Guest Lecture: Tim Leiner, MD, PhD

Posted on March 2021

How can machine learning improve patient care? That is the question Tim Leiner, MD, PhD will answer during his guest lecture. Dr. Leiner is a Professor of Radiology and Chair in Cardiovascular Imaging at Utrecht University Medical Center, in Utrecht, The Netherlands. His guest lecture at UW, “Bringing Machine Learning to the Clinic: Challenges and Opportunities” will take place virtually on Wednesday March 31st.

After obtaining his MD and PhD from Maastricht University Medical School, Dr. Leiner spent 18 months as a postdoctoral research fellow at the Cardiac MR Center at the Beth Israel Deaconess Medical Center/ Harvard Medical School in Boston. He then completed his Radiology residency at Maastricht University Medical Center, during which he spent three months at the Vascular Imaging Laboratory at the University of Washington. His research interests focus on the development and implementation of new MR and CT techniques for cardiovascular imaging. Dr. Leiner has served on numerous prestigious imaging committees and is currently President of the International Society for Magnetic Resonance in Medicine (ISMRM).

Machine learning and deep learning have the potential to greatly improve patient care. In Radiology, machine learning can be used for image analysis; patient selection and examination scheduling; image acquisition and reconstruction; using image data for prognostic purposes; and combining image data with information from electronic health records, laboratory and genetic data. However, when designing algorithms, it is important to take into account clinic workflow and how the technology can be implemented in clinical practice. Dr. Leiner will discuss these aspects of machine learning from a cardiovascular imaging perspective.

The thing he hopes people take away from his lecture is the broad impact machine learning will continue to have on radiology and medical imaging. “It will affect all steps of the imaging chain, from patient selection for imaging, image acquisition, and reconstruction, but also post-processing, reporting and extraction of prognostic information. In short, all aspects of radiology as we know it will be affected,” says Dr. Leiner.

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