Abstract: Described herein are systems and methods for modeling complex outcomes using similarity and machine learning algorithms. Machine learning algorithms and models can be implemented on platforms comprising one or more user interfaces and an insight engine. In these embodiments, insight engine comprises a machine learning software algorithm (or module) configured to ingest data and generate insights.

Abstract: Described herein are systems and methods for modeling complex outcomes using clustering and machine learning algorithms. Machine learning algorithms and models can be implemented on platforms comprising one or more user interfaces and an insight engine. In these embodiments, insight engine comprises a machine learning software algorithm (or module) configured to ingest data and generate insights.

Abstract: Described herein are systems and methods for modeling complex outcomes using clustering and machine learning algorithms. Machine learning algorithms and models can be implemented on platforms comprising one or more user interfaces and an insight engine. In these embodiments, insight engine comprises a machine learning software algorithm (or module) configured to ingest data and generate insights.

Abstract: The present disclosure describes imaging systems configured to identify features within image frames and improve the frames by implementing image quality adjustments. An ultrasound imaging system can include a transducer configured to acquire echo signals responsive to ultrasound pulses transmitted toward a target. The system can also include a user interface configured to display an image and one or more processors configured to identify one or more features within the image. The processors can cause the interface to display elements associated with at least two image quality operations specific to the identified feature. A first image quality operation can include a manual adjustment of a transducer setting, and a second image quality operation can include an automatic adjustment of the identified feature derived from reference frames including the identified feature. The processors can receive a user selection of one or more elements and apply the operations to modify the image.

Abstract: The present disclosure describes imaging systems configured to determine the accuracy of anatomical measurements obtained from image data. Systems may include an ultrasound transducer configured to acquire echo signals responsive to ultrasound pulses transmitted toward a target region. Systems can also include a graphical user interface configured to display a biometry tool widget, such as a caliper, for acquiring a measurement of an anatomical feature within the target region from at least one image frame generated from the ultrasound echoes. Systems can also include one or more processors configured to determine a confidence metric indicative of the accuracy of the measurement. The processors can also be configured to cause the graphical user interface to display a graphical indicator corresponding to the confidence metric. The processors can implement one or more neural networks, and can derive additional information, such as gestational age or weight, from the anatomical measurements acquired.

Abstract: Ultrasound image devices, systems, and methods are provided. A medical ultrasound imaging system, comprising an interface in communication with an ultrasound imaging component and configured to receive a first image representative of blood vessels of a brain of a patient while the ultrasound imaging component is positioned at a first imaging position with respect to the patient; and a processing component in communication with the interface and configured to apply a convolutional network (CNN) to the first image to produce a motion control configuration for repositioning the ultrasound imaging component from the first imaging position to a second imaging position associated with a transcranial examination, the CNN trained based on at least a known blood vessel topography.