176 results about "Diagnostic aid" patented technology

The invention discloses a diagnosis method and system for glenoid cavity and humeral head defect areas, and belongs to the technical field of image processing, and the method comprises the steps: obtaining the medical image data of a glenoid cavity and a humeral head to be processed; inputting the medical image data into a preset segmentation model to obtain a segmentation result of the glenoid cavity and the humerus head; based on the segmentation result, calculating the defect area and defect proportion of the glenoid cavity; and determining the defect degree of the humerus head based on the defect proportion. According to the diagnosis method and system for the glenoid cavity and humeral head defect area, an efficient and accurate intelligent diagnosis auxiliary tool is provided for clinic, and the good application prospect and popularization value are achieved.

The invention is suitable for the technical field of ultrasonic diagnosis, and provides an ultrasonic auxiliary diagnosis method and system, and the method comprises the steps: collecting ultrasonic data through an ultrasonic probe; transmitting the ultrasonic data from the ultrasonic probe to a millimeter wave communication module physically separated from the ultrasonic probe through a wired link; the millimeter wave communication module adopts a millimeter wave wireless communication technology to send the ultrasonic data to one of a plurality of millimeter wave access points deployed in the operation environment; transmitting the ultrasound data received by the millimeter wave access point to a remote computing platform through a wired bearer network configured to support deterministic network functionality; and receiving the ultrasonic data transmitted through the deterministic network function in the remote computing platform, performing ultrasonic data processing on the received ultrasonic data according to a preset processing strategy, generating diagnosis auxiliary data for ultrasonic auxiliary diagnosis, and providing the diagnosis auxiliary data for display equipment for display. The problem that existing ultrasonic diagnosis is insufficient in flexibility is solved.

The invention provides a multi-modal data fusion diagnosis assistance method and device, a storage medium and equipment, and the method comprises the steps: obtaining multi-modal data sent by a data collection module, the multi-modal data comprising at least two of 2D fundus color photo data, 3D OCT body data, OCTA blood flow parameters and wide-area fundus spliced image parameters; inputting the multi-modal data into a trained layered feature extraction module to obtain a multi-modal data feature vector; inputting the multi-modal data feature vector into a trained feature compensation-fusion module to obtain a complete multi-modal data fusion feature; and inputting the complete multi-modal data fusion features into a trained dynamic expert hybrid system to obtain structured diagnosis and treatment data and a text diagnosis auxiliary report, and sending the structured diagnosis and treatment data and the text diagnosis auxiliary report to a data display module.

The invention relates to an artificial intelligence diagnosis auxiliary method and device based on a medical image, equipment and a medium. According to the method, standardized images and interested area masks are extracted from medical images to serve as basic data, in combination with a pathological causal atlas matrix constructed by medical domain knowledge, the atlas matrix is utilized to guide an attention mechanism in a deep neural network to generate a causal-associated weighted feature map and attention distribution; further eliminating the influence of confusion variables through adversarial training and causal intervention loss processing so as to obtain a robust diagnosis model, and finally performing path search and confidence calculation on attention distribution and a pathological causal map based on an analysis result of the model on a target image. And a diagnosis decision path and a visual interpretation report conforming to clinical causal logic are generated, so that the false correlation feature interference is effectively inhibited while the diagnosis accuracy is ensured, and the transparency and clinical credibility of a model decision process are remarkably improved.

Proposed is a method, apparatus, and system for providing medical diagnosis assistance information showing whether a tissue related to a tissue image is normal or abnormal (e.g., cancer) using the AI technology. Proposed are a method, an apparatus, and a system for training an AI model for producing medical diagnosis assistance information in consideration of conditions for obtaining a tissue image in an endomicroscope system for digital biopsy.

The invention discloses a medical image auxiliary diagnosis system based on artificial intelligence, which is suitable for diagnosis assistance of fetal ultrasonic images and comprises an image preprocessing module, a fetal recognition module, an imaging optimization module, a micro structure enhancement module, a characteristic parameter extraction module, a diagnosis assistance module and an image quality control module. The image preprocessing module is used for performing primary processing on the original ultrasonic image; the fetus recognition module is used for recognizing the position and the state of a fetus in the maternal body according to the processed ultrasonic image; the imaging optimization module is used for optimizing imaging conditions for micro-structure imaging by intelligently adjusting the position and angle of a probe and combining three-dimensional reconstruction and image correction technologies according to a fetus recognition result; the micro structure enhancement module is used for identifying and enhancing a micro anatomical structure in the image; the feature parameter extraction module uses a deep learning model to automatically identify and extract key feature parameters in the ultrasonic image. The imaging quality is improved, and the reliability of auxiliary diagnosis is enhanced.

The invention provides an AI-based child respiratory system diagnosis assistance method and system, and relates to the technical field of diagnosis assistance, and the method comprises the steps: collecting multiple layers of breathing sound signals through a bionic sensing array, building an airflow track model, carrying out the signal enhancement, and generating an enhanced breathing sound signal. Generating three-dimensional breathing sound propagation data by using the acoustic reconstruction network, evaluating a breathing function in combination with airflow dynamics and acoustic parameters, tracking abnormal breathing sound and generating a characteristic distribution diagram; symptom evolution analysis is carried out through a medical knowledge inference engine, real-time monitoring data is output, accurate auxiliary diagnosis is achieved, and the diagnosis efficiency and accuracy of children respiratory system diseases are improved.

The invention discloses a detection device special for animal husbandry diagnosis based on computer vision. The system mainly comprises an image acquisition module, a livestock identification module, an environment regulation and control module, a diagnosis assistance module and the like. Multi-angle image acquisition is carried out on livestock through a high-resolution camera, and a neural network model trained by a deep learning technology is utilized to carry out accurate identification on livestock types and health states. The device automatically adjusts environmental conditions such as temperature and illumination according to an identification result so as to reduce the stress response of the livestock and improve the accuracy and efficiency of diagnosis. Medical staff check the recognition result and diagnosis data in real time through the human-computer interaction interface, and the system automatically records key parameters in the diagnosis process and generates a detailed diagnosis report. The device has portability and a safety protection function, a stable and comfortable operation environment is ensured to be provided in the diagnosis process, and the safety of livestock and medical staff is guaranteed.

The invention discloses a vascular ultrasound structured report system and a vascular ultrasound detection method, and belongs to the technical field of ultrasonic diagnosis. The system comprises a patient and examination information management module, an examination part selection module, an ultrasonic view description template module, a measured value processing module, a schematic diagram labeling module, an intelligent diagnosis auxiliary module and a report integration output module. By means of multi-module integration, templated text description, data tables and schematic diagrams, standardization, normalization and visualization of vascular ultrasound results can be achieved, and clinical diagnosis efficiency and quality are greatly improved.

The invention discloses a genetic disease pathogenic site sorting and diagnosis auxiliary method and system based on multi-modal artificial intelligence. The method comprises the following steps: firstly, collecting genetic disease data to construct a heterogeneous knowledge graph; the method comprises the following steps: acquiring patient data, and executing an analysis process: generating multi-modal feature representation of candidate pathogenic sites from four dimensions of variation features based on rules, a tissue specificity mechanism, a protein three-dimensional structure and real-time literature evidence; carrying out fusion sorting on the features by using a sorting model, and generating an interpretable report and a clinical examination suggestion based on the uncertainty of a sorting result; and after the doctor executes examination according to the suggestion and feeds back newly added data, the analysis process is repeated until a preset iteration target is achieved. According to the method, the limitation of a static analysis model is broken through, multi-dimensional evidence fusion and clinical workflow embedding are realized, and the interpretation accuracy and diagnosis efficiency of the pathogenic site of the genetic disease are remarkably improved.

The present invention relates to a diagnosis assistance device and method based on artificial intelligence processing of a radiographic image, wherein a disease diagnosis assistance device according to an embodiment of the present invention may comprise an information providing unit that constructs a diagnosis assistance model by learning a training radiographic image and additional information through a first feature processing unit, a second feature processing unit, and a feature fusion processing unit, determines a bone mineral density value and disease classification information with respect to a radiographic image to be diagnosed on the basis of the diagnosis assistance model, and provides disease diagnosis assistance information including the bone mineral density value, the disease classification information, and diagnosis basis information.

A pathological diagnosis support device comprises: an acquisition unit that acquires pathological images including cells as subjects; a determination unit that determines whether malignant cells are included in the pathological images by inputting the pathological images to a machine-trained first classification model; a classification unit that classifies the pathological images by class by inputting the pathological images to a second classification model machine-trained to classify the pathological image into any of a plurality of classes including one or more features suggesting the presence of malignant cells; and an output unit that outputs a classification result and supports a user's decision making.

The invention discloses a cervical cell segmentation method combining semantic condition diffusion and knowledge verification, which comprises the following steps: S1, constructing a cervical cancer diagnosis vertical field multi-modal large model, and performing fine tuning on an open-source multi-modal large model through a low-rank adaptation supervision fine tuning method to obtain the cervical cancer diagnosis vertical field multi-modal large model; s2, generating a semantic vector for guiding segmentation; s3, performing preliminary segmentation based on a conditional diffusion model, performing iterative denoising on the to-be-segmented image, and generating a preliminary cervical cell segmentation mask; and S4, performing segmentation post-processing based on domain knowledge: performing morphological processing on the preliminary segmentation mask to obtain a final cervical cell segmentation result. According to the method, large-scale text data can be fully utilized and converted into refined semantic information for guiding image segmentation, so that the challenge of scarcity of labeled samples is overcome, the performance, generalization ability and clinical applicability of a cervical cell abnormal region segmentation model are remarkably improved, and finally more accurate and more reliable cervical cell pathological diagnosis assistance is realized.

The present invention discloses a convolutional neural network-based MRI medical image correction method, system, and computer-readable storage medium. The method comprises: obtaining a brain MRI medical image and performing preprocessing; extracting lesion features of lacunar infarction from the preprocessed MRI medical image using a convolutional neural network, and locating the lesion area of the lacunar infarction; performing detail enhancement on the lacunar infarction lesion area, segmenting the lesion area, and optimizing the segmentation result; performing artifact repair on the segmented image, and generating a three-dimensional lesion visualization model based on the repaired image. The present invention can more efficiently and accurately identify the lesion area, improve image quality, and provide an efficient and accurate diagnostic aid for lacunar infarction through multiple processing such as MRI image quality assessment, lesion identification, segmentation, artifact repair, and three-dimensional visualization.

A diagnosis assistant apparatus according to an embodiment includes a processor. The processor acquires a first measured value of a biomarker in a first liquid sample that is collected from a subject before a physical stimulus is applied and a second measured value of the biomarker in a second liquid sample that is collected from the subject after the physical stimulus is applied. The processor performs a determination on at least one of presence / absence and a property of a lesion based on the first measured value and the second measured value.

The invention belongs to the technical field of medical ultrasonic diagnosis auxiliary equipment, and discloses an electric right heart acoustic radiography foaming device which comprises a device body, the device body comprises a base, a mounting plate is fixedly arranged on the rear side of the base, a driving assembly is arranged on the front side of the mounting plate, and the driving assembly comprises fixing frames fixed to the upper side and the right side of the mounting plate; a stepping motor is fixedly arranged at the top of the fixing frame, a ball screw is fixedly arranged at the tail end of a main shaft of the stepping motor, a sliding block is spirally arranged on the outer side of the ball screw in a sleeving mode, and a connecting rod is fixedly arranged at the bottom of the sliding block, so that accurate controllability of power transmission and visual adjustment of parameters in the contrast agent injection process are achieved; specifically, key parameters such as injection speed and dosage can be accurately set through the operation panel, a traditional manual injection mode is replaced, and the stability of the foaming effect of the contrast agent is guaranteed.

An AI-based endoscopic diagnostic aid system includes: an endoscope module providing an endoscopic image of internal organs of the body of a patient; an input module configured to be capable of inputting arbitrary medical information about the patient; a control module which analyzes the endoscopic image provided from the endoscope module through a pre-stored image processing program to detect lesion information, matches the detected lesion information with the medical information input from the input module through a pre-stored lesion diagnosis program while generating at least one diagnosis information of malignancy and malignancy probability corresponding to the matching result, and outputs a preset notification signal according to the lesion information and the diagnosis information; and a notification module which visually displays on an arbitrary screen according to the notification signal output from the control module.

1. The name of the design product: medical examination and diagnosis auxiliary device. 2. The use of the design product: used to assist physicians in examination, diagnosis and treatment during diagnosis and treatment, for example: can be used to assist in the diagnosis and treatment of digestive system diseases. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: design 1 perspective view 1. 5. Design 1 is designated as the basic design.

The present invention relates to an ultrasonic diagnostic apparatus and a diagnostic support method. A detection unit (33) uses a machine learning detection model to detect lesion candidates contained in display frame data (tomographic images). An exclusion processing unit (34) compares the feature vector of the detected lesion candidate with a feature vector group in an exclusion database to determine whether the detected lesion candidate corresponds to an exclusion object. A mark display control unit (30) limits the display of a mark notifying the lesion candidate when the detected lesion candidate corresponds to an exclusion object.

The possibility that an ADNC patient will develop Alzheimer's disease is predicted with high accuracy. A diagnosis assistance device 2 predicts a possibility that a subject who has ADNC will develop Alzheimer's disease within a prescribed period, the diagnosis assistance device 2 comprising a prediction unit 23 that predicts the possibility that the subject will develop Alzheimer's disease within a prescribed period, according to a machine-learned prediction algorithm D4.

To provide a pathological condition diagnosis support system and a pathological condition diagnosis support method capable of supporting highly reliable diagnosis of a pathological condition by suppressing clouding and staining of a stretchable sheet.SOLUTION: The pathological diagnosis support system 1 includes an extension device 10 for extending a tissue section P of a cell, an imaging device 30 for acquiring image data by imaging the tissue section P, and an image analysis device 40 for calculating an index of morbidity based on a mode of cracks generated in the tissue section P due to extension by analyzing the image data, and the extension device 10 supports a stretchable sheet 60 provided with the tissue section P and extends the tissue section P together with the stretchable sheet 60. The stretchable sheet 60 has at least the first sheet surface 61 provided with the tissue section P, and the number of micropores having a maximum width of 100 μm or less per predetermined area in the first sheet surface 61 is smaller than that of the polyurethane gel.SELECTED DRAWING: Figure 1

The application discloses a magnetocardiogram gradient instrument and an electrocardio collection circuit packaging structure and a signal processing system, relates to the technical field of medical equipment, and realizes high-density integration of hardware and reliable protection through a rigid protection frame formed by a circuit chip, a layer plate structure and a connecting assembly, shortens a signal transmission path, reduces the influence of environmental vibration, dust, moisture and electromagnetic disturbance on a synchronous sampling link, and provides a stable data basis for multi-modal fusion imaging. The signal processing system comprises multi-modal synchronous collection, noise suppression, data fusion imaging and a diagnosis auxiliary module, generates a high-precision space-time dynamic imaging atlas by constructing a cardiac electromagnetic field joint forward model and applying time and space double constraints, and outputs a cardiovascular risk assessment, a cardiac arrhythmia positioning and a myocardial ischemia quantification result in combination with machine learning. The application solves the problems of low integration, poor anti-interference and insufficient single modal detection precision of existing equipment.

This endoscopic diagnosis assistance device is provided with: an image acquisition unit that acquires an image of a lumen from an endoscope; a region determination unit that specifies a passing region of the lumen captured in the image; a feature determination unit that determines a feature of the passing region; and a diagnosis assistance information generation unit that generates assistance information for access or diagnosis of a predetermined passing region in the lumen on the basis of the characteristics of the passing region.

A diagnostic assistance apparatus according to an aspect of the present disclosure determines whether a body part of a target examinee captured in a target medical image is normal, by using a trained first classification model generated by unsupervised learning using a plurality of first learning medical images of normal cases and a trained second classification model generated by supervised learning using a plurality of learning data sets including normal cases and abnormal cases.

This invention relates to the field of intelligent brain disease diagnosis technology, specifically providing an auxiliary diagnostic method and system based on multimodal decoupled dynamic graph learning. The method includes: acquiring and preprocessing multimodal data (such as neuroimaging, genetic markers, etc.) of the subject; extracting common pathological information and modality-specific features through a shared encoder and modality-specific encoders respectively, and optimizing the separation process using a decoupling loss function; furthermore, fusing all modality embeddings using a multi-head self-attention mechanism with a masked matrix to generate initial node representations, where the mask is used to suppress modality self-attention; subsequently, performing hierarchical dynamic graph convolution based on the node representations: in each layer, dynamically updating the graph adjacency matrix by combining the current node representation with the original features, and iteratively optimizing the node representations through message passing; finally, inputting the optimized representations into a classifier to obtain disease prediction results. This invention improves the automation performance and reliability of diagnosis.

The invention relates to the technical field of image recognition, and particularly discloses an auxiliary blood tumor pathological diagnosis system and method based on artificial intelligence, and the system extracts a pathological image group of a patient through a pathological diagnosis auxiliary platform, analyzes the data of each pathological image, and judges the effective feature value of each pathological image; the effective feature values of the pathological images are compared with a predefined effective feature threshold value to obtain a comparison result, and the pathological diagnosis auxiliary platform judges whether the effective features of the pathological images are enhanced or not based on the comparison result; and extracting cell characteristic data in each pathological image according to each pathological image, judging the characteristic complexity of each pathological image, and performing difference analysis on the pathological image group to complete auxiliary blood tumor pathological diagnosis.

Provided is a method for diagnosing the prognosis and therapeutic effect for chemotherapy in patients with rheumatoid arthritis-associated lymphoproliferative disorder (RA-LPD), who have developed a lymphoproliferative disorder (LPD) during the treatment of rheumatoid arthritis (RA). This is a kit for diagnosing the prognosis and therapeutic effect for chemotherapy in rheumatic arthritis-associated lymphoproliferative disorder (RA-LPD) patients, who have developed LPD during RA treatment. The kit comprises: means for detecting an inositol 1,4,5-triphosphate receptor (ITPR) type 2 (ITPR2) gene mutation in a biological sample from a patient; and instructions for determining that the prognosis and therapeutic effect for chemotherapy are poor when an ITPR2 gene mutation is present in the biological sample from the patient. The LPD is diffuse large B-cell lymphoma (DLBCL). For the ITPR2 gene mutation, Chr12:rs26744460 is GT or GG. Use for the diagnosis of non-RA lymphoma patients is also possible.

To provide an image generation device, a trained model for image generation, a data structure for image generation, a program for image generation, and a diagnostic support method, for generating an image with reduced AN shadows from a biological image including the AN shadows.SOLUTION: A trained model for image generation is constructed by training a generative adversarial network, which is a type of a generative model, using original images X1-M created by pseudo-projecting virtual human body phantoms including a target tissue or organ O that contains AN and a target region T, and correct images X1-M. The trained model thus generates, from a biological image including AN shadows, an image containing only the AN shadows.SELECTED DRAWING: None

This application provides an AI-based diagnostic fee adjustment method and related apparatus. The method includes: acquiring basic diagnostic data of a target vehicle; performing preliminary AI diagnosis on the basic diagnostic data to obtain a first diagnostic result; the first diagnostic result includes a preliminary diagnostic report; determining a target diagnostic strategy based on the preliminary diagnostic report; the target diagnostic strategy includes any one of the following: AI-only diagnosis, AI-assisted and manual determination, or manual diagnosis only; determining a target diagnostic report based on the target diagnostic strategy; determining the AI ​​contribution based on the target diagnostic report; and determining the target diagnostic fee based on a preset basic fee standard and the AI ​​contribution. By constructing a differentiated human-machine collaboration process based on diagnostic confidence, quantifying the AI ​​contribution, and differentiating fees based on the AI ​​contribution, the accuracy of AI fault diagnosis applications and user engagement are improved.