303results about How to "Reduce misdiagnosis rate" patented technology

The invention discloses a deep convolutional neural network-based lung cancer preventing self-service health cloud service system. The system comprises a convolutional neural network used for deep learning and training identification, a segmentation module which segments out a lung region from a CT image based on a full convolutional neural network, a deep convolutional neural network used for lung cancer diagnosis classification, and a self-service health cloud service platform used for performing early prevention and treatment according to an identified suspected lung cancer type. According to the system, the automation and intelligentization level of mobile internet-based lung cancer screening can be effectively improved, more citizens can know and participate in self-service health detection, assessment and guidance, the sensitivity, specificity and accuracy of early lung cancer screening and clinical diagnosis are improved, the lung cancer can be early discovered, early diagnosed and early treated, and the self-health management capability is enhanced.

The invention discloses a method and system for recommending a doctor for a patient. According to the method and system for recommending the doctor for the patient, basic information of the patient and relevant information of the illness state of the patient are input into a client side (300), and the client side is connected to a former server (110) to submit a service request to a background system (100). According to the method and system for recommending the doctor for the patient, a medical database is retrieved by a similarity record retrieval system (130) which is designed on the background system (100) so that a similarity record search result (170) can be obtained; a designed doctor evaluation system (140) calculates the effective rates and the untoward effect rates within the ranges of all doctors relating to the similarity record search result (170) so that a list (190) of the effective rates and the untoward effect rates of the doctors can be generated; the list of the effective rates and the untoward effect rates of the doctors is sent to the client side through the former server (110) and the background system (100). The method and system for recommending the doctor for the patient have the advantages that the misdiagnosis rate is decreased obviously, the using efficiency of medical resources is improved, and the method and system can help the patient select the most suitable doctor through the client side.

The invention discloses a thyroid tumor pathological tissue section image classification method and a thyroid tumor pathological tissue section image classification device. The method comprises the steps of acquiring an original image set of classified thyroid tumor pathological tissue sections; automatically intercepting multiple area images including cells from each original image to serve as asub-image set; using the total or partial sub-image set as a training set; building a preliminary convolutional neural network model; training the preliminary convolutional neural network model by using the training set to acquire a mature convolutional neural network model; and classifying the classified thyroid tumor pathological tissue section images by using the mature convolutional neural network model. Cell nucleuses in the thyroid tumor pathological tissue sections are matched by using Gaussian laplace operator characteristics to find positions of cells, automatic image interception isimplemented in the area with many cells, so that the full-automatic cell image classification and cancer diagnosis are achieved, the workload of a doctor when in checking of the tissue section image can be greatly reduced, and the diagnosis accuracy is improved.

The embodiment of the invention provides a real-time labeling method for breast ultrasonic focus areas based on artificial intelligence. The method includes the steps that a breast ultrasonic image video is divided into picture sets according to the timestamp order at frames; the picture sets are sequentially detected according to a focus-area detection classification model, the BI-RADS type levelis determined, and meanwhile the focus areas are determined; contour lines of all the focus areas are labeled in pictures, wherein the types of the contour lines are related to the BI-RADS type level; all the frame pictures are anew synthesized to form a video according to the timestamp order. The embodiment of the invention also provides an establishing method for the focus-area detection classification model, and the establishing method is used for establishing the focus-area detection classification model in the real-time labeling method for the focus areas. The embodiment of the inventionalso provides a real-time labeling system for the breast ultrasonic focus areas based on artificial intelligence. According to the real-time labeling method and system for the breast ultrasonic focusareas based on artificial intelligence and the establishing method for the focus-area detection classification model in the embodiment, the focus recognition capability is enhanced, the misdiagnosisrate is reduced, and the embodiment assists doctors in giving a more-accurate suggestion.

The invention discloses a device for acquiring physiological condition information, which mainly comprises a physiological parameter detector and a communication module. The physiological parameter detector is used for detecting the physiological parameters of an organism and generating physiological parameter data. The communication module is used for wirelessly accessing a communication networkcapable of accessing an information management server, communicating with the information management server via the accessed communication network and transmitting the physiological parameter data asthe physiological condition information to the information management server. With the device, the remote acquisition of the physiological condition information can be implemented, therefore, the remote management of the physiological condition information can be further implemented, and the acquisition management process of the physiological condition information is simplified, which facilitatesto the application convenience of users.

The invention provides an intelligent auxiliary diagnosis and treatment system, which comprises: a receiving module used for receiving medical records; a classification module used for classifying the received medical records according to the disease types and storing the classified medical records into corresponding databases; a matching module used for matching the medical records with medical record templates in a medical record template library and obtaining the medical record templates with the matching degrees larger than a preset threshold value, wherein the medical record templates store the disease types and disease information corresponding to the disease types; and an auxiliary diagnosis module used for acquiring the matched disease types and the disease information corresponding to the disease type, giving corresponding historical diagnosis reference schemes by utilizing an artificial intelligence convolutional neural network learning method, and outputting the matched disease types and the disease information corresponding to the disease types for reference of doctors. According to the embodiment of the invention, the problem that the doctor level is insufficient in the current medical and health service is solved, the medical level of doctors can be improved, and the misdiagnosis rate of the doctors is effectively reduced.

The invention provides an image processing method and device based on a pathological tissue section image organization area. The image processing method based on the pathological tissue section image organization area comprises the steps: step 1, pre-processing a pathological tissue section image to obtain a sub image block; step 2, extracting sub image block characteristics on the basis of a deep convolutional neural network model, integrating the sub image block characteristics to obtain pathological tissue section image characteristics and pre-classifying the pathological tissue section image characteristics to obtain an abnormal pathological tissue section image; step 3, quantifying clinical hospital visiting information; step 5, using a multi-source data fusion technology to obtain fusion characteristics according to abnormal pathological tissue section image characteristics and the clinical hospital visiting information; and step 5, classifying the fusion characteristics by using a classifier. The image processing device based on the pathological tissue section image organization area comprises a pre-processing module, a characteristic extracting module, a classification module, a quantization module and a data fusion module. The image processing method and device based on the pathological tissue section image organization area, provided by the invention, have the advantage that a problem of poor organization area detection effect of the pathological tissue section image in the prior art is overcome.

The invention provides a capsule endoscope system capable of obtaining a real-time position and posture and a working method of the capsule endoscope system. The working method comprises the following steps: S1, collecting data of an inertial sensor from radio frequency communication equipment of a capsule endoscope, and carrying out operation processing on the obtained data on a remote server; carrying out data noise reduction through the remote server, and removing irregular sensor data; S2, carrying out position and posture solving according to accelerated velocity and angular velocity data obtained by an MEMS (Micro-electromechanical Systems) inertial sensor of the capsule endoscope, so as to obtain real-time position and posture information of the capsule endoscope; S3, carrying out error compensation on the obtained real-time position and posture information, and adjusting the obtained position and posture information values through operation, so as to enable positioning and posture determination of the capsule endoscope to be more accurate.

A thickness balancing method and device for a breast image. The thickness balancing method for the breast image includes the steps of: obtaining a breast image; filtering the breast image to obtain a low frequency breast image and a high frequency breast image; performing gray scale transformation on a preset area in the low frequency breast image to obtain a first image, a gray value of a preset area in the first image tending to be consistent with a gray value of a neighborhood, and the preset area referring to an area at a predetermined distance from an edge of the low frequency breast image; and reconstructing the first image and the high frequency breast image to generate a thickness-balanced breast image. The breast image after balancing which is obtained through the technical scheme of the invention has uniform gray scale while details of the breast image are not lost, satisfies actual clinical needs, the breast image after balancing is adopted to be diagnosed on a window level of a certain window width, and loss of edge information of the breast image does not occur, thereby lowering the rate of missed diagnosis, and improving the accuracy rate of diagnosis.

The invention discloses a deep learning-based lymphoma pathological image intelligent identification method. The method comprises the steps of preprocessing lymphoma pathological section image data; constructing a full convolutional neural network for segmenting a lymphatic tissue region, wherein the full convolutional neural network comprises an encoder sub-network and a decoder sub-network; constructing a lymphoma three-classification convolutional neural network under high resolution, wherein the lymphoma three-classification convolutional neural network is composed of six convolutional layers and three full-connection layers which are connected in sequence; and training the full convolutional neural network and the lymphoma three-classification convolutional neural network to finally obtain a lymphoma pathological section image classification model, and sequentially passing through the full convolutional neural network and the lymphoma three-classification convolutional neural network during testing to finally obtain a lymphoma classification result. Reliable intermediate data are provided for pathologists to judge lymphoma subtype categories, and auxiliary diagnosis referenceis provided for the pathologists to classify lymphoma subtypes by analyzing digitally scanned lymphoma pathological images, so that the pathologists are helped to quickly judge lymphoma conditions ofpatients.

The invention relates to a construction method of a stomach cancer image recognition model based on artificial intelligence. The image recognition model can accurately recognize a lesion site in a stomach cancer image.

The invention discloses a high-speed milling chatter on-line identification method based on an AR model. The method comprises the steps that (1) the state information of the milling process is acquired; (2) forced vibration frequency filtering is carried out on a signal; (3) chatter sensitive frequency band filtering is carried out on the signal; and (4) a model characteristic root index R(k) is constructed based on the difference of the AR model of the signal in a stable milling state and a chatter milling state, time varying AR(1) modeling is carried out on the signal in the stable milling process, and a recursive least square method with a forgetting factor is used for identification to obtain the variation of the model characteristic root R(k) of the model in the whole cutting process to identify chatter. Compared with a traditional chatter detection method, according to the method, characteristic information reflecting chatter and characteristic information irrelevant to the chatter are separated, and substantive characteristic parameters of a milling system are obtained; and the physical property of the milling chatter is represented substantially, sensitivity, precision and reliability of chatter detection are effectively improved, and the misdiagnosis rate and the missed diagnosis rate are decreased.

The invention provides a fast multiplex PCR identification diagnosis detection method of invasive fungus infection based on cfDNA (cell free DNA). The method can be used for identifying the invasive infection caused by clinic common and high-incidence Candida albicans, Candida tropicalis, Candida parapsilosis, Candida krusei, Candida glabrata and Aspergillus fumigtus. An amplification primer is designed according to characteristic genome segments of each fungus category and species; a detection fluorescence probe of a strain can be distinguished according to the amplification segment design; the real time PCR can be performed on a sample to be tested; high sensitivity of nested PCR and high specificity and multi-target performance advantages of multiple fluorescent hybrid probe PCR are integrated for identifying the fungus strain. The invention also provides a PCR diagnosis kit for the invasive fungus infection and application thereof.

The invention discloses an acquisition and management system for the physiological condition information, which mainly comprises an information acquisition terminal, an information management server and an information management terminal. The information acquisition terminal is used for acquiring the physiological condition of organism information, communicates with the information management server, and transmits the physiological condition information to the information management server. The information management server is used for storing and managing the physiological condition information transmitted from the information acquisition terminal. The information management terminal communicates with the information management server and is used for acquiring the physiological condition information from the information management server and analyzing and managing the acquired physiological condition information. With the method, the remote acquisition of the physiological condition information can be implemented, therefore, the remote management of the physiological condition information can be further implemented, and the acquisition management process of the physiological condition information is simplified, which facilitates to the application convenience of users.

The invention relates to a gastric cancer image recognition system and device and application thereof. The system comprises a data input module, a data preprocessing module, an image recognition modelconstruction module and a lesion recognition module. And meanwhile, the system can realize self-training, so that the lesion part in the gastric cancer image can be accurately identified.

The invention relates to a method and system for recommending a health diagnosis and treatment plan based on cloud computing. The method comprises the following steps: (1) a cloud server collects medical knowledge data and analyzes the medical knowledge data for storage; (2) a user terminal uploads a registered patient's electronic medical record to the cloud server, and the cloud server performspre-analysis of the patient's health condition; and (3) the user terminal uploads the current symptom of the patient to the cloud server, and the cloud server formulates the health diagnosis and treatment plan according to the current symptoms of the patient on the basis of medical knowledge data, pre-analysis data and the basic situation when the patient registers, and outputs one or more recommended diagnosis and treatment plan. Meanwhile, the invention also provides the system for recommending the health diagnosis and treatment plan based on the cloud computing. The method and system provide convenience for the patient to timely know the treatment, prevention and nursing method which can be adopted, avoids occurrence of medical accidents due to adoption of an outmoded diagnosis and treatment method by a doctor, effectively reduces the misdiagnosis rate and raises the working efficiency.

The present invention relates to a Down's syndrome screening method based on an isolation forest algorithm and a voting mechanism. The method comprises the following steps of: data preprocessing: adding samples into a data set; division of a data set: obtaining a training set A and a training set B to further perform cross division of the training set A to obtain a plurality of training subsets, obtaining a plurality of isolation forest models and corresponding abnormality scoring threshold values through training, performing voting of samples in the training set B to obtain the number of votes of each sample, obtaining a pre-determined threshold value and a pre-determined result of each sample in the training set B, and using a SVM (support vector machine) model to perform final determination. The Down's syndrome screening method can improve the abnormal detection rate and reduce the misdiagnosis rate.

An embodiment of the invention discloses an intelligent interrogation system based on XGBoost disease prediction and a method. The system comprises a client and a service end which are connected through a network. The client comprises an interrogation module and a display module. The service end comprises a data processing module, a data storage module and an XGBoost disease predicting module. Through inputting patient symptom information which is acquired by a client into a trained XGBoost multi-class model, a disease probability predicted value and predicted disease information are output. Furthermore the patient can directly check the related information on the client. Based on machine learning and big data technology, a self-service diagnosis service of the patient can be realized. Thesystem and the method further have advantages of supporting intelligent analysis and prediction on mass patient data, realizing high accuracy and high timeliness in prediction, and realizing simple operation and low cost.

The invention discloses a medical data transmitting and processing system and a method thereof. The system comprises a client side and a medical server. The method comprises the following steps: medical data of a patient is input from the client side and uploaded to the medical server, and/or medical data of the medical server is accessed and obtained; the medical server is used for storing the medical data of the patient, accepting access request from the client side and returning the corresponding data to the client side; the medical data of the patient comprises a pre-established special treatment plan in which at least one treatment project is included, the medical server receives treatment data reported from client side and records the treatment data into the corresponding treatment project. By adopting the invention, the medical data collecting efficiency can be improved, and the difficulty in operation for a user is decreased.

The invention discloses a thyroid nodule diagnosis method based on a deep learning network, and belongs to the field of image processing and artificial intelligence aided disease diagnosis. The methodcomprises the following steps: searching an ultrasonic original image and a pathological report of a thyroid nodule of a thyroid patient, and constructing a thyroid nodule database; preprocessing theultrasonic image; performing semantic segmentation on the ultrasonic image preprocessed in the step 2 through a Deeplab v3+ method based on Xception-JFT, and forming a semantic segmentation result graph; judging benign and malignant thyroid nodules based on a deep learning network; and forming a thyroid nodule diagnosis information report. According to the method, the Deeplab v3+ algorithm basedon Xeption-JFT is adopted to establish the thyroid ultrasound image segmentation network model, the optimal segmentation effect is achieved by continuously improving the backbone network Xception, nodule information can be automatically and rapidly recognized under high accuracy and high robustness, image features are automatically extracted for accurate segmentation to obtain a better diagnosis result, and an objective reference is provided for clinical diagnosis.