Abstract: A system is described for generating diagnostic information from a video sequence of ultrasound images acquired in “blind sweeps”, i.e., without operator seeing ultrasound images as they are acquired. We disclose two different types of machine learning systems for predicting diagnostic information: a “Temporal Accumulation” system and a “3-D Modeling Component” system. These machine learning systems could be implemented in several possible ways: using just one or the other of them in any given implementation, or using both of them in combination. We also disclose a computing system which implements (a) an image selection system including at least one machine learning model trained to identify clinically suitable images from the sequence of ultrasound images and (b) an image diagnosis/measurement system including of one or more machine learning models, configured to obtain the clinically suitable images identified by the image selection system and further process such images to predict health states.

Abstract: A system is described for conducting an ultrasound scan on a human subject. The system includes an ultrasound probe generating ultrasound image data and provisioned with one or more position sensors generating real time position and orientation data as to the position of the ultrasound probe and orientation in three-dimensional space during use of the probe; one or more machine learning models trained to correlate ultrasound images with probe position and orientation, wherein the one or more machine learning models receive images generated from the ultrasound probe; a feedback generator generating feedback data based on the current probe position determined by the position sensors; and a feedback display receiving the feedback data providing real-time suggestions to the user of the ultrasound probe for adjusting the probe position, orientation, pressure and/or other parameters of the ultrasound probe to improve the quality of the images generated from the ultrasound probe.

Abstract: An image capture system that is capable of capturing images of both small and large target objects includes a collapsible frame and at least one image capture device. The frame is free-standing and adapted to support the image capture device over a generally flat surface, on which a target object can also be supported. The image capture device can be activated to capture an image of the target object for subsequent viewing, storage, retrieval, processing, etc. The collapsible frame is constructed of a plurality of structural components adjustably connected together in a unique configuration that enables the entire frame to be transformed between a stored configuration when not in use, and a deployed configuration during use. The stored configuration is substantially more compact than the deployed configuration, which allows the frame to be easily transported between different locations.