ArticleArquivos brasileiros de cardiologia2021
Diagnostic Performance of a Machine Learning-Based CT-Derived FFR in Detecting Flow-Limiting Stenosis.
Article in Arquivos brasileiros de cardiologia, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The diagnostic performance of machine learning-based FFRCT for coronary artery disease: A meta-analysis.Open medicine (Warsaw, Poland) · 2025Article
- Cardiovascular Computed Tomography and Magnetic Resonance Imaging Guideline of the Brazilian Society of Cardiology and the Brazilian College of Radiology - 2024.Arquivos brasileiros de cardiologia · 2024Article
- Artificial Intelligence in Cardiovascular CT and MR Imaging.Life (Basel, Switzerland) · 2023Review
- 2021 Top 10 Articles in the Arquivos Brasileiros de Cardiologia and the Revista Portuguesa de Cardiologia.Arquivos brasileiros de cardiologia · 2022Article
- Machine learning applications in cardiac computed tomography: a composite systematic review.European heart journal open · 2022Review
- It is Time for Coronary Computed Tomography Angiography to be Incorporated into the SUS.Arquivos brasileiros de cardiologia · 2022Article
- Artificial intelligence in atherosclerotic disease: Applications and trends.Frontiers in cardiovascular medicine · 2022Review
- Artificial Algorithms Outperform Traditional Models in Predicting Coronary Artery Disease.Arquivos brasileiros de cardiologia · 2021Article
- One-Stop Shop for Non-Invasive Cardiovascular Imagers?Arquivos brasileiros de cardiologia · 2021Article
- Artificial intelligence in cardiology: The past, present and future.Indian heart journalArticle
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10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe non-invasive quantification of the fractional flow reserve (FFRCT) using a more recent version of an artificial intelligence-based software and latest generation CT scanner (384 slices) may show high performance to detect coronary ischemia.
objectivesTo evaluate the diagnostic performance of FFRCT for the detection of significant coronary artery disease (CAD) in contrast to invasive FFR (iFFR) using previous generation CT scanners (128 and 256- detector rows).
methodsRetrospective study with patients referred to coronary artery CT angiography (CTA) and catheterization (iFFR) procedures. Siemens Somatom Definition Flash (256-detector rows) and AS+ (128-detector rows) CT scanners were used to acquire the images. The FFRCT and the minimal lumen area (MLA) were evaluated using a dedicated software (cFFR version 3.0.0, Siemens Healthineers, Forchheim, Germany). Obstructive CAD was defined as CTA lumen reduction ≥ 50%, and flow-limiting stenosis as iFFR ≤0.8. All reported P values are two-tailed, and when <0.05, they were considered statistically significant.
resultsNinety-three consecutive patients (152 vessels) were included. There was good agreement between FFRCT and iFFR, with minimal FFRCT overestimation (bias: -0.02; limits of agreement:0.14-0.09). Different CT scanners did not modify the association between FFRCT and FFRi (p for interaction=0.73). The performance of FFRCT was significantly superior compared to the visual classification of coronary stenosis (AUC 0.93vs.0.61, p<0.001) and to MLA (AUC 0.93vs.0.75, p<0.001), reducing the number of false-positive cases. The optimal cut-off point for FFRCT using a Youden index was 0.85 (87% Sensitivity, 86% Specificity, 73% PPV, 94% NPV), with a reduction of false-positives.
conclusionMachine learning-based FFRCT using previous generation CT scanners (128 and 256-detector rows) shows good diagnostic performance for the detection of CAD, and can be used to reduce the number of invasive procedures.
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