116 results about "Diagnosis early" 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 discloses a construction method of a risk prediction model for early diagnosis of liver cancer. The construction method of the risk prediction model comprises the following steps: 1, preparing clinical data; 2, processing, analyzing and learning clinical data, and constructing a project database of a hepatocellular carcinoma clinical diagnosis path based on big data; 3, determining main diagnosis key points in the disease timing sequence diagnosis scheme and related indexes influencing the operation, and formulating a diagnosis and treatment path extraction rule; 4, constructing a quality-efficiency evaluation index system for the diagnosis and treatment path set, and calculating the early diagnosis probability and postoperative recurrence probability of the liver cancer; and 5, establishing a Markov model for early screening and diagnosis of liver cancer and risk prediction of postoperative recurrence. According to the method, the ANN principle is utilized, an HCC early diagnosis risk prediction model and an HCC clinical diagnosis and treatment path evaluation system are established, HCC high-risk groups are subjected to early recognition, and the HCC early diagnosis rate is increased; meanwhile, an optimal clinical diagnosis and treatment decision is provided for HCC treatment.

The invention discloses a cardiovascular three-dimensional optical coherence imaging system, and belongs to the technical field of medical instruments. A cardiovascular three-dimensional optical coherence imaging system comprises a host integration unit and an auxiliary diagnosis unit. The system solves the problem that accurate diagnosis and treatment requirements are difficult to meet in the prior art, interference signals are generated through the host integration unit, high-speed data processing is carried out in combination with the GPU, high-resolution cardiovascular tissue two-dimensional and three-dimensional images can be generated, powerful support is provided for early diagnosis and treatment of diseases, and the system is suitable for popularization and application. The auxiliary diagnosis unit establishes a cardiovascular disease diagnosis knowledge base based on a large amount of clinical case data, automatically identifies plaque types in a three-dimensional image by using a machine learning algorithm, can quickly and accurately provide diagnosis reference for doctors, and calculates a calcification radian score based on the distribution and geometric morphology of calcified plaques, so that the accuracy of diagnosis is improved. And the severity of lesion can be evaluated more accurately by quantitatively analyzing the poor attachment degree of the stent and the tissue prolapse volume.

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.

The invention belongs to the technical field of gene detection, and particularly relates to application of a CFTR gene mutation combination, an amplification reagent and a product in preparation of a CF risk assessment product. The CFTR gene mutation combination consists of an intron region mutation combination and an exon region mutation combination, based on the CFTR gene mutation combination, the invention further develops an amplification reagent and a CFTR gene mutation detection product, the amplification reagent and the CFTR gene mutation detection product comprise a primer combination for multiplex PCR amplification of the CFTR gene mutation combination, and the primer combination has the characteristics of high accuracy, high specificity and high sensitivity, can accurately detect related gene mutation, and provides a reliable basis for risk assessment and diagnosis of CF. When the amplification reagent and the CFTR gene mutation detection product are combined with sweat chlorine detection for use, the diagnosis rate of CF can be remarkably improved, and the amplification reagent has important application value in clinical diagnosis of CF and is expected to provide more powerful support for early diagnosis and treatment of CF patients.

The invention belongs to the technical field of gene engineering, and relates to an esophageal squamous cell carcinoma diagnostic kit based on a peripheral blood mononuclear cell methylation marker and application. The kit comprises a primer pair which is used for detecting the methylation of a cg05914150 site, a cg06769875 site, a cg07475126 site, a cg08433285 site, a cg10038907 site, a cg17968322 site, a cg19572487 site, a cg20184330 site, a cg22741248 site and a cg22823192 site. According to the detection kit provided by the invention, the type of a detected sample is peripheral blood, the sample is relatively easy to obtain and non-invasive, and the patient compliance is relatively good. Abnormal methylation changes mostly exist in the early progression process of tumors, early diagnosis of ESCC can be achieved through the methylation detection method provided by the invention, and the five-year survival rate of ESCC patients is effectively increased.

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.

The invention discloses an MDD early-stage dynamic diagnosis method based on a multi-modal attention network, and relates to the technical field of MDD diagnos.According to the method, early-stage diagnosis analysis is carried out on various types of mental diseases through multi-modal data dynamic fusion and a layered attention mechanism in combination with a multi-modal attention network diagnosis model; the fitting degree of the representation data of the target diagnosis user and various types of mental diseases is deeply analyzed, the diagnosis cycle for the target diagnosis user is further dynamically set, the situation of diagnosis errors caused by one-time diagnosis of multi-modal data is avoided, corresponding diagnosis cycles are customized for different target diagnosis users, and the diagnosis accuracy is improved. The accuracy, the timeliness and the clinical practicability of MDD early diagnosis are remarkably improved, the reliability of clinical decision and the resource allocation efficiency are further enhanced, and an innovative technical scheme is provided for precise prevention and control of mental diseases.

The invention belongs to the field of intelligent medical treatment, and particularly relates to a method, equipment, medium and program product for predicting RMPP based on lung microvascular changes. The method comprises the following steps: S101, acquiring a CT image of a pediatric MPP patient; s102, the CT image is extracted to obtain a quantitative PBV parameter, the quantitative PBV parameter comprises BV10%, and BV10% refers to the percentage of the blood volume in a blood vessel with the blood vessel cross section area larger than 10 mm < 2 > in the total lung blood volume; and S103, if BV10% is higher than a first threshold value, judging that the pediatric MPP patient develops into a prediction result with high RMPP risk, and otherwise, outputting a prediction result with low RMPP risk. According to the application, the prediction effect on the occurrence and development of the RMPP is found by quantitatively measuring the microvascular change in the CT, and the clinical value of early diagnosis of the RMPP is realized, so that the clinical early treatment is driven.

The invention discloses a biomarker for detecting venous thrombosis and application of the biomarker, and belongs to the technical field of molecular markers, the biomarker is ESM1, early diagnosis and positive prevention of venous thrombosis embolism can be achieved by monitoring the marker, and compared with a diagnosis method in the prior art, the biomarker has the advantages that the biomarker is simple and convenient to use. The marker provided by the invention is a plasma-derived biomarker, can avoid invasive diagnosis, is minimally invasive in sampling, is easy to detect, does not cause body injury, and does not need to use a large instrument. Meanwhile, the ESM1 and D-dimer combined detection improves the sensitivity and accuracy of the existing serological detection method (D-dimer detection) for judging thrombus, and can be used for early prevention and treatment effect evaluation of venous thrombosis.

The present invention relates to a method and system for early diagnosis of lesions configured to enable early diagnosis of lesions by including functions for preprocessing, lesion detection, statistical analysis, visualization, and personalization adjustment based on Whole Slide Image (WSI) data. More specifically, the invention relates to a method for early diagnosis of lesions based on Whole Slide Image (WSI) data using an early lesion diagnosis system, comprising: a step of receiving and preprocessing said WSI data; and a step of detecting lesions by applying an artificial intelligence model to the preprocessed high-resolution image.

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 relates to the technical field of medical diagnosis, in particular to a papillary thyroid carcinoma diagnosis method based on hsacirc0076710 and a kit. The invention provides a papillary thyroid carcinoma detection kit based on hsacirc0076710, the expression condition of circular RNA hsacirc0076710 in a living body can be accurately and rapidly detected, a new gene detection means can be provided for early diagnosis and prognosis of papillary thyroid carcinoma, meanwhile, the invention provides a papillary thyroid carcinoma diagnosis method based on hsacirc0076710, and the papillary thyroid carcinoma detection kit can be applied to diagnosis of papillary thyroid carcinoma. According to the method, diagnosis is carried out by detecting the expression level of hsacirc0076710 in a sample, the areas (AUC) under an ROC curve in tissue, a fine needle biopsy eluent and a serum sample are 0.851, 0.838 and 0.769 respectively, and the sensitivity reaches 83.7%-95.1%. Further analysis shows that the hsacirc0076710 has high expression and high expression of lymph node metastasis (OR = 3.21, plt; 0.001) and advanced TNM (stage III / IV), and can be used for early diagnosis and prognosis risk assessment of papillary thyroid carcinoma.

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.

Provided are a composition for diagnosing Down syndrome, a kit including the composition, a diagnostic method, and a method of providing information for diagnosing Down syndrome, the composition including an agent for measuring a methylation level of any one gene selected from the group consisting of MXRA8, MIB2, KIF26B, SP5, ZIC4, ENPEP, PITX2, SH3BP2, SEPP1, FLJ32255, SHROOM1, LINC00574, LOC154449, PRRT4, TMEM176B, MNX1, LOC101928483, EGFL7, NACC2, C9orf69, TLX1, FGF8, TACC2, CPXM2, NKX6-2, TLXINB, IQSEC3, PCDH8, F7, SOX9, PNMAL2, THBD, MAPK81P2, KLHDC7B, GPR143, IGHMBP2, MRGPRD, CHODL, NCAM2, CYYR1, GRIK1, OLIG2, CLIC6, SIM2, HLCS, MX2, MX1, TMPRSS2, SLC37A1, PDE9A, CBS, CRYAA, C21orf2, TRPM2, TSPEAR, LINC00162, SSR4P1, SLC19A1, LOC100129027, MCM3AP, YBEY, PRMT2, and ITSN1. Thus, Down syndrome can be diagnosed early with high accuracy, and the disclosure is expected to be applied as key a technology in the field of Down syndrome diagnosis.

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.

The application discloses a prediction model for pulmonary arterial hypertension diagnosis and severity judgment based on pulmonary arteriography, a construction method and application. By collecting and analyzing pulmonary arteriography and test information of pulmonary arterial hypertension patients and controls, the application constructs a clinical prediction model for pulmonary arterial hypertension diagnosis and severity judgment. The diagnosis model and the severity judgment model both contain five independent prediction variables. After the model is constructed, the model is visualized through a nomogram, and the performance is evaluated through an ROC curve, a DCA curve and a calibration curve. The model is tested through a machine learning method, and the results show that the model has good performance. In addition, through correlation analysis of continuous variables, the application further constructs a linear regression equation with mean pulmonary arterial pressure as the dependent variable. The application provides a new clue for non-invasive screening of pulmonary arterial hypertension and has important significance for expanding the disease screening population and early diagnosis.

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 invention discloses an early diagnosis marker for children refractory mycoplasma pneumonia (RMPP) and an RT-qPCR detection method, and relates to the technical field of disease detection. According to the invention, PBMC maps of a healthy control group in the early morbidity stage of RMPP sick children, in the early morbidity stage of mycoplasma pneumoniae pneumonia sick children and in the early morbidity stage of RMPP sick children are drawn for the first time by virtue of an scRNA-seq technology, and IGHM, NEAT1, IL32 and ACTG1 four-gene marker combination for early diagnosis of RMPP in children is innovatively discovered; a peripheral blood sampling scheme is combined to thoroughly solve throat swab sampling obstacles of young children; and a high-precision three-classification diagnosis model is constructed. According to the method disclosed by the invention, the diagnosis time is shortened from 7 days to 24 hours, the sensitivity is improved to 93-96%, the detection cost is reduced by 95%, a first molecular solution with high precision, low cost and clinical accessibility is provided for early RMPP intervention of children, and the severe case rate and medical burden are expected to be obviously reduced.

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 relates to the technical field of respiratory medicine diagnosis, in particular to a respiratory system disease prediction method and system. By combining multiple groups of physicochemical data and multiple groups of image group data, comprehensive disease prediction of patients at different age stages can be realized, and compared with single-factor judgment or single-type factor judgment in the prior art, the prediction result is more accurate; clinical doctors can accurately and rapidly carry out early diagnosis on patients.

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.