Class / Patent application number | Description | Number of patent applications / Date published |
600408000 | Using neural network or trainable (adaptive) system | 41 |
20090171183 | PATIENT SCAN TIME OPTIMIZATION FOR PET/SPECT IMAGING - An imaging system ( | 07-02-2009 |
20100076296 | Method and System for Automatic Detection of Coronary Stenosis in Cardiac Computed Tomography Data - A method and system for automatic coronary stenosis detection in computed tomography (CT) data is disclosed. Coronary artery centerlines are obtained in an input cardiac CT volume. A trained classifier, such as a probabilistic boosting tree (PBT) classifier, is used to detect stenosis regions along the centerlines in the input cardiac CT volume. The classifier classifies each of the control points that define the coronary artery centerlines as a stenosis point or a non-stenosis point. | 03-25-2010 |
20110172514 | METHOD FOR INCREASING THE ROBUSTNESS OF COMPUTER-AIDED DIAGNOSIS TO IMAGE PROCESSING UNCERTAINTIES - A classifier ( | 07-14-2011 |
20110257505 | ATHEROMATIC?: IMAGING BASED SYMPTOMATIC CLASSIFICATION AND CARDIOVASCULAR STROKE INDEX ESTIMATION - Characterization of carotid atherosclerosis and classification of plaque into symptomatic or asymptomatic along with the risk score estimation are key steps necessary for allowing the vascular surgeons to decide if the patient has to definitely undergo risky treatment procedures that are needed to unblock the stenosis. This application describes a statistical (a) Computer Aided Diagnostic (CAD) technique for symptomatic versus asymptomatic plaque automated classification of carotid ultrasound images and (b) presents a cardiovascular risk score computation. We demonstrate this for longitudinal Ultrasound, CT, MR modalities and extendable to 3D carotid Ultrasound. The on-line system consists of Atherosclerotic Wall Region estimation using AtheroEdge™ for longitudinal Ultrasound or Athero-CTView™ for CT or Athero-MRView from MR. This greyscale Wall Region is then fed to a feature extraction processor which uses the combination: (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability. Another combination uses: (a) Local Binary Pattern; (b) Law's Mask Energy and (c) Wall Variability. The output of the Feature Processor (from either of the combination) is fed to the Classifier which is trained off-line from the Database of similar Atherosclerotic Wall Region images. The off-line Classifier using combination one is trained from the significant features from (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability, selected using t-test. Using the combination two, the off-line Classifier uses grayscale features: (a) Local Binary Pattern; (b) Law's Mask Energy and (c) Wall Variability. Symptomatic ground truth information about the training, patients is drawn from cross modality imaging such as CT or MR or 3D ultrasound in the form of 0 or 1. Support Vector Machine (SVM) supervised classifier of varying kernel functions is used off-line for training. The Atheromatic™ system is also demonstrated for Radial Basis Probabilistic Neural Network (RBPNN), or Nearest Neighbor (KNN) classifier or Decision Trees (DT) Classifier for symptomatic versus asymptomatic plaque automated classification. The obtained training parameters are then used to evaluate the test set. The system also yields the cardiovascular risk score value on the basis of the four set of wall features in combination one and risk score using combination two. | 10-20-2011 |
20120172702 | DYNAMIC ADAPTIVE RESPIRATION COMPENSATION WITH AUTOMATIC GAIN CONTROL - A system for determining a location of an electrode of a medical device (e.g., a catheter) in a body of a patient includes a localization block for producing an uncompensated electrode location, a motion compensation block for producing a compensation signal (i.e., for respiration, cardiac, etc.), and a mechanism for subtracting the compensation signal from the uncompensated electrode location. The result is a corrected electrode location substantially free of respiration and cardiac artifacts. The motion compensation block includes a dynamic adaptation feature which accounts for changes in a patient's respiration patterns as well as intentional movements of the medical device to different locations within the patient's body. The system further includes an automatic compensation gain control which suppresses compensation when certain conditions, such as noise or sudden patch impedance changes, are detected. | 07-05-2012 |
20120184840 | Automated Measurement of Brain Injury Indices Using Brain CT Images, Injury Data, and Machine Learning - A decision-support system and computer implemented method automatically measures tee midline shift in a patient's brain using Computed Tomography (CT) images. The decision-support system and computer implemented method applies machine learning methods to features extracted from multiple sources, including midline shift, blood amount, texture pattern and other injury data, to provide a physician an estimate of intracranial pressure (ICP) levels. A hierarchical segmentation method, based on Gaussian Mixture Mode! (GMM), is used. In this approach, first an Magnetic Resonance Image (MRI) ventricle template, as prior knowledge, is used to estimate the region for each ventricle. Then, by matching the ventricle shape it) CT images to fee MRI ventricle template set, the corresponding MRI slice is selected. From the shape matching result, the feature points for midline estimation in CT slices, such as the center edge points of the lateral ventricles, are detected. The amount of shift, along with other information such as brain tissue texture features, volume of blood accumulated in the brain, patient demographics, injury information, and features extracted from physiological signals, are used to train a machine learning method to predict a variety of important clinical factors, such as intracranial pressure (ICP), likelihood of success a particular treatment, and the need and/or dosage of particular drugs. | 07-19-2012 |
20130131488 | MULTIMODAL DETECTION OF TISSUE ABNORMALITIES BASED ON RAMAN AND BACKGROUND FLUORESCENCE SPECTROSCOPY - Methods and apparatus for classifying tissue use features of Raman spectra and background fluorescent spectra. The spectra may be acquired in the near-infrared wavelengths. Principal component analysis and linear discriminant analysis of reference spectra may be used to obtain a classification function that accepts features of the Raman and background fluorescence spectra for test tissue and yields an indication as to the likelihood that the test tissue is abnormal. The methods and apparatus may be applied to screening for skin cancers or other diseases. | 05-23-2013 |
20130338479 | Apparatus And Method For Surgical Instrument With Integral Automated Tissue Classifier - A method and apparatus is described for optically scanning a field of view, the field of view including at least part of an organ as exposed during surgery, and for identifying and classifying areas of tumor within the field of view. The apparatus obtains a spectrum at each pixel of the field of view, and classifies pixels with a kNN-type or neural network classifier previously trained on samples of tumor and organ classified by a pathologist. Embodiments use statistical parameters extracted from each pixel and neighboring pixels. Results are displayed as a color-encoded map of tissue types to the surgeon. In variations, the apparatus provides light at one or more fluorescence stimulus wavelengths and measures the fluorescence light spectrum emitted from tissue corresponding to each stimulus wavelength. The measured emitted fluorescence light spectra are further used by the classifier to identify tissue types in the field of view. | 12-19-2013 |
20130345544 | MULTI-EXCITATION DIAGNOSTIC SYSTEM AND METHODS FOR CLASSIFICATION OF TISSUE - Methods and systems for in vivo classification of tissue are disclosed. The tissue is irradiated with light from multiple light sources and light scattered and fluoresced from the tissue is received. Distinct emissions of the sample are identified from the received light. An excitation-emission matrix is generated ( | 12-26-2013 |
20140249399 | Determining Functional Severity of Stenosis - A method for determining functional severity of a stenosis includes: (a) generating a simulated perfusion map from a calculated blood flow; (b) comparing the simulated perfusion map to a measured perfusion map to identify a degree of mismatch therebetween, the measured perfusion map representing perfusion in a patient; (c) modifying a parameter in a model used in calculating the blood flow when the degree of mismatch meets or exceeds a predefined threshold; (d) computing a hemodynamic quantity from the simulated perfusion map when the degree of mismatch is less than the predefined threshold, the hemodynamic quantity being indicative of the functional severity of the stenosis; and (e) displaying the hemodynamic quantity. Systems for determining functional severity of a stenosis are described. | 09-04-2014 |
20140296693 | PRODUCTS OF MANUFACTURE AND METHODS USING OPTICAL COHERENCE TOMOGRAPHY TO DETECT SEIZURES, PRE-SEIZURE STATES AND CEREBRAL EDEMAS - In alternative embodiments, the invention provides compositions, products of manufacture and medical devices, and methods, using optical coherence tomography to monitor for a physiological event and/or a state prior to the physiological event. The invention also provides computer program products and computer implemented methods to capture, analyze, and display optical coherence tomography images of neural tissue to measure, detect and/or monitor for a physiological event and/or a state prior to the physiological event. | 10-02-2014 |
20140316243 | SYSTEMS AND TECHNIQUES FOR DELIVERY AND MEDICAL SUPPORT - Systems and techniques for delivery and medical support are disclosed herein. In some embodiments, a drone delivery system may include receiving logic and communication logic. The receiving logic may be configured to receive a request signal indicative of a package request event proximate to a target device, wherein the request signal comprises sensor data indicative of conditions proximate to the target device or a request signal transmitted to the receiving device from the target device. The communication logic may be configured to instruct a drone to carry a package to the target device in response to the request signal. The drone may be configured to perform an environmental scan during transit to adjust a route to the target device. Other embodiments may be disclosed and/or claimed. | 10-23-2014 |
20150065850 | ACCURATE AND EFFICIENT POLYP DETECTION IN WIRELESS CAPSULE ENDOSCOPY IMAGES - A method for detecting polyps in endoscopy images includes pruning a plurality of two dimensional digitized images received from an endoscopy apparatus to remove images that are unlikely to depict a polyp, where a plurality of candidate images remains that are likely to depict a polyp, pruning non-polyp pixels that are unlikely to be part of a polyp depiction from the candidate images, detecting polyp candidates in the pruned candidate images, extracting features from the polyp candidates, and performing a regression on the extracted features to determine whether the polyp candidate is likely to be an actual polyp. | 03-05-2015 |
20150080702 | GENERATING COLONOSCOPY RECOMMENDATIONS - Computer-implemented methods of improved quantitative interpretation of cell index profiles are provided. | 03-19-2015 |
20150087957 | EARLY THERAPY RESPONSE ASSESSMENT OF LESIONS - For therapy response assessment, texture features are input for machine learning a classifier and for using a machine learnt classifier. Rather than or in addition to using formula-based texture features, data driven texture features are derived from training images. Such data driven texture features are independent analysis features, such as features from independent subspace analysis. The texture features may be used to predict the outcome of therapy based on a few number of or even one scan of the patient. | 03-26-2015 |
20150112182 | Method and System for Machine Learning Based Assessment of Fractional Flow Reserve - A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches. | 04-23-2015 |
20150148657 | ULTRASONOGRAPHIC IMAGES PROCESSING - A computerized method of adapting a presentation of ultrasonographic images during an ultrasonographic fetal evaluation. The method comprises performing an analysis of a plurality of ultrasonographic images captured by an ultrasonographic probe during an evaluation of a fetus, automatically identifying at least one location of at least one anatomical landmark of at least one reference organ or tissue or body fluid of the fetus in the plurality of ultrasonographic images based on an outcome of the analysis, automatically localizing a region of interest (ROI) in at least some of the plurality of ultrasonographic images by using at least one predefined locational anatomical property of the at least one anatomical landmark, and concealing the ROI in a presentation of the at least some ultrasonographic images during the evaluation. At least one anatomical landmark is imaged in the presentation and not concealed by the ROI. | 05-28-2015 |
20150297313 | MARKERLESS TRACKING OF ROBOTIC SURGICAL TOOLS - Appearance learning systems, methods and computer products for three-dimensional markerless tracking of robotic surgical tools. An appearance learning approach is provided that is used to detect and track surgical robotic tools in laparoscopic sequences. By training a robust visual feature descriptor on low-level landmark features, a framework is built for fusing robot kinematics and 3D visual observations to track surgical tools over long periods of time across various types of environments. Three-dimensional tracking is enabled on multiple tools of multiple types with different overall appearances. The presently disclosed subject matter is applicable to surgical robot systems such as the da Vinci® surgical robot in both ex vivo and in vivo environments. | 10-22-2015 |
20150313551 | TENDENCY DISCRIMINATION DEVICE, TASK EXECUTION ASSISTING DEVICE, TENDENCY DISCRIMINATION COMPUTER PROGRAM, AND TASK EXECUTION ASSISTING COMPUTER PROGRAM - A tendency discrimination device includes a discriminator to objectively discriminate a tendency of a person to be tested, based on brain information obtained by magnetic resonance imaging (MRI). In the generation of the discriminator, a gray matter volume and a diffusion anisotropy degree are calculated for a frontal pole of each of multiple test subjects as a region of interest, and machine learning is performed on the relationship of the information obtained by classifying results of a test for discriminating the tendencies of the multiple test subjects, to gray matter volumes and diffusion anisotropy degrees obtained by MRI for each of the multiple test subjects. | 11-05-2015 |
20150320365 | Characterizing States of Subject - Among other things, a user of a browser is exposed simultaneously to three interfaces: A viewing interface for at least one image of a subject that is stored on a device on which the browser is running, a decision support interface that aids the user in determining the state of the subject based on the image, and a template interface that aids the user in capturing uniform descriptive information about the state of the subject. At least two of the viewing interface, the decision support interface, and the template interface operate cooperatively so that actions of the user with respect to one of the two interfaces causes changes in content exposed by the other of the two interfaces. | 11-12-2015 |
20150342527 | OPTICAL PRESSURE SENSOR - A pressure sensor comprises an optical source configured to illuminate the tissue of a user, and an optical sensor configured to measure reflected illumination from the tissue. A compute system is configured to output a pressure between a surface of the optical sensor and the tissue as a function of the measured reflected illumination. | 12-03-2015 |
20150366532 | VALVE REGURGITANT DETECTION FOR ECHOCARDIOGRAPHY - A regurgitant orifice of a valve is detected. The valve is detected from ultrasound data. An anatomical model of the valve is fit to the ultrasound data. This anatomical model may be used in various ways to assist in valvular assessment. The model may define anatomical locations about which data is sampled for quantification. The model may assist in detection of the regurgitant orifice using both B-mode and color Doppler flow data with visualization without the jet. Segmentation of a regurgitant jet for the orifice may be constrained by the model. Dynamic information may be determined based on the modeling of the valve over time. | 12-24-2015 |
20160000327 | NON-TOUCH OPTICAL DETECTION OF VITAL SIGNS FROM AMPLIFIED VISUAL VARIATIONS - A microprocessor is operably coupled to a camera from which patient vital signs are determined. A temporal-variation-amplifier of at least two images is operable to generate a temporal variation, a vital-sign generator is operable to generate at least one vital sign from the temporal variation and a display device is operable to display the at least one vital sign. | 01-07-2016 |
20160000331 | APPARATUS OF NON-TOUCH OPTICAL DETECTION OF VITAL SIGNS OF REDUCED IMAGES FROM SPATIAL AND TEMPORAL FILTERS - A microprocessor is operably coupled to a camera from which patient vital signs are determined. A temporal variation of images from the camera is generated and amplified from which the patient vital sign, such as heart rate or respiratory rate, can be determined and displayed or stored. | 01-07-2016 |
20160000381 | APPARATUS OF NON-TOUCH OPTICAL DETECTION OF VITAL SIGNS FROM MULTIPLE FILTERS - A microprocessor is operably coupled to a camera from which patient vital signs are determined. A temporal variation of images from the camera is generated from multiple filters and then amplified from which the patient vital sign, such as heart rate or respiratory rate, can be determined and then displayed or stored. | 01-07-2016 |
20160038122 | ULTRASOUND DIAGNOSIS APPARATUS - Provided is an ultrasound diagnosis apparatus including: a data acquisition unit configured to acquire volume data for a head of an object; an image processor configured to detect a mid-sagittal plane (MSP) from the volume data, generate an MSP image corresponding to the MSP, detect at least one measurement plane based on the MSP, and generate at least one measurement plane image corresponding to the at least one measurement plane; and a display configured to display the MSP image and the at least one measurement plane image on a single screen. | 02-11-2016 |
20160045180 | Computer-Aided Analysis of Medical Images - A pair of medical images is analysed, the pair including a first image, which is a contrasted scan of a part in a human or animal body, and a second image, which is a native scan of the same part of the human or animal body. Anatomic structures are identified within both the first image and the second image. By using those anatomic structures, centerlines of vessels in the first image are mapped to the second image. Candidate calcified plaques are extracted in the second image, and calcified plaques out of the candidate calcified plaques are identified by a machine learning classifier. The positional information of the centerlines in the second image is used for extracting the candidate calcified plaques in the second image and/or for identifying the calcified plaques out of the candidate calcified plaques by the machine learning classifier. | 02-18-2016 |
20160066860 | Use of Machine Learning for Classification of Magneto Cardiograms - The use of machine learning for pattern recognition in magnetocardiography (MCG) that measures magnetic fields emitted by the electrophysiological activity of the heart is disclosed herein. Direct kernel methods are used to separate abnormal MCG heart patterns from normal ones. For unsupervised learning, Direct Kernel based Self-Organizing Maps are introduced. For supervised learning Direct Kernel Partial Least Squares and (Direct) Kernel Ridge Regression are used. These results are then compared with classical Support Vector Machines and Kernel Partial Least Squares. The hyper-parameters for these methods are tuned on a validation subset of the training data before testing. Also investigated is the most effective pre-processing, using local, vertical, horizontal and two-dimensional (global) Mahanalobis scaling, wavelet transforms, and variable selection by filtering. | 03-10-2016 |
20160073614 | System and Method for Detection of Lameness in Sport Horses and other Quadrupeds - A method of diagnosing lameness in quadrupeds utilizing computer vision and a computerized depth perception system to scan quadrupeds, such as sport horses, over time. The method enables a detailed analysis of the quadruped's movement, and changes thereof over time without the need for attaching sensors to the body of the horse, or requiring force plates or expensive high speed cameras. A processing system receives the input of this movement data and utilizes it to make a determination of severity of lameness signals of the animal. The system is inexpensive enough that non-specialists, such as non-veterinary trained quadruped owners, may install the system at an appropriate location such as a horse barn enabling identification of lameness early, to aid in objectively analyzing rehabilitation from injury, and relating changes in gait to performance changes. | 03-17-2016 |
20160073897 | NON-TOUCH DETECTION OF BODY CORE TEMPERATURE - In one implementation, an apparatus estimates body core temperature from an infrared measurement of an external source point using a cubic relationship between the body core temperature and the measurement of an external source point is described. In another implementation, a non-touch biologic detector estimates temperature from a digital infrared sensor and determines vital signs from a solid-state image transducer. In another implementation, a non-touch biologic detector determines vital signs from a solid-state image transducer and estimates body core temperature from an infrared measurement of an external source point using a cubic relationship between the body core temperature and the measurement of an external source point. | 03-17-2016 |
20160095565 | METHOD AND IMAGING SYSTEM FOR COMPENSATING FOR LOCATION ASSIGNMENT ERRORS IN PET DATA OCCURRING DUE TO A CYCLICAL MOTION OF A PATIENT - In a method for compensating for location assignment errors in PET data that occur due to a cyclical motion of a patient, three-dimensional training data of the patient are acquired with an image recording facility using a different modality from PET in different motion states of the cyclical motion. Model parameters of a statistical model are determined describing the cyclical motion, from the deviations of the training data in different motion states from displacement data describing a reference motion-state. A rule is determined for assigning measurement values of at least one measuring signal that can be recorded during the PET measurement process, and that describe motion states of the cyclical motion, to input parameters describing an instance of the statistical model. Measurement values are assigned to the PET data recorded for the respective recording time points. Displacement data for the PET data are determined using the assignment rule and the PET data are spatially displaced based on the displacement data. | 04-07-2016 |
20160106378 | SYSTEM AND METHOD FOR DETECTING AN ARRHYTHMIC CARDIAC EVENT FROM A CARDIAC SIGNAL - What is disclosed is a system and method for detecting an arrhythmic or non-arrhythmic event from a cardiac signal obtained from a subject. In one embodiment, a plurality of different cardiac signals are received and are transformed into frequency domain signals which, in turn, are changed such that a dominant frequency in each of the signals is substantially aligned to form a matrix of feature vectors. The feature matrix is used to train a classifier. A cardiac signal from the subject is received and transformed to a frequency domain signal. The frequency domain signal is changed such that a dominant is substantially aligned with a dominant frequency of signals used to train the classifier. The subject's frequency domain signal is provided as a new feature vector to the classifier. The classifier uses the new feature vector to classify the subject as having an arrhythmic or a non-arrhythmic event. | 04-21-2016 |
20160113612 | METHOD FOR THE FULLY AUTOMATIC DETECTION AND ASSESSMENT OF DAMAGED VERTEBRAE - A method is disclosed for the automatic determination of the bone density and a method is disclosed for the automatic detection and characterization of spinal column fractures. Both methods enable the fully automatic detection and assessment of damaged vertebrae and reliably enable an analysis of the state of the vertebrae with a high accuracy rate. A computed tomography system to carry out either of the methods is further disclosed. | 04-28-2016 |
20160135729 | SYSTEM AND METHOD FOR DETECTING CANCEROUS TISSUE FROM A THERMAL IMAGE - What is disclosed is a system and method for the detection of cancerous tissue by analyzing blocks of pixels in a thermal image of a region of exposed skin tissue. In one embodiment, matrices are received which have been derived from vectors of temperature values associated with pixels in blocks of pixels which have been isolated from a plurality of thermal images of both cancerous and non-cancerous tissue. The vectors are rearranged to form matrices. A thermal image of a subject is received. Blocks of pixels which reside within a region of exposed skin tissue are identified and isolated. For each identified pixel block, an image vector comprising temperature values associated with these pixels is formed. The vector is provided to a classifier which uses the matrices to classify tissue associated with this block of pixels as being either cancerous or non-cancerous tissue. | 05-19-2016 |
20160166209 | Method and System for Personalized Non-Invasive Hemodynamic Assessment of Renal Artery Stenosis from Medical Images | 06-16-2016 |
20160174902 | Method and System for Anatomical Object Detection Using Marginal Space Deep Neural Networks | 06-23-2016 |
20190142334 | DIAGNOSIS SYSTEM | 05-16-2019 |
20190142338 | EFFICACY AND/OR THERAPEUTIC PARAMETER RECOMMENDATION USING INDIVIDUAL PATIENT DATA AND THERAPEUTIC BRAIN NETWORK MAPS | 05-16-2019 |
20190142519 | GRAPHICAL USER INTERFACE FOR DISPLAYING AUTOMATICALLY SEGMENTED INDIVIDUAL PARTS OF ANATOMY IN A SURGICAL NAVIGATION SYSTEM | 05-16-2019 |
20220133259 | BLADDER MONITORING APPARATUS AND METHOD FOR CONTROLLING BLADDER MONITORING APPARATUS - Disclosed are a bladder monitoring apparatus which accurately determines a bladder status of a subject based on an ultrasonic image and a posture of the subject and a method for controlling the bladder monitoring apparatus. The bladder monitoring apparatus includes a processor, a memory configured to operably connected to the processor and store at least one code executed by the processor, and a transceiver configured to receive a reflection ultrasonic signal from a subject and a posture sensing signal obtained by sensing a posture of the subject based on a sensor, and the memory may store a code which is executed by the processor to cause the processor to determine the bladder status of the subject by applying the machine learning-based learning model to an ultrasonic image generated from the reflection ultrasonic signal and posture information generated based on the posture sensing signal. | 05-05-2022 |
20220133260 | Premature Birth Prediction - Systems and methods of predicting future medical events are based on the processing of medical image. The prediction of premature birth and estimation of gestational age based on ultrasound images are presented as illustrative examples. The new abilities to estimate the probability of future medical events, before they otherwise could be predicted, provides new avenues for the development of preventative treatments. | 05-05-2022 |