139 results about "Tissue characterization" patented technology

The invention discloses a subcutaneous injection site detection and recommendation system based on an intelligent algorithm, and aims to improve the safety of long-term subcutaneous injection and the drug absorption efficiency and reduce the risk of related complications such as subcutaneous hemorrhage, subcutaneous induration and subcutaneous fat hyperplasia at the same time. Comprising a subcutaneous tissue detection module for acquiring subcutaneous vascular distribution, fat layer thickness and induration state of a target part through infrared imaging and ultrasonic technologies and generating subcutaneous tissue characteristic data; the data processing module analyzes the feature data by using an image recognition algorithm, and recognizes a blood vessel dense region, induration and a part where fat hyperplasia has occurred; the injection part recommendation module is used for evaluating an analysis result of the data processing module based on a machine learning algorithm in combination with personalized injection historical data of the patient, and generating an optimal injection part recommendation scheme avoiding a risk area; the intelligent imaging and data analysis technology is utilized, the subcutaneous injection part selection accuracy is improved, and the patient comfort and the treatment effect are improved.

The invention provides a method and a device for evaluating and measuring microscopic mechanical properties of TC4 alloy, relates to the technical field of microscopic mechanical properties, and realizes accurate evaluation of material properties through combination of multi-scale characterization and numerical simulation. Firstly, a standard sample is prepared through selective laser melting, and residual stress field distribution is obtained through X-ray diffraction; dividing a stress field into sub-regions, and establishing a quantitative relationship between microscopic structure parameters and residual stress by combining SEM and EBSD analysis; the method comprises the following steps: acquiring local mechanical properties through a nanoindentation test, and constructing a stress-performance correlation model; and finally, establishing a finite element model, transmitting stress and tissue parameters by adopting trilinear interpolation and an inverse distance weighting method, verifying data reliability through a stress difference criterion, and calculating mechanical properties of each unit. According to the method, multi-scale correlation from microstructure characteristics to macroscopic mechanical properties is innovatively realized, so that the mechanical property distribution of the material is accurately evaluated.

The present disclosure provides methods and systems for identifying a tissue characteristic in a subject. Identifying a tissue characteristic may comprise accessing a database comprising a first set of data from a first image obtained from a first tissue region of the subject and a second set of data from a second image obtained from a second tissue region of the subject; computer processing the first set of data and the second set of data to (i) identify a presence or absence of one or more features indicative of the tissue characteristic in the first image, and (ii) classify the subject as being positive or negative for the tissue characteristic based on the presence or absence of the one or more features in the first image; and generating an electronic report which is indicative of the subject being positive or negative for the tissue characteristic.

The invention discloses a medical image analysis processing method based on AI, and relates to the technical field of image analysis, and the method comprises the steps: collecting multi-modal medical image data of a patient from an image device, and carrying out the preprocessing of the multi-modal medical image data; fusing the preprocessed multi-modal medical image data, extracting biological tissue features, constructing a biological tissue feature library, and marking abnormal biological tissue features in the biological tissue feature library; according to a medical image analysis task, combining with the marked biological tissue feature library to train an AI visual detection model so as to identify and classify different abnormal biological tissues; and automatically detecting an abnormal biological tissue area in the image by using an AI visual detection model. According to the method, high-precision analysis can be carried out on medical images through the AI technology, tiny abnormal changes can be detected, the severity of lesions can be evaluated and the development trend of diseases can be predicted through quantitative analysis on image features, and the risk of disease progression can be timely found and reduced when the lesions do not cause obvious symptoms.

The invention relates to the technical field of ultrasonic image processing systems, and provides an ultrasonic image processing system based on three-dimensional reconstruction, which comprises an ultrasonic data acquisition terminal, an image processing terminal, a three-dimensional reconstruction terminal, a quality evaluation terminal and a doctor interaction terminal, the ultrasonic data acquisition terminal is used for acquiring ultrasonic original radio frequency data and a B mode image and synchronously acquiring probe position information of the ultrasonic probe; the image processing terminal is used for performing image denoising, edge enhancement and tissue feature labeling according to the ultrasonic original radio frequency data and the B mode image, and extracting key section information for subsequent reconstruction; the three-dimensional reconstruction terminal is used for performing spatial reconstruction based on the key section data and the probe position information to form a three-dimensional ultrasonic image body; the quality evaluation terminal is used for scoring the three-dimensional reconstruction effect of the three-dimensional ultrasonic image body and generating three-dimensional reconstruction quality evaluation information; and the doctor interaction terminal is used for a doctor to check the three-dimensional ultrasonic image body and the three-dimensional reconstruction quality evaluation information in real time and generate and display optimization suggestion information. The method has the effect of improving the accuracy of three-dimensional reconstruction quality evaluation.

The invention provides a digital pathological section scanner control system based on the Internet of Things. The digital pathological section scanner control system comprises a user management module, a product classification module, an equipment management module, a pathological image data service module, a remote real-time monitoring and operation module and an intelligent fault diagnosis and early warning module. The system has the following beneficial effects: the product classification module realizes efficient classification management of pathological section scanner equipment; an intelligent section type identification sub-module in the pathological image data service module can automatically identify HE staining types and tissue features, and dynamically adjust scanning parameters according to staining quality, so that the stability of scanning quality is improved; a 3D digital slice reconstruction sub-module in the pathological image data service module can construct a three-dimensional organization structure view, so that the accuracy and reliability of pathological diagnosis are greatly improved; the intelligent fault diagnosis and early warning module adopts a machine learning technology to carry out fault prediction and real-time diagnosis, and equipment faults are effectively prevented.

The invention discloses a postpartum pelvic floor function evaluation system based on three-dimensional image reconstruction, which belongs to the technical field of medical image processing and biomechanical analysis and comprises an ultrasonic image acquisition module, a three-dimensional reconstruction module, a tissue elasticity analysis module, a rehabilitation scheme generation module and a visual display module. Performing three-dimensional space registration and feature point extraction through a multi-angle ultrasonic image sequence, and realizing three-dimensional reconstruction of a pelvic floor anatomical structure with the precision of 0.2 mm by using a multi-scale fusion network; a deep learning neural network is adopted to extract tissue features, and elastic modulus distribution is calculated through a strain analysis model; an individualized rehabilitation training plan is automatically generated according to an evaluation result, and the rehabilitation effect is improved by 75%; according to the postpartum pelvic floor function evaluation system and method, the three-dimensional visual display and rehabilitation process tracking functions are provided, accurate evaluation and scientific rehabilitation guidance of the postpartum pelvic floor function are achieved, the screening efficiency is improved by 320%, and the clinical application value is remarkable.

A surgical instrument comprises a controller configured to control application of RF or ultrasonic energy at a low level when displacement or intensity of a button is above a first threshold but below a second threshold higher than the first threshold, and control application of RF or ultrasonic energy at a high level when the displacement or intensity exceeds the second threshold. In another aspect, a surgical instrument comprises a first sensor configured to measure a tissue characteristic at a first location, a second sensor configured to measure the tissue characteristic at a second location, and a controller configured to, based at least in part on the measured tissue characteristic at the first location and the second location, control application of RF or ultrasonic energy.

Provided herein are systems and methods for tissue characterization that may be utilized for intraoperative cancer margin assessment. A tissue sample is resected from a patient and then placed in a system where it is imaged for autofluorescence characteristics to help identify regions of interest such as cancerous tissue margins. Based on the imaging results and tissue characterization, the resection of tissue in the subject can be adjusted.

According to the chronic obstructive pulmonary disease grading system and method based on the X-ray chest radiograph, the original X-ray chest radiograph is obtained, and the original X-ray chest radiograph is preprocessed; a conditional generative adversarial network is adopted to separate the preprocessed original X-ray chest radiograph, and a bone suppression image and a bone tissue feature image are obtained; inputting the bone tissue feature image into the constructed double-branch deep neural network to obtain a pulmonary parenchyma feature map and a rib feature map; obtaining a clinical variable, preprocessing the clinical variable, performing cross-modal fusion on the preprocessed clinical variable, the lung parenchyma feature map and the rib feature map, and generating a fusion feature vector; processing the fusion feature vector by combining a regression-classification output header model to obtain an FEV1% predicted value and a GOLD staging result; a conditional generative adversarial network, a double-branch deep neural network, cross-modal fusion and a joint regression-classification output head model are introduced, and low-cost, low-radiation and high-universality GOLD automatic grading can be realized.

The invention belongs to the technical field of steel performance intelligent analysis, and particularly relates to a high-strength and high-toughness rock drilling tool steel performance intelligent analysis method based on machine learning. The method comprises the following steps: firstly, collecting chemical components, process, microstructure images and performance data of rock drilling tool steel; then, a process-component prediction model and a microstructure image prediction model are constructed, and relations between component-process and microstructure characteristics and performance are mined respectively; then, the proportion of each component of the microstructure is obtained by combining finite element simulation with a genetic algorithm; predicting performance by using an XGBoost algorithm, comparing real indexes, and constructing an error feedback mechanism optimization model; and finally, solving an optimal parameter combination meeting a strength-toughness index by means of a particle swarm optimization algorithm. According to the method, multi-source data and multiple intelligent algorithms are fused, the defects of a traditional analysis method are effectively overcome, collaborative optimization of chemical components, the heat treatment process and the microstructure form is achieved, and the analysis precision and the production efficiency are improved.

The invention relates to the technical field of medical instruments, and discloses an interventional ultrasound method and system for minimally invasive surgery, and the method comprises the steps: obtaining ultrasound image data and operation state parameters; a three-dimensional ultrasonic image model is constructed through multi-layer scanning, and an initial tissue feature evaluation model is analyzed and generated; dynamic blood vessel data and operation constraint conditions are loaded, a comprehensive evaluation model is generated, and puncture point dynamic simulation is carried out; constructing a dynamic blood vessel prediction model, training to obtain a puncture point adjustment model, and generating initial adjustment parameters; obtaining dynamic adjustment simulation information based on the parameters and the model; constructing a multi-path optimization model, and carrying out iterative optimization to obtain an optimal path planning parameter; the safety puncture probability is inferred in combination with real-time images, and a real-time puncture strategy is adjusted to achieve dynamic matching. The system comprises a data acquisition module, an image modeling and analysis module and the like. The safety and accuracy of minimally invasive surgery puncture are improved, and the method is suitable for interventional ultrasound guided minimally invasive surgery scenes.

The invention, which belongs to the technical field of medical image processing and computer vision, discloses a multispectral imaging-assisted post-radiotherapy skin injury early detection system comprising a multispectral data acquisition module, a deep tissue feature extraction module, a three-dimensional lesion segmentation module and a space-time tracking evaluation module. The subcutaneous 2.5 cm depth tissue information is obtained through multispectral imaging at the wave band of 400-1350nm, blood perfusion, melanin concentration, collagen structure and other physiological parameters are inversed based on the radiation transfer theory, a three-dimensional medical image segmentation algorithm is adopted to achieve three-dimensional accurate segmentation of an injury area, a spatio-temporal evolution model is established to predict the injury development trend, and the damage development trend is predicted. The radioactive skin injury can be detected in the subclinical period, the detection time window is advanced by 5.2 days on average, the occurrence rate of severe dermatitis is reduced by 65%, and a basis is provided for clinical timely intervention.

The invention relates to the technical field of image recognition, in particular to a gynecological operation navigation method and system based on the minimally invasive technology, and the method comprises the steps: carrying out the feature recognition operation of a three-dimensional focus region structure diagram, and obtaining a focus surrounding tissue feature set; performing minimally invasive surgery scheme identification operation on the focus surrounding tissue feature set to obtain a predicted channel number; performing minimally invasive surgery path recognition operation based on the predicted channel number on the tissue feature set around the focus to obtain a minimally invasive path set, and acquiring necessary avoidance tissue sequences in a preset range region on each minimally invasive path in the minimally invasive path set to obtain a necessary avoidance tissue sequence set; according to the necessary avoiding tissue sequence set, the real-time instrument position information, the minimally invasive path set and the path distribution purpose type set, cutting operation is conducted on the target treatment area, and a minimally invasive channel set is obtained. The navigation accuracy of the minimally invasive surgery can be improved, and the safety of the minimally invasive surgery is improved.

One or more devices, systems, methods and storage mediums for performing intravascular imaging and / or optical coherence tomography (OCT) while detecting and / or characterizing one or more tissues are provided. Examples of applications include imaging, evaluating and diagnosing biological objects, such as, but not limited to, for Gastro-intestinal, cardio and / or ophthalmic applications, and being obtained via one or more optical instruments, such as, but not limited to, optical probes, catheters, capsules and needles (e.g., a biopsy needle). Preferably, the intravascular imaging devices, systems methods and storage mediums include or involve a method, such as, but not limited to, using one image, such as a carpet view, to detect and / or characterize the one or more tissues and / or to perform coregistration. Examples of identified or detected tissues include calcium, lipids, and other types of tissue.

A diagnostic platform for detecting and analyzing brain tumors includes a portable fiber Surface Enhanced Raman spectroscopy (SERS) module, a removable fiber probe for intraoperative use, disposable nanoplasmonic cartridges for individual sample loading, and an updatable spectral library collected from healthy and diseased samples. The platform is designed to analyze blood and tissue signatures and employ artificial intelligence (Al) models to classify SERS spectra based on an updatable proteomics and SERS spectral library encompassing distinct brain tumor types. The platform is further configured to utilize artificial intelligence (Al) algorithms for the analysis and classification of the recorded SERS spectra.

A medical RF apparatus using an RF pulse and a method of controlling the medical RF apparatus are provided. The medical RF apparatus includes an RF generator, which generates a test pulse for detecting characteristics of tissue, a monitoring unit, which monitors the change in the information on the tissue state while the test pulse is transmitted to the tissue, and a measurement unit, which determines the tissue characteristics of a patient by comparing the values monitored in the monitoring unit with the reference data.

This application relates to the field of medical image processing technology, and discloses a multimodal image recognition method and system for dynamic assessment of diabetic wound healing. The method includes acquiring infrared thermal imaging images, hyperspectral images, and visible light color images of a diabetic wound collected at the same time of visit; identifying the wound boundary between the diabetic wound and surrounding tissues in the visible light color image, and determining the central reference point of the visible light color image based on the wound boundary; establishing a virtual polar coordinate system based on the central reference point and preset anatomical reference markers; extracting tissue feature information characterizing the pathological state of the wound from the infrared thermal imaging image, hyperspectral image, and visible light color image, and mapping the spatial position of the tissue feature information in its corresponding image to the virtual polar coordinate system; normalizing the mapped tissue feature information, and calculating wound healing assessment indicators based on the normalized tissue feature information in the virtual polar coordinate system.

The utility model provides a laser diagnosis device comprising a first laser light source used for emitting diagnosis laser; the light deflector is connected with the output end of the first laser light source and is used for carrying out light beam deflection at a specific angle on the diagnosis laser; the optical fiber coupler is arranged at the output end of the optical deflector and is used for coupling the diagnosis laser with the deflected light beam into one optical fiber branch of the optical fiber array; the optical fiber array is connected with the optical fiber coupler and is used for transmitting the diagnosis laser coupled by the optical fiber coupler to a focusing microlens of the focusing microlens array; wherein the optical fiber branches in the optical fiber array are mutually independent; the focusing micro-lens array is connected with the optical fiber array and is used for focusing the diagnosis laser on the diseased tissue through a focusing micro-lens to generate a laser signal with tissue characteristic information; wherein each focusing microlens in the focusing microlens array is correspondingly connected with one optical fiber branch of the optical fiber array; the device is compact in structure, easy and convenient to operate and capable of conducting refined diagnosis on diseased tissue.

Changes in tissue stiffness have long been associated with disease. Systems and methods for determining the stiffness of tissues using ultrasonography may include a device for inducing a propagating shear wave in tissue and tracking the speed of propagation, which is directly related to tissue stiffness and density. The speed of a propagating shear wave may be detected by imaging a tissue at a high frame rate and detecting the propagating wave as a perturbance in successive image frames relative to a baseline image of the tissue in an undisturbed state. In some embodiments, sufficiently high frame rates may be achieved by using a ping-based ultrasound imaging technique in which unfocused omni-directional pings are transmitted (in an imaging plane or in a hemisphere) into a region of interest. Receiving echoes of the omnidirectional pings with multiple receive apertures allows for substantially improved lateral resolution.

The present invention provides a multi-physical field data fusion system and method, product, and medium for a biological probe, belonging to the fields of biosensing and tissue feature analysis, and solving the technical problem of how to accurately and effectively adapt to different biological tissues to fuse the multi-physical field data collected by the biological probe and extract key information, and then quickly and accurately determine the state of the biological tissue. By combining multi-physical field data fusion, parameter optimization, and adaptive model construction, the present invention can significantly improve the overall performance of the biological probe measurement system, fully explore the internal correlation between the physical field data for the state detection of biological tissues, avoid information loss and deviation caused by single physical field data, and provide multi-dimensional support for subsequent biometric analysis.

Systems and techniques are disclosed for improving real-time tissue characterizations during endovascular procedures. In some implementations, a method involves characterizing biological tissue for an endoluminal procedure. A first electrode is positioned within an endoluminal space adjacent to a region of biological tissue. A second electrode is positioned spatially separated from the first electrode. A variable-frequency input electrical signal is applied across the first electrode and the second electrode to generate an electrical field corresponding to the region. A voltage response is measured between the first electrode and the second electrode based on the electric field. A voltage phasor is determined based on the voltage response relative to the variable-frequency input electrical signal. A characteristic of the region is determined based on the voltage phasor.

A computer-implemented method and system are described. The method comprises the steps of: identifying a region having an electrical activation sequence within a chamber of the heart from an electrograph, the electrical activation sequence characterizing the region as a potential drive of an abnormal heart rhythm; for each sensing location at or substantially around the region, determining an earliest activation electrode site from a primary activation; for each determined earliest activated electrode site: calculating a value for each of a plurality of modifiers associated with the electrode site, the modifiers determined from electrogram data of the site, tissue characteristics of the site, or anatomical characteristics of the site; determining a sorting factor calculated according to the plurality of modifiers; sorting each of the earliest activated electrode sites according to a sorting factor; and outputting data identifying the region, the data changing a significance of each of the determined earliest activated electrode sites depending on the ranking.

The invention discloses a dynamic path planning method and system for anterior cruciate ligament tendon dissection, and relates to the technical field of ligament tendon dissection, and the method comprises the following steps: obtaining anatomical structure data of an anterior cruciate ligament tendon and tissue characteristic parameters of a to-be-dissected region; marking key monitoring points in the to-be-stripped area to obtain stripping monitoring points; counting the space distance and the tissue density difference value between the adjacent stripping monitoring points to obtain a to-be-measured space parameter; historical tissue stress distribution characteristics of different anatomical structure types are extracted from historical anterior cruciate ligament tendon dissection operation data, and according to the historical tissue stress distribution characteristics, a dissection path deviation value in the historical operation data and a path correction correlation factor between the to-be-measured spatial parameters are counted; according to the anatomical structure data and the tissue characteristic parameters of the current tendon to be stripped, the effects are that operation errors caused by path deviation are reduced, and the accuracy of operation is improved.

A system for characterizing a region of interest of a biological tissue that includes a sighting device that produces an image in reflection of an elementary surface of the biological tissue. The system further includes a microscopic analysis device that detects, in a detection pattern included in the elementary surface, a light beam emitted by the biological tissue in response to illumination of the biological tissue and generates microscopic analysis information, and to determine a tissue characterization parameter from the microscopic analysis information. The system further includes a processing module that localizes, relative to a surface image of the region of interest, each elementary surface image of various elementary surface images and produces a map element for the characterization parameter. The system further includes a display module that displays a tissue map.

A system and method of analysis for medical image data. An image of breast tissue is received, a region of interest (ROI) in the image is identified based on tissue characteristics, and a query image is defined. A hierarchy of lesion data in a lesion database is retrieved, the hierarchy being formed based on one or more relationships among a plurality of images, and the query image is positioned within the hierarchy of lesion data based on the tissue characteristics. A position of the query image within the hierarchy of the lesion data in the lesion database is determined, identifying one or more neighbor images of the query image from among the plurality of images based on the position, and statistics associated the one or more neighbor images are retrieved. Analytics associated with the query image based on the statistics are generated, and a graphic depicting the analytics is displayed.

This invention discloses a deep learning-based method for predicting orthodontic outcomes, belonging to the field of artificial intelligence-assisted technology. The method includes acquiring multimodal initial data of the target patient, comprising craniofacial CBCT 3D images, intraoral 3D scan models, and a digital design scheme for orthodontic appliances containing target tooth displacement data. Based on the CBCT 3D images, macroscopic geometric features of the jawbone are extracted using a macroscopic morphological encoder, and microscopic tissue features of the periodontal region are extracted using a microscopic tissue feature encoder. The macroscopic morphological encoder is a 3D convolutional neural network, and the microscopic tissue feature encoder is a local window attention network based on a visual Transformer. This invention can predict the dynamic effects of the interaction between tooth movement and adaptive skeletal remodeling, and is particularly suitable for predicting orthodontic outcomes in adolescents during their growth and development period, addressing the industry pain point of large systematic biases in static models in such scenarios.

Provided herein is an intravascular endoscope with an improved balloon design. The endoscope comprises an anterior balloon with a camera, light source, and working channel port for visualization and therapeutic access. One or more posterior balloons are positioned inferior to, at the same level as, or superior to the anterior balloon. Inflation of the one or more posterior balloons pushes the anterior balloon against the blood vessel wall while maintaining a channel for blood flow past the anterior balloon. The endoscope enables real-time, direct visualization of the interior of blood vessels for diagnosis and treatment of vascular diseases while preserving critical blood flow. Additional imaging modalities, such as ultraviolet and infrared, can be incorporated to enhance tissue characterization. Visual markers on devices inserted through the working channel improve visibility and guidance under endoscopic imaging. The endoscope is compatible with arteries and veins throughout the body and provides advantages over traditional imaging techniques for managing vascular disease.

The invention discloses a cardiac magnetic resonance report generation method and system based on a multi-dimensional tissue feature knowledge graph. The method comprises the following steps: performing motion correction and multi-view feature extraction on a cardiac magnetic resonance multi-sequence image to obtain an original numerical attribute of an instance node; constructing a dynamic knowledge graph containing tissue characteristics and medical priori, and generating a target reasoning chain with a clinical basis in combination with neural symbol reasoning; then, in a controlled generation stage, an inference chain is injected into a prefix tree, in a decoding step, mask physical shielding logic conflict description is utilized, and a pointer network is synchronously started to directly copy original numerical value attributes into a text sequence; and finally, executing consistency logic verification through a text restoration technology, and identifying and intercepting factual errors. Through the atlas constraint and numerical unvarnished transmission mechanism, the factual illusion problem of a generative model is effectively solved, and the accuracy and reliability of a heart diagnosis report are remarkably improved.

The invention belongs to the technical field of material microstructure state test characterization, and particularly provides a material microstructure quantitative characterization method coupling image enhancement and machine learning, which comprises the following steps: obtaining an observation sample microstructure image set through a microscopic observation method; carrying out image enhancement, multiple noise reduction and binarization processing on the microstructure image set; extracting features of the binarized image to a high-dimensional hidden space by using a deep convolutional neural network algorithm, and then performing dimension reduction and quantitative characterization on the high-dimensional features by using a consistent manifold algorithm; and carrying out physical interpretability analysis on the dimension reduction result of the microstructure characteristics of the material, and evaluating the characterization effect of the dimension reduction quantitative index on the microstructure characteristics of the material. According to the method, unified description and quantitative characterization of various complex microstructure morphology, size and evolution of the material are achieved, the cost and errors of a manual statistical method are greatly reduced, and the method has high invention innovation and good engineering application prospects.