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Medical University of Vienna spin-off "contextflow" receives seed funding

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(Vienna, 23 March 2018) The Medical University of Vienna's spinoff "contextflow" has just obtained seed funding. The start-up capital will allow it to scale up its activities and continue to develop its technology in the field of artificial intelligence. "contextflow" develops software to conduct rapid searches of large medical image databases.

The firm emerged from an EU project KHRESMOI ( at MedUni Vienna's Department of Biomedical Imaging and Image-guided Therapy and the Computational Imaging Research Lab ( that is housed there. It develops software to help radiologists with diagnosis by allowing them to search large image databases quickly using artificial intelligence.

The spinoff has just completed the seed funding process led by IST Cube and APEX Ventures. The investment will enable the company to scale up its activities to further develop technology in the field of artificial intelligence.

Medical imaging: finding and comparing relevant information quickly
Artificial intelligence is becoming a key technology in medicine, particularly in diagnostics and in support of decision-making. "contextflow" is developing technology in this area to help doctors by rapidly providing them with the relevant information they need for decision-making. This allows the enormous amount of knowledge that has been built up over many years to be used in individual diagnoses.

The Medical University of Vienna and "contextflow" are working closely together to refine techniques and to address and resolve clinically relevant problems.

The Computational Imaging Research Lab is a non-clinical division of MedUni Vienna's Department of Biomedical Imaging and Image-guided Therapy. The interdisciplinary research group is developing methodology at the interface between machine learning, artificial intelligence and precision medicine. Research projects are concerned with the identification of prognostic markers in image data and the development of predictive models and progression models.