iCAD and Solis CVD Alliance

iCAD and major breast imaging center company Solis Mammography announced plans to develop and commercialize AI that quantifies breast arterial calcifications (BACs) in mammograms to identify women with high cardiovascular disease (CVD) risks.

Through the multi-year alliance, iCAD and Solis will expand upon iCAD’s flagship ProFound AI solution’s ability to detect and quantify BACs, with the goal of helping radiologists identify women with high CVD risks and guide them into care.

iCAD and Solis’ expansion into cardiovascular disease screening wasn’t exactly expected, but recent trends certainly suggest that commercial AI-based BAC detection could be on the way: 

  • There’s also mounting academic and commercial momentum behind using AI to “opportunistically” screen for incidental findings in scans that were performed for other reasons (e.g. analyzing CTs for CAC scores, osteoporosis, or lung nodules).
  • Despite being the leading cause of death in the US, it appears that we’re a long way from formal heart disease screening programs, making the already-established mammography screening pathway an unlikely alternative.
  • Volpara and Microsoft are also working on a mammography AI product that detects and quantifies BACs. In other words, three of the biggest companies in breast imaging (at least) and one of the biggest tech companies in the world are all currently developing AI-based BAC screening solutions.

The Takeaway

Widespread adoption of mammography AI-based cardiovascular disease screening might seem like a longshot to many readers who often view incidentals as a burden and have grown weary of early-stage AI announcements… and they might be right. That said, there’s plenty of evidence suggesting that a solution like this would help detect more early-stage heart disease using scans that are already being performed.

Incidental Evolution

Last week brought a wave of studies that either highlighted how findings in common imaging exams could add value in completely different clinical areas, or showed how incidentals could find a home in established clinical workflows. That might not be welcomed news among the many radiologists who view incidentals as a clinical slippery slope, but it’s another sign that the incidental evolution is gaining momentum.

Left Atrial Dementia Marker – A new JAMA study showed that echocardiographic left atrial function measurements can be used to identify individuals with higher dementia risks, in addition to supporting cardiovascular diagnosis. Analysis of 4,096 participants’ echo exams and 6-year outcomes (75yr avg. age; 531 developed dementia) revealed that lower left atrial function (e.g. reservoir strain, conduit strain, contractile strain, active emptying fraction, emptying fraction) has a statistically significant association with developing dementia (1.43 to 1.98 hazard ratios).

BACs and CVD – A Kaiser Permanente study added more evidence supporting breast arterial calcifications’ value as a cardiovascular disease risk factor. The researchers analyzed 5,059 women’s digital mammography exams (26.5% w/ BACs), finding that women with BACs had a 51% higher risk of developing atherosclerotic CVD and a 23% higher risk of developing any type of CVD over 6.5-years. This is far from the first study to tie BACs to CVD risk, but it came with a high level of credibility (large/observational study, published on Circulation) and generated quite a bit of media attention.

Auto CAC Pathway – A Journal of Digital Imaging study highlighted how coronary artery calcium scores (CAC scores) could be integrated into standard cardiovascular disease (CVD) risk systems, potentially streamlining CAC AI adoption. The researchers used an FDA-cleared AI model (believed to be from Nanox AI) to screen 14,135 patients’ existing CTs (470 who experienced CVD within 5yrs) and then combined their CAC scores with the ACC/AHA’s PCE risk system. The AI-augmented PCE predictions outperformed standard PCE predictions (sensitivity: 57% vs. 53%; specificity: 70% vs. 67%), without requiring additional scans or diagnostic workflows.

Northwestern Follows-Up – A new NEJM study highlighted the impressive results of Northwestern Medicine’s lung nodule follow-up system, which uses NLP to identify suspicious nodules and then initiates a follow-up workflow (prompts physicians, notifies patients, tracks follow-ups). Over 13 months, the system screened over 570k imaging studies, flagging 29k exams for follow-up (77.1% sensitivity, 99.5% specificity, 90.3% PPV), and tracked over 2,400 follow-ups to completion.

The Takeaway
Last week’s batch of studies serve as yet another reminder that common imaging exams could serve broader clinical roles the future, either by creating new risk-based incidental pathways (LA function for dementia; BAC for CVD), catching more undetected incidentals (AI CAC scoring), or by formalizing how incidentals are brought into clinical pathways (e.g. adding CAC to PCEs; leveraging NLP for follow-ups).

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