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Diagnostic Accuracy...
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Coenen, AdriaanErasmus Univ, Netherlands
(författare)
Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve Result From the MACHINE Consortium
- Artikel/kapitelEngelska2018
Förlag, utgivningsår, omfång ...
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LIPPINCOTT WILLIAMS & WILKINS,2018
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printrdacarrier
Nummerbeteckningar
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LIBRIS-ID:oai:DiVA.org:liu-149477
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https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-149477URI
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https://doi.org/10.1161/CIRCIMAGING.117.007217DOI
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Språk:engelska
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Sammanfattning på:engelska
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Ämneskategori:ref swepub-contenttype
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Ämneskategori:art swepub-publicationtype
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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.
Ämnesord och genrebeteckningar
Biuppslag (personer, institutioner, konferenser, titlar ...)
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Kim, Young-HakUniv Ulsan, South Korea
(författare)
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Kruk, MariuszInst Cardiol, Poland
(författare)
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Tesche, ChristianMed Univ South Carolina, SC 29425 USA
(författare)
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De Geer, Jakob,1970-Linköpings universitet,Avdelningen för radiologiska vetenskaper,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Röntgenkliniken i Linköping(Swepub:liu)jakde64
(författare)
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Kurata, AkiraEhime Univ, Japan
(författare)
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Lubbers, Marisa L.Erasmus Univ, Netherlands
(författare)
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Daemen, JoostErasmus Univ, Netherlands
(författare)
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Itu, LucianSiemens SRL, Romania
(författare)
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Rapaka, SaikiranSiemens Healthcare, NJ USA
(författare)
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Sharma, PuneetSiemens Healthcare, NJ USA
(författare)
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Schwemmer, ChrisSiemens Healthcare GmbH, Germany
(författare)
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Persson, AndersLinköpings universitet,Avdelningen för radiologiska vetenskaper,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Röntgenkliniken i Linköping(Swepub:liu)andpe75
(författare)
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Schoepf, U. JosephMed Univ South Carolina, SC 29425 USA
(författare)
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Kepka, CezaryInst Cardiol, Poland
(författare)
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Yang, Dong HyunUniv Ulsan, South Korea
(författare)
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Nieman, KoenErasmus Univ, Netherlands; Stanford Univ, CA 94305 USA
(författare)
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Erasmus Univ, NetherlandsUniv Ulsan, South Korea
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:Circulation Cardiovascular Imaging: LIPPINCOTT WILLIAMS & WILKINS11:61941-96511942-0080
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Coenen, Adriaan
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Kim, Young-Hak
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Kruk, Mariusz
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Tesche, Christia ...
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De Geer, Jakob, ...
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Kurata, Akira
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Lubbers, Marisa ...
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Daemen, Joost
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Itu, Lucian
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Rapaka, Saikiran
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Sharma, Puneet
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Schwemmer, Chris
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Persson, Anders
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Schoepf, U. Jose ...
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Kepka, Cezary
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Yang, Dong Hyun
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Nieman, Koen
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