71 results about "Performed Diagnosis" patented technology

The invention relates to a device for risk assessment of nervous system diseases, and a processor of the device is configured to receive eye movement data and electroencephalogram data which are associated in a time sequence, and fuse a double-branch deep neural network in a model through multiple modes to assess the risk of the nervous system diseases, performing feature extraction and attention mechanism-based cross-modal feature fusion on the eye movement data and the electroencephalogram data to obtain cross fusion features; and outputting risk assessment results and risk assessment parameters of at least one type of nervous system diseases through an output layer of the multi-modal fusion assessment model. According to the method and the device, the problem that diagnosis sensitivity and universality are insufficient due to the fact that diagnosis only depends on a single information source in related technologies is solved, disease features can be more comprehensively constructed from two levels of neural activity and behavior response by fusing data of two modes of eye movement and electroencephalogram, parallel diagnosis is further performed for multiple types of diseases, and the diagnosis efficiency is improved. The sensitivity and applicability of neuropsychiatric diagnosis are improved, and particularly, the method has more remarkable accuracy in early stages such as mild cognitive impairment and the like.

The invention provides an auxiliary doctor seeing decision method and system based on multiple agents and a storage medium, and the method comprises the steps: carrying out the structural information collection of a target patient according to a pre-diagnosis agent, and obtaining the structural data of the patient; inputting the structured data of the patient into a diagnostic reasoning agent for diagnostic reasoning to obtain a diagnostic predicted disease; inputting the diagnosed and predicted disease and the current medical resource load information of the doctor-seeing hospital into a resource scheduling agent for diagnosis and treatment analysis to obtain a diagnosis and treatment recommendation scheme; inputting the diagnosis and treatment recommendation scheme into a medical insurance strategy agent for medical insurance analysis to obtain a medical insurance recommendation scheme; and combining the patient structured data, the diagnosis and prediction disease, the diagnosis and treatment recommendation scheme and the medical insurance recommendation scheme to obtain auxiliary treatment information. According to the embodiment of the invention, based on the auxiliary doctor-seeing information, the auxiliary doctor-seeing decision corresponding to the disease diagnosis, the diagnosis and treatment scheme and the medical insurance scheme can be effectively output, and the diversity of the auxiliary doctor-seeing decision function is guaranteed.

The invention provides a crop leaf disease image recognition method, which comprises the following steps: collecting crop leaves to capture a scab area image, obtaining original pixel-level data and a boundary contour, and obtaining an initial data set containing a scab form; angle matching and direction comparison are carried out on the extracted contour coordinates and the main extension direction angle and a preset vein trend template, and the coincidence degree of the extension direction and the vein trend is evaluated; according to the coincidence degree of the expansion direction and the leaf vein trend, identifying the difference between the expansion rate of the disease spot along the leaf vein direction and the expansion rate vertical to the leaf vein direction, and analyzing to obtain a directivity judgment standard after dynamic adjustment; performing diagnosis specificity strong characterization identification classification processing on the morphological characteristics of the non-uniform disease spots according to the dynamically adjusted directivity judgment standard, and determining the tightness degree of judgment between the disease spot morphology and the pathogen species; and integrating the main expansion direction and the vein trend distribution data according to pathogen types, and generating a disease type discrimination report.

A method of automatically diagnosing pneumonia in a patient includes obtaining values of two or more diagnostic parameters of the patient from a caregiver of the patient using an input / output interface device. The method includes applying, using a processor connected to an input / output interface, two or more diagnostic parameters to an electronic memory storing a plurality of pre-compiled pneumonia diagnostic models to identify an optimal diagnostic model for performing the diagnosis. The values of the two or more diagnostic markers are applied to the identified optimal diagnostic model to generate a diagnostic output. An input / output interface device is operated according to the diagnostic output to indicate to the caregiver the presence or absence of pneumonia in the patient. The caregiver may use the diagnostics to provide appropriate care for the patient. A pneumonia diagnostic model is from investigations of pneumonia-positive and non-pneumonia population of subjects.

The present application provides detection and prognosis of infectious diseases. In particular, the present application provides fragment length profiles of nucleic acid libraries, methods of generating fragment length profiles of nucleic acid libraries, and methods of using the fragment length profiles for diagnosis and / or prognosis. The present application further provides methods, compositions, and kits for determining the stage or site of infection in a subject.

In accordance with at least one aspect of this disclosure, a method for preparing a patient for a diagnostic procedure and / or for receiving a therapy using a disease targeted drug is provided. The method can include the steps of a) identifying one or more soluble targets detectable in plasma of the patient; and b) extracorporeally removing the soluble targets from the plasma of the patient. Extracorporeally removing the soluble targets from the plasma of the patient can include using plasmapheresis or immunoadsorption to prepare a patient for a procedure using disease targeted treatment or diagnostics. Additionally, or alternatively, dose modulation can be used to proportionally increase the concertation of the diagnostic / treatment agent relative to the concentration of the soluble target in the blood, improving the outcome treatment and diagnostic procedures over conventional methods.

The invention discloses an Alzheimer's disease classification method based on sMRI and PET brain images. The Alzheimer's disease classification method is used for auxiliary diagnosis of the Alzheimer's disease. The method comprises the following steps that firstly, a feature extraction network is used for extracting image features of sMRI and PET brain images, and the feature extraction network enhances the spatial position information capturing capacity and the redundant information inhibiting capacity of the network by embedding a coordinate attention mechanism and space-channel reconstruction convolution; secondly, realizing complementary fusion of two image features by using a parallel interaction network based on proxy cross attention; and finally, classifying by using a full connection layer as a classifier. According to the method provided by the invention, the pathological information of sMRI and PET brain images is combined, the Alzheimer's disease classification accuracy is improved, a doctor can be assisted in diagnosis, and the method has a wide application prospect in the field of computer-aided diagnosis.

The invention provides a method for pooling samples prior to a diagnostic screen for a non-infectious disease. The method includes pooling only a portion of the obtained samples and storing the other portion for later use. Pooling samples represents a more time and cost-effective way to perform diagnostic screening.

The invention discloses a multi-modal orthopedic infection diagnosis method and system based on double-flow encoder fusion, and the method comprises the steps: collecting medical images and clinical index data, extracting cross-slice features through an image encoder, calculating the importance weight based on information entropy, and achieving the self-adaptive weighted pooling to generate image embedding representation; mapping the clinical indexes by using a clinical encoder to generate a clinical embedded representation; identifying a mode dominant mode to perform diagnosis preference adjustment to generate interaction features; the measurement source feature semantic distance recognition complementary dimension is quantified into the collaboration degree, and difference self-adaptive weight fusion is generated to obtain joint representation; evaluating confidence, screening high-stability features to construct an importance map, and generating a preliminary classification result based on a high-confidence feature construction decision rule; and identifying a difficult sample based on a preliminary classification result, extracting boundary feature dimensions, constructing a boundary mode, quantifying sensitivity, dividing risk levels, constructing a hierarchical diagnosis system, and outputting a hierarchical diagnosis result, thereby realizing deep fusion, precise classification and risk layering of multi-modal data.

This invention discloses an AI-based automatic analysis color diagnosis system for fundus lesions, relating to the field of ophthalmic disease diagnosis technology. It includes a fundus image acquisition module and an AI color analysis module. This AI-based automatic analysis color diagnosis system for fundus lesions, through its fundus image acquisition module, AI color analysis module, and analysis model construction module, inputs acquired fundus color images into a fundus color image processing and analysis model to obtain fundus color feature data. It then identifies the patient's lesions, diagnoses the patient's ophthalmic disease based on the identified lesions, and determines the location of the lesions in the fundus images using the fundus color feature data. This constructs a fundus image color evaluation system, improving the accuracy and reliability of ophthalmic diagnosis and assisting in the diagnosis and treatment of fundus diseases.

Embodiments of the present disclosure relate generally to methods used to perform diagnosis and drug screening because the method includes an algorithm that is able to obtain images of immune cells in 3D tissue cultures, analyze such images to create immune cell infiltration maps, automatically score and identify drugs or agents that can be potentially clinically beneficial.

The invention discloses a marker group for screening and diagnosing prostatic cancer and application of the marker group, and belongs to the technical field of molecular biology. The marker group comprises at least four of polypeptides as shown in sequences SEQ ID NO. 1 to 46. The marker combination constructed by the invention has more excellent sensitivity and specificity when being used for auxiliary diagnosis and early screening of prostatic cancer. The auxiliary diagnosis result of the method is superior to that of a PCa diagnosis mode using mp-MRI (sensitivity is 83.45%, and specificity is 92.94%) recommended by a PCa diagnosis and treatment guide and a prostate image report data system of the European urinary society at present. An early screening result is superior to that of serum PSA (Prostate Sequence Assay) examination which is currently used as a first choice means for prostate cancer screening, the critical value is 4.0 ng / mL (strong recommendation in Guidelines and medium evidence grading), the sensitivity is 72.84%, and the specificity is 81.10%.

A method of diagnosing an infectious disease, including determining the severity of the disease, in a subject comprising measuring an expression level of at least one protein selected from the group consisting of TSG-14, AGER, ANG-2 and ST2 in a sample of the subject; and diagnosing the disease based on said expression level. Kits for carrying out the diagnosis are also disclosed.

The invention provides an electrocardio diagnosis method and system based on a large language model, and the method comprises the steps: extracting a plurality of electrocardio features from a to-be-diagnosed electrocardio signal based on the knowledge of electrocardio diagnosis diseases; inputting the electrocardiosignal to be diagnosed into a preset electrocardiograph time sequence model for electrocardiograph waveform analysis to obtain a first diagnosis tag and a diagnosis probability corresponding to the first diagnosis tag; searching a historical electrocardiosignal similar to the electrocardiosignal to be diagnosed, and acquiring a second diagnosis tag corresponding to the historical electrocardiosignal and a corresponding similarity; generating an electrocardio diagnosis process prompt based on the electrocardio diagnosis process knowledge and a thinking chain technology; and taking each electrocardio feature, the first diagnosis tag, the diagnosis probability, the second diagnosis tag and the similarity as an electrocardio diagnosis context, and inputting the electrocardio diagnosis context, the electrocardio diagnosis knowledge and the electrocardio diagnosis process prompt into a preset large language model for diagnosis to obtain a diagnosis result output by the large language model. The purpose of improving the electrocardio diagnosis accuracy is achieved.

The invention relates to an image recognition and expert system fused wheat disease intelligent diagnosis system and method, and belongs to the technical field of wheat disease diagnosis. Different processing modes are adopted for different detection results; for the condition that the disease image features are detected and the corresponding disease is unique, the disease result is directly output; a second expert system module performs further identification on the condition that the detected disease image features are not unique corresponding to the disease; and for the condition that no disease image feature is detected, the first expert system module performs diagnosis, if the disease type is diagnosed, a disease result is output, and if the disease type is not diagnosed, the manual judgment module can perform further diagnosis. According to the system and the method, all types of scenes such as obvious image features, similar image features, non-image feature dominant and unknown diseases can be covered, and intelligent diagnosis of various types of wheat diseases is realized.

To improve determination accuracy of a disease of an affected part which is difficult to determine from an image obtained by imaging a medical examination region of a patient.SOLUTION: The image processing apparatus includes a detection unit configured to detect a disease from an image obtained by capturing an examined region of a patient, and a correction unit configured to, when the detection unit detects a first disease, correct determination accuracy of the first disease based on a detection state of a second disease related to the first disease.SELECTED DRAWING: Figure 3

The present invention relates to compositions and methods for molecular profiling and diagnostics for genetic disorders and cancer, including but not limited to gene expression product markers associated with cancer or genetic disorders. In particular, the present invention provides algorithms and methods of classifying cancer, for example, thyroid cancer, methods of determining molecular profiles, and methods of analyzing results to provide a diagnosis.

The application discloses an AI-assisted pathological sample diagnosis method and a diagnosis system, relates to the technical field of medical image assistance, and comprises the following steps: acquiring a pathological sample from a digital pathology image library through a data access layer, and acquiring user information corresponding to the pathological sample from a clinical information library; performing multi-scale attention feature extraction and representation learning on the pathological sample through a multi-scale feature extraction network; quantifying the dynamic uncertainty of the pathological sample through a Bayesian neural network; performing multi-modal knowledge fusion; performing diagnosis decision process visualization output through a visualization module; evaluating and verifying the diagnosis system based on the visualization output result; the recognition ability of microstructure in the pathological sample and the confidence evaluation accuracy of the diagnosis result are significantly improved, the explainability and clinical practicability of the system are enhanced, incremental learning and long-term verification are supported, and the robustness, generalization ability and cost-effectiveness of the diagnosis system are effectively improved.

This invention provides a diagnostic method based on saccade analysis. The method includes: collecting eye movement displacement and time data of the subject to obtain a discrete eye movement velocity sequence; obtaining a denoised eye movement velocity sequence after denoising; selecting the velocity sequence to be processed using a moving window; determining the time length of the moving window based on the length of the velocity sequence and the calculation time requirements; determining an eye movement velocity threshold according to diagnostic needs; calculating local extrema of the velocity sequence within the moving window and determining the eye movement type and number of saccades based on the velocity threshold; calculating local extrema of the entire velocity sequence using this method to obtain the total number of saccades; diagnosing the subject's eye movement state based on the number of saccades and their time intervals, and determining whether detailed analysis of local data is needed to obtain a more accurate diagnostic result based on the diagnosis results. This invention can help doctors diagnose different types of saccades and their potential predictive conditions such as cognitive impairment and Parkinson's disease more quickly.

A method of determining the severity of the disease, in a subject comprising measuring an expression level of at least one protein selected from the group consisting of IP- 10, IL-6, IL- 10, DR5, ANG-2 and ST2 in a sample of the subject; and determining the severity of the disease based on said expression level. Kits for carrying out diagnosis are also disclosed.

1. The name of the design product: digital electroencephalograph (NL-4208). 2. The use of the design product: as a device for electrotherapy rehabilitation, for measuring brain electrical signals, and assisting in the diagnosis of brain diseases. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The invention relates to the technical field of medical instruments, in particular to a urinary pathology diagnosis device which comprises a base, a shell, a placement block, a puncture needle, a rotating ring, a placement box and the like, the shell is connected to the top of the base, the placement block is connected to the shell, the puncture needle is placed in the placement block, and the rotating ring is rotationally connected to the top of the shell. Six placing boxes for placing test tubes are uniformly connected to the top of the rotating ring in the circumferential direction at intervals. A pathological sample can be diagnosed through the pathological microscopic observer, the condition of a patient can be known, so that a reasonable treatment scheme can be formulated, a solution can be sucked into the test tube through the water pump, the solution is added, the quality and integrity of the pathological sample are ensured, subsequent analysis and diagnosis are facilitated, the test tube can be heated through the heater, and the test tube is convenient to use. The pathological sample is kept in an optimal state by keeping the temperature of the pathological sample, solution adding and temperature keeping can be automatically carried out, and the operation steps are reduced, so that the treatment difficulty of the pathological sample can be reduced.

The invention provides detection and prediction of infectious diseases. In particular, the present application provides fragment length profiles of nucleic acid libraries, methods of generating fragment length profiles of nucleic acid libraries, and methods of using fragment length profiles for diagnosis and / or prognosis. The present application further provides methods, compositions, and kits for determining the stage of infection or localization in a subject.

A quality defect diagnosis device for diagnosing quality defects occurring in a product is provided with: a data division unit for two-dimensionally dividing product data to be diagnosed, said product data being composed of job data and quality data, for each predetermined block; a data collection unit that extracts job data for each predetermined classification of quality defects on the basis of the tendency of occurrence of quality defects in the divided blocks, and collects the extracted job data for each block and for each region including two or more blocks; a quality defect determination unit that determines the presence or absence of occurrence of quality defects using a plurality of prediction models generated for each classification of quality defects and having learned the relationship between the aggregated job data and quality data; and a main cause estimation unit that estimates the main cause of the quality defect on the basis of the determination result of the quality defect.

The invention discloses a detection chip of an idiopathic membranous nephropathy marker molecule, a preparation method of the detection chip, and a detection method of IgG4 in serum by using the chip. The detection chip is formed by preparing a flexible Au nano finger ordered array structure, immersing the flexible Au nano finger ordered array structure into a PLA2R solution for incubation and drying, then collapsing and implanting the flexible Au nano finger ordered array structure into a groove of a biological carrier slide. During use, serum of a patient is injected into the chip, and Raman measurement is carried out for diagnosis. According to the invention, high-sensitivity and high-selectivity detection can be realized, the detection time is short, and the detection requirements of clinical idiopathic membranous nephropathy are met.

The invention relates to the technical field of medical artificial intelligence, in particular to an infectious disease symptom image recognition and diagnosis system based on deep learning, and the system comprises a multi-modal symptom image collection module which obtains infectious disease related multi-modal symptom image data; the intelligent sensing type symptom region positioning standardization module detects and positions the symptom key region in real time, and outputs standardized multi-modal data; the cross-modal fusion two-dimensional diagnosis module constructs a Transform-based multi-modal feature fusion model, extracts symptom core features, performs cross-modal association modeling, performs two-dimensional diagnosis in combination with a cross-disease transfer learning framework, and generates a preliminary diagnosis result; and the clinical semantic collaborative calibration and scheme generation module performs adaptive calibration, performs diagnosis optimization by fusing infectious disease symptom evolution priori knowledge, and synchronously generates clinical intervention suggestions and dynamic monitoring schemes. Therefore, the problems of weak feature fusion mechanism, disjunction of diagnosis results and clinical practice and the like in the prior art are solved.

This invention discloses a method for identifying comorbidity relationships in ophthalmology based on zero-model correction and adaptive node selection, belonging to the fields of medical big data processing, real-world electronic medical record analysis, disease comorbidity relationship mining, and graph data mining. The method includes: acquiring diagnostic data from ophthalmology patients' electronic medical records and performing diagnostic cleaning, coding standardization, and patient-level disease set construction; constructing a patient-disease bipartite graph based on the correspondence between patients and diseases, and statistically analyzing real disease co-occurrence relationships; while maintaining the distribution of the number of patients with diseases and the distribution of disease incidence frequency, randomly reconnecting the patient-disease bipartite graph to generate multiple zero-model background networks, and calculating the background correction advantage score of disease pairs relative to the random background; obtaining the initial association strength by combining the number of co-occurring individuals, relative risk, lift, and significance test results of disease pairs; calculating the adaptive node selection threshold based on the incidence frequency of disease nodes, the number of candidate edges, the distribution of candidate edge weights, and the zero-model advantage distribution; further calculating the importance score of the two-end nodes and the resampling stability score of disease pairs, fusing them to obtain a comprehensive credibility score for disease pairs; selecting candidate comorbidity relationships and classifying their credibility levels based on the comprehensive credibility score, and outputting the disease pairs, scoring results, credibility levels, and reasons for removal. This invention can reduce the impact of high-frequency diseases, differences in the number of patient diagnoses, and random co-occurrence on the results of ophthalmic comorbidity identification, avoid the excessive background connections of high-frequency diseases and the accidental deletion of low-frequency specific relationships caused by traditional uniform thresholds, and improve the reliability, stability, and interpretability of ophthalmic comorbidity identification.

An anti-NMI monoclonal antibody or an antigen-binding fragment thereof and uses thereof are provided. The antibody or the antigen binding fragment thereof has good binding activity with the NMI protein, has high affinity to the NMI protein, can be used for detecting the existence or level of the NMI protein in a sample, can be used for diagnosis, auxiliary diagnosis or prognosis evaluation of diseases or symptoms related to abnormally high-level and / or active NMI, can be used for removing the NMI protein in body fluid, and can be used for detecting the existence or level of the NMI protein. Preventing and treating diseases or conditions related to the abnormally high-level and / or active NMI.

The invention discloses a small kidney tumor preoperative diagnosis method, system, medium and device based on MRI, and belongs to the field of medical imaging diagnos.The method comprises the steps that clinical feature data and original MR I image data of a patient to be diagnosed and MRI interpretation feature data obtained through interpretation based on the original MRI image data under the blind method condition are obtained; performing tumor region segmentation in the original MRI image data to generate tumor three-dimensional volume-of-interest data; extracting a radiomics feature set from a non-fat suppression T2 weighted imaging sequence and a contrast enhancement T1 weighted imaging sequence in the original MRI image data based on the tumor three-dimensional volume-of-interest data, and performing weighted fusion calculation on each feature in the radiomics feature set to obtain a radiomics score; and inputting the clinical feature data, the MR I interpretation feature data and the radiomics score into a preset composite diagnosis model for diagnosis, and outputting a diagnosis result. By implementing the method, the accuracy of benign and malignant identification before the small kidney tumor operation can be improved.

The invention discloses a diagnosis and treatment target device for cytophagic lymphocytosis. The device comprises a detection module, a detection module and a control module, wherein the detection module is used for detecting the expression quantity of four genes, namely CCR2, CDKN1A, TNFSF10 and TP53INP2 in a patient sample of a current patient; the detection module is used for detecting the expression quantity of four genes, namely CCR2, CDKN1A, TNFSF10 and TP53INP2; the analysis module is used for carrying out difference analysis on the expression quantity and the expression quantity of a healthy control group to obtain a molecular characteristic spectrum related to the HLH; the diagnosis typing treatment module is used for performing diagnosis typing on the current patient according to a difference mode of the molecular characteristic spectrum and treating according to a diagnosis typing result, so that serious treatment toxicity such as myelosuppression and liver and kidney injury of a traditional chemotherapy regimen can be reduced, the safety and effectiveness of clinical treatment of the patient are improved, and higher specificity and accuracy are achieved; a foundation is laid for developing a novel diagnostic tool, and a direct and feasible candidate scheme and a theoretical basis are provided for developing a high-efficiency and low-toxicity HLH new therapy.