in sensitivity and strong in specificity, can improve the early diagnosis efficiency of the cerebral arterial thrombosis, and provides an important basis for clinical diagnosis, treatment and prognosis of the cerebral arterial thrombosis.

The invention provides an application technology for enabling early screening, early diagnosis and postoperative treatment judgment of gastric cancer by a multi-modal model algorithm, and belongs to the field of molecular biology, medical diagnosis and prevention. The method comprises the following steps: collecting a sample from human body liquid through a modern detection technology, detecting data of a plurality of molecular biomarkers and kit results associated with the gastric cancer or the cancer, and calculating by using a multi-modal model algorithm to find precancerous lesions, early gastric cancer or early cancer so as to reach the best early curing opportunity of a patient; the progression stage, pathological typing, clinical staging, metastatic state and postoperative curative effect inference of gastric cancer or cancer are analyzed and predicted, so that the cure effect of a patient can be improved and precise medical treatment can be realized in combination with a gastroscope pathological biopsy result; the sensitivity and the specificity precision of the kit approved by the country in the future are improved; the problem that early detection of medical early screening is difficult to solve is solved. The non-destructive detection and multi-modal model algorithm technology has wide usability, and has a demonstration effect on gastric cancer and other cancers.

Subject of the present invention is a method for the early diagnosis and / or early risk prediction and / or risk monitoring and / or severity prediction of reduced graft function in a kidney transplantation patient, comprising the steps of: determining the level of pro-enkephalin or a fragment thereof in a bodily fluid sample obtained from said patient; and correlating the level of the pro-enkephalin or fragment thereof in the sample with the diagnosis and / or risk and / or severity of a reduced graft function, wherein the reduced graft function is a reduced graft function (SGF) or delayed graft function (DGF) of the kidney.

A biomarker osteonectin (ON) for auxiliary diagnosis of acute chest pain diseases, and a use thereof. ON is closely associated with acute chest pain diseases such as AAD, AMI, and APE, ON is used as a biomarker, and a detection reagent thereof is prepared into an auxiliary diagnostic product, facilitating early diagnosis, long-term prognosis, and quality-of-life improvement for patients with the three diseases, especially AAD.