ENDRA Life Sciences to Enhance Ultrasound Offerings with AI and Machine Learning

January 22, 2019

ENDRA Life Sciences Inc. ("ENDRA") (NASDAQ: NDRA), a developer of enhanced ultrasound technologies, has partnered and established a collaboration with the Ladak Laboratory at Western University, in London, Ontario, Canada, to apply Artificial intelligence (AI) methodologies to ENDRA's TAEUS technology.

The collaboration, with an initial term of 12 months, will involve scientists from the Ladak Laboratory and ENDRA's applied science team and will be jointly directed by ENDRA's CTO Michael Thornton and Dr. Hanif Ladak, a professor of Medical Biophysics, Electrical and Computer Engineering, and Biomedical Engineering at Western University and the author of over 80 peer reviewed papers. The collaboration will apply advanced artificial intelligence to a variety of computational tasks related to TAEUS imaging applications including: image segmentation, artifact corrections and measurement calibration.

"AI technology is advancing clinical workflow and accuracy in ways that directly impact patient outcomes in many areas of healthcare technology, from rapidly triaging large amounts of radiology and pathology data, to helping customize patient treatment protocols," said Francois Michelon, CEO of ENDRA. "The future of broader access to and cheaper healthcare is by harnessing vast amounts of data in ways that improve patient health at much earlier stages than what is available today. This collaboration is about ENDRA looking ahead to areas that can have a meaningful impact on our mission of broadening access to better healthcare," concluded Michelon.

"We're excited to collaborate with Dr. Ladak and his research team as they have extensive experience in applying AI to image segmentation and signal processing tasks in biomedical applications," said ENDRA CTO Michael Thornton. "Although AI technology will not be a part of ENDRA's initial TAEUS fatty liver assessment product launching in Europe in 2019, we expect future ENDRA clinical products to include elements of AI technology. AI will not only support advanced TAEUS computational models, but also help automate some of the operator-dependent tasks -- which can be a source of measurement variability in manual clinical procedures," concluded Thornton.

"ENDRA's fatty liver application is particularly well suited to machine learning and computation by AI systems," added Dr. Hanif Ladak. "ENDRA's applied science team has already anticipated the need for large training data sets, so we will have a good starting point for building AI training networks and evaluating their performance."

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