Yale University

Brian Letzen

Pramod Bonde

James S Duncan

Clinton Wang

Julius Chapiro

One aspect of the invention provides a computer-implemented method of identifying one or more clinical factors associated with an artificial intelligence prediction. The computer implemented method includes: applying a previously trained deep neural network to one or more images for a subject to produce a prediction, the previously trained deep neural network comprising a plurality of nodes; comparing outputs of the nodes to previously identified patterns of node outputs for a plurality of individual clinical factors; and identifying which of the plurality of individual clinical factors are most correlated with the outputs of the nodes.

Interpretable deep machine learning for clinical radiology

The present invention provides a minimally-invasive percutaneous device that can be positioned within the body of a subject to aid in the movement or pumping of a bodily fluid. In one embodiment, the device comprises a plurality of pump units configured to transform from a first compressed configuration where the pump units are organized in a serial arrangement into a second expanded configuration where the pump units are reorganized into a parallel arrangement.

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Orange, CT, United States of America

Brian Letzen