contextflow SEARCH Lung CT offers clinical decision support for 19 disease patterns + lung nodules in lung CTs, providing relevant statistics, reference cases and differential diagnosis information directly in your PACS. Click a button below to see how SEARCH Lung CT can improve your daily workflow.
Comprehensive clinical decision support system integrated into your native viewer
SEARCH Lung CT supports radiologists in clinical routine with qualitative and quantitative insights for 19 image patterns and lung nodules.
Gain insight into what’s happening with a case before you even open it with information like the number of nodules detected and the percentage of lung affected by anomalies.
Visualize detected findings and nodules in color from within your native viewer.
Detected nodules can be accepted or rejected from within your viewer. Only those accepted will appear in the final report.
Quantifications are provided in the form of a structured report, which includes the following:
- Number of nodules detected
- Average diameter of largest nodule (mm)
- Lung coverage values (%) for anomalies
quantitative image analysis results are automatically sent to your PACS:
- Coverage values for lung anomalies (%)
- Coverage values for 7 key image patterns (%)
- Spatial distribution maps for image patterns
- 3D nodule detection results
Need additional information about your patient? One click from your PACS takes you to our Insights Screen containing even more complementary information for your analysis.
These maps guide you to the important parts of the CT – or show you there is nothing at all.
Understand how much of the lung is affected by the following patterns:
- Ground-glass opacity
- Reticular Pattern
We not only generate a general heatmap showing all anomalies; rather, we also generate a heatmap for each key disease pattern.
We detect and measure nodules in accordance with the following guidelines:
- British Thoracic Society guidelines for the investigation and management of pulmonary nodules
- European Union Position Statement on Lung Cancer Screening
- Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017
- International Early Lung Cancer Action Program: Screening Protocol, 2021
- Lung-RADS Version 1.1 2019
- Response Evaluation Criteria in Solid Tumors (RECIST)
Save clicks by retrieving information on differential diagnosis directly in our UI. It’s also perfect for training new radiologists.
3D Image Search
Find reference cases similar to your current patient
Still need additional information for your case? The Search Screen enables you to select a region of interest and receive similar reference cases from our knowledge base of thousands of patients. This enables you to compare and contrast your current patient with similar cases and to understand how we reached specific results – no more black box AI.
the search screen provides analysis and classifications for 19 image patterns in selected regions of interest:
- airway wall thickening
- ground glass
- mosaic perfusion pattern
- pulmonary cavity
- reticular pattern
- tree in bud
- non-specific: includes image patterns not
currently incorporated and patterns with no
evidence of pathological changes
Find cases similar to yours from a Knowledge Base of thousands of curated reference cases, thus enabling you to build confidence in the system’s results.
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.
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.
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.
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.
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.
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.
Chief Medical Information Officer & Head of Imaging IT and Value-Based Imaging at Erasmus MC