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

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: 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: A record collection device (201, 400) configured to collect records for an individual (452). The device includes: a user interface (440) configured to receive input from the user, a location module (410) configured to detect a location the device; and a processor (420) configured to: (i) automatically determine, by a processor of the record collection device, that the detected location corresponds to a location where a record may be generated or stored; (ii) receive, from the individual via the user interface, approval to request the record from the determined location; and (iii) send, in response to the individual's input, a request to the determined location for the individual's record.

Abstract: The present disclosure describes ultrasound imaging systems and methods configured to identify and remove image artefacts from ultrasound image frames by applying a neural network to the frames. Systems may include an ultrasound transducer configured to acquire echo signals responsive to ultrasound pulses transmitted toward a target region. One or more processors communicatively coupled with the ultrasound transducer may be configured to generate an image frame from the ultrasound echoes and apply a neural network to the image frame. The neural network determines whether an artefact is present in the image frame, and identifies the type of artefact detected. The processors can also generate an indicator conveying the presence of the artefact, which can be displayed on a user interface. The processors can further generate an instruction for adjusting the ultrasound transducer based on the presence and type of artefact present within the image frame.

Abstract: A vasculature imaging device includes an optical camera (10), a display (12), an electronic processor (14) connected to operate the optical camera and the display, and a non-transitory storage medium (16) storing instructions (18) readable and executable by the electronic processor to perform a vasculature imaging method (20). That method includes: operating the optical camera to acquire color video; computing a temporal variation of values of pixels of the color video; identifying pixels representing vasculature based on the temporal variation of the values of the pixels; and operating the display to present the color video with highlighting of the pixels representing vasculature. In some embodiments, the vasculature imaging device comprises a cellular telephone (cell phone) or other mobile device (22) with the camera and display being built-in components. The instructions may be an application (app) executable under a mobile operating system (24) run by the mobile device.

Abstract: The disclosure relates to systems, software and methods for diagnosis or prognosis of subjects for dementia, including, classification and treatment of subjects who have been diagnosed with or deemed at risk of dementia. The methods are based, in part, on the multimodal analysis of a plurality of features, e.g., genetic features such as SNPs or chromosome regions, including, loci or genes related thereto and structural brain features such as MRI images of brain or brain regions.

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: 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.