Abstract: A method of implementing an artificial intelligence based imaging platform for cardiopulmonary analysis comprises providing a multilayer convolutional network for cardiopulmonary analysis configured for segmenting data sets of cardiopulmonary scans into resolution voxels; supervised learning and validation of the platform by classification of tissue within classification voxels of a specific given training and validation data sets by the multilayer convolutional network for neurological tumor identification with each classification voxel of the training and validation data sets having a predetermined ground truth; and implementing the platform by classification of tissue within classification voxels of a specific given patient data sets by the multilayer convolutional network for cardiopulmonary analysis with each classification voxel of each data set assigned a label. An artificial intelligence based imaging platform implemented according to the method is disclosed.

Abstract: 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.

Abstract: The present invention provides an infrared and visible light fusion method, and belongs to the field of image processing and computer vision. The present invention adopts a pair of infrared binocular camera and visible light binocular camera to acquire images, relates to the construction of a fusion image pyramid and a significant vision enhancement algorithm, and is an infrared and visible light fusion algorithm using multi-scale transform. The present invention uses the binocular cameras and NVIDIATX2 to construct a high-performance computing platform and to construct a high-performance solving algorithm to obtain a high-quality infrared and visible light fusion image. The present invention constructs an image pyramid by designing a filtering template according to different imaging principles of infrared and visible light cameras, obtains image information at different scales, performs image super-resolution and significant enhancement, and finally achieves real-time performance through GPU acceleration.

Abstract: A machine-learning (ML) model may be trained to receive, as input, epigenetic data associated with a subject and to output a continuous value and/or a classification of a biochemical state and/or medical condition associated with a subject. For example, the biochemical state and/or medical condition may comprise an indication that the subject consumes alcohol and/or nicotine and/or that the subject is diabetic or is likely to become diabetic, to give a small non-limiting example. The epigenetic data may be derived from saliva and/or blood in some examples.

Abstract: The present teachings generally include techniques for characterizing the health of an animal (e.g., gastrointestinal health) using image analysis (e.g., of a stool sample) as a complement, alternative, or a replacement to biological specimen sequencing. The present teachings may also or instead include techniques for personalizing a health and wellness plan (including, but not limited to, a dietary supplement such as a customized formula based on a health assessment), where such a health and wellness plan may be based on one or more of the health characterization techniques described herein. The present teachings may also or instead include techniques or plans for continuous care for an animal, e.g., by executing health characterization and heath planning techniques in a cyclical fashion. A personalized supplement system (e.g., using a personalized supplement, personalized dosing device, and personalized packaging) may also or instead be created using the present teachings.

Abstract: Technologies are provided herein for selecting one or more subimages to be processed from a plurality of images received. A subset of images is identified, from the plurality of images received, that include a face image. The face image is assessed to generate assessment information regarding one or more of a size of the face image, a pose of the face in the face image, sharpness of the face image, and contrast quality of the face image. Each aspect of the assessment information is compared with corresponding criteria and, as a result of determining that the assessment information satisfies the criteria, facial recognition processing is performed on the face image.

Abstract: A super-resolution reconstruction preprocessing method of contrast-enhanced ultrasound images includes: acquiring an image set to be preprocessed; acquiring grayscale fluctuation signal of a pixel point in the registered contrast-enhanced ultrasound images to be preprocessed; performing denoise reconstruction operation on the image set to be preprocessed to obtain a reconstructed feature parameter image, and performing interpolation calculation on the reconstructed feature parameter image to obtain a sparse microbubble image. By analyzing the grayscale fluctuation signals of the collocated pixel point set in the plurality of frames of the registered contrast-enhanced ultrasound images to be preprocessed, a signal-to-noise ratio and a signal-to-background ratio are improved.

Abstract: A method of assessing and/or quantifying remineralization in a tooth model by exposing a tooth model to calcium and a calcium-binding fluorophore either sequentially or simultaneously under conditions sufficient to produce a remineralized/demineralized tooth model; exposing the remineralized/demineralized tooth model to a non-calcium binding fluorophore to produce an enhanced tooth model; and assessing and/or quantifying remineralization, demineralization, or both on the enhanced tooth model by determining the location and extent of calcium binding fluorophore and non-calcium binding fluorophore on the enhanced test tooth sample.

Abstract: Embodiments of the present disclosure provide an image processing method based on artificial intelligence (AI) and an image processing system. The method includes: obtaining a feature recognition result of an image by performing image processing on the image to recognize a feature of the image and the image being obtained by performing image acquisition on a section of a patient using a digital slide scanner to generate a whole slide image (WSI) as the image; determining an imaging area of the section within a field of view of an eyepiece of a microscope with which real-time imaging is performed on the section; determining, within the image, an image area corresponding to the imaging area of the section and acquiring, from the feature recognition result of the image, a target feature recognition result of the image area; and superimposing the target feature recognition result on the imaging area of the section.

Abstract: 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.

Abstract: A system and method for a procedure that can be performed on an appropriate subject. Procedures can include assembling any appropriate work piece or installing members into a work piece, such as an airframe, autoframe, etc. Regardless of the subject, generally the procedure can have a selected result that is efficacious. The efficacious result may be the desired placement of a device. The system and method can be used in confirming an efficacious result.

Abstract: Exemplary embodiments of the present disclosure include apparatus and methods to classify the plaque tissue present in the coronary artery using intravascular optical coherence tomography (IVOCT) images.

Abstract: Using artificial intelligence and data observed using sensors or imaging devices to prompt a patient to provide responses or perform actions and then observing the patient's responses to the prompts and performing an assessment resulting in a quantitative result. The quantitative result is then used to complete a clinical qualitative assessment of the patient.

Abstract: An electronic device includes an image sensor configured to capture a target to generate first image data, and a processor configured to perform directional interpolation on a first area of the first image data to generate first partial image data, perform upscale on a second area of the first image data to generate second partial image data, and combine the first partial image data and the second partial image data to generate second image data.

Abstract: A system and method for automatically monitoring a ruminant animal. The system includes a 3D camera system that obtains images from a region of interest, in particular the paralumbar fossa. An image processor determines the surface curvature in the region of interest, as a function of time. Based on the frequency with which this function attains local maxima, a health indication for the animal is generated.

Abstract: A three-dimensional (3D) appearing Pepper's Ghost artifice is created in a 3D virtual environment for viewing on at least one target device by using a source device that includes a database that stores a plurality of virtual screens of varying sizes and shapes, and an artifice processor. Motion metric data of a human is captured. Also, at least one two-dimensional (2D) live video feed of at least a portion of the human is captured. The artifice processor enables and stitches a Pepper's Ghost artifice of the human using the motion metric data of the human, the at least one 2D live video feed of the human, and a unique identifier of the human. At least one virtual screen is selected from the database using the Pepper's Ghost artifice of the human. The selected virtual screen is compatible with the at least one 2D live video feed of the human.

Abstract: 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.

Abstract: 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.

Abstract: In some implementations, a device may obtain a set of input images, of an object of interest, that includes images that have a plain background. The device may obtain a set of background images that includes images associated with the object of interest. The device may generate, for an input image, a first modified image of the input image that removes a plain background of the input image. The device may generate, for the input image, a second modified image of the input image that is based on the first modified image and a background image. The device may generate, for the input image, a training image that includes an indication of a location of the object of interest depicted in the training image. The device may provide the training image to a training set, that includes a set of training images, for a computer vision model.

Abstract: Methods and systems are directed to training an artificial intelligence engine. One system includes an electronic processor configured obtain a set of reports corresponding to a set of medical images, determine a label for a finding of interest, and identify one or more ambiguous reports in the set of repots. Ambiguous reports do not include a positive label or a negative label for the finding of interest. The electronic processor is also configured to generate an annotation for each of the one or more ambiguous reports in the set of reports, and train the artificial intelligence engine using a training set including the annotation for each of the one or more ambiguous reports and non-ambiguous reports in the set of reports. A result of the training is generation of a classification model for the label for the finding of interest.