18963 results about "Medical physics" patented technology

Various wearable health monitoring devices are described herein. Some implementations include one or more emitter chamber covers injection molded out of a first material comprising transparent glass, one or more detector chamber covers injection molded out of a second material comprising transparent glass, and a light barrier construct injection molded out of a third material over the one or more emitter chamber covers and the one or more detector chamber covers. The third material may be opaque and can comprise glass and an optically absorbing pigment. Some implementations include a circuit board, emitter(s) mounted to the circuit board and arranged within the emitter chamber cover(s), and detector(s) mounted to the circuit board and arranged within the detector chamber cover(s). The circuit board, emitter(s), detector(s), emitter chamber cover(s), and detector chamber cover(s) can form part of a sensor assembly connected to a housing of the wearable health monitoring devices.

Various wearable health monitoring devices are described herein. Some implementations include a housing including a front side and a back side configured to face tissue of the wearer when the device is worn; and a sensor assembly positioned by the back side of the housing. The sensor assembly can include emitters and detectors positioned on a surface of a substrate and a frame positioned adjacent to the surface of the substrate and configured to inhibit transmission of optical radiation through the frame. The frame can comprise an emitter chamber housing the emitters; and one or more detector chambers housing the one or more detectors.

A method of self-testing an imaging system of a sample handling apparatus is provided. Systems and non-transitory computer readable mediums performing the method are also provided.

The invention discloses an operating room intelligent monitoring method and system based on monitoring video recognition, and relates to the technical field of medical safety supervision, and the method comprises the steps: collecting a video through an operating room camera array, and constructing an operation region panoramic sequence through a residual neural network and an optical flow field; a double-branch target detection network is adopted to extract the position of a medical worker, the body position of a patient and the characteristics of surgical instruments, and a surgical scene model is constructed; performing trajectory tracking based on skeleton key point extraction and Kalman filtering, and constructing a dynamic graph of the operation process; generating an operation process state report by using the time sequence diagram convolutional network and a multi-head attention mechanism; and comparing the operation specification library through a knowledge distillation algorithm, and carrying out grading recording on abnormal events. According to the invention, efficient abnormal event tracking and recording functions are realized, technical support is provided for operation quality control and safety management, and the overall performance of operating room intelligent monitoring is improved.

The teachings generally provide for a hand-held medical robotic system for use with a surgical tool, the system comprising an instrument comprising a hand-held portion, a tool support coupled to the hand-held portion, and an actuator assembly operatively interconnecting to move the tool support in a plurality of degrees of freedom to place the surgical tool and a localizer. The system additionally comprises a control system configured to determine a state of the surgical tool and determine a position and / or orientation of a reference coordinate system relative to a known coordinate system, activate a guided mode based on a first relationship criteria between the state of the surgical tool and the reference coordinate system, and deactivate the guided mode based on a second relationship criteria between the state of the surgical tool and the reference coordinate system, wherein the first relationship criteria and the second relationship criteria are different.

Improved novel 3D imaging and reconstruction methods for quantitative and accurate 3D imaging used in multiple industries, segments, and applications within each. Improvement includes methods and hardware configured to improve performance and lower radiation and improve accessibility. Using simplified system matrix and datasets of little scatter interference in tomographic image acquisition and reconstruction, in some cases, using time continuity and spatial continuity to generate parameter data for substances within VOI for a number of key applications in medical and non-medical fields.

A method, system, and apparatus for a head guide support system for use in measuring a transcranial magnetic stimulation (TMS) treatment coil on a patient's head are described herein. The coil positioning apparatus, also called a coil positioning device or a coil angle guide, may include a support structure supported by the patient's head; a first instrument configured to indicate a position angle for placement of the TMS coil; a second instrument configured to indicate a distance from the support structure for placement of the TMS coil; and / or a third instrument configured to indicate an orientation angle for rotation of the TMS coil.

A wearable system can include an electronic device configured to measure one or more physiological parameters of a patient and a wearable device configured to operably position the electronic device. The electronic device can have at least one light emitter and at least one light detector and be configured to measure at least a pulse oximetry measurement. The wearable device can have a main body with a cavity configured to position the electronic device, a securement portion connected to the main body and configured to secure the main body to the patient, and storage component configured to store patient identification data associated with the patient. When the electronic device and the wearable device are secured to one another, the patient identification data can be transferred from the wearable device to the electronic device.

The invention relates to the technical field of medical instruments, and discloses a laparoscope and digestive endoscope combined positioning measurement system and method.The method comprises the steps of positioning mark initialization, image feature analysis, instrument path planning, dynamic positioning calibration, operation integrity evaluation and the like. Spatial matching is realized by setting multiple types of positioning marks and utilizing optical reflection signals, noise reduction and feature extraction are performed on endoscope imaging data to generate a measurement area boundary, an operation path is planned in combination with anatomical path information, the tail end position of an instrument is calibrated in real time, and operation integrity is evaluated. The system comprises a positioning module, an analysis module, a correction module and an integration module which are respectively used for realizing the functions of positioning mark setting, image processing and path generation, offset correction and operation evaluation. According to the system, the operation positioning precision and the operation safety are improved, intellectualization and precision of minimally invasive surgery are achieved, and the system is suitable for minimally invasive treatment of digestive system diseases.

A system, apparatus and method directed to placing a medical instrument in a patient body, where the system includes the medical instrument having a first optical fiber, a console and an interconnect having a second optical fiber to receive incident light from the console and propagate the incident light to the medical instrument. The interconnect includes a predetermined bend along its length, such that logic of the console may determine a positioning and an orientation of the medical instrument relative to the predetermined bend. Additionally, the logic may generate a display of the medical instrument based on the reflected light signals and the determination of the positioning and the orientation of the medical instrument relative to the predetermined bend, where the display may be rendered as an overlay on an ultrasound image.

Disclosed herein is a system, apparatus and method directed to placing a medical instrument in a vasculature of a patient body, where the medical instrument includes an optical fiber having one or more core fibers. The system also includes a console having non-transitory computer-readable medium storing logic that, when executed, causes operations of providing an incident light signal to the optical fiber, receiving a reflected light signal of the incident light, processing the reflected light signals associated with the optical fiber and determining a location of a distal tip of the medical instrument within the patient body. The medical instrument may be steerable in one of various methods including having a predetermined curvature, a distal tip that is magnetic, magnetized, metallic or ferrous and is steerable by an external magnetic device or a having a variable stiffness at a distal tip.

An automated damage severity assessment system and method is disclosed. The present system scans and assesses roof damage, and generates a damage severity report without the oversight of a roof inspector. The system comprises a central database of reference images and data that is constantly reviewed and updated to ensure accuracy of the reports, and aims to reduce the errors and waste inherent in the current methods.

A method and system for determining a regional weakening (RW) value in a blood vessel using ultrasonic imaging. A set of images of a portion of a given patient's body collected over time is received. A set of measurements representing the movement of the tissue of the blood vessel is received, and the set of measurements is determined based on the set of images. The movement of the wall and lumen at least partially indicates strain. A blood flow velocity value in the blood vessel is determined based on a Doppler image, and an RW parameter indicating the state of weakening in a region of the blood vessel is determined based on at least the set of measurements, thrombus burden, and blood flow velocity value. A map showing the circumference of the blood vessel including the regional weakening value and the strain value may be displayed.

The embodiment of the invention provides an intelligent hospital guide method and system based on BERT and feature fusion. Wherein biochemical information such as humidity and temperature of a postoperative wound is collected through a dressing sensor of the intelligent medical textile, and a wound healing activity index is generated. A wound image is synchronously obtained and processed to enhance edge sharpness and texture, and a detailed wound topological structure is formed. In combination with nursing record text analysis, a semantic map describing inflammation intensity and seepage trend is generated and is associated with biochemical data. A wound healing activity index, a wound topological structure and a semantic map are dynamically associated in a cross-modal mode, and the analysis precision is improved by adjusting specific parameters, such as the association between humidity and a necrotic tissue boundary and the relationship between layering density and a pH value. And generating a wound healing deviation parameter according to a comprehensive analysis result, evaluating a risk level and triggering a corresponding hospital guide decision. According to the technical scheme provided by the embodiment of the invention, the intelligent hospital guide efficiency and accuracy can be improved.

The invention relates to a multi-source data fusion management method and system for medical record information, and the method comprises the following steps: carrying out the data collection of an electronic medical record system, a medical image workstation and an inspection information system of a medical institution, and obtaining an original medical record data set; performing semantic mapping and concept association on the original medical record data set based on a preset medical ontology knowledge graph to obtain a semantic association medical record information network; performing dynamic time sequence feature analysis on the semantic association medical record information network through a time sequence feature extractor to obtain a time sequence medical record feature sequence; and carrying out heterogeneous data fusion on the time sequence medical record feature sequence to obtain a unified medical record data view, so that the technical problem that the capability of deeply understanding and mining medical record information is limited due to the fact that an existing medical record management method usually ignores semantic relevance between data is solved.

The invention discloses a multi-angle particle therapy system without a rotating rack and a using method thereof, and relates to the technical field of particle therapy equipment, and the treatment system comprises an accelerator, an irradiation system, a control system, a patient supporting system and an imaging system. The patient supporting system comprises a containing and fixing device, a two-dimensional translation system, a rotating system and a multi-degree-of-freedom mechanical positioning system, the containing and fixing device is connected with the two-dimensional translation system, the two-dimensional translation system is connected with the rotating system, and the two-dimensional translation system controls the relative position of a patient and a rotating center; the rotating system rotates a patient to an appointed angle of a treatment plan or cooperates with the imaging system to obtain a three-dimensional image of the patient, and the irradiation system irradiates a tumor target area; the use method comprises the following steps: generating a treatment plan by reconstructing a three-dimensional CT image, and completing irradiation at different angles according to the treatment plan. According to the treatment system, multi-angle irradiation on a patient can be realized under the condition that a rotating rack is not used, and the manufacturing period and cost of the particle treatment system can be reduced.

The invention discloses a multi-source heterogeneous medical data fusion and intelligent diagnosis method, and relates to the technical field of medical data processing and intelligent diagnosis, and the method comprises the specific steps: firstly, synchronously collecting medical images and clinical text data of a patient, and carrying out the correlation and integration to form a heterogeneous diagnosis data set; performing standardized feature extraction to obtain a feature set in a unified format; then constructing a parallel model, fusing features by using a cross-modal attention alignment technology, and guiding correction by means of a knowledge graph; and finally, the cross-modal diagnosis features are input into the reference model, automatic focus positioning is realized through processing, and a visual marker graph is output. Heterogeneous data of medical images and clinical texts are synchronously integrated, and the diagnosis feature reliability is improved through standardization processing, feature fusion and the like; a focus sensing mask is generated through comparison with a normal model, a multi-scale feature fusion technology is combined to realize automatic and accurate positioning of the focus, a large amount of labeled data is not needed, the process is simplified, and the diagnosis efficiency and accuracy are improved.

The invention discloses a renal clear cell carcinoma prognosis prediction method based on multi-mode MRI (Magnetic Resonance Imaging) image and digital pathological omics fusion. The method comprises the following steps: S1, collecting a training data set based on an MR image and a pathological image; s2, feature extraction of MR radiomics; s3, deep learning feature extraction of the pathological image; s4, an MR-pathological feature fusion module; and S5, deploying the network. According to the method, depth features with prognosis information are obtained from two scales of pre-treatment images and post-operation pathology, effective features are extracted by adopting image omics and a convolutional neural network mode according to data characteristics of MR images and pathology images, and depth fusion of the two types of features is completed in a hidden space through a multi-task guiding mode, so that the accuracy of the MR image and the pathology image is improved. A precise prognosis model with multi-scale information is provided, and the method has a relatively strong clinical application prospect and is of great significance for realizing precise immunotherapy and improving prognosis of a patient.

The invention discloses a tumor radiotherapy dose calibration method, and relates to the technical field of tumor radiotherapy, and the method comprises the steps: collecting multi-modal data in real time through 4D-CT, MRI and the like, extracting dynamic features through space-time registration, calculating a risk organ dose ratio and a tumor target dose deviation rate, and constructing a comprehensive evaluation function. The method has the advantages that the multi-modal dynamic data of the tumor target area are synchronously collected in real time, the space-time joint registration algorithm and the time sequence weight factor are combined, the limitation that existing static calibration depends on fixed parameters is broken through, and the method is suitable for the multi-modal dynamic data of the tumor target area. According to the method, 95% dose coverage of a tumor target region is guaranteed, and meanwhile, organ-endangering peak value receiving amount is controlled within a certain range of a clinical tolerance threshold value, so that the risk of excessive irradiation of normal tissues is reduced, and the radiotherapy dose calibration precision is prompted based on the deviation rate of a traditional method.

The invention provides a patient pathological data analysis and evaluation method for clinical nursing, and relates to the technical field of nursing informatization. Comprising the steps of collecting biochemical and pathological data of a patient and standardizing units and reference values, analyzing the dynamic trend of detection values and classifying change directions and amplitudes, matching pathological labels and establishing association with nursing items, comparing differences between nursing records and the pathological data and establishing a transverse index relationship, and judging whether the nursing task difference exceeds a threshold value or not and adjusting a nursing path generation scheme. By integrating multi-source pathological data and standardizing units and reference intervals, data consistency and comparability are ensured, the index fluctuation trend is dynamically tracked, the change direction and amplitude are quantitatively analyzed, disease course evolution is accurately captured, direct mapping between pathological categories and nursing items is established, logic binding is clear, intervention precision is improved, and the method is suitable for clinical application. Differences between nursing records and pathological data are transversely compared, nursing dynamic adaptation is optimized, a path scheme is rapidly adjusted through threshold judgment, and the lagging or misjudgment risk is reduced.

The invention relates to a visualization method of an electronic medical record system and a visual medical record system. The method comprises the following steps: constructing a system main page; providing entry options, basic information corresponding to the patient, chief complaint information and current medical history information in the navigation bar area; loading and displaying the three-dimensional human body model in the three-dimensional human body model display area; in response to analysis of the electronic medical record information of the patient, automatically identifying and extracting lesion part information described in the medical record information; performing visual labeling on the mapped diseased region on the three-dimensional human body model; setting human body system options in the three-dimensional human body model display area; and in response to the operation that the mouse of the user hovers on the lesion part label on the three-dimensional human body model, displaying an examination result and abnormal index information related to the lesion part. According to the invention, the defects of an existing electronic medical record system in the aspects of information acquisition efficiency, spatial information presentation and information interaction modes can be effectively overcome, and the readability of medical record information and the diagnosis and treatment efficiency of doctors are remarkably improved.

Embodiments of the present invention provide a therapeutic device. The therapeutic device includes an inner layer including a plurality of sensing locations and a plurality of stimulating locations constructed therewithin. Furthermore, the therapeutic device includes a plurality of stimulation elements located within the plurality of stimulating locations. The therapeutic device further includes a plurality of optical fibers including a plurality of respective first optical endpoints and a plurality of respective second optical endpoints, the plurality of first optical endpoints located within the plurality of sensing locations. Furthermore, the therapeutic device includes an optical sensor configured to receive electromagnetic radiation from the plurality of second optical endpoints. The therapeutic device also includes a control module configured to determine radiation characteristics of the electromagnetic radiation received by the optical sensor and activate one or more of the plurality of stimulation elements in correlation with the determined radiation characteristics.

The present disclosure provides a zero-shot low-dose Computed Tomography (CT) image denoising method and apparatus based on strip diffusion model. This method includes building a strip diffusion model which uses high fidelity of the diffusion model to complete end-to-end denoising of low-dose CT images with different doses, thicknesses and devices. Particularly, in the training process, only the normal-dose CT images are required, greatly reducing the data reliance of the model, and model training across dose and thickness condition can be carried out with the data of only one scenario. In a sampling process, strip scanning strategy is used in combination with overlapped strip information and the input low-dose CT images to solve the maximum a posteriori problem, thereby sequentially generating denoising results. The present disclosure only uses simple convolution and attention architecture and carries out extensive experiments on the datasets involving different doses and thicknesses.

The invention relates to the technical field of diagnosis and treatment path planning, in particular to an intelligent diagnosis and treatment path dynamic planning system based on symptom ontology reasoning, which comprises a symptom specialized mapping module, a holographic panoramic navigation module, a path optimization core module and a real-time feedback module, wherein the symptom specialized mapping module generates a dynamic medical resource recommendation parameter set by fusing a symptom ontology feature vector, medical institution treatment efficiency data and a specialized association rule base, and dynamic medical resource recommendation parameters comprise a multi-dimensional matching degree tensor and a treatment efficiency quantification matrix; the holographic panoramic navigation module receives the multi-dimensional matching degree tensor and constructs a three-dimensional medical resource topological graph, wherein the three-dimensional medical resource topological graph comprises hospital nodes and department connecting lines; the path optimization core module analyzes hospital nodes in the three-dimensional medical resource topological graph, and generates a path with optimal cost performance based on a multi-objective optimization algorithm of a treatment efficiency quantification matrix; and the real-time feedback module acquires and updates the treatment efficiency quantization matrix through a back propagation algorithm to form a closed-loop optimization system.

Disclosed are approaches to data acquisition, analysis, and computational forecasting that employs quantitative MRI data to predict the response of cancer to therapy. Example protocols detail how to acquire needed images followed by registration, segmentation, quantitative perfusion and diffusion analysis, model calibration, and prediction. The response of individual cancer patients to therapy is forecast by application of a biophysical, reaction-diffusion model to these data. Application of the protocol results in coregistered MRI data from at least two scan visits that quantifies an individual tumor's size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patient's tumor will respond to therapy. A modified therapy can be determined based on predicted response.

The invention discloses a cervical cancer close-range radiotherapy high-risk target area sketching method fused with a multi-modal image. The method comprises the following steps of collecting an MRI image scanned before radiotherapy and a CT image during radiotherapy of a cervical cancer close-range radiotherapy patient and annotation data of the MRI image and the CT image; the method comprises the following steps: preprocessing an MRI image scanned before radiotherapy and a CT image during radiotherapy, and converting annotation data into a three-dimensional tag image; on the basis of the preprocessed MRI image, the preprocessed CT image and the three-dimensional label image of the preprocessed MRI image, the preprocessed CT image and the three-dimensional label image of the preprocessed MRI image, registration of the MRI image and the CT image is conducted through a pre-constructed registration neural network model, feature extraction and fusion are conducted on the registered MRI image and the registered CT image through a pre-constructed high-risk target area automatic segmentation neural network model of a multi-scale cross-modal attention mechanism, and the high-risk target area automatic segmentation neural network model of the multi-scale cross-modal attention mechanism is obtained. Automatic delineation of a cervical cancer close-range radiotherapy high-risk target area is realized; according to the method, automatic segmentation of HR-CTV in close-range radiotherapy of cervical cancer is realized, high efficiency, accuracy and generalizability are realized, and intelligent support can be provided for clinical work.

The invention discloses an image segmentation method for evaluating a hepatocellular carcinoma neutron therapy dose, and relates to the technical field of neutron therapy. A 3D U-Net GAN network model is trained through a medical image data set; a generator model and a discriminator model in a 3D U-Net GAN network model are alternately trained by using a real image and a false image randomly generated by the generator model, so that the trained generator model can generate a segmentation result which is more accurate and rich in details for a medical image; the trained discriminator model can more accurately evaluate the authenticity of the output of the generator model, and the trained network can accurately reflect the anatomical structure of the patient, the distribution condition of 10B in the body and the radionuclide dynamics condition when segmenting the medical image, so that the information in the medical image can be accurately and fully displayed, and the medical image segmentation efficiency can be improved. The application is convenient.

The invention provides a magnetic resonance image self-supervision super-resolution reconstruction method and system based on a prior guide diffusion model. The method comprises the steps of image preprocessing, pseudo-pairing data set construction, prior condition construction, model training, model testing and three-dimensional isotropic volume reconstruction. According to the method, the dependence on a real high-resolution image is remarkably reduced by constructing a self-supervised pseudo-pairing mechanism, and high-quality reconstruction from anisotropic MRI to an isotropic image is realized; meanwhile, by introducing multiple medical priori including high-frequency residual, edge structure and region-of-interest guidance, detail retention and structural boundary definition of the image are effectively improved, the convergence speed and robustness of the model are improved while anatomical fidelity is guaranteed, and the method is suitable for large-scale popularization and application. And a technically feasible and clinically applicable solution is provided for high-resolution reconstruction of medical images.