29 results about "Differential diagnosis" patented technology

The invention relates to 4 serum peptide patterns for diagnostics of intracranial hemorrhagic stroke (ICH) in humans and differential diagnosis from acute ischemic stroke that are defined using a list of the 34 quantitative peptides that in sera of patients with ICH are increased 2 folds or more as compared with individuals without stroke and patients with acute ischemic stroke and a list of 16 qualitative peptides detectable in sera of the patients with ICH but not detectable in the sera of individuals without stroke and in sera of patients with acute ischemic stroke.

This disclosure belongs to the field of medical testing technology, and specifically relates to a classification, diagnostic, and prediction method, system, and electronic device for congenital neurodevelopmental disorders. Addressing the clinical diagnostic challenges of congenital neurodevelopmental disorders, this disclosure establishes for the first time a classification, diagnostic, and prediction method for congenital neurodevelopmental disorders based on whole-genome methylation signatures and customized SVM machine learning. This method achieves one-stop differential diagnosis across multiple mechanisms and diseases, filling a gap in existing technologies and providing a novel and efficient solution for the accurate diagnosis of congenital neurodevelopmental disorders.

The application discloses a farm veterinarian question and answer and auxiliary diagnosis method and system based on a large language model, which comprises the following steps: standardizing a colloquial query to obtain structured query information; based on the information, mixed retrieval is carried out from a hierarchical veterinarian knowledge base to obtain a candidate disease list and a multi-source evidence set; a preliminary diagnosis answer is generated according to the multi-source evidence set, semantic and evidence alignment evaluation is carried out, and evidence sufficiency scores are generated; the differential diagnosis attributes of each disease in the candidate disease list are compared to identify key differences, and information missing items are identified by comparison with standard symptom profiles; based on the scores, key differences and information missing items, a multi-round diagnosis enhanced retrieval framework and user interaction are adopted, the candidate disease list and the multi-source evidence set are updated, and a diagnosis report is generated in combination with veterinary drug compliance rules. The application integrates hierarchical knowledge graphs, multi-evidence alignment and multi-round diagnosis logic, improves the accuracy of veterinarian question and answer, reduces knowledge illusion, and ensures drug compliance.

The application provides a gender stratified lung nodule disease identification and diagnosis model and system. Specifically, based on a machine learning method, six male characteristic indexes and six female characteristic indexes are used to construct a gender stratified identification and diagnosis system. The results show that the model based on support vector machine has an AUC greater than 0.8 in the training set and the validation set, has good identification ability, and can accurately identify and judge whether the patient with normal serum angiotensin converting enzyme level is a lung nodule disease patient. The system effectively solves the short board of the existing diagnosis method of lung nodule disease, and realizes the identification and diagnosis by using a small amount of characteristic indexes, greatly shortens the diagnosis time, and has a wide clinical application prospect.

This invention discloses a methylation marker for the differential diagnosis of malignant and benign breast tumors. The invention provides the application of the methylated SH3PXD2B gene as a marker in product preparation; the product is used for at least one of the following purposes: (1) distinguishing between benign and malignant breast tumors; (2) distinguishing between benign breast tumors and different subtypes or stages of malignant breast tumors; (3) distinguishing between different subtypes or stages of malignant breast tumors. This invention has significant scientific and clinical application value for differentiating between benign and malignant breast tumors, different subtypes or stages of malignant breast tumors, and guiding the development of reasonable clinical treatment plans.

This invention provides a microfluidic chip for the multiplex differentiation and diagnosis of different avian influenza virus subtypes and its application. Specifically, it is a microfluidic chip based on TaqMan probes that can simultaneously and rapidly differentiate and diagnose 25 avian influenza subtypes, enabling high-throughput simultaneous detection of multiple gene targets. This invention utilizes high-throughput microfluidic chip technology to simplify the differential diagnosis procedure for avian influenza virus subtypes, resulting in more accurate and time-saving results. It is more suitable for rapid clinical diagnosis of avian influenza, providing technical support for rapid clinical detection, diagnosis, and prevention of avian influenza, and is of great significance for epidemic prevention and control and safeguarding public health security.

This invention relates to the quantitative detection of pigeon adenovirus and the simultaneous differential diagnosis of type I and type II. Specific primers and probes for pigeon adenovirus type I and II are designed using specific gene sequences. Real-time quantitative PCR can accurately detect and differentiate between pigeon adenovirus types I and II. Verification has shown that this technology can rapidly and accurately differentiate between PiAdV-I and PiAdV-II, exhibiting high specificity and sensitivity. Furthermore, it is cost-effective, easy to operate, and suitable for import / export quarantine and specialized laboratories for the detection and differentiation of PiAdV-I and PiAdV-II.

The application discloses a uterus tumor differential diagnosis method and system based on multi-parameter MRI imaging and belongs to the technical field of medical image processing and artificial intelligence diagnosis. The method aims at the problems existing in the current uterus tumor MRI diagnosis, such as strong subjective dependence, insufficient utilization of multi-parameter information, unreasonable feature screening and model construction. The method comprises the following steps: collecting T1WI, T2WI, T2 fat suppression sequence and DWI multi-parameter images; after format standardization, deartifacting and Z-score normalization, a model combining U-Net and attention mechanism is used to realize automatic segmentation of the lesions; multi-dimensional features are extracted and the optimal subset is screened by ANOVA filtering-RFE-SVM packaging method; a stacking integrated model is constructed for training, and the output includes the differential results and confidence of ordinary uterine fibroids, special type uterine fibroids and uterine sarcoma; the application integrates multi-parameter complementary information, improves the segmentation efficiency and feature quality, has strong model generalization ability and accurate diagnosis, is suitable for the actual situation of the scarcity of uterine sarcoma cases, and provides an objective diagnosis tool for clinical use.

The present application relates to a kind of biomarkers for Parkinson or early diagnosis of Parkinson, including the combination of PRKD2, TULP2, ALKAL2, RUFY1, HDAC9, LINC01600, EXT2, ARHGAP24 and CCDC102B.The biomarker provided by the present application can effectively diagnose Parkinson, the sensitivity and specificity reach 0.903, 0.792 respectively, and the AUC value is 0.854;At the same time, the AUC value for differential diagnosis of Parkinson similar diseases is 0.856.According to the biomarker for diagnosis, compared with some liquid biopsy markers currently available, it has the advantages of safe and non-invasive, easy to obtain sample, high accuracy, easy to operate and the like, and provides accurate judgment for early diagnosis of Parkinson.

The application provides a method for diagnosing and differentiating classical swine fever based on a classical swine fever E2 subunit vaccine, which comprises the following steps: immunizing pigs in a pig farm with the classical swine fever E2 subunit vaccine, detecting E0 antibodies and E2 antibodies after one year of immunization, and eliminating the pigs with E0 antibodies until both the E0 antibodies and the CSFV nucleic acid detection are negative, which indicates that the classical swine fever is purified. The classical swine fever E2 subunit vaccine comprises a nucleic acid fragment encoding an antigen protein with the amino acid sequence of SEQ ID NO: 5 inserted into a recombinant expression vector. The classical swine fever E2 subunit vaccine provided by the application has good immunoprotective efficacy on pigs, and a classical swine fever epidemic disease purification method is established by using the subunit vaccine. The application effect of the method in a pig breeding company for two years shows that the method can effectively purify the classical swine fever, which indicates that the purification method provided by the application is feasible and can be popularized and applied.

A decision support system that aids healthcare practitioners in making more informed clinical decisions and avoiding errors related to diagnosis and treatment of diseases. The system utilizes several data points from the patient history and clinical findings input by the patient and the doctor, to help the doctor make a more accurate diagnosis and develop a more informed treatment plan that incorporates not just drugs and procedures, but also dietary and lifestyle interventions. A system of the present disclosure comprises dosage calculator, symptom checker, differential diagnosis, drug interaction checker, side effect checker, nutritional analyzer and drug-food interaction checker input pathways and corresponding database compartments. Output data is generated in correspondence with patient input data and stored on a database server, where it is correlated with patient outcomes over time and improved through machine learning.

The present application relates to a kind of biomarkers, which is selected from one or more of the following: UBR2, NPY4R, KLRF2, HABP2, GLP1R, DR1, RUNX2, ARHGAP5, LINC02135, DDHD1, MIR4523, TC2N.The biomarker is especially used for diagnosing stroke and differential diagnosis of cerebral hemorrhage.The present application screens 12 characteristic biomarkers to construct model, adopts receiver operating characteristic (ROC) analysis to evaluate the performance of model, uses sklearn to calculate area under curve (AUC), the sensitivity and specificity of model reach 0.898, 0.818 respectively, AUC value is 0.913;While differential diagnosis of cerebral hemorrhage, sensitivity and specificity reach 0.864, 0-854 respectively, AUC value is 0.909.The present application combines 5hmC modification spectrum of plasma evDNA and machine learning algorithm for the first time, the characteristic biomarker and / or model screened has the advantages of strong specificity and high sensitivity, overcome the problem of low efficiency and poor accuracy in the process of identifying stroke and cerebral hemorrhage in prior art.

A method for differential diagnosis of gallbladder adenoma and cholesterol polyps based on the fusion of deep learning and clinical features in ultrasound images includes: performing deep learning on ultrasound images using a deep learning network to output an adenoma / polyp risk score; the deep learning network is selected from one of VGG, ResNet, DenseNet, GoogLeNet, Vision Transformer, and Inception; the adenoma / polyp risk score and clinical features are input into a trained machine learning model for learning to obtain the final probability of identifying the polyp as a gallbladder adenoma rather than a cholesterol polyp; the clinical features include: echo intensity, maximum polyp diameter, and age; the machine learning model is selected from one of Logistic Regression, Gradient Boosting, K-nearest neighbors, Gaussian naïve Bayes, AdaBoost, Random Forest, ExtraTrees, and SVM.

A breast ultrasound diagnosis method using weakly supervised deep-learning artificial intelligence comprises: an ultrasound image preprocessing step of generating input data including only an image region necessary for learning, by deleting personal information about a patient from a breast ultrasound image; a deep-learning step of receiving the input data, obtaining a feature map from the received input data by using a convolutional neural network (CNN) and global average pooling (GAP), and carrying out re-learning; and a differential diagnosis step of determining the input data as one of normal, benign, and malignant by using the GAP, and when the input data is determined to be malignant, calculating a probability of malignancy (POM) indicating accuracy of the determination.

FIELD: ophthalmology.SUBSTANCE: used for objective differential diagnostics of the anatomical form of the optic nerve disc (OND) deepening. Optical coherence tomography is performed on the Optovue RTVue XR Avanti System using the 3D Disc program. Images of the optic disc are obtained in the horizontal plane, a straight line is drawn between the visible boundaries of the retinal pigment epithelium (RPE) and the position and shape of the bottom of the OND deepening are assessed. When the depression is at the level of the RPE and the angle of its bottom is less than or equal to 90° a triangular shape is defined. If the depression is located above the RPE level and the angle of its bottom is more than 90° a flat shape is defined. If the depression is below the RPE level and the bottom angle is less than or equal to 90° a wedge shape is defined. If the depression is below the RPE level, and its bottom forms a base and two angles, a trapezoidal shape is defined.EFFECT: improving the accuracy, objectivity and reproducibility of determining the individual anatomical shape of the OND excavation by using a stable anatomical landmark – the RPE and standardized geometric parameters, which eliminates subjective error in diagnosis.1 cl, 7 dwg, 1 tbl, 3 ex

This application relates to a blood biomarker for the differential diagnosis of patients with Parkinson's disease and multiple system atrophy, and its uses. The blood biomarker contains at least SERPINA3. This blood biomarker can assist in the early diagnosis and continuous monitoring of patients with Parkinson's disease and multiple system atrophy. It is low-cost, simple to operate, fast to detect, minimally invasive, and readily accessible, making it suitable for most patients and possessing high clinical application value.

Provided in the present invention is a biomarker for the differential diagnosis of active pulmonary tuberculosis, wherein the biomarker is a TRANCE protein, and has an AUC value of >0.8 for the differential diagnosis of patients with active pulmonary tuberculosis, indicating that the biomarker screened in the present invention has a relatively high diagnostic efficacy on active pulmonary tuberculosis.

FIELD: neurology; laboratory diagnostics.SUBSTANCE: used for the differential diagnosis of Parkinson's disease not associated with mutations in the GBA1 gene, and multiple system atrophy. The patient's peripheral venous blood is collected, from which mononuclear cells are isolated by gradient centrifugation, followed by their differentiation into a primary culture of macrophages in the presence of the macrophage colony-stimulating growth factor M-CSF to ensure proliferation and differentiation of monocytes into mature macrophages. The obtained blood macrophage cells are applied to 903 filter cards at a concentration of 2×106 cells / ml. Whatman 903 Sample Collection Cards can be used as 903 filter cards. The activity of lysosomal enzymes is determined: glucocerebrosidase GCase, alpha-galactosidase GLA, acid sphingomyelinase ASMase, galactosylceramidase GALC by high-performance liquid chromatography in combination with tandem mass spectrometry. The value of the canonical linear discriminant function CLDF is calculated using the stated formula. If the value of CLDF is ≥ 43.44, the patient is diagnosed with multiple system atrophy. If the value of CLDF is < 43.44, Parkinson's disease is diagnosed in patients who do not have a mutation in the GBA1 gene.EFFECT: method enables reliable and accurate differential diagnosis of Parkinson's disease and multiple system atrophy by assessing the activity of lysosomal enzymes.2 cl, 2 dwg, 2 ex

The embodiment of the application provides a multi-role adaptive interaction method and system based on an oral medicine knowledge base, which comprises the following steps: when a query signal is received, a query role is identified, and query content is acquired; a preset query engine matched with the query role is called, a previously constructed knowledge base is accessed according to the currently called query engine and the query content, and a differential diagnosis set containing multiple diseases with a sorting relationship is acquired; the diseases in the differential diagnosis set are subjected to multi-dimensional differential diagnosis reasoning one by one according to the sorting relationship, and the comprehensive confidence of the reasoning result is correspondingly evaluated; when the comprehensive confidence is not less than a preset threshold, the disease corresponding to the comprehensive confidence is taken as a diagnosis disease, and a query report matched with the query role is output. The application can assist doctors in making professional clinical decisions and provide patients with convenient oral common sense popularization services.

This invention discloses a predictive model and its application for differentiating between alcohol use disorder (AUD) and alcoholic hepatitis (AH). By integrating the patient's basic clinical information, gut bacterial community characteristics, and gut fungal community structure, a multi-dimensional differentiation model is constructed. This model, through the joint analysis of gut microbiota (bacteria + fungi) and patient baseline data, provides an innovative and practical combination of molecular markers for the accurate differential diagnosis of AUD and AH.

This invention provides a collaborative reasoning diagnostic assistance method and system, relating to the field of large-scale model technology. First, a knowledge base of clinical cases of birth defects in children, containing medical reasoning rules, is constructed. Then, multimodal diagnostic information of a target child is received and decomposed to obtain the decomposed results. The decomposed results of the target child's multimodal diagnostic information are dynamically correlated and reasoned with the case data and medical reasoning rules in the knowledge base of clinical cases of birth defects using a teaching large-scale model, generating a structured differential diagnosis opinion, including the defect type, medical evidence chain, and feature matching. The structured differential diagnosis opinion is pushed to the clinician's terminal, feedback is collected, and the reasoning parameters of the teaching large-scale model and the knowledge base rules are collaboratively adjusted. This invention can comprehensively utilize multimodal information, improve diagnostic accuracy and reliability, and can be continuously optimized and improved.

This invention relates to the field of medical software development technology and discloses a system and storage medium for differentiating benign and malignant pulmonary nodules based on cfDNA methylation-radiomics-clinical studies. The system includes a multimodal data acquisition module, a feature extraction and alignment module, a multimodal fusion diagnosis module, and an interpretable report generation module. After collecting clinical information, low-dose CT images, and peripheral blood cfDNA methylation sequencing data from the subjects, features are extracted from each modality and mapped to a unified semantic space through contrastive learning. A collaborative attention mechanism is then used to dynamically weight and fuse the aligned multimodal features. The fused features are input into a deep neural network classifier to output a nodule malignancy probability score, and finally, an interpretable diagnostic report with risk stratification is generated. This invention solves the problems of insufficient multimodal heterogeneous data fusion, poor model robustness, and weak interpretability, achieving accurate differentiation between benign and malignant pulmonary nodules, and demonstrating good clinical applicability and promotional value.

To enable support in the differential diagnosis of heart disease. [Solution] An information processing system comprising: an answer input unit that receives input from a patient's answers to questions for differentiating mental illnesses; a video image acquisition unit that acquires video images of the patient; a biological response detection unit that analyzes the video images to detect changes in the patient's biological responses; and an estimation unit that estimates the mental illness the patient is suffering from by providing the received answers and detected changes in biological responses to a learning model that has learned the answers, changes in biological responses, and mental illnesses.

This invention relates to a method for diagnosing mycosis fungoides (MF) or eczema and / or differentiating MF from eczema or psoriasis, the method comprising: determining the expression of at least one biomarker in a sample; differentiating MF from eczema and / or psoriasis based on the expression of the at least one biomarker in the sample; and generating a differential diagnosis result based on the expression of the at least one biomarker in the sample. The invention also relates to a system for diagnosing mycosis fungoides (MF) or eczema and / or differentiating MF from eczema or psoriasis, the system comprising: a processing component configured to output at least one dataset; and an analysis component configured to analyze the at least one dataset, wherein the analysis component comprises: a determination module configured to determine the expression of at least one biomarker in a sample; a differentiation module configured to differentiate MF from eczema and / or eczema or psoriasis based on the expression of the at least one biomarker in the sample; and a result generation module configured to generate a differential diagnosis result based on the expression of the at least one biomarker in the sample. Furthermore, the present invention relates to a kit for diagnosing eczema or mycosis fungoides, and / or a kit for distinguishing MF from eczema or psoriasis, said kit comprising at least one means for quantifying the expression of at least one biomarker in at least one sample.

The present invention relates to the field of in vitro molecular biology diagnostic reagents, and provides a use of primers and probes for detecting prostate cancer specific methylation markers in preparation of a prostate cancer detection reagent. Primers and probes are designed on the basis of partial regions or the full length of positive strands of differentially methylated target regions, namely chr10:111767101-111767600, chr4:54965801-54966100, and chr16:88717311-88717610, of prostate cancer specific methylation markers; the combination of methylation markers allows for effective distinguishing of prostate cancer from benign prostatic hyperplasia or other cancers of the urinary system, thereby helping to address the issue of false positives in prostate cancer screening and significantly improving the detection sensitivity of non‑digital rectal examination urine. The present invention provides an important reference for clinicians in early diagnosis and differential diagnosis of prostate cancer.

This invention relates to the field of tumor detection technology. It proposes a method for differential diagnosis and prediction of xanthogranulomatous cholecystitis (XGC) based on preoperative enhanced CT images. The method includes: acquiring enhanced CT images of the gallbladder wall in the arterial and portal venous phases; performing image segmentation and feature extraction; performing dimensionality reduction on the arterial phase features, portal venous phase features, and the combined dual-phase features to obtain corresponding feature datasets, which are then divided into training and testing sets; training three machine learning prediction models based on the three training sets to obtain the optimal machine learning prediction model; validating the performance of the optimal machine learning prediction model using the testing set to obtain a differential diagnosis prediction model; and inputting the feature data of the enhanced abdominal CT image to be predicted into the differential diagnosis prediction model for identification. This invention establishes a better differential diagnosis prediction model by analyzing the imaging characteristics of XGC and thick-walled GBC, providing a basis for treatment decisions.

The application provides a differential model based on CT enteroclysis imageomics and clinical features, and relates to the technical field of medical treatment.The differential model comprises the data of clinical information, laboratory examination, image examination, enteroscopy and pathological examination, extracts the imageomics of intestinal wall and mesenteric fat characteristics based on CT enteroclysis, and constructs a differential diagnosis model of Crohn's disease and intestinal tuberculosis through machine learning.The differential diagnosis model of Crohn's disease and intestinal tuberculosis is constructed by integrating the clinical features and the imageomics parameters of CT enteroclysis, and is significantly better than a single model.The imageomics characteristics reveal the essential differences of the two diseases in the texture of intestinal wall and mesenteric fat.The nomogram constructed by the application provides a convenient visual tool for the clinic, and is particularly suitable for precise diagnosis in tuberculosis high-incidence areas.

The application provides an immunohistochemical combined gene detection system for early screening of skin lymphoma, which integrates ten function modules of intelligent sample pretreatment, multi-target synchronous immunohistochemical staining, high-resolution digital pathology scanning, laser microdissection assisted micro-nucleic acid extraction, skin lymphoma specific gene panel amplification, high-throughput sequencing, multi-modal data fusion, intelligent risk assessment, dynamic follow-up early warning and standardized report generation. The system realizes accurate matching of spatial localization of immunophenotype and high-sensitivity gene variation detection on the same tissue section, and automatically outputs individualized risk score and diagnosis suggestion by fusing multi-dimensional data through artificial intelligence algorithm. Compared with the traditional method, the detection rate and interpretation consistency of early skin lymphoma are significantly improved, the whole process is automatically and standardized, and the system is suitable for clinical early screening, differential diagnosis and dynamic monitoring, and has important application prospect.

The application discloses application of an antigen peptide MG15 and an antibody thereof in preparation of a rheumatoid arthritis diagnosis product, and belongs to the technical field of disease diagnosis. The application aims to solve the problem of lack of a serological diagnosis marker with high sensitivity and high specificity for RA (especially anti-CCP antibody negative RA), and relevant detection products show significant diagnostic value. The antigen peptide MG15 has an amino acid sequence of GNSSESESKTTHAYT or BSA-C-GNSSESESKTTHAYT, and is used for detecting the content of an epitope antibody capable of being specifically recognized by the antigen peptide MG15 in a biological sample of a patient. The epitope antibody has obvious differences between RA patients, healthy people and other easily-confused rheumatism and immunological disease patients, has a sensitivity of 47.47% and a specificity of 90.16% in serum RA diagnosis, can effectively identify and diagnose RA, and has unique diagnostic value in anti-CCP antibody negative RA patients.