The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography
| Autor(a) principal: | |
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| 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/10362/134761 |
Resumo: | Publisher Copyright: © 2022 The Authors |
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The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiographyArtificial intelligenceAtherosclerosisCCTACoronary artery diseaseCoronary computed tomography angiographyImage qualityRadiology Nuclear Medicine and imagingPublisher Copyright: © 2022 The AuthorsObjectives: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. Background: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. Methods: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). Results: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had <50% stenosis in all vessel territories evaluated by QCA. Average AI analysis time was 10.3 ± 2.7 min. On a per vessel basis, there were significant differences only in sensitivity for ≥50% stenosis based on contrast type (iso-osmolar 70.0% vs non isoosmolar 92.1% p = 0.0345) and iodine concentration (<350 mg/ml 70.0%, 350-369 mg/ml 90.0%, 370–400 mg/ml 90.0%, >400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. Conclusion: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. Condensed abstract: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient's BMI or heart rate at time of scan affect the software's diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters.NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)RUNJonas, Rebecca A.Barkovich, EmilChoi, Andrew D.Griffin, William F.Riess, JoannaPinto Marques, 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 S.Bom, Michiel J.Thompson, Randall C.Jang, James J.Ridner, MichaelRowan, ChrisAvelar, ErickGénéreux, PhilippeKnaapen, Paulde Waard, Guus A.Pontone, GianlucaAndreini, DanieleGuglielmo, MarcoAl-Mallah, Mouaz H.Jennings, Robert S.Crabtree, Tami R.Earls, James P.2022-03-17T23:23:03Z2022-042022-04-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article10application/pdfhttp://hdl.handle.net/10362/134761eng0899-7071PURE: 42050886https://doi.org/10.1016/j.clinimag.2022.01.016info: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:RCAAP2024-10-21T01:36:18Zoai:run.unl.pt:10362/134761Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-10-21T01:36:18Repositório 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 |
The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography |
| title |
The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography |
| spellingShingle |
The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography Jonas, Rebecca A. Artificial intelligence Atherosclerosis CCTA Coronary artery disease Coronary computed tomography angiography Image quality Radiology Nuclear Medicine and imaging |
| title_short |
The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography |
| title_full |
The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography |
| title_fullStr |
The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography |
| title_full_unstemmed |
The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography |
| title_sort |
The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography |
| author |
Jonas, Rebecca A. |
| author_facet |
Jonas, Rebecca A. Barkovich, Emil Choi, Andrew D. Griffin, William F. Riess, Joanna Pinto 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 C. Jang, James J. Ridner, Michael Rowan, Chris Avelar, Erick Généreux, Philippe Knaapen, Paul de Waard, Guus A. Pontone, Gianluca Andreini, Daniele Guglielmo, Marco Al-Mallah, Mouaz H. Jennings, Robert S. Crabtree, Tami R. Earls, James P. |
| author_role |
author |
| author2 |
Barkovich, Emil Choi, Andrew D. Griffin, William F. Riess, Joanna Pinto 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 C. Jang, James J. Ridner, Michael Rowan, Chris Avelar, Erick Généreux, Philippe Knaapen, Paul de Waard, Guus A. Pontone, Gianluca Andreini, Daniele Guglielmo, Marco Al-Mallah, Mouaz H. Jennings, Robert S. Crabtree, Tami R. 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 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 |
Jonas, Rebecca A. Barkovich, Emil Choi, Andrew D. Griffin, William F. Riess, Joanna Pinto 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 C. Jang, James J. Ridner, Michael Rowan, Chris Avelar, Erick Généreux, Philippe Knaapen, Paul de Waard, Guus A. Pontone, Gianluca Andreini, Daniele Guglielmo, Marco Al-Mallah, Mouaz H. Jennings, Robert S. Crabtree, Tami R. Earls, James P. |
| dc.subject.por.fl_str_mv |
Artificial intelligence Atherosclerosis CCTA Coronary artery disease Coronary computed tomography angiography Image quality Radiology Nuclear Medicine and imaging |
| topic |
Artificial intelligence Atherosclerosis CCTA Coronary artery disease Coronary computed tomography angiography Image quality Radiology Nuclear Medicine and imaging |
| description |
Publisher Copyright: © 2022 The Authors |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022-03-17T23:23:03Z 2022-04 2022-04-01T00:00:00Z |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
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publishedVersion |
| dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/134761 |
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http://hdl.handle.net/10362/134761 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
0899-7071 PURE: 42050886 https://doi.org/10.1016/j.clinimag.2022.01.016 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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10 application/pdf |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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mluisa.alvim@gmail.com |
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