Abstract: Systems, methods, models, and training data for models are discussed, for determining vehicle positioning, and in particular identifying tailgating. Simulated training images showing vehicles following other vehicles, under various conditions, are generated using a virtual environment. Models are trained to determine following distance between two vehicles. Trained models are used to in detection of tailgating, based on determined distance between two vehicles. Results of tailgating are output to warn a driver, or to provide a report on driver behavior.

Abstract: According to the present application, a computer-implemented method of predicting thyroid eye disease is disclosed. The method comprising: preparing a conjunctival hyperemia prediction model, a conjunctival edema prediction model, a lacrimal edema prediction model, an eyelid redness prediction model, and an eyelid edema prediction model, obtaining a facial image of an object, obtaining a first processed image and a second processed image from the facial image, wherein the first processed image is different from the second processed image, obtaining predicted values for each of a conjunctival hyperemia, a conjunctival edema and a lacrimal edema by applying the first processed image to the conjunctival hyperemia prediction model, the conjunctival edema prediction model, and the lacrimal edema prediction model, and obtaining predicted values for each of an eyelid redness and an eyelid edema by applying the second processed image to the eyelid redness prediction model and the eyelid edema prediction model.

Abstract: Methods and systems for detecting a vertebral fracture within an x-ray. One method includes receiving a chest x-ray image and identifying a plurality of vertebrae represented in the chest x-ray image. The method further includes extracting a plurality of image patches from the chest x-ray image, each image patch of the plurality of image patches including a portion of the chest x-ray image representing one of the plurality of vertebrae identified in the chest x-ray image. The method further includes sequencing the plurality of image patches into an ordered sequence of image patches, and assigning, with a deep learning model applied to the ordered sequence of image patches, a classification to each of the plurality of image patches indicating whether the image patch represents a fractured vertebra or an unfractured vertebra.

Abstract: A computing apparatus for aiding visualization of lesions in a medical image includes a communicator and a processor. The processor is configured to receive a user input for selecting a single point in the medical image to modify a lesion mask representing a lesion area in them medical image, determine a modified lesion mask corresponding to the received user input among a plurality of pre-generated plurality of candidate lesion masks, and provide the modified lesion mask with the medical image.

Abstract: A computer implemented method, a data processing system and a computer program product to determine a likelihood of pneumothorax of a patient, the method including assessing a digital image of a chest x-ray of the patient, applying a standard detection pipeline to the digital image, applying a confounding factor detector to the digital image, and applying a high-resolution detection pipeline to the digital image.

Abstract: Systems and methods are disclosed for grouping cells in a slide image that share a similar target, comprising receiving a digital pathology image corresponding to a tissue specimen, applying a trained machine learning system to the digital pathology image, the trained machine learning system being trained to predict at least one target difference across the tissue specimen, and determining, using the trained machine learning system, one or more predicted clusters, each of the predicted clusters corresponding to a subportion of the tissue specimen associated with a target.

Abstract: Disclosed herein are system, method, and computer program product embodiments for distinguishing a disease state from a non-disease state in an image. An embodiment operates by receiving an image of a target area over a network. The embodiment then corrects for background noise in the image by applying a semantic segmentation filter to obtain a segmented image. The sematic segmentation filter may be trained to remove the background noise from the image. The embodiment then determines, using a trained artificial intelligence (AI) model and the segmented image, at least one classification for the target area. The embodiment finally causes the display of the at least one classification and disease information on a user device associated with a user. The trained AI model may be trained using at least augmented images obtained from a set of images to correct for at least an imbalance in the set of images.

Abstract: A method of detecting the presence of a prostate cancer in a human subject comprising the steps of (a) obtaining a histologically normal prostate tissue sample from the patient and (b) quantifying the epithelial thickness or gland lumen roundness of the tissue, wherein an increase in epithelial thickness or a decrease in gland lumen roundness indicates the presence of prostate cancer or a prostate cancer field defect.

Abstract: One aspect of the present invention is to provide systems and methods that improve the accuracy of an assay that comprise at least one or more parameters each having a random error.

Abstract: A data processing method that is suitable for obtaining quantitative information from data obtained by OCT imaging. The image processing method includes acquiring original image data corresponding to a three-dimensional image of a cultured embryo obtained by optical coherence tomography imaging of the embryo and executing a region segmentation the three-dimensional image into a plurality of regions on the basis of the original image data. In the region segmentation, a local thickness calculation is performed on the three-dimensional image to determine an index value indicating a size of an object included in the three-dimensional image, the three-dimensional image is segmented into a region indicated by the index value greater than a predetermined first threshold and a region indicated by the index value less than the first threshold, and each of the regions resulting from the segmentation is further segmented by the watershed algorithm.

Abstract: Methods and apparatus for evaluating an impact of injury to brain networks or regions are provided. The method comprises receiving MRI data of a brain of an individual, including a first volumetric dataset recorded using first imaging parameters and a second volumetric dataset recorded using second imaging parameters, combining, on a voxel-by-voxel basis, first MRI data based on the first volumetric dataset and second MRI data based on the second volumetric dataset to produce a volumetric injury map, performing a structural-functional analysis of one or more brain networks or regions by refining the volumetric injury map using a volumetric eloquence map that specifies eloquent brain tissue within the one or more brain networks or regions to determine an impact of injury within the one or more brain networks or regions, and displaying a visualization of the determined impact of injury within the one or more brain networks or regions.

Abstract: A method and system are disclosed for outputting augmented reality information to a first user. In an embodiment, the method includes acquiring first information, including image information, depth information, coordinate information and combinations thereof, the first information relating to at least one of a medical device and a medical examination of a patient; creating the augmented reality information, relating to the medical device and/or the medical examination of the patient, based on the first information; and outputting the augmented reality information such that the augmented reality information is perceivable in a field of view of the first user.

Abstract: According to some embodiments, disclosed are systems and methods for a novel framework that performs management of a location and the individuals operating therein based on determined fatigue data of such individuals. The framework may track a person (e.g., a user) at or around a location. Such tracking may be performed based on captured digital imagery of the user via a set of strategically positioned cameras at the location. In some embodiments, as soon as a user begins working, or upon detection by a camera(s), the framework may cause the camera(s) to begin capturing footage of the user, which may be fed, uploaded and/or streamed to a fatigue detection system that determines fatigue data related to the user. Such fatigue data may be leveraged to control which jobs certain users are performing, while reassigning other users based on safety decisions formed from their respective fatigue data.

Abstract: Systems and methods for designing and implementing patient-specific surgical procedures and/or medical devices are disclosed. In some embodiments, a method includes receiving a patient data set of a patient. The patient data set is compared to a plurality of reference patient data sets, wherein each of the plurality of reference patient data sets is associated with a corresponding reference patient. A subset of the plurality of reference patient data sets is selected based, at least partly, on similarity to the patient data set and treatment outcome of the corresponding reference patient. Based on the selected subset, at least one surgical procedure or medical device design for treating the patient is generated.

Abstract: A system and method configured to better identify patient-specific anatomical landmarks, measure anatomical parameters and features, and predict the patient's need for surgery within a predetermined time period. In some embodiments, the system and method is configured to predict the likelihood or risk that a patient will require total hip arthroplasty. In some embodiments, the present invention includes machine learning technology Some embodiments of the present invention include a first ML machine configured to received medical images as inputs and identify anatomical landmarks as outputs; a measurement module to measure joint space width, hip dysplasia angles, and/or leg length differential; and a second ML machine configured to receive the anatomical measurements and patient demographic data as inputs and produce a risk or likelihood that the patient will require surgery within a certain time frame.

Abstract: According to the present application, a computer-implemented method of predicting thyroid eye disease is disclosed. The method comprising: preparing a conjunctival hyperemia prediction model, a conjunctival edema prediction model, a lacrimal edema prediction model, an eyelid redness prediction model, and an eyelid edema prediction model, obtaining a facial image of an object, obtaining a first processed image and a second processed image from the facial image, wherein the first processed image is different from the second processed image, obtaining predicted values for each of a conjunctival hyperemia, a conjunctival edema and a lacrimal edema by applying the first processed image to the conjunctival hyperemia prediction model, the conjunctival edema prediction model, and the lacrimal edema prediction model, and obtaining predicted values for each of an eyelid redness and an eyelid edema by applying the second processed image to the eyelid redness prediction model and the eyelid edema prediction model.

Abstract: A method for remotely assessing oral health of a user of a mobile device by obtaining, using the mobile device (40), at least one digital image (1) of said user's (30) oral cavity (31) and additional non-image data (2) comprising anamnestic information about the user (30). The digital image (1) is processed both using a statistical object detection algorithm (20) to extract at least one local visual feature (3) corresponding to a medical finding related to a sub-region of said user's oral cavity (31); and also using a statistical image recognition algorithm (21) to extract at least one global classification label (4) corresponding to a medical finding related to said user's oral cavity (31) as a whole. An assessment (10) of the oral health of said user (30) is determined based on the local visual feature(s) (3), the global classification label(s) (4) and the non-image data (2).

Abstract: Devices, systems and methods related to techniques for performing four-chamber segmentation of echocardiograms are disclosed. In one example aspect, a method for generating segmented image data based on an input echocardiogram includes receiving an input echocardiogram that includes information associated with four chambers of a heart, performing segmentation on the information associated with the four chambers using an adversarial model that comprises a first artificial neural network with multiple layers, and combining data from selected layers of the first artificial neural network to generate an output image that includes the segmented four chambers of the heart.

Abstract: A method for matching a reference object and a test object includes providing a test object in a field of sensing of at least one sensor disposed at a vehicle. A volume match is determined based on a volume of the reference object and a volume of the test object. A distance match is determined based on a center and orientation of the reference object and a center and orientation of the test object. An angle match is determined based on a yaw angle of the reference object and a yaw angle of the test object. A superposition of the volume match, the distance match, and the angle match is determined based on a multiplication of the volume match, the distance match, and the angle match. A degree of matching of the reference object and the test object is determined based on the superposition.

Abstract: Systems and methods for determining a disease state of image elements are disclosed herein. An example method may include providing quantitative and non-quantitative images, determining a correspondence map of the quantitative images to an atlas, and determining candidate elements of the quantitative images by comparing the elements of the quantitative images to atlas elements of the atlas. The example method may also include localizing the candidate elements in the non-quantitative images, classifying the candidate elements based on properties of the non-quantitative images, and determining a disease state of the candidate elements.