37 results about "Timely diagnosis" patented technology

SOLUTION: To provide a cardiac monitoring system and a method for using such a system, in which preferable embodiments of the system and method detecting cardiac information through a wearable device and then recording the same, and extracting data features from the recorded cardiac information, the extracted data features being analyzed and used for clinical diagnosis.EFFECT: A biological monitoring system can smooth and / or enhance patient experience, for achieving more precise and timely diagnosis of cardiac arrhythmia.SELECTED DRAWING: Figure 11

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.

The invention provides an otoscope image processing system for establishing database model analysis based on deep learning, and relates to the technical field of image processing, the otoscope image processing system comprises an acquisition layer, a processing layer, an extraction layer, a modeling layer and an analysis layer, the processing layer adopts an AI image expansion algorithm to carry out fusion splicing and interference elimination processing on acquired images, and the extraction layer adopts a mixed feature extraction method to extract color, texture and shape features of the images processed by the processing layer and introduce MPEG-7 descriptors to express the features; according to the method, otoscope images of a patient are collected, features and feature values of the images are extracted, a focus global shared image library is constructed through deep learning modeling, the focus global shared image library is compared with the otoscope images of the patient for training, and corresponding focuses are determined, so that different ear diseases can be automatically identified and diagnosed by learning and analyzing a large amount of ear image data, and the diagnosis accuracy is improved. And more accurate and timely diagnosis information is provided for doctors.

The invention relates to the technical field of medical instruments, in particular to an automatic association test method and system, and aims to reduce unnecessary test items, shorten detection time and improve detection efficiency. Through intelligent judgment and optimization of detection items, excessive detection is avoided, the examination cost of a patient is reduced, and the economic burden is relieved; manual intervention is reduced, and the detection efficiency is improved; the detection items are automatically increased or decreased according to the preset rule, so that the time waste of secondary application test is avoided, and the detection efficiency is improved. The test is reduced without influencing the clinical diagnosis, and meanwhile, the detection items can be intelligently added according to the rule, so that waste and aggravation of the economic burden of the patient caused by excessive test and examination are avoided, the potential diagnosis risk caused by missing of the additional detection items set in the rule is avoided, and the illness state can be diagnosed in time. Therefore, the problem of resource waste caused by excessive detection in traditional detection is solved.

The invention provides multifunctional ophthalmology intelligent examination equipment which comprises a base and an examination equipment body. The front side of the examination equipment body is connected with a chin support and a forehead support through supports. First teeth are evenly arranged on the opposite side walls of the quadrangular prism, first tooth blocks are slidably arranged in the first sliding grooves respectively, a first driving assembly is arranged on the chin support, and a positioning assembly is arranged on the forehead support; fixing columns are arranged on the two sides of the chin support respectively, the opposite side walls of the fixing columns are connected with fixing sleeves through elastic telescopic pieces respectively, second telescopic disinfection blocks are arranged at the upper ends of the inner rods respectively, make contact with the working face of the forehead support and slide in the length direction, and first telescopic disinfection blocks are arranged at the lower ends of the opposite side walls of the sleeves respectively. A second driving assembly is arranged at the lower end of the chin support. The heights of the chin support and the forehead support can be conveniently and rapidly adjusted, and the examination efficiency is improved; disinfection is convenient, fast, timely, clean and sanitary, the experience feeling of a patient is improved, and cross infection is effectively avoided; and the system is connected with the Internet, so that doctors can diagnose conditions timely.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.

The invention discloses a lupus nephritis marker and application thereof, and belongs to the technical field of biomarkers. The marker for the lupus nephritis, disclosed by the invention, comprises one or more of CD8, CD38 and PD-1. By detecting specific cell surface markers in patient blood, early diagnosis of LN is assisted in a minimally invasive mode, and based on deep comparative analysis of LN patients and healthy people, the specificity and sensitivity of the biomarkers in LN diagnosis are ensured through screening of the biomarkers. In the aspect of differential diagnosis, a clinician is assisted to distinguish LN from other types of kidney diseases, such as membranous nephropathy (MN) and anti-neutrophil cytoplasm antibody related vasculitis (AAV), by analyzing specific biomarker modes of different nephropathy. The dependence on kidney puncture is reduced, related potential risks and inconvenience are avoided, and meanwhile more accurate and timely diagnosis is provided for an SLE patient.

The invention relates to the technical field of sepsis monitoring, and discloses a sepsis monitoring method and system based on multi-modal data, and the method comprises the steps: setting a plurality of sepsis patient types based on influence factors, and generating a plurality of disease course period sequences which comprise a plurality of disease course periods, each disease course period is mapped with a corresponding multi-modal data set; comparing and analyzing the multi-modal data sets of the plurality of disease course cycle sequences to obtain feature data of each disease course cycle, change features of each feature data and a weight coefficient, and constructing a sepsis monitoring model; the real-time characteristic data of the to-be-monitored patient is obtained and input into the sepsis monitoring model, the monitoring result is obtained, whether the early warning instruction is generated or not is judged, the complexity of sepsis and the individual difference of the patient are fully considered, the monitoring accuracy is improved, and powerful support is provided for timely diagnosis and treatment of the sepsis patient.

The present application provides an intelligent monitoring and diagnosis method, a vehicle-mounted central control system, a diagnostic system, and a vehicle. The method includes obtaining the vehicle's current state type in response to receiving abnormal health status information of a target user sent by a smart home monitoring device; determining the information type corresponding to the abnormal health status information; and generating and sending health status information corresponding to the abnormal health status information based on the vehicle's current state type and the information type. The intelligent health monitoring and diagnosis method provided in the present application can promptly send the abnormal health status information of the target user to relevant personnel associated with the target user, so that the relevant personnel can promptly understand the abnormal health status of the target user, thereby enabling the target user to receive timely diagnosis and treatment with the help of relevant personnel, thereby avoiding the dangerous situation where the target user cannot receive timely help and diagnosis due to abnormal health.

The invention discloses a heartbeat and pulse monitoring system for critical nursing, and relates to the technical field of heartbeat and pulse monitoring, the system comprises the following components: a non-contact sensing module which is used for capturing a heart micro-motion signal of a patient by integrating advanced radar and infrared sensors, converting the heart micro-motion signal into an electronic signal for filtering and feature extraction, and sending the electronic signal to a central processing module; according to the method, the discomfort caused by the fact that a sensor makes contact with the skin is avoided, non-contact monitoring of the heartbeat pulse of a patient is achieved, the stability and accuracy of data can still be kept when the patient slightly moves and moves, filtering, feature extraction and anomaly detection are conducted on captured heart micro-motion signals of the patient in combination with a deep learning algorithm, and the accuracy of the heart micro-motion signals is improved. The high-precision recognition module ensures that the system can still keep high accuracy even under the conditions that the patient moves and external noise interferes, medical staff can find abnormal changes of heartbeat pulses of the patient in time, and powerful support is provided for timely diagnosis and treatment.

The invention relates to the technical field of medical data processing, and discloses a tremor classification model construction method, system and device, and a medium, and the method comprises the steps: learning clinical data, motion function scoring data, emotion scoring data, cognitive function scoring data and brain image data corresponding to a tremor type PD patient group and an ET patient group through a machine learning model; a high-precision tremor classification model is obtained through training, the distinction degree of PD and ET can be remarkably improved, and the accuracy rate is higher. Good adaptability is still achieved under the complex conditions of disease course stage differences, atypical symptoms and the like; the method has the advantages of being simple in operation, capable of rapidly giving classification results, helping medical staff to make timely diagnosis decisions and formulate individualized treatment strategies, providing feasible thoughts for other nervous system diseases with similar diagnosis difficulties, and having wide application prospects in the fields of medical image analysis and artificial intelligence diagnosis.

The invention provides a wearable wireless stethoscope early warning device for monitoring acute attack of an asthma patient, the wearable wireless stethoscope early warning device comprises a vest, a first binding belt and a second binding belt, the first binding belt and the second binding belt are arranged on two sides of the vest, two adjusting belts are arranged above the vest, and the two adjusting belts are connected with the vest; a miniature sound signal sensor, a miniature lung sound discriminator and a signal microprocessor are arranged in the vest, the miniature sound signal sensor is used for collecting breathing sound signals, and the miniature lung sound discriminator is used for recognizing dry rale, wet rale and normal breathing sound. The breathing sound of an asthma patient, especially a child patient, is monitored in real time, abnormal breathing sound characteristics are accurately recognized, timely early warning of acute attack is achieved, by means of wireless transmission and a remote diagnosis technology, a doctor can remotely obtain illness state information of the patient, timely diagnosis and treatment guidance is provided for the patient, and the patient experience is improved. The risk caused by acute attack of asthma is reduced, and the health management level of patients is improved.

Colorectal cancer (CRC) is one of the leading causes of cancer-related mortality in men and women. Uncontrolled proliferation of CRC cells is pivotal in colorectal tumorigenesis and cyclooxygenase-2 (COX-2) is an important regulatory enzyme in this process. Timely diagnosis is the key to life-saving management of CRC, which is under-diagnosed because colorectal aberrant crypt foci, hyper plastic polyps, micro edemas are often missed with conventional colonoscopy. Disclosed herein are imaging agents tagged to nanobodies specific for COX-2 protein that can selectively accumulate and retain in COX-2 expressing cells and colorectal adenomas, resulting in specific and sensitive of early detection, while reducing background and off-target systemic toxicity. The prospect of combining structural and functional imaging readouts obtained with fluorescence-aided colonoscopy measurements would provide powerful diagnostic information for improved management of colorectal carcinogenesis.

This proposal discloses the field of medical testing technology, and particularly relates to a marker for non-alcoholic fatty liver disease and its application. The sequences of the markers are shown in SEQ ID No. 1, SEQ ID No. 2, SEQ ID No. 3, and SEQ ID No. 4. Any of the markers shown can be used to prepare a preparation or detection tool for treating or assisting in the treatment of non-alcoholic fatty liver disease. It can quickly determine whether a patient is at risk of developing non-alcoholic fatty liver disease or whether non-alcoholic fatty liver disease has already occurred, allowing for timely diagnosis or treatment. Furthermore, compared to existing detection methods, the detection reagents or tools prepared using the markers are less expensive and more efficient, and will not adversely affect the patient during the detection process.

The invention discloses an intelligent medical remote diagnosis system based on multi-source data fusion, and relates to the field of intelligent medical treatment, and the system comprises a sensing module which is used for configuring an operation cycle, and sensing the physical state information of a user in real time based on the operation cycle; the screening module is used for traversing the user body state information sensed by the sensing module, and screening effective information from all the user body state information; according to the system, various physical state information related to sleep is collected in real time, including electroencephalogram, electrocardiogram, oxygen concentration and the like, so that the sleep condition of a patient is comprehensively monitored, the system adopts specific screening and analysis methods in data processing, key information can be accurately extracted, sleep risks can be deeply analyzed, and the sleep quality of the patient is improved. By means of recording and comparison of historical analysis results, the system can timely diagnose whether the current sleep of the patient has risks or not and can also judge whether the patient has risk tendency or not.

The present application relates to a kind of multiplex immunohistochemistry image generation method, comprising: obtaining H&E dataset and paired mIHC dataset and pre-processing;mIHC generation model is constructed;mIHC generation model is trained;H&E image in test set is input to the mIHC generation model after training and obtains the generated mIHC image result, and the generation result of mIHC image is visualized.The present application can quickly and accurately obtain mIHC image by mIHC generation model using easily obtained H&E image, and avoids traditional chemical dyeing process, reduces the cost of obtaining mIHC image, provides convenient and accurate tool for the acquisition of mIHC image, to provide pathological information such as cancer, and can shorten the diagnosis and analysis cycle of disease, is conducive to the timely diagnosis and treatment of patient;The mIHC image generated by mIHC generation model using H&E image and H&E image are strictly registered, so H&E image and mIHC image can be accurately combined to diagnose disease, to improve the accuracy of disease diagnosis analysis.

The invention relates to the technical field of auxiliary bone marrow paracentesis equipment, in particular to a portable auxiliary bone marrow paracentesis ultrasonic positioning instrument which comprises an ultrasonic probe module, an image processing unit, a positioning algorithm module, a mechanical positioning device, a user interaction interface and a power management module. The method has the advantages that by means of the ultrasonic technology, the anatomical structure of the bone marrow puncture part is clearly displayed in real time, doctors are helped to accurately determine the puncture point, the needle inserting angle and depth, puncture accuracy is remarkably improved, important tissue is prevented from being punctured by mistake, the success rate is increased, and the method is especially suitable for patients with complex anatomical structures or obesity and the like with high puncture difficulty; the instrument is portable in design, small in size, light in weight and convenient to carry and move, can be used at any time in various scenes such as wards, operating rooms, emergency rooms and temporary medical points in remote areas, is not limited by fixed sites, improves the utilization efficiency of medical resources, and provides convenience for timely diagnosis of patients.

The present invention belongs to the field of bioinformatics, and specifically relates to a method and system for predicting the risk of tumor cell metastasis. The method of the present invention predicts the S of the tumor cell to be detected based on the gene expression information of the tumor cell to be detected. EMT , S Tra and S comm ; Among them, S EMT Used to indicate the epithelial-mesenchymal transition ability of tumor cells, S Travel Indicates the survival ability of tumor cells in blood circulation, S comm Indicates the ability of tumor cells to invade tissues or organs; then calculate the risk factor S of the tumor cells to be tested for metastasis Merta The method of the present invention can assist doctors in timely diagnosis and targeted treatment of cancer patients in the early stage of cancer metastasis, and can guide the research and development of new anti-cancer drugs, which is conducive to improving the prevention and treatment rate and cure rate of cancer.

The present invention relates to the technical field of intelligent medical care. Provided is a smart medical nursing interactive image processing algorithm based on 5G and Internet of Things technology. The smart medical nursing interactive image processing algorithm comprises: receiving real-time image data from a medical device by means of a 5G network; using the Internet of Things to perform pre-processing, including denoising, deblurring and image enhancement, on the received image data; and inputting the pre-processed image data into a deep learning model for feature extraction and classification. Real-time image data from a medical device is received by means of a 5G network, thereby greatly shortening the data transmission time, such that a doctor and a nurse can more quickly acquire information of a patient's state of illness, thus making timely diagnosis and treatment decisions. A medical nursing instruction is automatically generated on the basis of an image classification result, thereby reducing the time and errors in manual intervention, and improving the efficiency of medical nursing.

The utility model relates to the field of excrement collection, in particular to a household excrement sample collection structure, which comprises a box body, a collection tube and a lining, sample collection codes are arranged on the box body, a bar code strip I is adhered on the surface of the box body, the collection tube is arranged on the lining, and the lining and the collection tube are arranged inside the box body. The collecting tube comprises a transparent tube body, a screw cap and a spoon, a bar code strip II consistent with the bar code strip I in information is adhered to the surface of the transparent tube body, the spoon is fixed on the lower side in the screw cap, the screw cap is in threaded connection with the transparent tube body, two groups of through holes are symmetrically formed in the screw cap, sealing films are arranged in the through holes, and two groups of sealing covers are rotationally connected to the top of the screw cap; the diameter of the sealing cover is larger than that of the through hole, opening and closing of the corresponding sealing cover and insertion and extraction of the sealing film penetrating through the inner side are controlled, cover opening is not needed in the whole process, peculiar smell emission is reduced, and by means of the collecting structure, the sampling process is simplified, the detection efficiency is improved, timely diagnosis and treatment of a patient are guaranteed, medical resources are optimized, and medical experience is improved.

The application relates to a flow meter calibration method, device, computer equipment and storage medium, which comprises the following steps: acquiring characteristic data of a target flow meter, wherein the characteristic data comprises acceleration and flow value of the target flow meter; determining whether the state of the target flow meter is abnormal according to the characteristic data; if the state of the target flow meter is abnormal, determining a diagnosis parameter of the target flow meter according to the characteristic data; and searching for an abnormal reason corresponding to the diagnosis parameter in a pre-established diagnosis database according to the diagnosis parameter, wherein the diagnosis database comprises a mapping relationship between diagnosis parameters and abnormal reasons of a template flow meter. The above method can monitor and record abnormal vibration data of the target flow meter in real time, and can diagnose the reason for the abnormal vibration of the target flow meter in a timely manner.

This invention discloses an IoT-based smart agricultural product pest and disease diagnosis system and method, belonging to the field of smart agricultural product technology. To address the problem of the wide variety of crop pests and diseases, and the potential for poor identification of rare or newly emerging pests and diseases, the IoT-based smart agricultural product pest and disease diagnosis system includes an image acquisition unit, a feature extraction unit, a diagnostic decision-making unit, and a remote interaction unit. The image acquisition unit uses cameras and sensors deployed in the farmland to capture real-time image data of the crop growth environment and pests and diseases, ensuring timely diagnosis. The feature extraction unit establishes a pest and disease feature database and constructs a key feature recognition model, achieving intelligent processing of pest and disease images, identifying and diagnosing multiple pest and disease types. The remote interaction unit improves user convenience, reduces the cost of manual inspection and diagnosis, increases agricultural production efficiency, and improves crop yield and quality.

The invention relates to a multi-immunohistochemical image generation method. The method comprises the following steps: acquiring a Hamp; the E data set and the paired mIHC data set are preprocessed; an mIHC generation model is constructed; training an mIHC generation model; testing the Hamp in the test set; and the E image is input into the trained mIHC generation model to obtain a generated mIHC image result, and the mIHC image generation result is visualized. According to the method, the model is generated through the mIHC, and the Hamp which is easy to obtain is utilized; the mIHC image can be rapidly and accurately obtained through the E image, the traditional chemical staining process is avoided, the cost of obtaining the mIHC image is reduced, a convenient and accurate tool is provided for obtaining the mIHC image, and therefore pathological information such as cancer is provided, the diagnosis and analysis period of diseases can be shortened, and timely diagnosis and treatment of patients are facilitated; a model is generated through mIHC, and Hamp is utilized; the mIHC image and the Hamp generated by the E image; the E image is strictly registered, so that the E image can be accurately combined with the Hamp; the E image and the mIHC image are used for disease diagnosis, so that the accuracy of disease diagnosis and analysis is improved.

This invention discloses a deep learning-based electrocardiogram (ECG) image classification method and system, belonging to the field of intelligent medical and image classification technology. The technical problem this invention aims to solve is how to utilize deep learning to classify and predict ECG images, reducing the workload of professional physicians while improving the accuracy and effectiveness of ECG image classification, thus providing effective assistance for timely diagnosis of patients' conditions. The technical solution adopted is as follows: The method specifically involves: selecting ECG images; performing denoising preprocessing on the ECG images; extracting waveform feature points from the denoised ECG images; classifying the images using a support vector machine (SVM) model and a linear regression model; and predicting the ECG images to be classified using a long short-term memory (LSTM) model.

The application belongs to the technical field of medical diagnosis, and specifically discloses a diabetes mellitus combined with tuberculosis marker and application thereof. It is found for the first time that the transcription level and protein level of activin A receptor 2A (ACVR2A), angiostatin and chemokine ligand 14 (CXCL14) are inversely proportional to the severity of diabetes mellitus combined with tuberculosis, and can be used as a diabetes mellitus combined with tuberculosis marker to indirectly reflect whether the crowd is suffering from diabetes mellitus combined with tuberculosis and the development of the disease, so as to timely diagnose and treat. A new diabetes mellitus combined with tuberculosis detection kit can be prepared according to the three markers, and is applied to public medical treatment. The three markers can effectively improve the diagnosis efficiency of diabetes mellitus combined with tuberculosis, and have high sensitivity and strong specificity.

The invention discloses a quick access and security management system applied to an OFD format electronic medical record, and relates to the technical field of electronic medical record management, and the system comprises a quick access module, a security management module and a system management module; the quick access module comprises an index establishment unit, a cache management unit and a parallel access unit, and is used for establishing an index for the OFD format electronic medical record, managing cache and realizing parallel access to improve the access speed; according to the method, rapid access to the electronic medical record in the OFD format is achieved through a series of technical means, the medical record searching time is greatly shortened by establishing the index for the medical record key information and conducting cache management, meanwhile, the system resource potential is fully excavated through the parallel access technology, medical staff can obtain the needed medical record information more rapidly, and the medical record searching efficiency is improved. Therefore, the medical working efficiency is effectively improved, and powerful support is provided for timely diagnosis and treatment of patients.