Tag: contextflow

RevealDx & contextflow’s Lung CT Alliance

Posted on by Jake Fishman

RevealDx and contextflow announced a new alliance that should advance the companies’ product and distribution strategies, and appears to highlight an interesting trend towards more comprehensive AI solutions.

The companies will integrate RevealDx’s RevealAI-Lung solution (lung nodule characterization) with contextflow’s SEARCH Lung CT software (lung nodule detection and quantification), creating a uniquely comprehensive lung cancer screening offering. 

contextflow will also become RevealDx’s exclusive distributor in Europe, adding to RevealDx’s global channel that includes a distribution alliance with Volpara (exclusive in Australia/NZ, non-exclusive in US) and a platform integration deal with Sirona. 

The alliance highlights contextflow’s new partner-driven strategy to expand SEARCH Lung CT beyond its image-based retrieval roots, coming just a few weeks after announcing an integration with Oxipit’s ChestEye Quality AI solution to identify missed lung nodules.

In fact, contextflow’s AI expansion efforts appear to be part of an emerging trend, as AI vendors work to support multiple steps within a given clinical activity (e.g. lung cancer assessments) or spot a wider range of pathologies in a given exam (e.g. CXRs):

  • Volpara has amassed a range of complementary breast cancer screening solutions, and has started to build out a similar suite of lung cancer screening solutions (including RevealDx & Riverain).
  • A growing field of chest X-ray AI vendors (Annalise.ai, Lunit, Qure.ai, Oxipit, Vuno) lead with their ability to detect multiple findings from a single CXR scan and AI workflow. 
  • Siemens Healthineers’ AI-RAD Companion Chest CT solution combines these two approaches, automating multiple diagnostic tasks (analysis, quantification, visualization, results generation) across a range of different chest CT exams and organs.

The Takeaway

contextflow and RevealDx’s European alliance seems to make a lot of sense, allowing contextflow to enhance its lung nodule detection/quantification findings with characterization details, while giving RevealDx the channel and lung nodule detection starting points that it likely needs.

The partnership also appears to represent another step towards more comprehensive and potentially more clinically valuable AI solutions, and away from the narrow applications that have dominated AI portfolios (and AI critiques) before now.

Content-Based AI Efficiency

Posted on by Jake Fishman

A new study out of Austria provided solid evidence that content-based image retrieval systems (CBIRS) enhance radiologists’ reading efficiency, while potentially improving their diagnostic accuracy.

Eight radiologists reviewed chest CTs from 108 patients with suspected diffuse parenchymal lung disease (DPLD), leveraging contextflow’s AI-based SEARCH Lung CT CBIRS with half of the exams. 

Using the radiologists’ CT image regions of interest, the CBIRS would search a database of 6,542 chest CTs to identify similar scans, providing the rads with the three most likely disease patterns and supporting information (e.g. a list of potential differential diagnoses). The CBIRS’ added “context” had a notable impact on the radiologists:

  • Reducing their average reading time by 31.3% (197 vs. 287 seconds) 
  • Reducing resident and attending radiologists’ reading time by 27% and 35% 
  • Improving overall diagnostic accuracy by over 7pts (42.2% vs. 34.7%; not statistically significant)

These reading time reductions came despite the fact that radiologists were more likely to search for additional information when using the CBIRS (72% vs. 43% of cases). That’s partially because CBIRS allowed greater speed improvements when radiologists searched for more information (110 seconds faster vs. without CBIRS) than when rads didn’t search for more info (39 seconds faster).

The Takeaway
This study presents a rare example of how imaging AI can significantly improve radiologists’ efficiency, while amplifying their current workflows and diagnostic decision-making processes. It’s also the second study in the last year suggesting that CBIRS might improve diagnostic accuracy, although the authors encourage more research into CBIRS’ accuracy impact to know for sure.

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