PRESENTED BY
Julie Sufana

NOMINATED BY
Technische Universität Wien

 

Facing increasing workloads, radiologists must choose between longer working hours or decreased time evaluating images. About 20% of cases require additional research from many sources, requiring up to 20 minutes each with a questionable rate of success.

contextflow’s 3D image-based search engine uses deep learning to put the knowledge encoded in millions of medical images and reports at radiologists’ fingertips, saving time and money. Simply mark a region of interest in an image, and we instantly return reference cases and associated knowledge necessary for differential diagnosis. No more paging through books, guessing the right keyword or consulting various text-based reference search engines. Case-relevant information is only a few clicks away! Additionally, our software can help prioritize time-critical patients.

Our solution is currently being validated on Lung CT scans and can be extended to additional pathologies and organs. Moreover, we enable clinics to share information across institutional borders and benefit from a collaborative growing knowledge base, generating clinical value from existing data. Our solution is currently being tested with 7+ international partners.

contextflow is a spin-off of the Medical University of Vienna (MUW), Technical University of Vienna (TU) and European research project KHRESMOI. Founded in 2016, we received the BCS Search Industry Most Promising Startup Award 2016, the 2017 Digital Innovation Award by the Austrian Ministry of Education, Science & Research, and were selected as one of 19 startups out of 700+ applications for the 2018 Philips HealthWorks accelerator. Most recently, the Central European Startup Awards awarded us Best Healthcare Startup – Austria.

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