223 results about "Tissue type" patented technology

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 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 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 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.

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 relates to the technical field of wound image intelligent identification, and discloses a wound type detection method based on image segmentation and a storage medium. The method comprises the following steps: synchronously acquiring a wound time sequence image and multivariate physiological data, generating a fusion characteristic spectrum, and adaptively generating a preliminary region mask according to the fusion characteristic spectrum through a dynamic path selection network; then, mask cutting is utilized to obtain a high-attention area image, and the high-attention area image is input into the cascade segmentation network after enhancement processing; according to the network, the first stage outputs a refined wound contour, and then the second stage carries out tissue type classification segmentation on the interior under the constraint of the contour. Organizational composition and spatial distribution features are extracted according to the segmentation result, and a predefined knowledge base is matched to output a type result. According to the method, by fusing the multi-modal data and the cascade segmentation architecture, the precision and robustness of wound segmentation under complex conditions are improved, so that more accurate wound type automatic judgment is realized.

The invention discloses a postoperative pathology assessment method, survival prediction method and system for esophageal cell carcinoma, and the method comprises the steps: obtaining and preprocessing WSI image data, obtaining cut small images of the WSI image data in a low-magnification view angle, a medium-magnification view angle and a high-magnification view angle, and sequentially inputting the cut small images into a selective spatial state model through feature extraction; updating the diagnosis states through a long and short term memory network, and adjusting the contribution degree of each diagnosis state through a selective memory weight; fusing all diagnosis states, and outputting the probability of tissue types through a full connection layer and a Softmax function; calculating the percentage of the residual living tumor, and obtaining a pathological assessment result according to the percentage of the residual living tumor. According to the method, the diagnosis process of doctors can be simulated, the workload of pathologists can be reduced, the evaluation consistency and universality are improved, and the survival rate can be predicted and clinical treatment decision making can be assisted through the constructed survival prediction model.

The invention provides regulatory elements, as well as vectors containing the same that may be used to stimulate transcription of a gene of interest in certain tissue types. The transcription regulatory elements described herein may be operably linked to a transgene, such as acid alpha-glucosidase (GAA), so as to promote expression of the GAA transgene in a cell, such as a muscle cell, liver cell, or neuron. The transcription regulatory elements described herein may be operably linked to a therapeutic transgene and used for the treatment of various disorders, such as lysosomal storage diseases, and particularly Pompe disease.

The invention discloses a colorectal cancer pathological image analysis platform based on a CTE-MI L deep learning architecture, and relates to the technical field of pathological image recognition. Comprising the following steps: collecting and preprocessing a full-slice image of a known pathological region through a foreground training image processing module, and generating a foreground extraction training data set; and constructing a Ti ssue-Seg segmentation network module, training a model based on the training data set, and outputting a foreground mask of the pathological tissue. And the feature unit screening module is used for gridding the foreground mask, calculating the feature expression score of the patch image and screening out the high-response patch image. And constructing a CFE feature extraction network module, carrying out specific adjustment on model parameters based on high-response patch, and carrying out weak supervision training. The attention mechanism optimization module introduces a TFAM attention mechanism, performs feature fusion according to attention scores, and improves the accuracy of pathological tissue type recognition.

A wound assessment method is provided. The wound assessment method includes receiving an image that includes an L-shaped color calibration card and a wound; performing an image preprocessing on the image, based on the L-shaped color calibration card, to obtain an adjusted image, where the image preprocessing includes a distortion correction and a color calibration; obtaining a wound image excluding the L-shaped color calibration card, based on the adjusted image, where the wound image includes a plurality of regions; and classifying each of the plurality of regions of the wound image into one of wound tissue types, to obtain a wound tissue type distribution of the wound, where the wound tissue types include a granulation tissue, a slough tissue, and an eschar tissue.

The invention discloses a pig SNP chip construction method based on gene regulatory network characteristics, and relates to the technical field of animal genetic breeding, and the pig SNP chip construction method comprises the following steps: constructing a pig tissue specificity multi-level gene regulatory network related to target traits and tissue types; performing function annotation and network comprehensive feature extraction on the whole genome SNP based on the gene regulation network; and carrying out score sorting and screening on the extracted network comprehensive characteristics, screening SNP variation sites with regulation function potential, constructing a pig SNP site set, and preparing the SNP chip. By introducing tissue / character related gene regulatory network information, functional priority ranking and screening are performed on SNP loci in a whole genome range, so that the character interpretation ability and breeding value estimation accuracy of SNP in a chip are remarkably improved.

Example systems and methods for generating virtual immunofluorescence stains for tissue samples are provided. A computing device receives a slide image of a target tissue sample of a particular tissue type. The computing device selects a first trained machine learning (ML) model to generate virtual immunofluorescence (IF) stains of a first type for the particular tissue type based on a user input. The first trained ML model is trained at least based on a first set of stain images of a plurality of training tissue samples with stains of the first type. The computing device generate a virtually stained image of the target tissue sample with the virtual IF stains of the first type using the first trained ML model. The computing device displays the virtually stained image of the target tissue sample with the virtual IF stains of the first type.

A method is provided for precision dosing of electrical stimulation of the brain. The method includes determining a location of each voxel of a plurality of voxels in a reference frame of an electro-stimulation device including a plurality of electrodes positioned on a head of a subject. The method also includes obtaining measurements that indicate a tissue type at each voxel inside the head of the subject based on an imaging device. The method also includes determining, with a processor, a value of one or more parameters of the electro-stimulation device based on the tissue type measurements at each voxel such that the electro-stimulation device is configured to generate a value of one or more parameters of an electric field at each voxel inside the head of the subject to improve the treatment outcome of the subject.

Systems and methods for ablating target tissue, measuring parameters during ablation such as temperature of the target tissue, and predicting volume of the ablation based on the measured parameters are provided. The system may include a switching antenna for both heating of target tissue and radiometry to monitor the temperature of the heated tissue, and a processor for calculating the temperature of the target tissue, segmenting medical images, and predicting volume of the ablation based on radiometric signals indicative of the target tissue temperature. The predicted ablation volume may be adapted to account for tissue boundaries and anatomical structures. The processor further may determine properties of the target tissue such as tissue type.

Improved allografts having controlled and consistent properties and useful for soft tissue repair, including breast reconstruction, and other surgical procedures, are disclosed. In one embodiment, an improved acellular dermal matrix (ADM) is produced from a skin sample, where the ADM consists essentially of a single dermal tissue type (e.g., papillary or reticular) and comprises a collagen matrix having substantially uniform density and porosity. In another embodiment, two ADMs are produced from the same dermal tissue type (e.g., papillary or reticular) of the same skin sample, each of which consists essentially of reticular dermis, and comprises a collagen matrix having substantially uniform density and porosity. All such improved ADMs are suitable for use in breast reconstruction and other tissue repair and modification surgery procedures.

The invention belongs to the technical field of medical equipment, and particularly discloses plasma operation equipment and a using method thereof, and the plasma operation equipment comprises a host used for generating plasma; the control machine is connected with the host and is used for controlling the host to operate; the resectoscope equipment comprises an inner sheath, an outer sheath, an electrode and a cleaning device, the inner sheath is in a slender rod shape, and a limiting block is connected to the inner sheath in a sliding and sleeving mode. Compared with traditional plasma operation equipment, the mode that the traditional plasma operation equipment depends on a pedal to control cutting parameters is abandoned, and the cutting time, the cutting interval and the cutting current parameters of the main machine are set, regulated and controlled innovatively through the independent control machine; therefore, the medical staff can accurately input parameters on the control machine before the operation according to the operation site, the tissue type and the individual difference of patients, the pedal does not need to be treaded frequently during the operation, the operation steps are effectively reduced, and the probability of operation errors is reduced.