Abstract: A saliency mapping method includes displaying video clip imagery of an organ in a display of a computer and submitting the imagery to a neural network trained to produce a probability of an existence of a physical feature of the organ. In response, the probability is received along with a pixel-wise mapping of dispositive pixels in the imagery resulting in the probability. Variations of the imagery are then repeatedly resubmitted to the neural network, each including a change to one or more of the pixels in the imagery. Thereafter, for each resubmission, a change is measured in the probability and then correlated to the changed pixels. Finally, a graphical indicator is overlain on the display of the imagery corresponding to each of the pixels determined through the repeated resubmission to be dispositive based upon a threshold measured change in probability.

Abstract: Embodiments of the present invention provide a method, system and computer program product for video clip quality determination in medical device imaging and diagnosis. In an embodiment of the invention, a method for video clip quality determination in medical imaging includes first retrieving from a data store into memory of a host computer, video clip imagery of a target organ. Then, a quality value assigned to the video clip imagery may be identified in connection with the retrieved video clip imagery. A measurement of the target organ is computed based upon the retrieved video clip imagery. Finally, a confidence in respect to the computed measurement is determined based upon the identified quality value and the confidence determination displayed in a user interface provided by the host computer.

Abstract: Embodiments of the invention provide a method, system and computer program product for artificially intelligent ejection fraction determination. In a method for artificially intelligent ejection fraction determination, a neural network is loaded into memory of a computer, that has been trained with different sets of cardiac imaging data acquired during imaging of a ventricle for different hearts and a known ejection fraction for each of the sets. Then, a contemporaneous set of imaging data is acquired of a ventricle of a heart and the contemporaneous set of imaging data is provided to the neural network. Finally, an ejection fraction determination output by the neural network is displayed in a display of the computer without tracing a ventricle boundary of the heart.