Overview:
In the coming years, the medical industry will see increased demand for personalized medical solutions. Stratasys and Axial3D have partnered to take on this growth and enable routine and accessible patient-specific care. This webinar will introduce Axial3D founder Daniel Crawford and Stratasys Medical Business Manager Evan Hochstein and feature an engaging conversation about patient-specific applications that impact medical device development.
Whether you’re just starting, or looking to scale your patient-specific programs, you’ll need accurate, scalable, secure and affordable solutions that can take your medical device business to the next level.
What we’ll cover:
The end-to-end workflow from getting your patient data to the production of the device
How AI driven segmentation can speed up your process
How you can cost-effectively implement or start scaling your patient-specific programs
Real-life examples and hear success stories
Q/A
Evan Hochstein has spent 11 years successfully building additive manufacturing programs for many companies. With Stratasys Evan has built deep relationships with large providers and customers seeking the best medical additive manufacturing solutions. In his role as Medical Business Manager for Stratasys, Evan has worked to provide expertise to the medical AM field and has pushed the envelope of what is possible, always focusing on patients and customers. He has received multiple certifications and awards for his work in the field and his support of mentorship and education initiatives. He takes most pride in being a national finalist for the “Woodie Flowers” Award, celebrating mentors who work in robotics and engineering design.
Daniel Crawford is the Founder & Chief Strategy Officer of Axial3D, a company creating automated solutions to make patient specific surgery routine practice in hospitals globally. Daniel graduated with a BSc Hons in Biomedical Engineering and an MSc in Medical Visualization and Human Anatomy. He has over 10 years of experience in medical device development and deployment into clinical settings, 7 of which are specifically related to medical 3D printing and automated applications. Daniel is responsible for the strategy within Axial3D, including its machine learning algorithms for use in creating 3D printed models from 2D medical scans.
