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AI Evaluation of Stenosis on Coronary CT Angiography, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve

2023-02Artigo científico Acesso aberto
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dc.contributor.authorGriffin, William F
dc.contributor.authorChoi, Andrew D
dc.contributor.authorRiess, Joanna S
dc.contributor.authorMarques, Hugo
dc.contributor.authorPinto Marques, Hugo
dc.contributor.authorChang, Hyuk-Jae
dc.contributor.authorChoi, Jung Hyun
dc.contributor.authorDoh, Joon-Hyung
dc.contributor.authorHer, Ae-Young
dc.contributor.authorKoo, Bon-Kwon
dc.contributor.authorNam, Chang-Wook
dc.contributor.authorPark, Hyung-Bok
dc.contributor.authorShin, Sang-Hoon
dc.contributor.authorCole, Jason
dc.contributor.authorGimelli, Alessia
dc.contributor.authorKhan, Muhammad Akram
dc.contributor.authorLu, Bin
dc.contributor.authorGao, Yang
dc.contributor.authorNabi, Faisal
dc.contributor.authorNakazato, Ryo
dc.contributor.authorSchoepf, U Joseph
dc.contributor.authorDriessen, Roel S
dc.contributor.authorBom, Michiel J
dc.contributor.authorThompson, Randall
dc.contributor.authorJang, James J
dc.contributor.authorRidner, Michael
dc.contributor.authorRowan, Chris
dc.contributor.authorAvelar, Erick
dc.contributor.authorGénéreux, Philippe
dc.contributor.authorKnaapen, Paul
dc.contributor.authorde Waard, Guus A
dc.contributor.authorPontone, Gianluca
dc.contributor.authorAndreini, Daniele
dc.contributor.authorEarls, James P
dc.contributor.institutionNOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
dc.contributor.pblElsevier Science B.V., Amsterdam.
dc.date.accessioned2022-04-04T22:38:11Z
dc.date.available2022-04-04T22:38:11Z
dc.date.issued2023-02
dc.descriptionCopyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.
dc.description.abstractOBJECTIVES: The study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography angiography (AI-QCT) analyses to core lab-interpreted coronary computed tomography angiography (CTA), core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR). BACKGROUND: Clinical reads of coronary CTA, especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. AI-based solutions applied to coronary CTA may overcome these limitations. METHODS: Coronary CTA, FFR, and QCA data from 303 stable patients (64 ± 10 years of age, 71% male) from the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia) trial were retrospectively analyzed using an Food and Drug Administration-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. RESULTS: Disease prevalence was high, with 32.0%, 35.0%, 21.0%, and 13.0% demonstrating ≥50% stenosis in 0, 1, 2, and 3 coronary vessel territories, respectively. Average AI-QCT analysis time was 10.3 ± 2.7 minutes. AI-QCT evaluation demonstrated per-patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 94%, 68%, 81%, 90%, and 84%, respectively, for ≥50% stenosis, and of 94%, 82%, 69%, 97%, and 86%, respectively, for detection of ≥70% stenosis. There was high correlation between stenosis detected on AI-QCT evaluation vs QCA on a per-vessel and per-patient basis (intraclass correlation coefficient = 0.73 and 0.73, respectively; P < 0.001 for both). False positive AI-QCT findings were noted in in 62 of 848 (7.3%) vessels (stenosis of ≥70% by AI-QCT and QCA of <70%); however, 41 (66.1%) of these had an FFR of <0.8. CONCLUSIONS: A novel AI-based evaluation of coronary CTA enables rapid and accurate identification and exclusion of high-grade stenosis and with close agreement to blinded, core lab-interpreted quantitative coronary angiography. (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia [CREDENCE]; NCT02173275).en
dc.description.versionproof
dc.description.versionpublished
dc.format.extent2657709
dc.identifier.doi10.1016/j.jcmg.2021.10.020
dc.identifier.issn1936-878X
dc.identifier.otherPURE: 42309356
dc.identifier.otherPURE UUID: 4c628e50-2958-4ba7-a5d6-935eaf359d1e
dc.identifier.otherPubMed: 35183478
dc.identifier.otherORCID: /0000-0003-3540-0488/work/110981261
dc.identifier.otherScopus: 85147128903
dc.identifier.otherWOS: 000954714300007
dc.identifier.urihttp://hdl.handle.net/10362/135838
dc.language.isoeng
dc.peerreviewedyes
dc.subjectartificial intelligence
dc.subjectatherosclerosis
dc.subjectcoronary artery disease
dc.subjectcoronary computed tomography
dc.subjectcoronary CTA
dc.subjectfractional flow reserve
dc.subjectquantitative coronary angiography
dc.titleAI Evaluation of Stenosis on Coronary CT Angiography, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserveen
dc.title.subtitleA CREDENCE Trial Substudyen
dc.typejournal article
degois.publication.firstPage193
degois.publication.issue2
degois.publication.lastPage205
degois.publication.titleJacc: Cardiovascular Imaging
degois.publication.volume16
dspace.entity.typePublication
person.familyNamePinto Marques
person.givenNameHugo
person.identifier.orcid0000-0003-3540-0488
person.identifier.scopus-author-id24537571800
rcaap.rightsopenAccess
relation.isAuthorOfPublicationa3ed0804-c7c1-437a-b08c-931f4de4b3b4
relation.isAuthorOfPublication.latestForDiscoverya3ed0804-c7c1-437a-b08c-931f4de4b3b4

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