AI Evaluation of Stenosis on Coronary CT Angiography, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2022 |
| Outros Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10362/135838 |
Resumo: | Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved. |
| id |
RCAP_3bea63ef66289d833a9436f5ad9218de |
|---|---|
| oai_identifier_str |
oai:run.unl.pt:10362/135838 |
| network_acronym_str |
RCAP |
| network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| repository_id_str |
7160 |
| spelling |
AI Evaluation of Stenosis on Coronary CT Angiography, Comparison With Quantitative Coronary Angiography and Fractional Flow ReserveA CREDENCE Trial Substudyartificial intelligenceatherosclerosiscoronary artery diseasecoronary computed tomographycoronary CTAfractional flow reservequantitative coronary angiographyCopyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.OBJECTIVES: 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).NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)RUNGriffin, William FChoi, Andrew DRiess, Joanna SMarques, HugoChang, Hyuk-JaeChoi, Jung HyunDoh, Joon-HyungHer, Ae-YoungKoo, Bon-KwonNam, Chang-WookPark, Hyung-BokShin, Sang-HoonCole, JasonGimelli, AlessiaKhan, Muhammad AkramLu, BinGao, YangNabi, FaisalNakazato, RyoSchoepf, U JosephDriessen, Roel SBom, Michiel JThompson, RandallJang, James JRidner, MichaelRowan, ChrisAvelar, ErickGénéreux, PhilippeKnaapen, Paulde Waard, Guus APontone, GianlucaAndreini, DanieleEarls, James P2022-04-04T22:38:11Z2023-022023-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/135838eng1936-878XPURE: 42309356https://doi.org/10.1016/j.jcmg.2021.10.020info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:14:06Zoai:run.unl.pt:10362/135838Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:48:30.894661Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
| dc.title.none.fl_str_mv |
AI Evaluation of Stenosis on Coronary CT Angiography, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve A CREDENCE Trial Substudy |
| title |
AI Evaluation of Stenosis on Coronary CT Angiography, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve |
| spellingShingle |
AI Evaluation of Stenosis on Coronary CT Angiography, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve Griffin, William F artificial intelligence atherosclerosis coronary artery disease coronary computed tomography coronary CTA fractional flow reserve quantitative coronary angiography |
| title_short |
AI Evaluation of Stenosis on Coronary CT Angiography, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve |
| title_full |
AI Evaluation of Stenosis on Coronary CT Angiography, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve |
| title_fullStr |
AI Evaluation of Stenosis on Coronary CT Angiography, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve |
| title_full_unstemmed |
AI Evaluation of Stenosis on Coronary CT Angiography, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve |
| title_sort |
AI Evaluation of Stenosis on Coronary CT Angiography, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve |
| author |
Griffin, William F |
| author_facet |
Griffin, William F Choi, Andrew D Riess, Joanna S Marques, Hugo Chang, Hyuk-Jae Choi, Jung Hyun Doh, Joon-Hyung Her, Ae-Young Koo, Bon-Kwon Nam, Chang-Wook Park, Hyung-Bok Shin, Sang-Hoon Cole, Jason Gimelli, Alessia Khan, Muhammad Akram Lu, Bin Gao, Yang Nabi, Faisal Nakazato, Ryo Schoepf, U Joseph Driessen, Roel S Bom, Michiel J Thompson, Randall Jang, James J Ridner, Michael Rowan, Chris Avelar, Erick Généreux, Philippe Knaapen, Paul de Waard, Guus A Pontone, Gianluca Andreini, Daniele Earls, James P |
| author_role |
author |
| author2 |
Choi, Andrew D Riess, Joanna S Marques, Hugo Chang, Hyuk-Jae Choi, Jung Hyun Doh, Joon-Hyung Her, Ae-Young Koo, Bon-Kwon Nam, Chang-Wook Park, Hyung-Bok Shin, Sang-Hoon Cole, Jason Gimelli, Alessia Khan, Muhammad Akram Lu, Bin Gao, Yang Nabi, Faisal Nakazato, Ryo Schoepf, U Joseph Driessen, Roel S Bom, Michiel J Thompson, Randall Jang, James J Ridner, Michael Rowan, Chris Avelar, Erick Généreux, Philippe Knaapen, Paul de Waard, Guus A Pontone, Gianluca Andreini, Daniele Earls, James P |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.contributor.none.fl_str_mv |
NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM) RUN |
| dc.contributor.author.fl_str_mv |
Griffin, William F Choi, Andrew D Riess, Joanna S Marques, Hugo Chang, Hyuk-Jae Choi, Jung Hyun Doh, Joon-Hyung Her, Ae-Young Koo, Bon-Kwon Nam, Chang-Wook Park, Hyung-Bok Shin, Sang-Hoon Cole, Jason Gimelli, Alessia Khan, Muhammad Akram Lu, Bin Gao, Yang Nabi, Faisal Nakazato, Ryo Schoepf, U Joseph Driessen, Roel S Bom, Michiel J Thompson, Randall Jang, James J Ridner, Michael Rowan, Chris Avelar, Erick Généreux, Philippe Knaapen, Paul de Waard, Guus A Pontone, Gianluca Andreini, Daniele Earls, James P |
| dc.subject.por.fl_str_mv |
artificial intelligence atherosclerosis coronary artery disease coronary computed tomography coronary CTA fractional flow reserve quantitative coronary angiography |
| topic |
artificial intelligence atherosclerosis coronary artery disease coronary computed tomography coronary CTA fractional flow reserve quantitative coronary angiography |
| description |
Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved. |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022-04-04T22:38:11Z 2023-02 2023-02-01T00:00:00Z |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/135838 |
| url |
http://hdl.handle.net/10362/135838 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
1936-878X PURE: 42309356 https://doi.org/10.1016/j.jcmg.2021.10.020 |
| dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
| instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
| instacron_str |
RCAAP |
| institution |
RCAAP |
| reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
| repository.mail.fl_str_mv |
|
| _version_ |
1799138086219677696 |