Improving patient identification for advanced cardiac imaging through machine learning-integration of clinical and coronary CT angiography data
Jan Walter Benjamins; Ming Wai Yeung; Teemu Maaniitty; Antti Saraste; Riku Klén; Pim van der Harst; Juhani Knuuti; Luis Eduardo Juárez‐Orozco · 2021 · International Journal of Cardiology
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
BACKGROUND: Standard computed tomography angiography (CTA) outputs a myriad of interrelated variables in the evaluation of suspected coronary artery disease (CAD). But an important proportion of obstructive lesions does not cause significant myocardial ischemia. Nowadays, machine learning (ML) allows integration of numerous variables through complex interdependencies that optimize classification and prediction at the individual level. We evaluated ML performance in integrating CTA and clinical variables to identify patients that demonstrate myocardial ischemia through PET and those who ultimat
Abstract by Jan Walter Benjamins; Ming Wai Yeung; Teemu Maaniitty; Antti Saraste; Riku Klén; Pim van der Harst; Juhani Knuuti; Luis Eduardo Juárez‐Orozco, International Journal of Cardiology (2021) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1016/j.ijcard.2021.04.009