100 results about "Diagnostic aid" patented technology

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 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 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.

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.

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.

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.

The objective is to provide a diagnostic support device, a diagnostic support device, a trained model for diagnostic support, a diagnostic support method, a diagnostic support program, and a data structure for diagnostic support for diagnosing which of two diseases a patient has. [Solution] Multiple biometric data from patients with bilateral aldosteronism (disease A) and multiple biometric data from patients with unilateral primary aldosteronism (disease B) are used as training data, and multiple training explanatory variables are generated from the multiple training biometric data. Based on this, a diagnostic support device, a trained model for diagnostic support, a diagnostic support method, a diagnostic support program, and a data structure for diagnostic support are constructed that can predict (assist in diagnosis) or determine whether a subject suspected of having disease A or disease B has either disease A or disease B.

The invention discloses a multi-modal fusion maternal and child medical image intelligent identification and diagnosis auxiliary system and method, and belongs to the technical field of medical health, and the system comprises a data obtaining module which is used for obtaining a medical image of a related part of a patient, the related part comprises an area corresponding to gland tissue, related processing is carried out on the medical image, and the medical image is obtained; the correlation processing includes distinguishing the medical image into an image corresponding to a gland and an image corresponding to connective tissue. According to the method, by obtaining and distinguishing gland and connective tissue medical images, extracting edge features and monitoring area changes, the lesion site can be accurately judged, the distance change between the blood vessel and the blood vessel group can be quantitatively evaluated, then the lesion development trend is scientifically judged, a comprehensive and objective diagnosis basis is provided for doctors, and the diagnosis efficiency is improved. And the diagnosis accuracy and the clinical decision scientificity are effectively improved.

Provided is an endoscopic image diagnosis assistance processor (2) that stably and efficiently identifies an organ under observation. The endoscopic image diagnosis assistance processor (2) comprises: a site inference unit (13) that uses a first model to infer a site confidence level that an endoscopic image is an image of a site; a site identification unit (14) that uses the site confidence level to identify a site under observation in the endoscopic image; an organ identification unit (15) that identifies a first organ candidate corresponding to the site under observation; an organ inference unit (17) that uses a second model to infer a second organ candidate corresponding to the endoscopic image; and an organ determination unit (18) that determines the first organ candidate as the organ under observation in the endoscopic image when the first organ candidate and the second organ candidate are the same, and does not determine the organ under observation or determines the organ under observation on the basis of a predetermined condition when the first organ candidate and the second organ candidate are different.

An endoscope diagnosis assistance method is a diagnosis assistance method for an endoscope having an imaging unit for acquiring an image of a lumen at a tip section, the method comprising: detecting an acquisition position, which is a position within the lumen at which the tip section acquires the image, detecting a speed at which the tip section of the endoscope advances and retreats in the lumen, and determining the speed at which the tip section of the endoscope advances and retreats in the lumen, the speed at which the tip section of the endoscope advances and retreats in the lumen; it is determined whether or not the speed of the tip section of the endoscope passing through a region of interest, which is a region that can be determined under predetermined conditions, is within an appropriate observation speed range.

Provided is a diagnostic imaging assistance device (10) that assists in determining the invasion depth of a lesion (90) in an endoscopic image. The diagnostic imaging assistance device (10) comprises: a data acquisition unit (12) that acquires three-dimensional data pertaining to a lesion (80) which has been extracted on the basis of an endoscopic image; a curve acquisition unit (14) that acquires, from the three-dimensional data, at least one cross-sectional curve (90L) of the lesion (90) which has a convex shape; a feature extraction unit (15) that extracts at least one prescribed feature amount from the cross-sectional curve (90L); and a computation unit (16) that calculates an invasion depth score of the lesion (90) on the basis of the feature amount.

Embodiments relate to a method for assisting the diagnosis of cardioembolic stroke, in which the method is performed by a processor and is to assist the diagnosis of cardioembolic stroke by using chest radiographic images, the method comprising the steps of: acquiring chest radiographic images to be classified of a subject; and acquiring cardioembolic stroke diagnosis auxiliary information from the chest radiographic images to be classified of the subject, by using a neural network model, wherein the neural network model is configured to extract features from the chest radiographic images to be classified of the subject, and determine, based on the extracted features, whether the subject belongs to a group indicative of having cardioembolic stroke.

There are provided a medical image processing apparatus, an endoscope system, a diagnosis assistance method, and a program capable of coping with an erroneous determination while utilizing an observation completion determination in which image processing is used and of suppressing oversight of an observation target part by a doctor. The medical image processing apparatus includes at least one processor. The at least one processor acquires a medical image; makes, on the basis of the medical image, an observation completion determination as to whether observation is completed for a target part; performs display control for causing a determined result of the observation completion determination to be displayed on a display device, receives a user input including an instruction for correcting display content indicating the determined result of the observation completion determination, and causes corrected content based on the user input to be reflected in the display.

The invention relates to the technical field of medical image processing, and particularly discloses a medical image processing method and a medical image processing system, which are characterized in that pixels are divided by dynamically calculating an adaptive threshold, a Gaussian mixture model is used for denoising, an image is enhanced by a multi-scale Retinex enhancement algorithm, and a tissue boundary is highlighted. And constructing a U-Net network model combined with an attention mechanism, training by using a large amount of annotation data, optimizing by using a cross entropy loss function and a stochastic gradient descent algorithm, inputting a preprocessed image, and outputting a segmentation result. And extracting multiple types of features for the segmented tissues, constructing a diagnosis model by combining an SVM classifier with multi-modal features, determining parameters through a grid search method and cross validation, and inputting new image features to assist doctors in diagnosis. According to the method, the adaptive threshold is dynamically calculated to remove noise, the U-Net network model combined with the attention mechanism is utilized to perform image segmentation and construct the disease diagnosis model, the quality of medical image processing and the accuracy of disease diagnosis are improved, and more reliable diagnosis assistance is provided for doctors.

Provided is a pathological diagnosis assistance method in which a microscopic observation image data is acquired by specifying a position, the image data is classified by tissue type using AI, the classification result is integrated as a lesion site, and thus the diagnosis is assisted. Provided is a pathological diagnosis assistance method that can provide an assistance technology for performing pathological diagnosis with high precision and high efficiency by HE staining, which is commonly used by pathologists. Further, a pathological diagnosis assistance system, a pathological diagnosis assistance program, and a learned model are provided.

The present application provides a kind of medical image processing device, endoscope system, diagnostic aid method and program, can be in the observation of using the determination of using image processing is completed at the same time processing error determination, can inhibit the oversight of physician to observation object site.Medical image processing device has at least one processor.At least one processor obtains medical image, based on medical image, whether observation of object site is completed observation completion determination, carries out the display control of the determination result of observation completion determination is displayed on display, accepts the user input including the instruction of the display content of the determination result indicating observation completion determination is modified, and the content modified by user input is reflected in display.

AI-Based Cervical Cancer Detection and Diagnostic Assistance Device

The embodiment of the invention relates to a distributed multi-mode dermatosis combined diagnosis auxiliary device and method, the device comprises a computing center end and a plurality of distributed participation ends, the distributed participation ends are combined with the computing center end to carry out federated learning so as to obtain a final diagnosis model, and the final diagnosis model is adopted to assist in dermatosis auxiliary diagnosis. According to the technical scheme provided by the embodiment of the invention, the diagnosis model assistance is generated by adopting the multimode data and the improved federated learning method, so that the accuracy and comprehensiveness of the final diagnosis model are improved, and the data convergence speed in the federated learning process is effectively accelerated on the premise of ensuring the accuracy.

An endoscopic diagnosis assistance method includes: detecting an acquisition position that is a position within a lumen whose image is acquired by an imaging portion provided at a distal end portion of an endoscope; detecting a speed at which the distal end portion of the endoscope is advanced into or retracted from the lumen; and determining, based on an inference model, whether or not the speed of the distal end portion of the endoscope passing through an attention region capable of being determined under a predetermined condition is within a predetermined speed range for the attention region relative to the detected acquisition position, wherein the inference model was obtained by machine learning using: previously captured image frames captured in multiple cases; and a result of determining the attention region for the previously captured image frames to annotate an examination speed appropriate for examining the attention region, as training data.