We can populate your worklist with the following quantifications:
- Number of nodules detected
- Average diameter of largest nodule (mm)
- Lung coverage values (%) for anomalies
Visualize detected findings and nodules in color from within your native viewer.
Confirm detected nodules from within your native viewer – only confirmed nodules appear in final report
Quantifications can be pre-populated into your structured reporting tool
I have been following contextflow’s progress practically since the company’s founding, and their traction in the area of chest 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.
Chief Medical Information Officer & Head of Imaging IT and Value-Based Imaging at Erasmus MC
I really like the transparency of contextflow as opposed to other black box AI solutions. It’s designed to support my workflow while leaving the final decision up to me.
Vice Chair and Head of Imaging Informatics at the Department of Radiology at Freiburg University Medical Center
In the world of AI, it’s crucial to use it safely, be clear about what it does, and make ethical choices. This means moving forward with innovation in a responsible way, creating a future that’s both advanced and thoughtful.
MD MSc MRCS FRCR Consultant Radiologist, Artificial Intelligence Lead NHS SW London Imaging Network
For interstitial lung disease, we use contextflow on a daily clinical basis. We now put the major information into the radiology report. And this is what our clinicians expect from us: to be able to quantify the disease and especially to quantify disease progression in order to improve clinical decision making.
Managing Senior Physician at the University Department of Radiology at the Medical University of Innsbruck
One of the great features of contextflow is the TIMELINE view, which offers the possibility to actually analyse follow-up scans. And that has a lot of value for our clinical practice because patients will return to our practice for follow-up imaging.
Head of the Imaging Services Group at the Department of Radiology, Leiden University Medical Center
I use contextflow in any routine scan performed, for example, for staging, or for other disease evaluation. It helps me a lot to recognize patterns in patients where you’d not expect or where we cannot clearly see the pathology behind it. So it helps us a lot as a double checker.
Radiology Resident & Clinician Scientist at the Medical University of Mainz
contextflow 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.
Former President of the European Society of Medical Imaging Informatics (EuSoMII), Radiologist at St. Nikolaus Hospital in Eupen
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.
Head of Radiology at Vienna General Hospital
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.
MD, Chief of Interventional Radiology, Providence Little Company of Mary Medical Centers
We are very interested in using tools based on artificial intelligence like contextflow to support the decision in the diagnostic process based on the image.
Lluís Donoso Bach
President of the International Society of Radiology