SEARCH Lung CT
Clinical decision support for 19 image patterns in lung CTs + nodules detection

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.

PACS Integration

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.

Smart Worklist
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.

Secondary Capture
Visualize detected findings and nodules in color from within your native viewer.

Accept/reject nodules
Detected nodules can be accepted or rejected from within your viewer. Only those accepted will appear in the final report.

Structured 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

PDF Report
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

Quantitative Insights

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.

Anomaly Heatmaps
These maps guide you to the important parts of the CT – or show you there is nothing at all.

Pattern Quantification
Understand how much of the lung is affected by the following patterns:

  • Consolidation
  • Effusion
  • Emphysema
  • Ground-glass opacity
  • Honeycombing
  • Pneumothorax
  • Reticular Pattern

Pattern Heatmaps
We not only generate a general heatmap showing all anomalies; rather, we also generate a heatmap for each key disease pattern.

Nodule Detection
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)

Reference literature
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.

Pattern Analysis
the search screen provides analysis and classifications for 19 image patterns in selected regions of interest:

  • airway wall thickening
  • atelectasis
  • bronchiectasis
  • bulla
  • consolidation
  • cyst
  • effusion
  • emphysema
  • ground glass
  • honeycombing
  • mass
  • mosaic perfusion pattern
  • nodule
  • pneumothorax
  • pulmonary cavity
  • reticular pattern
  • tree in bud
  • non-specific: includes image patterns not
    currently incorporated and patterns with no
    evidence of pathological changes

 

 

Reference Cases
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.

Testimonials

lluís-donoso-bach-contextflow

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

boris-brkljacic-contextflow

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

christian-herold-contextflow

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

anand-patel-contexflow

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