767 results about "Aided diagnosis" patented technology

Described are systems, media, and methods for applying deep convolutional neural networks to medical images to generate a real-time or near real-time diagnosis.

A method for using computer-aided diagnosis (CAD) for digital tomosynthesis mammograms (DTM) including retrieving a DTM image file having a plurality of DTM image slices; applying a three-dimensional gradient field analysis to the DTM image file to detect lesion candidates; identifying a volume of interest and locating its center at a location of high gradient convergence; segmenting the volume of interest by a three dimensional region growing method; extracting one or more three dimensional object characteristics from the object corresponding to the volume of interest, the three dimensional object characteristics being one of a morphological feature, a gray level feature, or a texture feature; and invoking a classifier to determine if the object corresponding to the volume of interest is a breast cancer lesion or normal breast tissue.

The invention relates to a realization method and system for electronic medical record post-structuring and auxiliary diagnosis. A combination mode of multiple types of distance measurement is used: a character string editing distance refers to a minimum number of replacement, insertion and deletion operations required for converting a character into another character string; a Jaro-Winkler distance measures similarity between two character strings and is used for repeated recording detection; a geometric mean value of a Chinese character distance and a Chinese character input method is adopted as comprehensive similarity measurement for measuring similarity between characteristic texts; characteristic ranking is realized by using a TF-IDF method and is used for assessing the importance of characteristic terms relative to documents in a file set or a corpus library, and the importance of the characteristic terms is in direct proportion to an occurrence frequency in the documents and is in inverse proportion to an occurrence document in the corpus library; and files are converted to be in a file format of PU learning of a positive example data set and an unlabelled data set according to the generated characteristic terms, and through the PU learning, the system automatically recommends related diagnoses for clinical medical personnel to refer.

A cerebral ischemia diagnosis assisting apparatus comprises a storing portion for storing multi-slices or volume data with regard to the head portion of a subject, an image generating portion for generating a tomography image of a brain from the multi-slices or the volume data, an image processing portion for processing the tomography image for generating a contrast highlighting image and a brain sulci highlighting image or either thereof, and a display portion for displaying the tomography image along with the contrast highlighting image and the brain sulci highlighting image.

The invention discloses a tumor malignant risk stratification auxiliary diagnosis system of an artificial intelligence medical image. The system comprises a data acquisition module, a data preprocessing module, a model establishment module, a model verification and optimization module, a stratification diagnosis module and a database platform. The tumor malignant risk stratification auxiliary diagnosis system of the invention is based on artificial intelligence technology, successive stratification of the malignant risk of the tumor can be achieved, the clinical diagnosis thinking is simulated, based on the high-precision ability of detecting benign lesions and malignant tumors of artificial intelligence model, and the space-occupying lesions with definite imaging features are diagnosed automatically. As a result, the system can substantially assist the clinical management decision of space-occupying lesions, improve the existing work flow of clinical diagnosis, increase the confidenceof doctors in diagnosis, reduce the work pressure, reduce the anxiety of patients with low-risk malignant lesions, greatly improve the diagnostic rate of benign lesions and malignant tumors, and hopefully realize the landing implementation of artificial intelligence clinical auxiliary diagnosis.

The present invention provides a dermoscopy image automatic segmentation method based on a full convolutional neural network. The method comprises the following four steps: 1: obtaining of dermoscopy images and true value graphs; 2: performing structure design of a full convolutional neural network; 3: performing design of feature fusion and a per-pixel segmentation method; and 4: performing network training and segmentation. According to the steps mentioned above, an end-to-end depth convolutional neural network is obtained through training to perform accurate segmentation of the dermoscopy images and allow a small area skin lesion area to be effective so as to solve actual problems that the skin lesion area segmentation is not good to influence the subsequent diagnosis accuracy in the dermatology computer auxiliary diagnosis system.

A service provider network includes an automated support module that receives stream of network packets from subscribers and separates this streams into packet flows for analysis. The automated support module may be an intermediate device that examines each packet flow to detect subscriber characteristics for each subscriber. The automated support module may apply a set of analysis rules to the detected characteristics to aid diagnosis of reported or potential network errors.

The invention provides methods of monitoring amyotrophic lateral sclerosis (ALS) disease development or progression and monitoring an ALS therapy in an individual by determining the presence or absence of Herv-K/HML-2 expression in a biological sample from the individual. The invention is also directed to methods for aiding diagnosis of ALS by determining expression of Herv-K/HML-2 in a biological sample from the individual. The invention is also directed to methods of reducing Herv-K/HML-2 expression in infected cells and individuals. The invention includes reagents for use in these methods.

The invention discloses a breast cancer computer auxiliary diagnosis method and system based on galactophore X-ray radiograph. A galactophore X-ray radiograph for diagnosis is input into the system of the invention firstly, and is treated through an extracting module in a region of interest, a partitioning module and a feature extracting module in a region of doubtful lump, hereby a series of relative feature values about the doubtful lump region; then the feature values are input into a trained classifier which classifies and identifies the doubtful lump region and lastly the computer automatic examined final result of portioning the doubtful lump region is located on the input galactophore X-ray radiograph for diagnosis and the calculated relative feature values of the region are displayed to a roentgenologist according to requirements to indicate the roentgenologist about the region needing special attention and relative important parameters of the region. The invention can improve the accurateness and efficiency of the breast cancer diagnosis by the roentgenologist to some extent and help the roentgenologist to bring forward a diagnostic opinion and a therapeutic schedule more objectively and effectively.

A method of characterizing a mass within a digital mammogram. A region of interest is identified that includes the mass and surrounding tissue. A border outline of the mass is identified. A rectangular image is formed wherein each column of the image is formed by repetition of steps. A vector is employed for each of a set of ray angles, wherein the vector extends from a central point of the mass and intersects the border outline at an intersection point. A starting pixel along the vector is identified, between the intersection point and the central point, at a first distance before the intersection point. An ending pixel along the vector is identified at a second distance beyond the intersection point. Pixels along the vector, from the starting pixel to the ending pixel, are remapped as the respective column in the rectangular image. Texture features are extracted from the formed rectangular image.

A computer-assisted diagnosis method for assisting diagnosis of anatomical structures in a digital volumetric medical image of at least one lung includes identifying an anatomical structure of interest in the volumetric digital medical image. The anatomical structure of interest is automatically segmented, in real-time, in a predefined volume of interest (VOI). Quantitative measurements of the anatomical structure of interest are automatically computed, real-time. A result of the segmenting step and a result of the computing step are displayed, in real-time. A likelihood that the anatomical structure of interest corresponds to a disease or an area warranting further investigation is estimating, in real-time, based on predefined criteria and the quantitative measurements. A warning is generated, in real-time, when the likelihood is above a predefined threshold.

Methods for modulating macrophage proliferation in an individual afflicted with or at risk for a macrophage-associated disease are provided. The methods employ a polyamine analog, or salt or protected derivative thereof. Macrophage proliferation has been implicated in a number of serious disorders, including AIDS (HIV)-associated dementia, AIDS-associated non-Hodgkin's lymphoma, and Alzheimer's disease. The invention also provides methods for aiding diagnosis and monitoring therapy of a macrophage-associated non-HIV associated dementia, especially Alzheimer's disease. The invention also provides methods of delaying development of macrophage-associated non-HIV associated dementias, including Alzheimer's disease, which entail administration of an agent which modulates macrophage proliferation.

The invention relates to the field of medical image processing, in particular to a method for automatically identifying a liver tumor type in an ultrasonic image. The method can provide auxiliary diagnosis when a doctor diagnoses a liver lesion according to a CEUS medical image, wherein the auxiliary diagnosis comprises the process of giving out a lesion identification result, the time when the lesion is relatively remarkable and a position where the lesion is relatively remarkable. The method specifically comprises: representing a CEUS image with a plurality of regions of interest (ROI); distinguishing different lesions by expressions and changes of the ROI in time and space; and representing a space-time relationship among the ROI by establishing models in time and space at the same time, wherein the models can determine relatively proper ROI and related parameters of the models according to existing CEUS lesion samples with an iterative learning method. After a sample is given out, the proper ROI can be determined by removing part of improper ROI in advance and a fast search method, and reference diagnosis is given out for the lesion.

The invention provides a method and a system for automatically analyzing a mammary gland ultrasonic image, identifying a mammary gland focus and then carrying out focus area contour extraction and parameter calculation. According to the invention, the convolutional neural network is used to construct the recognition classification recognition and segmentation extraction model, the breast lesion area in the video or image can be recognized, the lesion area contour can be automatically extracted, and the parameters of the lesion area can be calculated. According to the method and the system, theauxiliary diagnosis accuracy is remarkably improved, repeated operations of doctors are greatly reduced, and the robustness is good.

The invention discloses a senile dementia monitoring system based on a mobile internet. The senile dementia monitoring system based on the mobile internet comprises an intelligent terminal, a cloud server and a brain electrical monitoring device, wherein the electrical brain monitoring device is used for acquiring brain electrical information of senile dementia patients or healthy people in real time, and sending the brain electrical information to the intelligent terminal; the intelligent terminal is used for receiving the brain electrical information, completing cognitive-psychological assessment, inputting clinic information, and uploading the brain electrical information, the cognitive-psychological assessment and the clinic information to the cloud server through the mobile internet; the cloud server is used for receiving MRI (Magnetic Resonance Imaging) image information uploaded by hospitals and information uploaded by the intelligent terminal so as to complete auxiliary diagnosis and generate corresponding nursing instruction suggestions, and then feeding auxiliary diagnosis results and the nursing instruction suggestions back to the intelligent terminal. According to the senile dementia monitoring system based on the mobile internet, the mobile internet technology is utilized, the automatic auxiliary diagnosis of senile dementia can be realized, the accuracy of the diagnosis is improved, and the prevention and the early detection of the senile dementia can be realized favorably.

The invention provides a temporomandibular joint movement reconstruction method and a system thereof. The method is characterized by collecting an image sequence of a mark point track of open and close mouth mandible movements of a photographed person; determining three-dimensional coordinates of all the mark points on an intraoral fixed mark device in each frame of image, acquiring a movement track of each mark point in a space, and collecting a CBCT image of the intraoral fixed mark device and collecting a CBCT image of the photographed person; registering the mark point of the CBCT image of the intraoral fixed mark device with the mark point acquired by a camera through photographing, and acquiring a transformation matrix of a mark point movement; and using the transformation matrix to carry out rigid body transformation on the CBCT image of the photographed person, acquiring a CBCT image sequence of the photographed person and acquiring a temporomandibular movement image. In the invention, real movement conditions of mandible condyle process and a temporal bone joint surface of a human-body temporomandibular joint can be acquired, the method and the system can be used for auxiliary diagnosis of a temporomandibular joint disease and dynamic analysis of joint motion, temporomandibular joint diagnosis means are increased, doctor diagnosis accuracy and efficiency can be increased, and medical quality and efficiency can be increased too.

Uterine cervical cancer Computer-Aided-Diagnosis (CAD) according to this invention consists of a core processing system that automatically analyses data acquired from the uterine cervix and provides tissue and patient diagnosis, as well as adequacy of the examination. The data can include, but is not limited to, color still images or video, reflectance and fluorescence multi-spectral or hyper-spectral imagery, coherent optical tomography imagery, and impedance measurements, taken with and without the use of contrast agents like 3-5% acetic acid, Lugol's iodine, or 5-aminolevulinic acid. The core processing system is based on an open, modular, and feature-based architecture, designed for multi-data, multi-sensor, and multi-feature fusion. The core processing system can be embedded in different CAD system realizations. For example: A CAD system for cervical cancer screening could in a very simple version consist of a hand-held device that only acquires one digital RGB image of the uterine cervix after application of 3-5% acetic acid and provides automatically a patient diagnosis. A CAD system used as a colposcopy adjunct could provide all functions that are related to colposcopy and that can be provided by a computer, from automation of the clinical workflow to automated patient diagnosis and treatment recommendation.

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 a diabetic retinopathy fundus photography standard image generation method, which comprises the following steps: 1) enabling a collected non-standard fundus image to be generated into a new sample image through a generation model; 2) carrying out local feature extraction on the new sample image; and 3) comparing local features of the non-standard image with local features of a standard image in a discrimination model, if the local features are consistent, outputting the new sample image, that is, the generated standard image, and if the local features are not consistent, adjusting the new sample image. The provided method is simple and effective; the definition of the generated standard image reaches requirement of an intelligent aided diagnosis system; and accuracy of diagnosis is improved.