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.
