Lightlab Imaging, Inc.

Kyle Savidge

Ajay Gopinath

Shimin Li

Robert Steinbrecher

In part, the disclosure relates to methods, and systems suitable for evaluating image data from a patient on a real time or substantially real time basis using machine learning (ML) methods and systems. Systems and methods for improving diagnostic tools for end users such as cardiologists and imaging specialists using machine learning techniques applied to specific problems associated with intravascular images that have polar representations. Further, given the use of rotating probes to obtain image data for OCT, IVUS, and other imaging data, dealing with the two coordinate systems associated therewith creates challenges. The present disclosure addresses these and numerous other challenges relating to solving the problem of quickly imaging and diagnosis a patient such that stenting and other procedures may be applied during a single session in the cath lab.

Systems and methods for classification of arterial image regions and features thereof

In part, the disclosure relates to systems and methods to assess stent/scaffold expansion in a vessel on an expedited time scale after stent/scaffold placement and expansion. In one embodiment, the method generates a first representation of a stented segment of the blood vessel indicative of a level of stent expansion; determines using the detected stent struts, a first end of the stent and a second end of the stent; and generate a second representation of the segment of the blood vessel by interpolating a lumen profile using an offset distance from the first end and the second end.

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Medford, MA, United States of America

Kyle Savidge