Tag: CAC score

Echo AI Coronary Artery Calcium Scoring

Posted on by Jake Fishman

A Cedars-Sinai-led team developed an echocardiography AI model that was able to accurately assess coronary artery calcium buildup, potentially revealing a safer, more economical, and more accessible approach to CAC scoring.

The researchers used 1,635 Cedars-Sinai patients’ transthoracic echocardiogram (TTE) videos paired with their CT-based Agatston CAC scores to train an AI model to predict patients’ CAC scores based on their PLAX view TTE videos. 

When tested against Cedars-Sinai TTEs that weren’t used for AI training, the TTE CAC AI model detected…

  • Zero CAC patients with “high discriminatory abilities” (AUC: 0.81)
  • Intermediate patients “modestly well” (≥200 scores; AUC: 0.75)
  • High CAC patients “modestly well” (≥400 scores; AUC: 0.74)

When validated against 92 TTEs from an external Stanford dataset, the AI model similarly predicted which patients had zero and high CAC scores (AUCs: 0.75 & 0.85).

More importantly, the TTE AI CAC scores accurately predicted the patients’ future risks. TTE CAC scores predicted one-year mortality similarly to CT CAC scores, and they even improved overall prediction of low-risk patients by downgrading patients who had high CT CAC scores and zero TTE CAC scores.

The Takeaway

CT-based CAC scoring is widely accepted, but it isn’t accessible to many patients, and concerns about its safety and value (cost, radiation, incidentals) have kept the USPSTF from formally recommending it for coronary artery disease surveillance. We’d need a lot more research and AI development efforts, but if TTE CAC AI solutions like this prove to be reliable, it could make CAC scoring far more accessible and potentially even more accepted.

Chest CT’s Untapped Potential

Posted on by Jake Fishman

A new AJR study out of Toronto General Hospital highlighted the largely-untapped potential of non-gated chest CT CAC scoring, and the significant impact it could have with widespread adoption.

Current guidelines recommend visual CAC evaluations with all non-gated non-contrast chest CTs. However, these guidelines aren’t consistently followed and they exclude contrast-enhanced chest CTs.

The researchers challenged these practices, performing visual CAC assessments on 260 patients’ non-gated chest CT exams (116 contrast-enhanced, 144 non-contrast) and comparing them to the same patients’ cardiac CT CAC scores (performed within 12-months) and ~6-year cardiac event outcomes.

As you might expect, visual contrast-enhanced and non-contrast chest CT CAC scoring:  

  • Detected CAC with high sensitivity (83% & 90%) and specificity (both 100%)
  • Accurately predicted major cardiac events (Hazard ratios: 4.5 & 3.4)
  • Had relatively benign false negatives (0 of 26 had cardiac events)
  • Achieved high inter-observer agreement (κ=0.89 & 0.95)

The Takeaway

Considering that CAC scores were only noted in 37% of the patients’ original non-contrast chest CT reports and 23% of their contrast-enhanced chest CT reports, this study adds solid evidence in favor of more widespread CAC score reporting in non-gated CT exams.

That might also prove to be good news for the folks working on opportunistic CAC AI solutions, noting that AI has (so far) seen the greatest adoption when it supports processes that most radiologists are actually doing.

Incidental Evolution

Posted on by Jake Fishman

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).

UCSF Automates CAC Scoring

Posted on by Jake Fishman

UCSF is now using AI to automatically screen all of its routine non-contrast chest CTs for elevated coronary artery calcium scores (CAC scores), representing a major milestone for an AI use case that was previously limited to academic studies and future business strategies.

UCSF’s Deployment – UCSF becomes the first medical center to deploy the end-to-end AI CAC scoring system that it developed with Stanford and Bunkerhill Health earlier this year. The new system automatically identifies elevated CAC scores in non-gated / non-contrast chest CTs, creating an “opportunistic screening pathway” that allows UCSF physicians to identify high-CAC patients and get them into treatment.

Why This is a Big Deal – Over 20m chest CTs are performed in the U.S. annually and each of those scans contains insights into patients’ cardiac health. However, an AI model like this would be required to extract cardiac data from the majority of CT scans (CAC isn’t visible to humans in non-gated CTs) and efficiently interpret them (there’s far too many images). This AI system’s path from academic research to clinical deployment seems like a big deal too.

The Commercial Impact – Most health systems don’t have the AI firepower of Stanford and UCSF, but they certainly produce plenty of chest CTs and should want to identify more high-risk patients while treatable (especially if they’re also risk holders). Meanwhile, there’s growing commercial efforts from companies like Cleerly and Nanox.AI to create opportunistic CAC screening pathways for all these health systems that can’t develop their own CAC AI workflows (or prefer not to).

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