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Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve Result From the MACHINE Consortium
Erasmus Univ, Netherlands.
Univ Ulsan, South Korea.
Inst Cardiol, Poland.
Med Univ South Carolina, SC 29425 USA.
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2018 (English)In: Circulation Cardiovascular Imaging, ISSN 1941-9651, E-ISSN 1942-0080, Vol. 11, no 6, article id e007217Article in journal (Refereed) Published
Abstract [en]

Background: Coronary computed tomographic angiography (CTA) is a reliable modality to detect coronary artery disease. However, CTA generally overestimates stenosis severity compared with invasive angiography, and angiographic stenosis does not necessarily imply hemodynamic relevance when fractional flow reserve (FFR) is used as reference. CTA-based FFR (CT-FFR), using computational fluid dynamics (CFD), improves the correlation with invasive FFR results but is computationally demanding. More recently, a new machine-learning (ML) CT-FFR algorithm has been developed based on a deep learning model, which can be performed on a regular workstation. In this large multicenter cohort, the diagnostic performance ML-based CT-FFR was compared with CTA and CFD-based CT-FFR for detection of functionally obstructive coronary artery disease. Methods and Results: At 5 centers in Europe, Asia, and the United States, 351 patients, including 525 vessels with invasive FFR comparison, were included. ML-based and CFD-based CT-FFR were performed on the CTA data, and diagnostic performance was evaluated using invasive FFR as reference. Correlation between ML-based and CFD-based CT-FFR was excellent (R=0.997). ML-based (area under curve, 0.84) and CFD-based CT-FFR (0.84) outperformed visual CTA (0.69; Pamp;lt;0.0001). On a per-vessel basis, diagnostic accuracy improved from 58% (95% confidence interval, 54%-63%) by CTA to 78% (75%-82%) by ML-based CT-FFR. The per-patient accuracy improved from 71% (66%-76%) by CTA to 85% (81%-89%) by adding ML-based CT-FFR as 62 of 85 (73%) false-positive CTA results could be correctly reclassified by adding ML-based CT-FFR. Conclusions: On-site CT-FFR based on ML improves the performance of CTA by correctly reclassifying hemodynamically nonsignificant stenosis and performs equally well as CFD-based CT-FFR.

Place, publisher, year, edition, pages
LIPPINCOTT WILLIAMS & WILKINS , 2018. Vol. 11, no 6, article id e007217
Keywords [en]
area under curve; computed tomography angiography; coronary artery disease; hemodynamics; machine learning
National Category
Cardiac and Cardiovascular Systems
Identifiers
URN: urn:nbn:se:liu:diva-149477DOI: 10.1161/CIRCIMAGING.117.007217ISI: 000435564000003PubMedID: 29914866OAI: oai:DiVA.org:liu-149477DiVA, id: diva2:1231076
Available from: 2018-07-05 Created: 2018-07-05 Last updated: 2019-05-02

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De Geer, Jakob

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Division of Radiological SciencesFaculty of Medicine and Health SciencesCenter for Medical Image Science and Visualization (CMIV)Department of Radiology in Linköping
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