319 results about "Tissue type" patented technology

The invention provides a multi-modal image analysis-based robot-assisted wound treatment method and system, and relates to the technical field of robots, and the method comprises the steps: collecting a multi-modal image of a to-be-treated wound, determining an image registration result, obtaining a wound region image through a multi-scale fusion algorithm and wound region segmentation, and carrying out the type classification of wound tissues. A wound tissue type classification result is obtained; performing surrounding shooting on a wound area to obtain a wound multi-view image, registering subsequent depth maps frame by frame by taking a first frame of depth map as a reference, accumulating point cloud data, determining a wound three-dimensional point cloud, generating a wound three-dimensional reconstruction result by combining a curved surface evolution algorithm, and determining a wound parameter measurement result; based on the wound tissue type classification result and the wound parameter measurement result, a robot path planning algorithm is adopted, the optimal movement track of the robot is generated, the movement parameters of the robot are adjusted in real time through a robot control algorithm of vision and force feedback, and wound adjuvant therapy is completed.

A robotic surgical system includes one or more robotic actuators configured to interact with biological tissue during a surgical procedure. A plurality of sensors include at least one of fiber Bragg grating sensors, piezoelectric strain sensors, or magnetostrictive sensors to capture real-time mechanical, elasticity, or deformation data from biological tissues. deep learning engine trained on a dataset comprising tissue mechanical responses across multiple tissue types, pathological states, and patient demographics. Pre-contact predictive adjustment profiles are generated for anticipated tissue interactions using preoperative imaging data registered to intraoperative coordinates. Intraoperative deviations are detected from predicted mechanical behavior and autonomously recalibrate actuator forces. Upcoming surgical maneuvers are anticipated based on prior task sequences and adjust actuator stiffness or damping properties in preparation for anticipated contact. An emergency override of actuator forces is provided via an anomaly detection module when real-time sensor data deviates beyond a threshold from the predicted safe mechanical response range. A feedback loop iteratively refines the deep learning engine during the procedure using supervised learning updates, anomaly detection, and reinforcement learning strategies. The reinforcement learning model is optionally shared across procedures to optimize distributed actuator force patterns for minimizing localized and cumulative tissue stress.

The invention provides a surgical process monitoring method and system based on machine vision, and the method comprises the steps: firstly obtaining a surgical scene real-time image sequence containing the dynamic images of a surgical instrument operation region and a biological tissue region; secondly, extracting a surgical instrument identification result (including an instrument type and a space posture) and a biological tissue segmentation result (including a tissue type and a region boundary), then monitoring an instrument operation track and a biological tissue region deformation parameter based on the results, generating a dynamic operation state parameter, and according to a predefined surgical stage safety threshold value, determining a biological tissue region deformation parameter; multi-modal anomaly detection is carried out on the dynamic operation state parameters, an abnormal event type and a risk level are determined, finally, a visual operation monitoring report is generated according to the abnormal event type and the risk level, the report covers abnormal area marks, risk prompt information and correction suggestions, comprehensive intelligent monitoring of the operation process is achieved, and the operation safety and the success rate are improved in an auxiliary mode.

A process for tissue modelling from a medical image of a subject, for forming a three-dimensional (3D) model of a region of interest (ROI) of a subject with one or more tissue types, said process including the steps of (i) utilising a rule-based method that automatically generates the weak annotation, initial seed area from a medial image (210b); (ii) utilising a proposal generation method that integrates the multi-scale image features and anatomical prior (220b); and (iii) a comprehensive loss for CNN training that optimizes the pixel classification and feature distribution simultaneously (230).

To provide a manufacturing system and method for creating multiple tissue constructs from cells.SOLUTION: A system can include a thaw subsystem (if the cells are provided in a frozen state), an expansion subsystem, a concentration subsystem, and a tissue maturation subsystem. Each of these subsystems is modular and can be reconfigured, and a process can be repeated depending on the specific tissue process being implemented. Multiple tissue types can be combined in multiple bioreactors. The activities of multiple bioreactors can be coordinated and controlled in an automated manner by a supervisor controller. The supervisor controller can receive user input at the start of the process, and can manage the process thereafter, alerting the user if user actions are required.SELECTED DRAWING: None

A tissue analysis system and method process hyperspectral images to classify tissue types within the images. A presence or level of tissue oxygenation is estimated for each tissue type as well as a PPG perfusion. The estimated tissue oxygenation and PPG perfusion information are combined to provide a tissue status for each tissue type. The system and method uses remote PPG sensing.

The invention belongs to the technical field of molecular markers, and particularly relates to an anti-disease gene of corn grain rot, a molecular marker and application. The nucleotide sequence of the gene ZmRLK2 is as shown in SEQ ID NO. 1. According to the invention, the corn grain rot resistance candidate gene ZmRLK2 is obtained by carrying out correlation analysis on a whole genome of the corn grain rot and screening SNP marker sites. Sequence comparison of the gene finds that two allele types exist in a natural material, and molecular markers are developed for variation of the allele. The method is not influenced by tissue types, development stages, environmental factors and the like, the molecular marker technology is simple, rapid and easy to automation, the inoculation and identification cost is greatly saved, and the method can be used for corn molecular breeding.

The invention discloses an anti-cancer drug synergistic effect prediction method based on deep learning, and the method can employ a network model which comprises a data input module, a drug sub-network module, a CrossNet module and a collaborative score prediction module. The data input is used for inputting drug pair-cell line characteristic data and cell line types, tissue types and cancer subtype data under the combined action of the two drugs, and the drug pair-cell line characteristic data comprises drug chemical characteristic data of the two drugs and cancer cell line gene expression data under the combined action of the two drugs; the input of the medicine sub-network module and the input of the CrossNet module are data output by the data input module; and the input of the collaborative score prediction module is the output of the drug sub-network module and the output of the CrossNet module. The method provided by the invention can more accurately and reliably predict whether the anticancer drugs have a synergistic effect.

The present invention relates to nanostructured conjugates, more specifically to nanostructured fusion proteins suitable for the selective delivery of their conjugated therapeutic agents to specific cell and tissue types. It also relates to nanoparticles comprising such nanostructured proteins and the therapeutic uses thereof.

The invention relates to the technical field of biomedical materials, and discloses a thrombectomy stent support material and a preparation process thereof, the thrombectomy stent support material comprises the following components by mass: 60-70% of a polysebacic diglyceride prepolymer; 25-35% of a dynamic covalent bond cross-linking agent; 5%-10% of carboxylated nano-diamond; 3 to 5 percent of temperature-sensitive and photo-thermal dual-response microspheres; the dynamic covalent bond cross-linking agent is composed of 4-formyl phenylboronic acid and hexamethylenediamine according to a molar ratio of 1: (1-3); the microsphere is prepared from poly (N-isopropylacrylamide), a gold nanorod and a recombinant tissue type plasminogen activating agent, and by combining a low-temperature 3D printing technology, the material can form a complex bionic structure, the mechanical strength is dynamically adjusted along with the physiological environment after implantation, and the material is safely degraded after functions are completed. The material is suitable for the scenes of cardiovascular and cerebrovascular stents, bone repair instruments and the like, has treatment accuracy, manufacturing suitability and clinical safety, and solves the problems of non-degradability, sudden drug release, mechanical mismatch and the like of traditional implants.

The invention discloses a flexible puncture needle motion trail control prediction system and method. The system comprises a mechanical arm, a needle feeding mechanism, a flexible puncture needle, an ultrasonic probe, a signal demodulation instrument and an upper computer. Wherein the mechanical arm is used for adjusting the posture of a needle feeding point; the needle inserting mechanism is used for adjusting the puncture movement radius and needle inserting and rotating operations; the head rigidity of the flexible puncture needle is adjustable, the bending radius of the flexible puncture needle is in direct proportion to the extension length, and an optical fiber sensor is arranged to collect the body posture and cutting force of the needle body; the ultrasonic probe is used for collecting tissue information; the signal demodulator is responsible for acquiring and processing optical fiber and ultrasonic signals; the upper computer comprehensively processes operation area information such as the position and posture of the puncture needle, the bending radius, the needle tip cutting force and the contact tissue type in the puncture process, a neural network algorithm is adopted for predicting and visually displaying the puncture track, and obstacle avoidance and precise puncture are achieved by adjusting the puncture track of the puncture needle. The puncture track can be effectively adjusted and visually predicted, the puncture time is shortened, and the puncture quality is improved.

The invention provides an ultrasonic cutter parameter optimization method for frozen tissue slicing, and belongs to the technical field of medical instruments. The ultrasonic cutter parameter optimization method for the frozen tissue section comprises the following steps: collecting vibration frequency, amplitude, feeding speed, load power and cut tissue type in a frozen tissue cutting process of an ultrasonic cutter as an initial parameter group, and establishing a mapping relation between the initial parameter group and frozen tissue temperature change; calculating a contribution value of the initial parameter group, comparing the contribution value with a preset threshold value, determining the parameter group greater than the preset threshold value as a key parameter group, and determining the parameter group less than or equal to the preset threshold value as a non-key parameter group; by means of the ultrasonic knife, the problem that an existing ultrasonic knife cannot achieve accurate control over temperature changes in the tissue slicing process can be solved.

The invention provides a method for constructing a CT-MRI (Computed Tomography-Magnetic Resonance Imaging) personalized three-dimensional heart model based on multi-modal imaging, which comprises the following steps of: firstly, carrying out myocardial and infarction region segmentation on a CMR-LGE image, and carrying out myocardial and intramyocardial adipose tissue segmentation on a CE-CT image to generate a corresponding Label image; then, a high-precision three-dimensional volume mesh model of the corresponding ventricle is constructed based on the segmentation results of the two types of images; and then, mapping the corresponding Label image with the tissue type to a corresponding finite element model according to the coordinates of the grid, and obtaining a corresponding personalized ventricular model with the tissue type. An interpolation method based on a general ventricular coordinate system is adopted, data of an MR ventricular model with infarct tissue attributes is transferred to a CT ventricular model, and therefore a mixed CT-MRI ventricular model is constructed. And finally, storing the constructed CT-MRI ventricular model data with the integrated tissue attributes as a standard geometric and topological file format.

The invention provides a method for analyzing Alzheimer's disease based on a multi-task graph convolutional network (GCN) of an attention mechanism, and aims to improve the accuracy of disease diagnosis and clearly identify a brain region related to the Alzheimer's disease. Most existing Alzheimer disease diagnosis models only depend on medical image data and ignore clinical score information related to diseases, so that brain areas affected by the diseases cannot be effectively recognized; optimization is achieved through the following steps that firstly, segmentation based on tissue types is conducted on brain areas, and initial weights are randomly distributed to each area; thirdly, a multi-task attention unit is introduced, and cross interaction between medical images and clinical scores is promoted by capturing shared feature information between brain areas and between tasks; finally, a specific task layer is designed for each task, and accurate prediction of the Alzheimer's disease and related brain areas is carried out. Experimental results show that the accuracy of the method is superior to that of an existing method on an Alzheimer's disease neuroimaging proposal (ADNI) data set, and the brain area related to the disease can be clearly recognized.

The invention provides a data enhancement method and system based on a pathological full-slice image and a storage medium, and the method comprises the steps: segmenting an obtained pathological full-slice image WSI into a plurality of image blocks, extracting a WSI representation from the WSI, and extracting an image block representation from each image block; obtaining condition input of a pre-trained first condition diffusion model based on the extracted WSI representation, inputting the noise image into the pre-trained first condition diffusion model, and outputting to obtain predicted noise WSI representation and predicted noise tissue type distribution; determining a tissue type contained in the predicted noise tissue type distribution, obtaining condition input of a pre-trained second condition diffusion model based on a category embedding vector corresponding to the determined tissue type and predicted noise WSI representation, and respectively inputting each image block representation into the pre-trained second condition diffusion model, and outputting to obtain a corresponding prediction noise image block representation. The survival prediction accuracy can be improved under the condition that the WSI number of the patient is insufficient.

The invention provides a cancer survival prediction method based on a pathological image, and relates to the technical field of artificial intelligence and medical image analysis. The method comprises the following steps: firstly, dividing an acquired pathological image to obtain a plurality of image blocks; and obtaining the feature vector of each image block and identifying the tissue type to which each image block belongs. Calculating spatial proximity, feature similarity and tissue type compatibility between the image blocks according to the center coordinates of the image blocks, the feature vectors and the tissue type to which each image block belongs; and based on a calculation result, constructing a dynamic heterogeneous graph, and performing feature extraction to obtain a multi-scale feature graph of the dynamic heterogeneous graph. And performing multi-prototype learning on the basis of the belonging organization type of the image block and a cross-category attention mechanism to generate multiple prototypes. And finally, the multi-scale feature map and the multiple prototypes are aggregated to obtain final image representation, and cancer survival prediction is performed based on the final image representation, so that the prediction accuracy of the cancer survival state is improved.

According to the invention there is provided a method of estimating an amount of at least one biomarker for a subject, based on an image of the subject imaged by a medical imaging method, the method comprising: processing the image to identify at least one tissue type within a region of interest of the image and determining an amount of each identified tissue type; associating each identified tissue type with a measure of biomarker production; calculating a total amount of biomarker production for the identified tissue types.

The invention provides a special endoscope thyroid nerve monitoring bipolar electrode, and relates to the technical field of medical apparatuses and instruments. A radio frequency output electrode is arranged in a functional electrode unit and used for applying high-frequency current to carry out tissue cutting and blood coagulation, and a nerve stimulation electrode is used for applying low-frequency current to carry out nerve stimulation and monitoring. The two electrodes work cooperatively, the impedance detection electrode can collect a voltage signal after the low-frequency current passes through the tissue, and the impedance modeling unit converts the collected voltage signal to obtain an impedance value. The position positioning module determines the position of a working point between the two electrode ends. In the operation process, the initial impedance value of the working point at the current position serves as the type impedance to judge the tissue type of the working point, tissue cutting is conducted along with a radio frequency output electrode, the impedance value change of the working point at the current position is monitored, and the tissue cutting result is judged in combination with the previously determined tissue type. And the safety and the overall efficiency of the surgical operation are improved.

A tracking system and a method for 3D tracking of a medical device, equipped of at least one ultrasound sensor, inside an anatomical region of a subject having at least one layer of a first tissue type at least partially surrounding a volume having at least one second tissue type, wherein the properties of ultrasounds propagation in the first and second tissue type are different.

The present invention relates to nanostructured conjugates, more specifically to nanostructured fusion proteins suitable for the selective delivery of their conjugated therapeutic agents to specific cell and tissue types. It also relates to nanoparticles comprising such nanostructured proteins and the therapeutic uses thereof.

The invention provides a universal method for preparing animal tissue mononuclear suspension. A universal solution is provided for preparation of animal tissue single cell nucleuses, the method is suitable for different tissue types of various species, and the problem of unstable extraction caused by tissue differences in a traditional method is solved. Specifically, through multi-stage grinding and three dynamic quality inspection feedback mechanisms, hardness and cracking difficulty of different tissues are adapted, redundant operation is reduced to avoid damage of nuclear membranes caused by excessive grinding or excessive purification, nuclear membrane integrity and RNA stability are ensured, and extracted cell nucleuses are good in state and relatively high in integrity and can meet the requirement of single cell sequencing.

Devices, systems and methods are provided for treating a variety of conditions with PEF energy. PEF energy is typically characterized as high voltage pulsed energy that is configured to be delivered in one or more doses. The one or more doses create a lesion in the tissue. Lesion dimensions vary depending on the parameters of the PEF waveform and the dose, among other influences such as tissue type, etc. Therefore, different waveforms of PEF energy and different doses of PEF energy provide different results, particularly different lesion sizes and types. Systems, devices, and methods are provided that allow a physician or user to control the PEF energy delivered to provide a desired result and / or provide predictive information with which the user can utilize to generate a desired result.

Embodiments herein disclosed relate to computer-implemented method and corresponding systems for providing a trained function (TFA) which function is configured to provide a classification result (CR) for a whole slide image (WSI) depicting tissue according to a plurality of tissue types (HE, CA, OT). Methods and systems are based on generating (S30) intermediate classification results (ICR) by inputting the set (WSI_T) into a first trained function (TF1), selecting (S40) classification results (SCR) from the intermediate classification results (ICR), inputting (S50) image data extracted from the set (WSI_T) corresponding to the selected classification results (SCR) into a second trained function (TF2) so as generate predictive classification results, and adapting (S60) the second trained function (TF2) based on a comparison of the selected classification results (SCR) with the predictive classification results.

The invention is a system and method for establishing an electronic-enhanced methodology for wound care assessment and treatment. Initially, a wound image is received from a mobile device, and the image is analyzed via artificial intelligence to standardize the wound area and tissue type. The wound area and tissue type analytics are then combined with point-of-care wound assessment information manually entered in a proprietary platform. The technological and point-of-care data points are then electronically combined to trigger a standardized dressing guideline with embedded evidence-based standards of care for wound treatment. The electronically generated dressing guideline is then modified over time using machine learning to identify patient and wound characteristics in combination with specific standardized dressing guidelines that result in optimal wound healing.

The embodiment of the invention provides a method and equipment for dynamically adjusting a motion control mode based on an intraoperative visual field environment, a medium and a program product, and relates to the field of intelligent medical treatment. The method comprises the following steps: acquiring an intraoperative view image and a preset basic force threshold value according to the tissue type of a to-be-operated site; calculating an intraoperative tissue real-time deformation rate based on the intraoperative view image, and calculating a real-time force safety threshold according to a preset basic force threshold and the tissue real-time deformation rate; receiving real-time contact force of the instrument and the tissue, comparing the real-time contact force with a real-time force safety threshold value, and if the real-time contact force is smaller than or equal to the real-time force safety threshold value, outputting an instruction of damping force in direct proportion to the real-time contact force; if the real-time contact force is larger than the real-time force safety threshold value, an emergency braking program instruction is output. According to the method, factors, such as tissue deformation and illumination change, which cause instability of the surgical field of view in the surgical environment are introduced, so that the environmental adaptability and dynamic adjustment of the field of view mechanism in the robot-assisted surgical operation process are improved.

The present inventions provide covalently surface modified adeno-associated viruses can comprise genes of interest (GOIs) and advantageously can be targeted to certain cell and tissue types for preventative and therapeutic purposes. The covalently surface modified adeno-associated viruses also can be mutated to detarget certain tissues and organs, such as the liver. The present inventions further provide systems and methods for engineering adeno-associated virus (AAV) to create covalently surface modified adeno-associated viruses, and methods of purifying such covalently surface modified adeno-associated viruses. The inventions further include covalently surface modified adeno-associated viruses and preparations and products comprising such covalently surface modified adeno-associated viruses.

The invention provides a special steel forging process microstructure intelligent prediction method based on machine learning, and relates to the technical field of microstructures. Comprising the steps of collecting process parameter data, microstructure data and dislocation motion data. And obtaining an influence coefficient according to the influence degree of the dislocation motion data on the dynamic recrystallization behavior, processing the microstructure data according to the influence coefficient to obtain optimized dynamic recrystallization behavior data, and adjusting the process parameter data according to the optimized dynamic recrystallization behavior data to obtain structure type data. And for the optimized generalized regression neural network model, inputting tissue type data, and outputting to obtain a microstructure prediction result. According to the method, the regression neural network model is optimized by using the fruit fly optimization algorithm, the microstructure prediction result can be accurately output, the influence of the dynamic recrystallization behavior on microstructure formation can be predicted, better microstructure and performance can be obtained, and the production risk is reduced for special steel forging production.

The invention provides a puncture visualization method based on ultrasonic imaging, and the method comprises the steps: obtaining a low-frequency ultrasonic image and a real-time ultrasonic image under the ultrasonic frequency of a current stage, and the ultrasonic frequency of each stage comprises the low-frequency ultrasonic frequency and the real-time ultrasonic frequency of the stage; the low-frequency ultrasonic image and the real-time ultrasonic image at the same stage have the same size and pixels are in one-to-one correspondence; determining the position of a needle point based on the real-time ultrasonic image of the current stage, and fusing the real-time ultrasonic image of the current stage with the low-frequency ultrasonic image to obtain a fused ultrasonic image of the current stage; determining the tissue type on the current puncture path based on the fusion ultrasonic image at the current stage; and determining the ultrasonic frequency of the next stage based on the position of the needle tip and the tissue type on the current puncture path. By means of the mode, intelligent control over the puncture process is achieved, the operation burden of doctors is relieved, and the doctors can complete puncture operation more quickly and more accurately.

The invention discloses a low-dose CT (Computed Tomography) imaging method and equipment based on semantic priori knowledge guidance. In order to solve the problems of excessive smoothness of images, detail loss and the like in low-dose CT imaging, semantic prior information is extracted through a pre-trained visual model by utilizing significant differences of different anatomical structures (such as bones, soft tissues, gas regions and the like) on noise levels, so that different tissue types are effectively distinguished, and noise modes of specific regions are captured. Meanwhile, a semantic perception module is provided, detail features concerned by the noise reduction network and high-level anatomical semantic features captured by the semantic network are aligned and fused, and it is ensured that the anatomical structure is perceived more accurately in the denoising process. Further, a segmentation network is introduced behind the noise reduction network, and a multi-task learning strategy is adopted to realize joint optimization of noise reduction and semantic segmentation. According to the design, the denoising performance of the image is improved, key anatomical details are effectively reserved, and clearer image support is provided for clinical diagnosis.

The invention belongs to the technical field of medical image processing, and particularly relates to a multi-modal pathological data conjoint analysis method based on deep learning. According to the method, a ResNet50 network is utilized, a pathological section image, a fluorescence image and a lasing image are respectively collected for deep learning, pathological tissues are divided into five tissue types, and super-multi-classification is realized; and through a weight voting system, carrying out weight comprehensive voting on the three-mode 5 classification results to obtain voting results with different colors representing different organization types. According to the multi-modal data conjoint analysis method for the pathological tissue, high-dimensional information orthogonal to original two-dimensional pathological section imaging is introduced, so that a relatively rough data set can be adopted to realize optimization judgment, and the judgment robustness is improved. And pathologists can be efficiently assisted to diagnose and analyze pathological sections in real time.