This paper is in the following e-collection/theme issue:

RCTs - Protocols/Proposals (eHealth) (669) Artificial Intelligence (2132) Coronary Heart Disease (109) Decision Support for Health Professionals (1540) mHealth in a Clinical Setting (743) Clinical Information and Decision Making (1916) Computer-Aided Diagnosis (252) Cardiac Risk and Cardiac Risk Calculators (102) Machine Learning (2077)

Published on in Vol 14 (2025)

This is a member publication of University of Toronto

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/71726, first published .
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

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

Authors of this article:

Jeremy Petch1, 2, 3, 4 Author Orcid Image ;   Juan Pablo Tabja Bortesi3, 5 Author Orcid Image ;   Tej Sheth1, 2 Author Orcid Image ;   Madhu Natarajan1, 2 Author Orcid Image ;   Natalia Pinilla-Echeverri1, 2 Author Orcid Image ;   Shuang Di3, 6 Author Orcid Image ;   Shrikant I Bangdiwala1, 7 Author Orcid Image ;   Karen Mosleh8 Author Orcid Image ;   Omar Ibrahim1, 2 Author Orcid Image ;   Kevin R Bainey9 Author Orcid Image ;   Julian Dobranowski10, 11, 12 Author Orcid Image ;   Maria P Becerra3 Author Orcid Image ;   Katie Sonier3 Author Orcid Image ;   Jon-David Schwalm1, 2, 8 Author Orcid Image
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