detection software
for chest CT
integrated directly
in your PACS

contextflow is your comprehensive chest CT guide


Quantitative and qualitative insights for lung cancer, ILD and COPD directly in your viewer

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Comprehensive chest CT insights directly in your native viewer

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Comprehensive quantitative profiling of a patient with every chest CT

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contextflow ADVANCE Chest CT provides radiologists comprehensive computer-aided detection support for suspected lung cancer, ILD and COPD cases. Its main features are designed to help save time and improve reporting quality: nodule detection and quantification, nodule tracking over time, quantitative lung tissue analysis for key image findings, and qualitative analysis of 19 image patterns plus reference cases and differential diagnosis information.

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DETECT / Nodule Detection

Detection & quantification of nodules from within your native viewer. Nodule characterization proven to reduce false positives and flag at-risk patients sooner.

TIMELINE / Nodule Tracking

Understand how your patient’s nodules change over time. Save time preparing for follow ups and multidisciplinary team meetings.

INSIGHTS / Lung Tissue Analysis

Anomaly heatmaps show overall distribution of detected disease patterns. Quantification & individual heatmaps for 8 key image patterns.

SEARCH / 3D Image Search

Qualitative analysis of 19 image patterns in chest CT. Links to differential diagnosis literature. Retrieval of similar cases to yours from a curated knowledge base.

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Latest Research
Average report reading time reduced 31% in clinical study with the Medical University of Vienna

Publication / Abstracts
contextflow’s scientific roots as a spinoff of the Medical University of Vienna means peer-reviewed research forms the foundation of everything we develop.



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We are very interested in using tools based on artificial intelligence like contextflow SEARCH to support the decision in the diagnostic process based on the image.

Lluís Donoso Bach

President of the International Society of Radiology


At Dubrava University Hospital, we take pride in providing the best care possible to our patients. There are many AI radiology solutions, but we agreed to the proof of concept with contextflow because their solution provides real value, particularly for new residents.

Boris Brkljacic

President of the European Society of Radiology


We’re very interested in using AI to improve the hospital experience for both doctors and patients; contextflow’s use of deep learning, particularly for lung diseases, is exactly the type of technology we want to evaluate. I very much look forward to the results.

Christian Herold

Head of Radiology at Vienna General Hospital

I really like the transparency of contextflow SEARCH as opposed to other black box AI solutions. It’s designed to support my workflow while leaving the final decision up to me.

Elmar Kotter

Vice Chair and Head of Imaging Informatics at the Department of Radiology at Freiburg University Medical Center


After using and advising several radiology AI software companies, I can say that what contextflow offers is actually the next generation of AI products to support the radiologist, not replace them. Their general approach means they recognize all relevant findings, not just one.

Anand Patel

MD, Chief of Interventional Radiology, Providence Little Company of Mary Medical Centers

contextflow SEARCH Lung CT is one of the applications that certainly fits radiology’s current needs and can simplify the analysis of complex lung pathology. With the right insights and technology, we can succeed in introducing AI in a very attractive way to radiology departments on a global scale.

Erik Ranschaert

Former President of the European Society of Medical Imaging Informatics (EuSoMII), Radiologist at St. Nikolaus Hospital in Eupen

I have been following contextflow’s progress practically since the company’s founding, and their traction in the area of lung CT is impressive. Being able to shape clinical decision support tools that myself and colleagues can benefit from in clinical practice is a big motivator. We’re literally shaping the future.

Jacob Visser

Chief Medical Information Officer & Head of Imaging IT and Value-Based Imaging at Erasmus MC


Markus Holzer

AI in radiology will increasingly be adopted in clinical routine, augmenting radiologists in mundane and simple tasks as well as providing them with all the relevant context to enable fast and high quality reporting

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