b-rayZ
Alexander Ciritsis is a seasoned professional in the fields of medical physics and data science, currently serving as CTO and Co-Founder of b-rayZ since 2019, where efforts are focused on enhancing breast cancer early detection through AI medical software collaboration with radiologists and breast cancer centers. Concurrently, Alexander holds a position at Universitätsspital Zürich as a Medical Physicist and Data Scientist, emphasizing the development and clinical evaluation of innovative medical software solutions, particularly in machine learning and deep learning for medical image analysis. Previous roles include Clinical Research Scientist at Uniklinik RWTH Aachen and Project Manager at X-Alliance GmbH. Alexander Ciritsis boasts an extensive education background, including a Venia Legendi in Radiology from the University of Zurich, a PhD in Medical Science from RWTH Aachen University, and two master's degrees in Production System Engineering and Biomedical Engineering from RWTH Aachen University and FH Aachen University of Applied Sciences, respectively.
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b-rayZ
b-rayZ is a spinoff of the University Hospital of Zurich. We develop AI software that supports the daily work of the radiologist in mammography with real-time assessment of image quality and breast density. We believe that AI can supplement visual inspection of radiologists in quality assurance and breasts density assessment. With us, you can focus on your clinical work and relay on better image quality. INNOVATION BY THE SIDE OF THE SPECIALIST Early detection of breast cancer by mammography is difficult in patients with a dense breast tissue and in poor quality images. Nowadays after each exam, radiologists rate mammographies in terms of breast density and diagnostic quality, which is costly, slow and doctor-dependent practice. The b-box, uses AI algorithms to emulate doctor decision and provides real-time feedback on breast density and image quality. OUR VISION b-rayZ is devoted to the development of AI solutions for radiology. Our products are developed by keeping in mind the needs of radiologists. • How can the radiologist provide higher quality examinations and reports with less efforts and lower costs? • How can the specialist take advantage of AI solutions without loosing the control on the medical procedures?