1918 results about "Non invasive" patented technology

The present invention relates to a magnetism generation coil that provides non-invasive treatment by applying a magnetic field to nerve cells or muscle cells of the body. The magnetism generation coil of the present invention comprises: a coil bundle consisting of a collection of a plurality of unit coil strands of a copper material; and a hollow tube surrounding the coil bundle and formed of a flexible material. The unit coil strands forming the magnetism generation coil of the present invention may be formed of an oxygen-free copper (OFC) coil having a low ratio of oxygen to copper.

The present invention relates to a helmet-type magnetic stimulation device comprising an inner sheath in contact with a skull part when the device is worn. The helmet-type magnetic stimulation device of the present invention is a helmet-type magnetic stimulation device for applying a magnetic field to nerve cells in the skull part or scalp cells so as to provide a non-invasive treatment, and includes a dome part providing a space in which the skull part is accommodated when the helmet-type stimulation device is worn; and an inner sheath inserted into the dome part to adjust the inner diameter of the dome part, wherein the inner sheath includes: a circumferential part disposed in an area which annularly surrounds the skull part in order to adjust the inner diameter of the dome part; and a connection part connected to one end and the other end of the circumferential part so as to connect the one end and the other end while crossing the upper side of an opening formed by the circumferential part.

A non-invasive physiological sensor system implemented as a smart watch or other wearable device includes an emitter for emitting light and a detector for collecting light after the light interacts with a tissue of a wearer. The system can measure light intensity at one or more radial distances to estimate attenuation parameter values that can be used to correct physiological parameter bias across subjects. In addition or alternatively, the system can determine whether a detector is positioned sufficiently proximate to an obstructing tissue (for example, an artery or vein) so as to cause inaccurate measurements by the detector.

Devices and methods for the non-invasive stimulation of nerves, such as the vagus nerve, include a housing and an electrode coupled to the housing. The electrode is configured to be positioned adjacent to, or in contact with, an outer skin surface of a patient. A source of energy is coupled to the housing and operably coupled to the electrode. The source of energy emits an electrical impulse to the electrode such that the electrical impulse passes through the outer skin surface of the patient to a nerve at a target region in the patient sufficient to modulate the nerve. The electrical impulse comprises burst periods and constant periods, wherein each burst period includes 2 to 20 pulses and has a duration of less than 20,000 microseconds.

The invention provides a rectal cancer neoadjuvant chemoradiotherapy curative effect prediction method and system based on magnetic resonance image analysis, and aims to improve accurate prediction of curative effects of patients with local advanced rectal cancer (LARC). According to the method, multi-modal magnetic resonance images (T1WI, T2WI, DWI and DCE-MRI) are obtained, qualitative and quantitative analysis is combined, tumor morphology, diffusion characteristics and microcirculation parameters are extracted, clinical and pathological data of a patient are integrated, and a multi-dimensional feature set is constructed. Curative effect prediction is performed through machine learning and a deep learning model, and the model can predict treatment results of complete remission, partial remission, ineffectiveness and the like in a classified manner. The system accurately predicts the reaction of a patient to neoadjuvant radiotherapy and chemotherapy through a non-invasive method, supports formulation of a personalized treatment scheme, reduces excessive treatment, optimizes the treatment effect, and has good clinical application value.

A system is provided for determining a target neurostimulation device position. The system comprises: one or more cameras arranged to capture one or more images of a body part of a subject; a neurostimulation device arranged to provide a neurostimulation to the body part of the subject; and a computer comprising a processor and a computer-readable medium arranged to: receive the one or more images from the one or more cameras; determine, by the processor using computer vision to process the one or more images, a three-dimensional (3D) target neurostimulation device position located relative to the body part of the subject; wherein the target neurostimulation device position comprises a 3D translational position metric indicating an area of operation of the neurostimulation device located relative to the body part of the subject. The described system aims to provide a non-invasive manner of providing neurostimulation which reduces the complexity of resources required, the time taken and the level of skill required, while providing a greater level of consistency of treatment provided.

A histotripsy therapy system configured for the treatment of tissue is provided, which may include any number of features. Provided herein are systems and methods that provide efficacious non-invasive and minimally invasive therapeutic, diagnostic and research procedures. In particular, provided herein are optimized systems and methods that provide targeted, efficacious histotripsy in a variety of different regions and under a variety of different conditions without causing undesired tissue damage to intervening / non-target tissues or structures.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

A histotripsy therapy system configured for the treatment of tissue is provided, which may include any number of features. Provided herein are systems and methods that provide efficacious non-invasive and minimally invasive therapeutic, diagnostic and research procedures. Additional embodiments herein provide coupling systems for histotripsy including various reservoirs or ultrasound medium containers (UMC) and coupling constraints configured to interface with a reservoir or UMC during a histotripsy procedure.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

A histotripsy therapy system configured for the treatment of tissue is provided, which may include any number of features. Provided herein are systems and methods that provide efficacious non-invasive and minimally invasive therapeutic, diagnostic and research procedures. In particular, provided herein are optimized systems and methods that provide targeted, efficacious histotripsy in a variety of different regions and under a variety of different conditions without causing undesired tissue damage to intervening / non-target tissues or structures.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

The invention discloses a visual time sequence feature network implementation method based on multi-module feature fusion. The system comprises a visual time sequence feature encoder VTFE, a multi-scale self-attention fusion module MSAF, an efficient feature reconstruction engine EFRE and an adaptive feature refinement processor AFRP. The VTFE module fuses a convolutional neural network and an extended long-short-term memory network, and realizes high-precision capture of spatial-temporal features. The MSAF module processes characteristic frequency domain information through fast Fourier transform, and the recognition capability of complex boundaries and noise is enhanced. The EFRE module optimizes feature reconstruction through depth separable convolution and jump connection to maintain global consistency. The AFRP module integrates multi-level features and improves the detail expression ability through deep convolution and an attention mechanism. The method is excellent in performance on a large-scale OCT data set, the segmentation precision of the retina edema and the recognition capability of a fine structure are remarkably improved, and an efficient and non-invasive auxiliary tool is provided for early diagnosis of ophthalmic diseases.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

A system for non-invasively adjusting the curvature of a spine includes a housing having a first end and a second end, a first rod having a first end telescopically disposed within a cavity of the housing along a first longitudinal axis at the first end of the housing and having a first threaded portion extending thereon, and a second end configured to be coupled to a first portion of a spinal system of a subject, a second rod having a first end telescopically disposed within the cavity along a second longitudinal axis at the second end of the housing and having a second threaded portion extending thereon, and a second end configured to be coupled to a second portion of the spinal system of the subject, a driving member rotatably disposed within the cavity and configured to be activated from a location external to the body of the subject.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention belongs to the technical field of intelligent transportation, and discloses a driving state recognition method and system based on multi-modal fusion. According to the method, the wrist movement information of the driver is collected through the non-invasive physiological bracelet, the wrist movement information is combined with traditional physiological signals, driving performance data and the like, and the accuracy and robustness of driving state recognition are improved. The invention provides a feature dynamic weighting model, the model captures a long-term dependency relationship in a time sequence by using an LSTM module, and weights the features among different modes by using a cross attention mechanism module, so that the dynamic importance of each feature can be adjusted according to different driving states, and the accuracy of driving is improved. And deep interaction and feature enhancement of different modal data are realized by using a Transform module. According to the method, the XGBoost model is utilized, driving state classification is carried out based on the weighted multi-modal features, and different driving states can be accurately distinguished.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

Provided are multi-wavelength phototherapy devices, systems and methods for the treatment of a disorder or disease, including multi-wavelength low level light therapy (“PBM”), in particular to multi-wavelength PBM and other phototherapy systems and methods for improving functionality in and / or restoring functionality to a cell and / or tissue through the coordinated and targeted delivery to the cell or tissue of two or more doses of light having distinct wavelengths, wherein the two or more doses of light, when delivered in a coordinated fashion, can stimulate the activity of two or more light sensitive factors that, when activated, provide and / or enhance a desired target cell functionality. Ophthalmic phototherapy devices, systems, and treatment methods to expose an eye to selected multi-wavelengths of light to promote the healing of damaged or diseased eye tissue. The devices include a housing having an interior; an eyepiece disposed on the housing and configured and arranged for placement of an eye of the patient adjacent the eyepiece; a first light source producing a first light beam having a first therapeutic wavelength and disposed within the housing; a second light source producing a second light beam having a second therapeutic wavelength and disposed within the housing, where the second therapeutic wavelength differs from the first therapeutic wavelength by at least 25 nm.

The invention discloses an acupuncture treatment effect real-time evaluation system based on a brain-computer interface technology, and belongs to the technical field of acupuncture treatment effect evaluation, and the acupuncture treatment effect real-time evaluation system comprises a brain-computer data collection module, an acupuncture data acquisition module, a treatment effect real-time evaluation module, a patient portrait generation module and a long-time treatment effect comprehensive evaluation module, comprising an electroencephalogram sensor which collects electroencephalogram signals of a patient before treatment and in the treatment process based on a non-invasive brain-computer interface technology and transmits the electroencephalogram signals to a follow-up module, and the acupuncture data acquisition module comprises a high-definition camera which is used for collecting actions of a doctor in the treatment process and judging acupuncture points and acupuncture depth of the current patient in the treatment process. According to the acupuncture and moxibustion treatment effect real-time evaluation system based on the brain-computer interface technology, by combining the brain-computer interface technology and acupuncture and moxibustion treatment characteristics, an acupuncture and moxibustion effect real-time evaluation scheme is established based on individual differences of patients, so that the accuracy and effectiveness of acupuncture and moxibustion treatment are improved.

The invention relates to the technical field of medical diagnosis, in particular to a dysphagia detection and automatic scoring system based on a high-resolution acoustic sensor. Comprising a three-dimensional curved surface type acoustic sensing array module, a self-adaptive environment noise compensation module, a swallowing stage dynamic segmentation module, a multi-dimensional feature extraction engine, a swallowing abnormal mode classifier and a dynamic weight scoring model. A self-adaptive environment noise compensation module, a time-frequency domain feature extraction module and a dynamic weight scoring model are combined, the swallowing process is monitored and scored in real time, the system automatically identifies and analyzes acoustic features of an oral cavity preparation period, a pharyngeal period and an esophageal period, and through multi-dimensional feature fusion, dynamic weighting and neural network optimization, the swallowing process is monitored and scored in real time. The method has the advantages of being non-invasive, easy and convenient to operate, high in real-time performance and the like, and can be widely applied to early screening, evaluation and follow-up visit of clinical dysphagia.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

The present invention relates to an electrical nerve stimulator configured to deliver an electrical signal to a user to entrain Mu-band neural oscillations and / or Beta-band neural oscillations. The electrical nerve stimulator may be used in the treatment of neurological conditions associated with desynchronization of neural oscillations, such as Tourette's syndrome. The electrical nerve stimulator may be wearable on a wrist of a user, and may be configured to deliver rhythmic stimulation at a frequency falling within the same frequency range of Mu-band or Beta-band neural oscillations. The invention provides a convenient means of treating conditions such as Tourette's syndrome without the need for specialist medical equipment or facilities. Also disclosed is a method of delivering an electrical signal to a user to entrain Mu-band neural oscillations and / or Beta-band neural oscillations.