Abstract: The present disclosure describes ultrasound imaging systems and methods configured to generate ultrasound images based on undersampled ultrasound data. The ultrasound images may be generated by applying a neural network trained with samples of known fully sampled data and undersampled data derived from the known fully sampled data to a acquired sparsely sampled data. The training of the neural network may involve training adversarial generative network including a generator and a discriminator. The generator is trained with sets of known undersampled data until the generator is capable of generating estimated image data, which the classifier is incapable of differentiation as either real or fake, and the trained generator may then be applied to unknown undersampled data.

Abstract: Systems and computer implemented method of generating a simulated image of a baby based on an ultrasound image of a fetus. A method comprises acquiring an ultrasound image of the fetus, and using a machine learning model to generate the simulated image of the baby, based on the ultrasound image of the fetus, wherein the simulated image of the baby comprises a prediction of how the fetus will look as a baby.

Abstract: A method (100) for generating a textual description of a medical image, comprising: receiving (130) a medical image of an anatomical region, the image comprising one or more abnormalities; segmenting (140) the anatomical region in the received medical image from a remainder of the image; identifying (150) at least one of the one or more abnormalities in the segmented anatomical region; extracting (160) one or more features from the identified abnormality; generating (170), using the extracted features and a trained text generation model, a textual description of the identified abnormality; and reporting (180), via a user interface of the system, the generated textual description of the identified abnormality.

Abstract: A method (100) for generating a textual description of a medical image, comprising: receiving (130) a medical image of an anatomical region, the image comprising one or more abnormalities; segmenting (140) the anatomical region in the received medical image from a remainder of the image; identifying (150) at least one of the one or more abnormalities in the segmented anatomical region; extracting (160) one or more features from the identified abnormality; generating (170), using the extracted features and a trained text generation model, a textual description of the identified abnormality; and reporting (180), via a user interface of the system, the generated textual description of the identified abnormality.

Abstract: Clinical assessment devices, systems, and methods are provided. A clinical assessment system, comprising a processor in communication with an imaging device, wherein the processor is configured to receive, from the imaging device, a sequence of image frames representative of a contrast agent perfused subjects tissue across a time period; classify the sequence of image frames into a plurality of first tissue classes and a plurality of second tissue classes based on a spatiotemporal correlation among the sequence of image frames by applying a predictive network to the sequence of image frames to produce a probability distribution for the plurality of first tissue classes and the plurality of second tissue classes; and output, to a display in communication with the processor, the probability distribution for the plurality of first tissue classes and the plurality of second tissue classes.

Abstract: The invention discloses an apparatus for converting data and for assessing data. The apparatus comprises a processor. For converting data, the processor is configured to train a neural network arrangement to generate a first vector to represent input data, each element of the first vector representing a defined feature of the input data. For assessing data, the processor is configured to provide a neural network trained to generate a first vector representing input data; provide input data to the trained neural network; and generate, using the trained neural network, a first vector representing the input data, wherein each element of the generated first vector represents a defined feature of the input data. Methods and a computer program product are also disclosed.

Abstract: Methods and systems for detecting false alarms. Methods and systems described herein may receive data associated with an alarm signal using an interface, extract at least one artifact feature from the received data, and then receive a classification of the alarm signal as a true positive or false positive based on the at least one extracted artifact feature. The classifier may be configured to execute an ensemble of decision trees.

Abstract: A imaging system includes a camera, a display and a processor. The camera has color video acquisition capability, and is mounted to a distal end of an interventional instrument insertable within an object, the camera providing image frames for imaging vasculature of the object, each image frame including multiple pixels providing corresponding signals, respectively. The processor is programmed to receive the signals; amplify variations in at least one of color and motion of the signals corresponding to the multiple pixels; determine spatial phase variability, frequency and signal characteristics of at least some of the amplified signals corresponding to the multiple pixels, respectively; identify pixels indicative of abnormal vascularity based on the spatial phase variability, frequency and/or signal characteristics; create a vascular map corresponding to each, where each vascularity map includes a portion of the object having the abnormal vascularity; and operate the display to display each vascularity map.

Abstract: Techniques disclosed herein relate to identifying individuals in digital images. In some embodiments, a digital image(s) that captures a scene containing one or more people may be acquired. The single digital image may be applied as input across a single machine learning model. In some implementations, the single machine learning model may be trained to perform a non-facial feature recognition task and a face-related recognition task. Output may be generated over the single machine learning model based on the input. The output may include first data indicative of non-facial features of a given person of the one or more people and second data indicative of at least a location of a face of the given person in the digital image relative to the non-facial features. In various embodiments, the given person may be identified based at least in part on the output.

Abstract: Various embodiments relate to a method and system for automatically generating a medical document during a medical visit in a controlled environment, the method including the steps of monitoring, by a network monitoring module, a network to capture use of medical equipment connected to the network, detecting, by an atomic action video recognition module, predefined atomic actions in the controlled environment, extracting, by a patient-medical provider conversation recognition module, clinical information from a conversation between a patient and a medical provider, matching, by a visit graph generation module, the use of medical equipment and the predefined atomic actions to an atomic actions and CPT codes database of known uses of medical equipment and predefined atomic actions, generating, by the visit graph generation module, an event graph based on the use of medical equipment, the predefined atomic actions and the extracted clinical information and translating, by a medical document generator, the event

Abstract: A method of detecting decline in activities of daily living (ADLs) over time, the method including gathering a plurality of image data of a subject over a period of time, preprocessing the image data to obtain a plurality of standardized images, segmenting out a feature from each of the image data, providing the segmented features to a trained model to identify possible changes in the features over time, classifying the possible changes as evidence, and using the evidence to calculate a risk score.

Abstract: Techniques disclosed herein relate to identifying individuals in digital images. In some embodiments, a digital image(s) that captures a scene containing one or more people may be acquired. The single digital image may be applied as input across a single machine learning model. In some implementations, the single machine learning model may be trained to perform a non-facial feature recognition task and a face-related recognition task. Output may be generated over the single machine learning model based on the input. The output may include first data indicative of non-facial features of a given person of the one or more people and second data indicative of at least a location of a face of the given person in the digital image relative to the non-facial features. In various embodiments, the given person may be identified based at least in part on the output.

Abstract: Various embodiments of the present disclosure are directed to a salient medical imaging controller (80) employing an artificial intelligence engine (40) and a graphical user interface (70). In operation, the artificial intelligence engine (40) includes one or more machine learning models (42) trained to render a feature assessment of a medical image. The graphical user interface (70) provides a user interaction with the artificial intelligence engine (40) to manipulate a salient visualization of the feature assessment of the medical image by the machine learning model(s) (42).

Abstract: A method (100) for generating a textual description of a medical image, comprising: receiving (130) a medical image of an anatomical region, the image comprising one or more abnormalities; segmenting (140) the anatomical region in the received medical image from a remainder of the image; identifying (150) at least one of the one or more abnormalities in the segmented anatomical region; extracting (160) one or more features from the identified abnormality; generating (170), using the extracted features and a trained text generation model, a textual description of the identified abnormality; and reporting (180), via a user interface of the system, the generated textual description of the identified abnormality.

Abstract: Techniques disclosed herein relate to identifying individuals in digital images. In some embodiments, a digital image may be acquired (802) that captures an environment containing at least a first subject. A first portion of the digital image depicting the first subject may be segmented (806) into a plurality of superpixels. For each superpixel of the plurality of superpixels: a semantic label may be assigned (810) to the superpixel; features of the superpixel may be extracted (812); and a measure of similarity between the features extracted from the superpixel and features extracted from a reference superpixel identified in a reference digital image may be determined (814), wherein the reference superpixel has a reference semantic label that matches the semantic label assigned to the superpixel. Based on the measures of similarity associated with the plurality of superpixels, it may be determined (818) that the first subject is depicted in the reference image.

Abstract: The present disclosure describes ultrasound imaging systems and methods for ultrasonically inspecting biological tissue, such as liver and for automatically identifying and acquiring a view suitable for hepatic-renal echo-intensity ratio quantification, using one or more neural networks, which may be trained to perform image classification, segmentation, or a combination thereof to compute a confidence metric and to provide a recommendation for measurement ROIs placement on the image.

Abstract: Various embodiments of the present disclosure are directed to a raw diagnostic machine for a medical diagnosis of raw medical imaging data generated by a medical imaging machine as opposed to a medical diagnosis of a medical image conventionally reconstructed from the raw medical imaging data. In operation, the raw diagnostic engine includes a medical imaging diagnostic controller implementing a dimension reduction pre-processor for selecting or extracting one or more dimension reduced feature vectors from the raw medical imaging data, and further implementing a raw diagnostic artificial intelligence engine for rendering a diagnostic assessment of the raw medical imaging data as represented by the dimension reduced feature vector(s). The medical imaging diagnostic controller may further control a communication of the diagnostic assessment of the raw medical imaging data (e.g., a display, a printing, an emailing, a texting, etc.).

Abstract: Clinical assessment devices, systems, and methods are provided. A clinical assessment system, comprising a processor in communication with an imaging device, wherein the processor is configured to receive, from the imaging device, a sequence of image frames representative of a contrast agent perfused subjects tissue across a time period; classify the sequence of image frames into a plurality of first tissue classes and a plurality of second tissue classes based on a spatiotemporal correlation among the sequence of image frames by applying a predictive network to the sequence of image frames to produce a probability distribution for the plurality of first tissue classes and the plurality of second tissue classes; and output, to a display in communication with the processor, the probability distribution for the plurality of first tissue classes and the plurality of second tissue classes.

Abstract: Systems and computer implemented method of generating a simulated image of a baby based on an ultrasound image of a fetus. A method comprises acquiring an ultrasound image of the fetus, and using a machine learning model to generate the simulated image of the baby, based on the ultrasound image of the fetus, wherein the simulated image of the baby comprises a prediction of how the fetus will look as a baby.

Abstract: A non-transitory computer-readable medium stores a preferences database (16); instructions readable and executable by at least one electronic processor (20) to perform a proposed radiation treatment plan review process (100), including: via a reviewing graphical user interface (GUI) (28), presenting a proposed radiation treatment plan to a reviewer; via the reviewing GUI, receiving one of (i) an acceptance of the proposed radiation treatment plan or (ii) a rejection of the proposed radiation treatment plan in combination with annotations of the rejected proposed radiation treatment plan from the reviewer; and updating radiation treatment plan preferences of the reviewer stored in the preferences database based on the acceptance of the proposed radiation treatment plan or based on the annotations of the rejected proposed radiation treatment plan; and instructions readable and executable by at least one electronic processor (32) to perform a radiation treatment planning process (200) including: optimizing radia