52 results about "Histology type" patented technology

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.

The application relates to the technical field of medical data processing, and discloses a macroscopic cross-section database construction method, a dose determination method, a device and equipment, which comprises the following steps: setting multiple boron concentration values for the tissue types corresponding to a human body model; selecting corresponding neutron source parameters for the tissue types and the multiple boron concentration values, wherein the neutron source parameters comprise energy range values; dividing the energy range values into multiple discrete neutron energy groups; performing particle transport simulation based on the selected neutron source parameters, jumping between different neutron energy groups in the simulation process, and obtaining neutron reaction rates and neutron fluxes of the tissue types under the multiple boron concentration values and the multiple neutron energy groups; and obtaining neutron macroscopic cross-section values of the tissue types under the multiple boron concentration values and the multiple neutron energy groups based on the neutron reaction rates and the neutron fluxes, and constructing a multi-group macroscopic cross-section database. The application can improve the calculation efficiency of the radiation dose and improve the adaptability to individual metabolic differences during dose calculation.

Techniques and systems for using spectroscopy, and / or image-based sensing with an intravascular blood pump are provided. The techniques generally include capturing a spectral response to light of a measured tissue and / or blood. Based on the specific needs, the method may include, e.g., determining a type of the measured tissue based on the spectral response, determining if the type of the measured tissue matches a target tissue type, determining a health of the measured tissue based on the spectral response, determining a treatment plan based on the health of the measured tissue, determining a type and / or quantity of components and / or characteristics in the blood, and / or determining a degree of heart recovery based on the type and / or quantity of the components and / or characteristics in the blood.

The present application relates to the technical field of vascular interventional treatment instruments, and discloses a system and method for the selective laser ablation of restenosis after the treatment of Budd-Chiari syndrome, comprising a controller, a pulse emission module and a reference element built into a catheter, the system is based on the reference element for periodic self-calibration, collects and processes the associated photoacoustic signals to identify the tissue type, and then realizes the selective ablation of the diseased tissue and the boundary self-stop when the healthy tissue is touched. The present application converts the traditional open-loop energy delivery into a closed-loop self-control process with real-time information interaction capability, so that the ablation operation end can in-situ perceive the tissue boundary and realize autonomous stop, thereby avoiding the problem of damaging the healthy tissue due to uncontrollable energy application, improving the safety of the operation, and having the advantages of high precision, few complications, repeatable treatment and the like.

The invention relates to a laser therapeutic instrument, a laser therapeutic instrument operation control device and a storage medium. The laser therapeutic instrument comprises a processor and a memory. Wherein the memory is used for storing computer programs; the processor executes the following steps when running a computer program: acquiring static characteristics and dynamic characteristics of a target object; based on the static features and the dynamic features, combining a tissue characteristic map and a prediction model to obtain a change trend prediction result of the target object; the tissue characteristic map comprises static characteristic reference values of different tissue types / skin types and a historical change rule curve of dynamic characteristics along with time; the prediction model is obtained based on constitutive equation training of the target object; on the basis of the variation trend prediction result, working parameters during operation on each region of the target object are determined in combination with an energy gradient distribution algorithm; and on the basis of the working parameters, real-time parameters of corresponding components in the laser therapeutic instrument are adjusted, and treatment of the target object is completed. Therefore, the accuracy and the stability of treatment are improved.

The invention relates to the technical field of vascular interventional therapy instruments, and discloses an excimer laser ablation system and method for restenosis after Budd-Chiari syndrome interventional therapy, the excimer laser ablation system comprises a controller, a pulse transmitting module and a reference element arranged in a catheter, and the system performs periodic self-calibration based on the reference element; according to the method, associated photoacoustic signals are collected and processed to identify tissue types, selective ablation of lesion tissues and boundary self-stop when healthy tissues are touched are further achieved, traditional open-loop energy delivery is converted into a closed-loop self-control process with real-time information interaction capacity, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. The ablation operation tail end can sense the tissue boundary in situ and achieve automatic stopping, so that the problem that healthy tissue is damaged due to uncontrollable energy application is solved, the safety of an operation is improved, and the advantages of being high in precision, few in complication, capable of achieving repeated treatment and the like are achieved.

In some aspects, a method, a system, or a non-transitory computer-readable storage medium are described for training one or more models to predict ulcerative colitis (UC) severity based on human-interpretable image features extracted from a whole-slide image, including acts of accessing a plurality of annotated whole-slide images associated with a plurality of UC patients, wherein each of the plurality of annotated whole-slide images includes at least one annotation describing a cell-type label or a tissue-type segmentation for a portion of the whole-slide image, extracting a plurality of human-interpretable image features based on cell-type labels and tissue-type segmentations associated with the plurality of annotated whole-slide images, training a statistical model based on the plurality of human-interpretable image features to predict the UC severity for a whole-slide image, and storing the trained model on at least one storage device.

The invention relates to an artificial intelligence-based abdominal cavity operation auxiliary control method, device and equipment and a medium. The method comprises the following steps: acquiring kinematics data of the intelligent surgical instrument and physical data in contact with tissues; geometric-physical fusion correction is carried out on the input pressure to obtain effective clamping pressure, and a contact quality index is calculated; extracting multi-dimensional electrical characteristics and obtaining tissue type labels through an AI tissue classifier; a target working state is generated through verification of the neural finite-state machine; and during plasma cutting, the residual life of the liquid film is predicted through LSTM, and a corresponding control signal is generated. By adopting the method, the accuracy, the safety and the stability of auxiliary control of the abdominal surgery can be improved, and reliable support is provided for surgery development.

The invention discloses a duodenum mucosa remodeling system based on pulse field ablation and a control method, and the system comprises a multi-modal sensing ablation catheter which is used for collecting multi-modal data related to duodenum ablation through a multi-modal sensor; the multi-sensing data fusion unit is used for extracting key features after preprocessing the multi-modal data, and fusing the key features according to a preset algorithm to obtain a multi-modal feature vector; the real-time tissue feature analysis module is used for recognizing the tissue type of the current ablation point location in real time according to the multi-modal feature vector and analyzing the dynamic change of tissue features; the self-adaptive parameter adjustment engine is used for determining corresponding ablation parameters according to the tissue type of the current ablation point location and the dynamic change of the tissue characteristics; and the motion compensation and prediction system is used for predicting the motion trail of the duodenum in a future preset time period. The problem that in the prior art, a duodenum mucosa remodeling system is poor in remodeling effect is solved.

A multispectral photonic imaging system and associated methods are disclosed for classifying wound tissue and generating customized therapeutic wound dressings. The system employs wavelength-specific optical sources and integrated image sensors to capture multispectral reflectance data from a wound, enabling computational classification of distinct tissue types such as granulation, slough, and necrosis. A three-dimensional (3D) digital model of the wound is constructed using spatial data from depth sensors. The classified 3D wound model facilitates automated design of patient-specific dressings, featuring heterogeneous regions tailored with therapeutic compounds like antimicrobial agents, analgesics, collagen scaffolds, and hydrogel matrices precisely aligned to corresponding tissue classifications. Additional imaging modalities, including thermal and ultrasound sensors, can be integrated to enhance classification accuracy and inform targeted therapeutic placement, providing a comprehensive and personalized approach to wound management for medical and veterinary applications.

A laser or ultrasonic instrument is used to remove tissue during a surgery, such as to form one or more pilot holes in a vertebra or a window in bone. Where a laser is used, interrogative laser pulses can be used to obtain information, such as detecting depth or tissue type.

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

The invention discloses a prior tissue prototype guided weak supervision digital pathological image hybrid enhancement method, which comprises the following steps of: firstly, acquiring a digital pathological image of an interested pathological region, and pre-processing the digital pathological image; secondly, feature extraction is carried out to obtain image block-level features, and the image block-level features of one digital pathological image form slice-level features; then, a priori tissue type set of the digital pathological image is defined, slice-level features are clustered, each cluster corresponds to one tissue type, and the cluster center of each cluster is calculated to serve as a priori tissue prototype of the tissue type; and finally, calculating the distance from the intra-cluster image block-level features to the priori tissue prototype based on cosine similarity, performing intra-cluster hybrid enhancement, and performing inter-cluster hybrid enhancement to obtain a digital pathological image generated by hybrid enhancement. According to the method, the internal structural features of the tissue morphology are effectively mined, the uncertainty and noise interference in the pseudo digital pathological image generation process are remarkably reduced, and effective data enhancement is performed.

Devices and methods are disclosed for characterizing tissue samples using radiofrequency (RF) analysis. RF reflection or transmission spectra are analyzed to determine dielectric and conductivity properties that correlate with tissue type. In some embodiments, one or more probe tips contact the tissue simultaneously or sequentially at different locations on the tissue samples, enabling spatially resolved RF spectral measurements across the sample. The system may include a positioner for translating probe tips relative to the sample, a carrier for holding a biopsy stylet or tissue substrate, and an analyzer for classifying tissue as healthy, benign, or malignant. The approach enables rapid, non-destructive discrimination of tissue types for diagnostic, therapeutic, or research applications.

For various purposes, a nucleosome signal pattern using fragmentation at a location around a target site is provided. For example, a nucleosome signal pattern may be used to determine methylation levels of a target site (e.g., a CpG site). The signal may be associated with a nucleosome pattern of a cfDNA molecule within a genomic region that is differentially methylated in a target tissue type by having different methylation levels (or levels, e.g., as a pattern) relative to one or more other tissue types (e.g., blood cells). The nucleosome signal pattern can be compared to one or more reference patterns with known methylation levels. Another exemplary method may determine a lesion level in a subject. Another example may determine a proportional concentration of DNA for a particular tissue type.

The application discloses a diving decompression sickness dynamic prediction and risk assessment system of multi-tissue parallel computation, relates to the fields of diving medicine, high-pressure medicine and human physiology modeling, and comprises a tissue unit planning module, a central blood circulation and coupling module and a model calculation and risk assessment module. The system divides a human body into a plurality of core physiological tissue units, and plans each tissue unit as a hybrid model composed of three sub-units of "fast-middle-slow". Then, the system simulates a human circulatory system by defining cardiac output, arterial blood inert gas partial pressure and dynamically distributing blood flow according to the tissue type, so as to construct a simulation model. Finally, according to the environmental pressure-time profile corresponding to a diving plan and the breathing gas composition, the simulation model is used to parallelly calculate the inert gas tension of all tissue units and sub-units, so as to be used for dynamic prediction and risk assessment of decompression sickness. The application provides a higher safety margin for saturation diving and repeated diving.

Methods, devices and systems for analyzing cfDNA fragments in a biological sample. For example, techniques may use a total amount of cfDNA fragments terminally located at three or more different positions around different types of CpG sites to detect cancer. Another technique may use the amount of cfDNA fragments located in a set of open chromatin regions (OCRs) to detect cancer. Another technique may use abundance of long cfDNA fragments on a gene to estimate the expression level of the gene. Another technique may use abundance of long cfDNA fragments to identify open chromatin regions. Another technique may use abundance of long cfDNA fragments located in a set of OCRs to determine a proportional concentration of free DNA of a first tissue type. Another technique may use abundance of long cfDNA fragments to classify nuclease activity.

The current disclosure provides methods and systems to reduce an amount of noise in image data. In one example, a method for creating synthetic computed tomography (CT) images for training a model to reduce an amount of noise in acquired CT images is proposed, comprising performing a tissue segmentation of reference images of an anatomical region of a subject to determine a set of different tissue types of the reference images; and generating synthetic CT images of the reference images by assigning CT image values of the synthetic CT images based on the different tissue types.

The present disclosure relates to electric surgical instruments and methods of identifying tissue types therewith, and provides a surgical instrument comprising: an end effector configured to clamp tissue; an electric motor configured to actuate the end effector; and a controller in communication with the electric motor and configured to determine a stress and a strain of the tissue, identify a tissue type of the tissue based on the determined stress and strain of the tissue, and set an operating parameter of the surgical instrument based on the identified tissue type of the tissue.

The disclosure is directed to an electrosurgical generator comprising: an electromagnetic signal supply unit for generating electromagnetic, EM, energy to be conveyed along each of a plurality of channels. Each of the plurality of channels is arranged to convey electromagnetic energy at a different frequency. The electrosurgical generator further comprises an output port selectively connectable to each of the plurality of channels. The output port is connectable to an electrosurgical instrument for delivering the electromagnetic energy to biological tissue. The electrosurgical generator further comprises a tissue identification unit comprising a controller configured to: detect a signal indicative of a load impedance at the output port; determine a set of complex impedance values from the detected signals; identify a tissue type from the set of complex impedance values; and generate an output for communicating the identified tissue type to a user.

The utility model relates to ultrasonic surgical equipment, the ultrasonic surgical equipment comprises a host and a cutter connected to the host, the cutter is provided with an impedance identification contact, an impedance identification module, a data processing module and a main control module are arranged in the host, the impedance identification module outputs an excitation signal to a measured target through the impedance identification contact, and the data processing module outputs the excitation signal to the measured target through the main control module. The data processing module is used for determining a tissue type according to the received impedance data and outputting a type identification electric signal corresponding to the tissue type, and the main control module is used for adjusting ultrasonic energy output to the cutter according to the type identification electric signal. According to the ultrasonic operation equipment provided by the embodiment of the invention, the tissue type corresponding to the measured target is acquired in real time, the output is adjusted, and the adjustment accuracy of the output energy of the ultrasonic operation equipment is improved.

The invention relates to a method for judging the impedance change trend through a high-frequency electrotome, and belongs to the field of medical instruments. The method comprises the following steps: detecting the initial impedance of the biological tissue at the end part of the electrotome and judging the tissue type based on the initial impedance; in the energy output process, tissue impedance is detected at the set frequency, and the current impedance change slope is calculated; judging the overall trend of the current impedance change, and selecting a corresponding target slope value based on the tissue type and the trend; comparing the current impedance change slope with a target slope value to obtain a slope deviation; adjusting the output power of the electrotome based on the slope deviation; and repeating the steps until the blood coagulation of the tissue is completed. The tissue impedance change slope is monitored in real time, the output energy of the electrotome is dynamically adjusted, the electrotome can adapt to the characteristics of different tissues, excessive heating or insufficient energy is avoided, the consistency of the coagulation effect is improved, and tissue thermal damage and surgical risks are reduced.

A tissue analysis system and method process images to derive a remote PPG perfusion map from PPG amplitude levels obtained from the images as well as a PPG delay map from PPG relative delays between the PPG signals for each image region (pixel) with respect to a reference signal. The images are segmented into one or more tissue regions based on the PPG delay map (or segmentation information identifying tissue regions derived from the PPG delay map are received as input). The different tissue regions have distinct PPG delay characteristics. A level of perfusion can then be determined separately for each tissue region. Thus PPG delay information is used to enable different tissue types to be identified.

A method of analyzing a tissue is provided that includes: administering a fluorescent agent to a tissue; producing a first excitation light that is configured to produce a fluorescence emission from the fluorescent agent administered to the tissue; producing a second excitation light that is configured to produce an autofluorescence emission from a biomolecule of interest present within the tissue; using a photodetector to detect the fluorescence emissions and produce first signals representative thereof, and produce second signals representative of the autofluorescence emission; producing an image using the first and second signals. The image includes a first, second and third portions representative of tissue types present within the tissue; and analyzing the tissue to identify diseased tissue and to distinguish it from the first and second tissue types.

The invention provides a puncture tissue characteristic recognition system based on multi-modal fusion. The puncture tissue characteristic recognition system comprises a model training module. The module is used for acquiring label-free force, vibration and impedance signals and sample data with a rough tissue type label; an encoder network is trained through cross-modal contrast learning, and modal-independent robust fusion feature representation is generated by taking different modal signal features at the same moment as positive samples and taking different modal signal features at different moments as negative samples; determining causal and non-causal feature subsets by using a causal discovery algorithm; a causal knowledge-based regularization loss function is constructed according to the causal knowledge-based regularization loss function, wherein the causal knowledge-based regularization loss function comprises two items which enable a causal attention vector to approach 1 in a causal feature subset dimension weight and approach 0 in a non-causal feature subset dimension weight; and finally, supervising and finely adjusting the encoder network by using related data to generate an organization recognition model. According to the method, a high-precision and high-generalization-ability organization recognition model can be constructed without massive accurate annotation data, and the problem that model training application is difficult due to the fact that high-quality annotation data are difficult to obtain is solved.

This invention relates to the field of medical device technology, specifically to an intelligent energy control method and system for an electrosurgical energy platform. The method includes: real-time acquisition of multi-dimensional electrophysiological parameters at the tissue-electrode contact point during electrosurgical procedures, including tissue impedance, voltage, current, and phase difference; inputting the acquired multi-dimensional electrophysiological parameters into a pre-trained tissue type recognition model to identify the currently contacting tissue type and determine its corresponding energy safety threshold; dynamically calculating the optimal energy output parameters based on the identified tissue type and surgical operation mode; and real-time monitoring of tissue state changes, automatically adjusting energy output or triggering a safety protection mechanism when the tissue state reaches a preset critical value. This invention achieves adaptive intelligent control of the electrosurgical energy platform through a combination of multi-parameter fusion sensing and artificial intelligence algorithms, improving surgical safety and accuracy, reducing tissue thermal damage, and lowering the risk of surgical complications.

The invention relates to the technical field of pathological image processing and medical artificial intelligence auxiliary diagnosis, and discloses a colorectal cancer prognosis analysis system and method fusing tissue and block characteristics, and the system comprises an image block cutting and standardization module which generates a standardized image block set under different multiplying power; the ResNet50 classifier module is used for carrying out organization classification on the image blocks; the tissue type specific feature extraction module is used for extracting multi-scale survival correlation features; the tissue spatial feature extraction and quantitative analysis module is used for reconstructing a multi-tissue type distribution diagram and calculating quantitative spatial features such as the number of lymphocytes and the intraperitoneal ratio after morphological repair; and the multi-modal feature fusion and survival prediction module is used for fusing the pathological deep features, the spatial segmentation features and the clinical data, and outputting an individualized prognosis risk score by using a survival model. According to the method, a macrostructure and microcosmic details are effectively integrated, and the accuracy and interpretability of colorectal cancer survival risk layering are improved.

The invention provides a deep learning-based scalp health condition detection method and system, and relates to the technical field of image data processing, and the method comprises the steps: obtaining dermatoscope images and optical coherence tomography data of different scalp regions; constructing a voxel model with epidermis morphological characteristics and subcutaneous tissue scattering characteristics according to optical coherence tomography data; performing multi-layer convolution and pooling operation on the voxel model by using a pre-trained convolutional neural network to extract a high-dimensional feature vector; mapping the high-dimensional feature vector to a low-dimensional space by using a manifold learning algorithm, and calculating a target distance; on the basis of the target distance, the scalp area where the tissue type is the scar lesion type is marked as the irreversible alopecia area, so that detection of the scalp health condition is completed, subcutaneous hair follicle structure disappearance and fibrosis scar tissue are accurately distinguished under the non-invasive condition, and the detection accuracy is improved. The accuracy of scar alopecia judgment and the reliability of irreversible alopecia area recognition are improved.

This invention discloses an endoscopic electronic staining method based on spectral unmixing. It separates the intrinsic tissue spectrum from background noise under LED white light using an improved spectral unmixing algorithm; constructs a multi-scale feature enhancement module to simultaneously enhance mucosal texture and vascular structure; designs a dynamic staining mapping function to achieve adaptive matching between staining parameters and tissue type; and finally, ensures the reliability of the staining effect through dual-index verification. This invention solves core problems such as tissue spectral overlap, low staining contrast, and poor adaptability under ordinary LED white light and CMOS imaging conditions. The method of this invention leverages algorithm optimization to unlock the potential of the imaging system, achieving software-defined staining effects.

The invention discloses a pathological microtome freezing device based on the AI technology, the pathological microtome freezing device comprises a shell, a plurality of glass slides for fixing tissues and a pretreatment mechanism for quickly freezing the tissues are arranged in the shell, the pretreatment mechanism comprises a wheel rotating disc fixedly connected with the top of the shell, and a mounting cavity is formed in the wheel rotating disc; the mounting cavity communicates with the feeding port and the discharging port, and the side, close to the discharging port, of the wheel rotating disc is higher than the side, close to the feeding port, of the wheel rotating disc. The mounting cavity is sequentially communicated with a flushing mechanism, a refrigerating mechanism and a recycling mechanism clockwise, the flushing mechanism is communicated with the feeding port, and the refrigerating mechanism is communicated with the discharging port. The method has the beneficial effects that the problems that in the traditional pathological tissue quick-freezing process, ice crystals destroy cell structures, freezing parameters are difficult to adapt to different tissue types and sizes, errors are large due to dependence on manual operation and the like are solved, intelligent control over the whole tissue freezing process is achieved, the freezing parameters are accurately matched, ice crystals are reduced, the slicing quality is guaranteed, and the working efficiency is improved. And the uncertainty caused by human intervention is reduced.