Massachusetts General Hospital
Boston, Massachusetts, United States
Arinc Ozturk, MD is a researcher for Center for Ultrasound Research & Translation (CURT), Department of Radiology, Massachusetts General Hospital and Instructor for Harvard Medical School. His research focuses on ultrasound imaging of liver and early diagnosis of nonalcoholic fatty liver disease (NAFLD) and other chronic liver diseases. He is studying ultrasound elastography techniques for fibrosis quantification, and sonographic fat quantification techniques for steatosis quantification. He is also interested in ultrasound device development and artificial intelligence for ultrasound images. He is currently the co-chair of American Institute of Ultrasound in Medicine (AIUM) / Quantitative Imaging Biomarkers Alliance (QIBA) PEQUS Ultrasound Attenuation committee and AIUM Liver fat quantification task force.
His other committee commitments include World Federation of Ultrasound in Medicine and Biology (WFUMB) Safety committee, Radiological Society of North America (RSNA) QIBA Shear Wave Speed Committee, Society of Abdominal Radiology (SAR) Liver Fibrosis Disease Focused Group, AIUM Elastography community, AASLD NAFLD Special Interest Group, and AIUM Artificial Intelligence community. He serves as a reviewer for several journals including, Journal of Ultrasound in Medicine, Ultrasound in Medicine and Biology, Abdominal Radiology, European Journal of Radiology, American Journal of Roentgenology and Radiology. He is an associate editor for WFUMB Open journal. He published many papers about ultrasound elastography and sonographic fat quantification techniques and wrote textbook chapters about liver fibrosis imaging and cirrhosis imaging. His published papers about elastography imaging for nonalcoholic steatohepatitis and the value of artificial intelligence in liver fibrosis imaging have been cited many times. He is currently working on several industry and NIH funded projects to address the limitations of ultrasound elastography and fat quantification methods, and building databases to develop artificial intelligence algorithms to diagnose NAFLD at early stages by using the ultrasound and histopathology images.
Tuesday, March 28, 2023
8:00 AM – 8:12 AM