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Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography for Low-Risk Patients Screened With Artificial Intelligence: Protocol for the CarDIA-AI Randomized Controlled Trial

Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography for Low-Risk Patients Screened With Artificial Intelligence: Protocol for the CarDIA-AI Randomized Controlled Trial

The model has been updated using more recent referral data and has undergone external validation using 2008-2023 province-wide data to confirm generalizability (Petch et al, unpublished paper, April 2025). The integration of this model into clinical practice may enable a more accurate assessment of patient risk and improve diagnostic efficiencies. A decision support tool that incorporates such an AI model within a centralized triage pathway may facilitate patient selection for first-line CCTA (Figure 1).

Jeremy Petch, Juan Pablo Tabja Bortesi, Tej Sheth, Madhu Natarajan, Natalia Pinilla-Echeverri, Shuang Di, Shrikant I Bangdiwala, Karen Mosleh, Omar Ibrahim, Kevin R Bainey, Julian Dobranowski, Maria P Becerra, Katie Sonier, Jon-David Schwalm

JMIR Res Protoc 2025;14:e71726