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P. Karlsberg R, S. Nurmohamed N, G. Quesada C, A. Samuels B, Dohad S, R. Anderson L, Crabtree T, K. Min J, D. Choi A, P. Earls J. Performance of an artificial intelligence-guided quantitative coronary computed tomography algorithm for predicting myocardial ischemia in real-world practice. IJC HEART & VASCULATURE 2024; 53:101433. [PMID: 38868318 PMCID: PMC11167428 DOI: 10.1016/j.ijcha.2024.101433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 05/07/2024] [Accepted: 05/24/2024] [Indexed: 06/14/2024]
Affiliation(s)
- Ronald P. Karlsberg
- Cardiovascular Research Foundation of Southern California – Cedars-Sinai Smidt Heart Institute, Los Angeles, CA, USA
| | - Nick S. Nurmohamed
- The George Washington University School of Medicine, Washington, DC, USA
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Carlos G. Quesada
- Cardiovascular Research Foundation of Southern California – Cedars-Sinai Smidt Heart Institute, Los Angeles, CA, USA
| | - Bruce A. Samuels
- Cardiovascular Research Foundation of Southern California – Cedars-Sinai Smidt Heart Institute, Los Angeles, CA, USA
| | - Suhail Dohad
- Cardiovascular Research Foundation of Southern California – Cedars-Sinai Smidt Heart Institute, Los Angeles, CA, USA
| | - Lauren R. Anderson
- Cardiovascular Research Foundation of Southern California – Cedars-Sinai Smidt Heart Institute, Los Angeles, CA, USA
| | | | | | - Andrew D. Choi
- The George Washington University School of Medicine, Washington, DC, USA
| | - James P. Earls
- The George Washington University School of Medicine, Washington, DC, USA
- Cleerly Inc., Denver, CO, USA
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2
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Quintana RA, von Knebel Doeberitz P, Vatsa N, Liu C, Ko YA, De Cecco CN, van Assen M, Quyyumi AA. Intra- and inter-reader reproducibility in quantitative coronary plaque analysis on coronary computed tomography angiography. Curr Probl Cardiol 2024; 49:102585. [PMID: 38688396 PMCID: PMC11146670 DOI: 10.1016/j.cpcardiol.2024.102585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 04/20/2024] [Indexed: 05/02/2024]
Abstract
PURPOSE Coronary artery plaque burden, low attenuation non-calcified plaque (LAP), and pericoronary adipose tissue (PCAT) on coronary CT angiography (CCTA), have been linked to future cardiac events. The purpose of this study was to evaluate intra- and inter reader reproducibility in the quantification of coronary plaque burden and its characteristics using an artificial intelligence-enhanced semi-automated software. MATERIALS AND METHODS A total of 10 women and 6 men, aged 52 (IQR 49-58) underwent CCTA using a Siemens Somatom Force, Somatom Definition AS and Somatom Definition Flash scanners. Two expert readers utilized dedicated semi-automatic software (vascuCAP, Elucid Bioimaging, Wenham, MA) to assess calcified plaque, low attenuation plaque and PCAT. Readers were blinded to all clinical information and repeated their analysis at 6 weeks in random order to minimize recall bias. Data analysis was performed on the right and left coronary arteries. Intra- and inter-reader reproducibility was compared using Pearson correlation coefficient, while absolute values between analyses and readers were compared with paired non-parametric tests. This is a sub-study of the Specialized Center of Research Excellence (SCORE) clinical trial (5U54AG062334). RESULTS A total of 64 vessels from 16 patients were analyzed. Intra-reader Pearson correlation coefficients for calcified plaque volume, LAP volume and PCAT volumes were 0.96, 0.99 and 0.92 for reader 1 and 0.94, 0.94 and 0.95 for reader 2, respectively, (all p < 0.0001). Inter-reader Pearson correlation coefficients for calcified plaque volume, LAP and PCAT volumes were 0.92, 0.96 and 0.78, and 0.99, 0.99 and 0.93 on the second analyses, all had a p value <0.0001. There was no significant bias on the corresponding Bland-Altman analyses. CONCLUSION Volume measurement of coronary plaque burden and PCAT volume can be performed with high intra- and inter-reader agreement.
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Affiliation(s)
- Raymundo A Quintana
- Cardiovascular Imaging Section, Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Philipp von Knebel Doeberitz
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Nishant Vatsa
- Department of Medicine, Division of Cardiology, Emory University School of Medicine, Atlanta, GA, USA
| | - Chang Liu
- Department of Medicine, Division of Cardiology, Emory University School of Medicine, Atlanta, GA, USA; Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Yi-An Ko
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Carlo N De Cecco
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA; Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Emory University School of Medicine, Atlanta, GA, USA
| | - Marly van Assen
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA; Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Emory University School of Medicine, Atlanta, GA, USA
| | - Arshed A Quyyumi
- Department of Medicine, Division of Cardiology, Emory University School of Medicine, Atlanta, GA, USA.
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3
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Lee SN, Lin A, Dey D, Berman DS, Han D. Application of Quantitative Assessment of Coronary Atherosclerosis by Coronary Computed Tomographic Angiography. Korean J Radiol 2024; 25:518-539. [PMID: 38807334 PMCID: PMC11136945 DOI: 10.3348/kjr.2023.1311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/29/2024] [Accepted: 03/23/2024] [Indexed: 05/30/2024] Open
Abstract
Coronary computed tomography angiography (CCTA) has emerged as a pivotal tool for diagnosing and risk-stratifying patients with suspected coronary artery disease (CAD). Recent advancements in image analysis and artificial intelligence (AI) techniques have enabled the comprehensive quantitative analysis of coronary atherosclerosis. Fully quantitative assessments of coronary stenosis and lumen attenuation have improved the accuracy of assessing stenosis severity and predicting hemodynamically significant lesions. In addition to stenosis evaluation, quantitative plaque analysis plays a crucial role in predicting and monitoring CAD progression. Studies have demonstrated that the quantitative assessment of plaque subtypes based on CT attenuation provides a nuanced understanding of plaque characteristics and their association with cardiovascular events. Quantitative analysis of serial CCTA scans offers a unique perspective on the impact of medical therapies on plaque modification. However, challenges such as time-intensive analyses and variability in software platforms still need to be addressed for broader clinical implementation. The paradigm of CCTA has shifted towards comprehensive quantitative plaque analysis facilitated by technological advancements. As these methods continue to evolve, their integration into routine clinical practice has the potential to enhance risk assessment and guide individualized patient management. This article reviews the evolving landscape of quantitative plaque analysis in CCTA and explores its applications and limitations.
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Affiliation(s)
- Su Nam Lee
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Cardiology, Department of Internal Medicine, St. Vincent's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Andrew Lin
- Monash Cardiovascular Research Centre, Victorian Heart Institute, Monash University and MonashHeart, Monash Health, Melbourne, Australia
| | - Damini Dey
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Donghee Han
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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Yoshida K, Tanabe Y, Hosokawa T, Morikawa T, Fukuyama N, Kobayashi Y, Kouchi T, Kawaguchi N, Matsuda M, Kido T, Kido T. Coronary computed tomography angiography for clinical practice. Jpn J Radiol 2024; 42:555-580. [PMID: 38453814 PMCID: PMC11139719 DOI: 10.1007/s11604-024-01543-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 01/28/2024] [Indexed: 03/09/2024]
Abstract
Coronary artery disease (CAD) is a common condition caused by the accumulation of atherosclerotic plaques. It can be classified into stable CAD or acute coronary syndrome. Coronary computed tomography angiography (CCTA) has a high negative predictive value and is used as the first examination for diagnosing stable CAD, particularly in patients at intermediate-to-high risk. CCTA is also adopted for diagnosing acute coronary syndrome, particularly in patients at low-to-intermediate risk. Myocardial ischemia does not always co-exist with coronary artery stenosis, and the positive predictive value of CCTA for myocardial ischemia is limited. However, CCTA has overcome this limitation with recent technological advancements such as CT perfusion and CT-fractional flow reserve. In addition, CCTA can be used to assess coronary artery plaques. Thus, the indications for CCTA have expanded, leading to an increased demand for radiologists. The CAD reporting and data system (CAD-RADS) 2.0 was recently proposed for standardizing CCTA reporting. This RADS evaluates and categorizes patients based on coronary artery stenosis and the overall amount of coronary artery plaque and links this to patient management. In this review, we aimed to review the major trials and guidelines for CCTA to understand its clinical role. Furthermore, we aimed to introduce the CAD-RADS 2.0 including the assessment of coronary artery stenosis, plaque, and other key findings, and highlight the steps for CCTA reporting. Finally, we aimed to present recent research trends including the perivascular fat attenuation index, artificial intelligence, and the advancements in CT technology.
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Affiliation(s)
- Kazuki Yoshida
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, 791-0295, Japan
| | - Yuki Tanabe
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, 791-0295, Japan.
| | - Takaaki Hosokawa
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, 791-0295, Japan
| | - Tomoro Morikawa
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, 791-0295, Japan
| | - Naoki Fukuyama
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, 791-0295, Japan
| | - Yusuke Kobayashi
- Department of Radiology, Matsuyama Red Cross Hospital, Bunkyocho, Matsuyama, Ehime, Japan
| | - Takanori Kouchi
- Department of Radiology, Juzen General Hospital, Kitashinmachi, Niihama, Ehime, Japan
| | - Naoto Kawaguchi
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, 791-0295, Japan
| | - Megumi Matsuda
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, 791-0295, Japan
| | - Tomoyuki Kido
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, 791-0295, Japan
| | - Teruhito Kido
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, 791-0295, Japan
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Nurmohamed NS, Cole JH, Budoff MJ, Karlsberg RP, Gupta H, Sullenberger LE, Quesada CG, Rahban H, Woods KM, Uzzilia JR, Purga SL, Aquino M, Hoffmann U, Min JK, Earls JP, Choi AD. Impact of atherosclerosis imaging-quantitative computed tomography on diagnostic certainty, downstream testing, coronary revascularization, and medical therapy: the CERTAIN study. Eur Heart J Cardiovasc Imaging 2024; 25:857-866. [PMID: 38270472 PMCID: PMC11139521 DOI: 10.1093/ehjci/jeae029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 12/26/2023] [Accepted: 01/17/2024] [Indexed: 01/26/2024] Open
Abstract
AIMS The incremental impact of atherosclerosis imaging-quantitative computed tomography (AI-QCT) on diagnostic certainty and downstream patient management is not yet known. The aim of this study was to compare the clinical utility of the routine implementation of AI-QCT versus conventional visual coronary CT angiography (CCTA) interpretation. METHODS AND RESULTS In this multi-centre cross-over study in 5 expert CCTA sites, 750 consecutive adult patients referred for CCTA were prospectively recruited. Blinded to the AI-QCT analysis, site physicians established patient diagnoses and plans for downstream non-invasive testing, coronary intervention, and medication management based on the conventional site assessment. Next, physicians were asked to repeat their assessments based upon AI-QCT results. The included patients had an age of 63.8 ± 12.2 years; 433 (57.7%) were male. Compared with the conventional site CCTA evaluation, AI-QCT analysis improved physician's confidence two- to five-fold at every step of the care pathway and was associated with change in diagnosis or management in the majority of patients (428; 57.1%; P < 0.001), including for measures such as Coronary Artery Disease-Reporting and Data System (CAD-RADS) (295; 39.3%; P < 0.001) and plaque burden (197; 26.3%; P < 0.001). After AI-QCT including ischaemia assessment, the need for downstream non-invasive and invasive testing was reduced by 37.1% (P < 0.001), compared with the conventional site CCTA evaluation. Incremental to the site CCTA evaluation alone, AI-QCT resulted in statin initiation/increase an aspirin initiation in an additional 28.1% (P < 0.001) and 23.0% (P < 0.001) of patients, respectively. CONCLUSION The use of AI-QCT improves diagnostic certainty and may result in reduced downstream need for non-invasive testing and increased rates of preventive medical therapy.
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Affiliation(s)
- Nick S Nurmohamed
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
- Division of Cardiology, Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA
| | - Jason H Cole
- Cardiology Associates of Mobile, Mobile, AL, USA
| | - Matthew J Budoff
- Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Ronald P Karlsberg
- Cardiovascular Research Foundation of Southern California, Cedars-Sinai Heart Institute, Beverly Hills, CA
| | - Himanshu Gupta
- Division of Cardiac Imaging, Valley Heart and Vascular Institute, Valley Health System, Ridgewood, NJ, USA
| | | | - Carlos G Quesada
- Cardiovascular Research Foundation of Southern California, Cedars-Sinai Heart Institute, Beverly Hills, CA
| | - Habib Rahban
- Cardiovascular Research Foundation of Southern California, Cedars-Sinai Heart Institute, Beverly Hills, CA
| | | | | | | | | | | | | | - James P Earls
- Division of Cardiology, Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA
- Cleerly Inc., Denver, CO, USA
| | - Andrew D Choi
- Division of Cardiology, Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA
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6
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Stamate E, Piraianu AI, Ciobotaru OR, Crassas R, Duca O, Fulga A, Grigore I, Vintila V, Fulga I, Ciobotaru OC. Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years. Diagnostics (Basel) 2024; 14:1103. [PMID: 38893630 PMCID: PMC11172021 DOI: 10.3390/diagnostics14111103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/12/2024] [Accepted: 05/23/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. RESULTS We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. CONCLUSIONS The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.
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Affiliation(s)
- Elena Stamate
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Alin-Ionut Piraianu
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Oana Roxana Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
| | - Rodica Crassas
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Oana Duca
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Ionica Grigore
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Vlad Vintila
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Clinical Department of Cardio-Thoracic Pathology, University of Medicine and Pharmacy “Carol Davila” Bucharest, 37 Dionisie Lupu Street, 4192910 Bucharest, Romania
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Octavian Catalin Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
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7
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Ibrahim S, Reeskamp LF, de Goeij JN, Hovingh GK, Planken RN, Bax WA, Min JK, Earls JP, Knaapen P, Wiegman A, Stroes ESG, Nurmohamed NS. Beyond early LDL cholesterol lowering to prevent coronary atherosclerosis in familial hypercholesterolaemia. Eur J Prev Cardiol 2024; 31:892-900. [PMID: 38243822 DOI: 10.1093/eurjpc/zwae028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/16/2023] [Accepted: 01/15/2024] [Indexed: 01/22/2024]
Abstract
AIMS Familial hypercholesterolaemia (FH) patients are subjected to a high lifetime exposure to low density lipoprotein cholesterol (LDL-C), despite use of lipid-lowering therapy (LLT). This study aimed to quantify the extent of subclinical atherosclerosis and to evaluate the association between lifetime cumulative LDL-C exposure and coronary atherosclerosis in young FH patients. METHODS AND RESULTS Familial hypercholesterolaemia patients, divided into a subgroup of early treated (LLT initiated <25 years) and late treated (LLT initiated ≥25 years) patients, and an age- and sex-matched unaffected control group, underwent coronary CT angiography (CCTA) with artificial intelligence-guided analysis. Ninety genetically diagnosed FH patients and 45 unaffected volunteers (mean age 41 ± 3 years, 51 (38%) female) were included. Familial hypercholesterolaemia patients had higher cumulative LDL-C exposure (181 ± 54 vs. 105 ± 33 mmol/L ∗ years) and higher prevalence of coronary plaque compared with controls (46 [51%] vs. 10 [22%], OR 3.66 [95%CI 1.62-8.27]). Every 75 mmol/L ∗ years cumulative exposure to LDL-C was associated with a doubling in per cent atheroma volume (total plaque volume divided by total vessel volume). Early treated patients had a modestly lower cumulative LDL-C exposure compared with late treated FH patients (167 ± 41 vs. 194 ± 61 mmol/L ∗ years; P = 0.045), without significant difference in coronary atherosclerosis. Familial hypercholesterolaemia patients with above-median cumulative LDL-C exposure had significantly higher plaque prevalence (OR 3.62 [95%CI 1.62-8.27]; P = 0.001), compared with patients with below-median exposure. CONCLUSION Lifetime exposure to LDL-C determines coronary plaque burden in FH, underlining the need of early as well as potent treatment initiation. Periodic CCTA may offer a unique opportunity to monitor coronary atherosclerosis and personalize treatment in FH.
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Affiliation(s)
- Shirin Ibrahim
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands
| | - Laurens F Reeskamp
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands
| | - Jim N de Goeij
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands
| | - G Kees Hovingh
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands
| | - R Nils Planken
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Willem A Bax
- Department of Internal Medicine, Northwest Clinics, Alkmaar, the Netherlands
| | | | - James P Earls
- Cleerly Inc., Denver, CO, USA
- The George Washington University School of Medicine, 2150 Pennsylvania Avenue NW, Washington, 0037 DC, USA
| | - Paul Knaapen
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, the Netherlands
| | - Albert Wiegman
- Department of Paediatrics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Erik S G Stroes
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands
| | - Nick S Nurmohamed
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands
- The George Washington University School of Medicine, 2150 Pennsylvania Avenue NW, Washington, 0037 DC, USA
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, the Netherlands
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8
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Onnis C, van Assen M, Muscogiuri E, Muscogiuri G, Gershon G, Saba L, De Cecco CN. The Role of Artificial Intelligence in Cardiac Imaging. Radiol Clin North Am 2024; 62:473-488. [PMID: 38553181 DOI: 10.1016/j.rcl.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Artificial intelligence (AI) is having a significant impact in medical imaging, advancing almost every aspect of the field, from image acquisition and postprocessing to automated image analysis with outreach toward supporting decision making. Noninvasive cardiac imaging is one of the main and most exciting fields for AI development. The aim of this review is to describe the main applications of AI in cardiac imaging, including CT and MR imaging, and provide an overview of recent advancements and available clinical applications that can improve clinical workflow, disease detection, and prognostication in cardiac disease.
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Affiliation(s)
- Carlotta Onnis
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA; Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, SS 554 km 4,500 Monserrato, Cagliari 09042, Italy. https://twitter.com/CarlottaOnnis
| | - Marly van Assen
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA. https://twitter.com/marly_van_assen
| | - Emanuele Muscogiuri
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA; Division of Thoracic Imaging, Department of Radiology, University Hospitals Leuven, Herestraat 49, Leuven 3000, Belgium
| | - Giuseppe Muscogiuri
- Department of Diagnostic and Interventional Radiology, Papa Giovanni XXIII Hospital, Piazza OMS, 1, Bergamo BG 24127, Italy. https://twitter.com/GiuseppeMuscog
| | - Gabrielle Gershon
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA. https://twitter.com/gabbygershon
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, SS 554 km 4,500 Monserrato, Cagliari 09042, Italy. https://twitter.com/lucasabaITA
| | - Carlo N De Cecco
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA; Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University, Emory University Hospital, 1365 Clifton Road Northeast, Suite AT503, Atlanta, GA 30322, USA.
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Bär S, Nabeta T, Maaniitty T, Saraste A, Bax JJ, Earls JP, Min JK, Knuuti J. Prognostic value of a novel artificial intelligence-based coronary computed tomography angiography-derived ischaemia algorithm for patients with suspected coronary artery disease. Eur Heart J Cardiovasc Imaging 2024; 25:657-667. [PMID: 38084894 PMCID: PMC11057943 DOI: 10.1093/ehjci/jead339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 05/01/2024] Open
Abstract
AIMS Coronary computed tomography angiography (CTA) imaging is used to diagnose patients with suspected coronary artery disease (CAD). A novel artificial intelligence-guided quantitative computed tomography ischaemia algorithm (AI-QCTischaemia) aims to identify myocardial ischaemia directly from CTA images and may be helpful to improve risk stratification. The aims were to investigate (i) the prognostic value of AI-QCTischaemia amongst symptomatic patients with suspected CAD entering diagnostic imaging with coronary CTA and (ii) the prognostic value of AI-QCTischaemia separately amongst patients with no/non-obstructive CAD (≤50% visual diameter stenosis) and obstructive CAD (>50% visual diameter stenosis). METHODS AND RESULTS For this cohort study, AI-QCTischaemia was calculated by blinded analysts amongst patients with suspected CAD undergoing coronary CTA. The primary endpoint was the composite of death, myocardial infarction (MI), or unstable angina pectoris (uAP) (median follow-up 6.9 years). A total of 1880/2271 (83%) patients had conclusive AI-QCTischaemia result. Patients with an abnormal AI-QCTischaemia result (n = 509/1880) vs. patients with a normal AI-QCTischaemia result (n = 1371/1880) had significantly higher crude and adjusted rates of the primary endpoint [adjusted hazard ratio (HRadj) 1.96, 95% confidence interval (CI) 1.46-2.63, P < 0.001; covariates: age/sex/hypertension/diabetes/smoking/typical angina]. An abnormal AI-QCTischaemia result was associated with significantly higher crude and adjusted rates of the primary endpoint amongst patients with no/non-obstructive CAD (n = 1373/1847) (HRadj 1.81, 95% CI 1.09-3.00, P = 0.022), but not amongst those with obstructive CAD (n = 474/1847) (HRadj 1.26, 95% CI 0.75-2.12, P = 0.386) (P-interaction = 0.032). CONCLUSION Amongst patients with suspected CAD, an abnormal AI-QCTischaemia result was associated with a two-fold increased adjusted rate of long-term death, MI, or uAP. AI-QCTischaemia may be useful to improve risk stratification, especially amongst patients with no/non-obstructive CAD on coronary CTA.
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Affiliation(s)
- Sarah Bär
- Turku PET Centre, Turku University Hospital, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland
- Department of Cardiology, Bern University Hospital Inselspital, Bern, Switzerland
| | - Takeru Nabeta
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Teemu Maaniitty
- Turku PET Centre, Turku University Hospital, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland
- Department of Clinical Physiology, Nuclear Medicine, and PET, Turku University Hospital, Hämeentie 11, 20540 Turku, Finland
| | - Antti Saraste
- Turku PET Centre, Turku University Hospital, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland
- Heart Center, Turku University Hospital, University of Turku, Turku, Finland
| | - Jeroen J Bax
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | | | - Juhani Knuuti
- Turku PET Centre, Turku University Hospital, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland
- Department of Clinical Physiology, Nuclear Medicine, and PET, Turku University Hospital, Hämeentie 11, 20540 Turku, Finland
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10
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van Assen M, Beecy A, Gershon G, Newsome J, Trivedi H, Gichoya J. Implications of Bias in Artificial Intelligence: Considerations for Cardiovascular Imaging. Curr Atheroscler Rep 2024; 26:91-102. [PMID: 38363525 DOI: 10.1007/s11883-024-01190-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/16/2024] [Indexed: 02/17/2024]
Abstract
PURPOSE OF REVIEW Bias in artificial intelligence (AI) models can result in unintended consequences. In cardiovascular imaging, biased AI models used in clinical practice can negatively affect patient outcomes. Biased AI models result from decisions made when training and evaluating a model. This paper is a comprehensive guide for AI development teams to understand assumptions in datasets and chosen metrics for outcome/ground truth, and how this translates to real-world performance for cardiovascular disease (CVD). RECENT FINDINGS CVDs are the number one cause of mortality worldwide; however, the prevalence, burden, and outcomes of CVD vary across gender and race. Several biomarkers are also shown to vary among different populations and ethnic/racial groups. Inequalities in clinical trial inclusion, clinical presentation, diagnosis, and treatment are preserved in health data that is ultimately used to train AI algorithms, leading to potential biases in model performance. Despite the notion that AI models themselves are biased, AI can also help to mitigate bias (e.g., bias auditing tools). In this review paper, we describe in detail implicit and explicit biases in the care of cardiovascular disease that may be present in existing datasets but are not obvious to model developers. We review disparities in CVD outcomes across different genders and race groups, differences in treatment of historically marginalized groups, and disparities in clinical trials for various cardiovascular diseases and outcomes. Thereafter, we summarize some CVD AI literature that shows bias in CVD AI as well as approaches that AI is being used to mitigate CVD bias.
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Affiliation(s)
- Marly van Assen
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA.
| | - Ashley Beecy
- Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Information Technology, NewYork-Presbyterian, New York, NY, USA
| | - Gabrielle Gershon
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
| | - Janice Newsome
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
| | - Hari Trivedi
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
| | - Judy Gichoya
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
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11
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Ferencik M. Expanding Options for Evaluating Hemodynamic Significance of Coronary Stenosis from Computed Tomography Angiography. JACC Cardiovasc Imaging 2024:S1936-878X(24)00111-6. [PMID: 38573287 DOI: 10.1016/j.jcmg.2024.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 02/26/2024] [Indexed: 04/05/2024]
Affiliation(s)
- Maros Ferencik
- Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Oregon, USA.
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12
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Nurmohamed NS, Danad I, Jukema RA, de Winter RW, de Groot RJ, Driessen RS, Bom MJ, van Diemen P, Pontone G, Andreini D, Chang HJ, Katz RJ, Stroes ESG, Wang H, Chan C, Crabtree T, Aquino M, Min JK, Earls JP, Bax JJ, Choi AD, Knaapen P, van Rosendael AR. Development and Validation of a Quantitative Coronary CT Angiography Model for Diagnosis of Vessel-Specific Coronary Ischemia. JACC Cardiovasc Imaging 2024:S1936-878X(24)00039-1. [PMID: 38483420 DOI: 10.1016/j.jcmg.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 11/30/2023] [Accepted: 01/11/2024] [Indexed: 03/27/2024]
Abstract
BACKGROUND Noninvasive stress testing is commonly used for detection of coronary ischemia but possesses variable accuracy and may result in excessive health care costs. OBJECTIVES This study aimed to derive and validate an artificial intelligence-guided quantitative coronary computed tomography angiography (AI-QCT) model for the diagnosis of coronary ischemia that integrates atherosclerosis and vascular morphology measures (AI-QCTISCHEMIA) and to evaluate its prognostic utility for major adverse cardiovascular events (MACE). METHODS A post hoc analysis of the CREDENCE (Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia) and PACIFIC-1 (Comparison of Coronary Computed Tomography Angiography, Single Photon Emission Computed Tomography [SPECT], Positron Emission Tomography [PET], and Hybrid Imaging for Diagnosis of Ischemic Heart Disease Determined by Fractional Flow Reserve) studies was performed. In both studies, symptomatic patients with suspected stable coronary artery disease had prospectively undergone coronary computed tomography angiography (CTA), myocardial perfusion imaging (MPI), SPECT, or PET, fractional flow reserve by CT (FFRCT), and invasive coronary angiography in conjunction with invasive FFR measurements. The AI-QCTISCHEMIA model was developed in the derivation cohort of the CREDENCE study, and its diagnostic performance for coronary ischemia (FFR ≤0.80) was evaluated in the CREDENCE validation cohort and PACIFIC-1. Its prognostic value was investigated in PACIFIC-1. RESULTS In CREDENCE validation (n = 305, age 64.4 ± 9.8 years, 210 [69%] male), the diagnostic performance by area under the receiver-operating characteristics curve (AUC) on per-patient level was 0.80 (95% CI: 0.75-0.85) for AI-QCTISCHEMIA, 0.69 (95% CI: 0.63-0.74; P < 0.001) for FFRCT, and 0.65 (95% CI: 0.59-0.71; P < 0.001) for MPI. In PACIFIC-1 (n = 208, age 58.1 ± 8.7 years, 132 [63%] male), the AUCs were 0.85 (95% CI: 0.79-0.91) for AI-QCTISCHEMIA, 0.78 (95% CI: 0.72-0.84; P = 0.037) for FFRCT, 0.89 (95% CI: 0.84-0.93; P = 0.262) for PET, and 0.72 (95% CI: 0.67-0.78; P < 0.001) for SPECT. Adjusted for clinical risk factors and coronary CTA-determined obstructive stenosis, a positive AI-QCTISCHEMIA test was associated with an HR of 7.6 (95% CI: 1.2-47.0; P = 0.030) for MACE. CONCLUSIONS This newly developed coronary CTA-based ischemia model using coronary atherosclerosis and vascular morphology characteristics accurately diagnoses coronary ischemia by invasive FFR and provides robust prognostic utility for MACE beyond presence of stenosis.
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Affiliation(s)
- Nick S Nurmohamed
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA.
| | - Ibrahim Danad
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Ruurt A Jukema
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Ruben W de Winter
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Robin J de Groot
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Roel S Driessen
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Michiel J Bom
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Pepijn van Diemen
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Gianluca Pontone
- Department of Cardiovascular Imaging, Centro Cardiologico Monzino, IRCCS, Milan, Italy
| | - Daniele Andreini
- Division of University Cardiology, IRCCS Ospedale Galeazzi Sant'Ambrogio, Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Hyuk-Jae Chang
- Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea
| | - Richard J Katz
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA
| | - Erik S G Stroes
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Hao Wang
- Cleerly Inc, Denver, Colorado, USA
| | | | | | | | | | - James P Earls
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA; Cleerly Inc, Denver, Colorado, USA
| | - Jeroen J Bax
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Andrew D Choi
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA
| | - Paul Knaapen
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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13
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Nurmohamed NS, Bom MJ, Jukema RA, de Groot RJ, Driessen RS, van Diemen PA, de Winter RW, Gaillard EL, Sprengers RW, Stroes ESG, Min JK, Earls JP, Cardoso R, Blankstein R, Danad I, Choi AD, Knaapen P. AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD. JACC Cardiovasc Imaging 2024; 17:269-280. [PMID: 37480907 DOI: 10.1016/j.jcmg.2023.05.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/17/2023] [Accepted: 05/30/2023] [Indexed: 07/24/2023]
Abstract
BACKGROUND The recent development of artificial intelligence-guided quantitative coronary computed tomography angiography analysis (AI-QCT) has enabled rapid analysis of atherosclerotic plaque burden and characteristics. OBJECTIVES This study set out to investigate the 10-year prognostic value of atherosclerotic burden derived from AI-QCT and to compare the spectrum of plaque to manually assessed coronary computed tomography angiography (CCTA), coronary artery calcium scoring (CACS), and clinical risk characteristics. METHODS This was a long-term follow-up study of 536 patients referred for suspected coronary artery disease. CCTA scans were analyzed with AI-QCT and plaque burden was classified with a plaque staging system (stage 0: 0% percentage atheroma volume [PAV]; stage 1: >0%-5% PAV; stage 2: >5%-15% PAV; stage 3: >15% PAV). The primary major adverse cardiac event (MACE) outcome was a composite of nonfatal myocardial infarction, nonfatal stroke, coronary revascularization, and all-cause mortality. RESULTS The mean age at baseline was 58.6 years and 297 patients (55%) were male. During a median follow-up of 10.3 years (IQR: 8.6-11.5 years), 114 patients (21%) experienced the primary outcome. Compared to stages 0 and 1, patients with stage 3 PAV and percentage of noncalcified plaque volume of >7.5% had a more than 3-fold (adjusted HR: 3.57; 95% CI 2.12-6.00; P < 0.001) and 4-fold (adjusted HR: 4.37; 95% CI: 2.51-7.62; P < 0.001) increased risk of MACE, respectively. Addition of AI-QCT improved a model with clinical risk factors and CACS at different time points during follow-up (10-year AUC: 0.82 [95% CI: 0.78-0.87] vs 0.73 [95% CI: 0.68-0.79]; P < 0.001; net reclassification improvement: 0.21 [95% CI: 0.09-0.38]). Furthermore, AI-QCT achieved an improved area under the curve compared to Coronary Artery Disease Reporting and Data System 2.0 (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.023) and manual QCT (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.040), although net reclassification improvement was modest (0.09 [95% CI: -0.02 to 0.29] and 0.04 [95% CI: -0.05 to 0.27], respectively). CONCLUSIONS Through 10-year follow-up, AI-QCT plaque staging showed important prognostic value for MACE and showed additional discriminatory value over clinical risk factors, CACS, and manual guideline-recommended CCTA assessment.
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Affiliation(s)
- Nick S Nurmohamed
- Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands; Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA. https://twitter.com/NickNurmohamed
| | - Michiel J Bom
- Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Ruurt A Jukema
- Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Robin J de Groot
- Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Roel S Driessen
- Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Pepijn A van Diemen
- Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Ruben W de Winter
- Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Emilie L Gaillard
- Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Ralf W Sprengers
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Erik S G Stroes
- Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | | | - James P Earls
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA; Cleerly Inc, Denver, Colorado, USA
| | - Rhanderson Cardoso
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ron Blankstein
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ibrahim Danad
- Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Andrew D Choi
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA.
| | - Paul Knaapen
- Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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Leipsic JA, Chandrashekhar Y. Novel Analytics for Coronary CT Angiography: Advancing Our Understanding of Risk and Mechanisms of MI. JACC Cardiovasc Imaging 2024; 17:345-347. [PMID: 38448132 DOI: 10.1016/j.jcmg.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
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15
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Hanneman K, Playford D, Dey D, van Assen M, Mastrodicasa D, Cook TS, Gichoya JW, Williamson EE, Rubin GD. Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e296-e311. [PMID: 38193315 DOI: 10.1161/cir.0000000000001202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.
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16
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Chen M, Almeida SO, Sayre JW, Karlsberg RP, Packard RRS. Distal-vessel fractional flow reserve by computed tomography to monitor epicardial coronary artery disease. Eur Heart J Cardiovasc Imaging 2024; 25:163-172. [PMID: 37708371 PMCID: PMC11032197 DOI: 10.1093/ehjci/jead229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/26/2023] [Accepted: 09/08/2023] [Indexed: 09/16/2023] Open
Abstract
AIMS Coronary computed tomography angiography (CTA) and fractional flow reserve by computed tomography (FFR-CT) are increasingly utilized to characterize coronary artery disease (CAD). We evaluated the feasibility of distal-vessel FFR-CT as an integrated measure of epicardial CAD that can be followed serially, assessed the CTA parameters that correlate with distal-vessel FFR-CT, and determined the combination of clinical and CTA parameters that best predict distal-vessel FFR-CT and distal-vessel FFR-CT changes. METHODS AND RESULTS Patients (n = 71) who underwent serial CTA scans at ≥2 years interval (median = 5.2 years) over a 14-year period were included in this retrospective study. Coronary arteries were analysed blindly using artificial intelligence-enabled quantitative coronary CTA. Two investigators jointly determined the anatomic location and corresponding distal-vessel FFR-CT values at CT1 and CT2. A total of 45.3% had no significant change, 27.8% an improvement, and 26.9% a worsening in distal-vessel FFR-CT at CT2. Stepwise multiple logistic regression analysis identified a four-parameter model consisting of stenosis diameter ratio, lumen volume, low density plaque volume, and age, that best predicted distal-vessel FFR-CT ≤ 0.80 with an area under the curve (AUC) = 0.820 at CT1 and AUC = 0.799 at CT2. Improvement of distal-vessel FFR-CT was captured by a decrease in high-risk plaque and increases in lumen volume and remodelling index (AUC = 0.865), whereas increases in stenosis diameter ratio, medium density calcified plaque volume, and total cholesterol presaged worsening of distal-vessel FFR-CT (AUC = 0.707). CONCLUSION Distal-vessel FFR-CT permits the integrative assessment of epicardial atherosclerotic plaque burden in a vessel-specific manner and can be followed serially to determine changes in global CAD.
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Affiliation(s)
- Michael Chen
- Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California, 10833 Le Conte Ave., CHS Building Room 43-268, Los Angeles, CA 90095, USA
| | - Shone O Almeida
- Cardiovascular Research Foundation of Southern California, Beverly Hills, CA, USA
| | - James W Sayre
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Ronald P Karlsberg
- Cardiovascular Research Foundation of Southern California, Beverly Hills, CA, USA
- Cedars-Sinai Smidt Heart Institute, Los Angeles, CA, USA
| | - René R Sevag Packard
- Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California, 10833 Le Conte Ave., CHS Building Room 43-268, Los Angeles, CA 90095, USA
- Cardiovascular Research Foundation of Southern California, Beverly Hills, CA, USA
- Ronald Reagan UCLA Medical Center, Los Angeles, CA, USA
- Veterans Affairs West Los Angeles Medical Center, Los Angeles, CA, USA
- Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
- Molecular Biology Institute, University of California, Los Angeles, CA, USA
- California NanoSystems Institute, University of California, Los Angeles, CA, USA
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17
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Lima MR, Lopes PM, Ferreira AM. Use of coronary artery calcium score and coronary CT angiography to guide cardiovascular prevention and treatment. Ther Adv Cardiovasc Dis 2024; 18:17539447241249650. [PMID: 38708947 PMCID: PMC11075618 DOI: 10.1177/17539447241249650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 03/08/2024] [Indexed: 05/07/2024] Open
Abstract
Currently, cardiovascular risk stratification to guide preventive therapy relies on clinical scores based on cardiovascular risk factors. However, the discriminative power of these scores is relatively modest. The use of coronary artery calcium score (CACS) and coronary CT angiography (CCTA) has surfaced as methods for enhancing the estimation of risk and potentially providing insights for personalized treatment in individual patients. CACS improves overall cardiovascular risk prediction and may be used to improve the yield of statin therapy in primary prevention, and possibly identify patients with a favorable risk/benefit relationship for antiplatelet therapies. CCTA holds promise to guide anti-atherosclerotic therapies and to monitor individual response to these treatments by assessing individual plaque features, quantifying total plaque volume and composition, and assessing peri-coronary adipose tissue. In this review, we aim to summarize current evidence regarding the use of CACS and CCTA for guiding lipid-lowering and antiplatelet therapy and discuss the possibility of using plaque burden and plaque phenotyping to monitor response to anti-atherosclerotic therapies.
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Affiliation(s)
- Maria Rita Lima
- Department of Cardiology, Hospital Santa Cruz, Centro Hospitalar Lisboa Ocidental, Av. Prof. Dr. Reinaldo dos Santos, Carnaxide, Lisbon 2790-134, Portugal
| | - Pedro M. Lopes
- Department of Cardiology, Hospital Santa Cruz, Centro Hospitalar Lisboa Ocidental, Carnaxide, Portugal
| | - António M. Ferreira
- Department of Cardiology, Hospital Santa Cruz, Centro Hospitalar Lisboa Ocidental, Carnaxide, Portugal
- UNICA – Cardiovascular CT and MR Unit, Hospital da Luz, Lisbon, Portugal
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18
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van Rosendael AR, Crabtree T, Bax JJ, Nakanishi R, Mushtaq S, Pontone G, Andreini D, Buechel RR, Gräni C, Feuchtner G, Patel TR, Choi AD, Al-Mallah M, Nabi F, Karlsberg RP, Rochitte CE, Alasnag M, Hamdan A, Cademartiri F, Marques H, Kalra D, German DM, Gupta H, Hadamitzky M, Deaño RC, Khalique O, Knaapen P, Hoffmann U, Earls J, Min JK, Danad I. Rationale and design of the CONFIRM2 (Quantitative COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) study. J Cardiovasc Comput Tomogr 2024; 18:11-17. [PMID: 37951725 PMCID: PMC10923095 DOI: 10.1016/j.jcct.2023.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 09/28/2023] [Accepted: 10/08/2023] [Indexed: 11/14/2023]
Abstract
BACKGROUND In the last 15 years, large registries and several randomized clinical trials have demonstrated the diagnostic and prognostic value of coronary computed tomography angiography (CCTA). Advances in CT scanner technology and developments of analytic tools now enable accurate quantification of coronary artery disease (CAD), including total coronary plaque volume and low attenuation plaque volume. The primary aim of CONFIRM2, (Quantitative COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) is to perform comprehensive quantification of CCTA findings, including coronary, non-coronary cardiac, non-cardiac vascular, non-cardiac findings, and relate them to clinical variables and cardiovascular clinical outcomes. DESIGN CONFIRM2 is a multicenter, international observational cohort study designed to evaluate multidimensional associations between quantitative phenotype of cardiovascular disease and future adverse clinical outcomes in subjects undergoing clinically indicated CCTA. The targeted population is heterogenous and includes patients undergoing CCTA for atherosclerotic evaluation, valvular heart disease, congenital heart disease or pre-procedural evaluation. Automated software will be utilized for quantification of coronary plaque, stenosis, vascular morphology and cardiac structures for rapid and reproducible tissue characterization. Up to 30,000 patients will be included from up to 50 international multi-continental clinical CCTA sites and followed for 3-4 years. SUMMARY CONFIRM2 is one of the largest CCTA studies to establish the clinical value of a multiparametric approach to quantify the phenotype of cardiovascular disease by CCTA using automated imaging solutions.
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Affiliation(s)
| | | | - Jeroen J Bax
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Rine Nakanishi
- Department of Cardiovascular Medicine, Toho University Graduate School of Medicine, Tokyo, Japan
| | - Saima Mushtaq
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Gianluca Pontone
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy; Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - Daniele Andreini
- Division of University Cardiology, IRCCS Galeazzi Sant'Ambrogio, Department of Biomedical and Clinical Sciences, University of Milan, Italy
| | - Ronny R Buechel
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital and University of Zurich, Zurich, Switzerland
| | - Christoph Gräni
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Gudrun Feuchtner
- Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Toral R Patel
- Cardiology at Stroobants Heart and Vascular Institute and UVA Cardiology, Lynchburg, VA, United States of America
| | - Andrew D Choi
- Cardiology and Radiology, George Washington University, Washington, DC, United States of America
| | - Mouaz Al-Mallah
- Department of Cardiology, Houston Methodist, Houston, TX, United States of America
| | - Faisal Nabi
- Department of Cardiology, Houston Methodist, Houston, TX, United States of America
| | - Ronald P Karlsberg
- Cardiovascular Research Foundation of Southern California, Cedars Sinai Heart Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States of America
| | - Carlos E Rochitte
- Heart Institute, InCor, University of São Paulo Medical School, São Paulo, Brazil
| | - Mirvat Alasnag
- Cardiac Center, King Fahd Armed Forces Hospital, Jeddah, Saudi Arabia
| | - Ashraf Hamdan
- Department of Cardiology, Rabin Medical Center, Petah Tikva, Israel
| | - Filippo Cademartiri
- Department of Imaging, Fondazione Monasterio/CNR, Pisa, Italy & SYNLAB IRCCS SDN, Naples, Italy
| | - Hugo Marques
- UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisboa and Católica Medical School, Portugal
| | - Dinesh Kalra
- Division of Cardiology, Department of Medicine, University of Louisville School of Medicine, Louisville, KY, United States of America
| | - David M German
- Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, United States of America
| | - Himanshu Gupta
- Cardiac Imaging, Heart and Vascular Institute, Valley Health System, Ridgewood, NJ, United States of America
| | - Martin Hadamitzky
- Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany
| | - Roderick C Deaño
- Department of Medicine, Division of Cardiovascular Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
| | - Omar Khalique
- Division of Cardiovascular Imaging, St. Francis Hospital & Heart Center, Roslyn, NY, United States of America
| | - Paul Knaapen
- Department of Cardiology, Amsterdam University Medical Center, Location VUMC, Amsterdam, The Netherlands
| | - Udo Hoffmann
- Cleerly, Inc, Denver, CO, United States of America
| | - James Earls
- Cleerly, Inc, Denver, CO, United States of America
| | - James K Min
- Cleerly, Inc, Denver, CO, United States of America
| | - Ibrahim Danad
- Department of Cardiology, Amsterdam University Medical Center, Location VUMC, Amsterdam, The Netherlands; Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands.
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19
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Omori H, Matsuo H, Fujimoto S, Sobue Y, Nozaki Y, Nakazawa G, Takahashi K, Osawa K, Okubo R, Kaneko U, Sato H, Kajiya T, Miyoshi T, Ichikawa K, Abe M, Kitagawa T, Ikenaga H, Saji M, Iguchi N, Ijichi T, Mikamo H, Kurata A, Moroi M, Iijima R, Malkasian S, Crabtree T, Min JK, Earls JP, Nakanishi R. Determination of lipid-rich plaques by artificial intelligence-enabled quantitative computed tomography using near-infrared spectroscopy as reference. Atherosclerosis 2023; 386:117363. [PMID: 37944269 DOI: 10.1016/j.atherosclerosis.2023.117363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 10/08/2023] [Accepted: 10/19/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND AND AIMS Artificial intelligence quantitative CT (AI-QCT) determines coronary plaque morphology with high efficiency and accuracy. Yet, its performance to quantify lipid-rich plaque remains unclear. This study investigated the performance of AI-QCT for the detection of low-density noncalcified plaque (LD-NCP) using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS). METHODS The INVICTUS Registry is a multi-center registry enrolling patients undergoing clinically indicated coronary CT angiography and IVUS, NIRS-IVUS, or optical coherence tomography. We assessed the performance of various Hounsfield unit (HU) and volume thresholds of LD-NCP using maxLCBI4mm ≥ 400 as the reference standard and the correlation of the vessel area, lumen area, plaque burden, and lesion length between AI-QCT and IVUS. RESULTS This study included 133 atherosclerotic plaques from 47 patients who underwent coronary CT angiography and NIRS-IVUS The area under the curve of LD-NCP<30HU was 0.97 (95% confidence interval [CI]: 0.93-1.00] with an optimal volume threshold of 2.30 mm3. Accuracy, sensitivity, and specificity were 94% (95% CI: 88-96%], 93% (95% CI: 76-98%), and 94% (95% CI: 88-98%), respectively, using <30 HU and 2.3 mm3, versus 42%, 100%, and 27% using <30 HU and >0 mm3 volume of LD-NCP (p < 0.001 for accuracy and specificity). AI-QCT strongly correlated with IVUS measurements; vessel area (r2 = 0.87), lumen area (r2 = 0.87), plaque burden (r2 = 0.78) and lesion length (r2 = 0.88), respectively. CONCLUSIONS AI-QCT demonstrated excellent diagnostic performance in detecting significant LD-NCP using maxLCBI4mm ≥ 400 as the reference standard. Additionally, vessel area, lumen area, plaque burden, and lesion length derived from AI-QCT strongly correlated with respective IVUS measurements.
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Affiliation(s)
- Hiroyuki Omori
- Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan
| | - Hitoshi Matsuo
- Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan
| | - Shinichiro Fujimoto
- Department of Cardiovascular Biology and Medicine, Juntendo University, Graduate School of Medicine, Tokyo, Japan
| | - Yoshihiro Sobue
- Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan
| | - Yui Nozaki
- Department of Cardiovascular Biology and Medicine, Juntendo University, Graduate School of Medicine, Tokyo, Japan
| | - Gaku Nakazawa
- Department of Cardiology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Kuniaki Takahashi
- Department of Cardiology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Kazuhiro Osawa
- Department of General Internal Medicine 3, Kawasaki Medical School General Medical Center, Okayama Red-Cross Hospital, Okayama, Japan
| | - Ryo Okubo
- Toho University Omori Medical Center, Tokyo, Japan
| | | | - Hideyuki Sato
- Edogawa Hospital Tokyo, Japan; Department of Radiological Technology, Juntendo University Hospital, Tokyo, Japan
| | | | - Toru Miyoshi
- Department of Cardiovascular Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Graduate School of Medicine, Okayama, Japan
| | - Keishi Ichikawa
- Department of Cardiovascular Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Graduate School of Medicine, Okayama, Japan
| | | | - Toshiro Kitagawa
- Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan
| | - Hiroki Ikenaga
- Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan
| | - Mike Saji
- Toho University Omori Medical Center, Tokyo, Japan; Department of Cardiology, Sakakibara Heart Institute, Tokyo, Japan
| | - Nobuo Iguchi
- Department of Cardiology, Sakakibara Heart Institute, Tokyo, Japan
| | - Takeshi Ijichi
- Department of Cardiology, Tokai University, School of Medicine, Kanagawa, Japan
| | - Hiroshi Mikamo
- Department of Cardiology, Toho University Sakura Medical Center, Chiba, Japan
| | - Akira Kurata
- Department of Cardiology, Shikoku Cancer Center, Department of Radiology, Ehime University Graduate School of Medicine, Ehime, Japan
| | - Masao Moroi
- Department of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Raisuke Iijima
- Department of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | | | | | | | - James P Earls
- Cleerly Inc., CO, USA; George Washington University School of Medicine and Health Sciences, Washington, DC, USA
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20
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Dundas J, Leipsic JA, Sellers S, Blanke P, Miranda P, Ng N, Mullen S, Meier D, Akodad M, Sathananthan J, Collet C, de Bruyne B, Muller O, Tzimas G. Artificial Intelligence-based Coronary Stenosis Quantification at Coronary CT Angiography versus Quantitative Coronary Angiography. Radiol Cardiothorac Imaging 2023; 5:e230124. [PMID: 38166336 PMCID: PMC11163244 DOI: 10.1148/ryct.230124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 09/12/2023] [Accepted: 10/02/2023] [Indexed: 01/04/2024]
Abstract
Purpose To evaluate the performance of a new artificial intelligence (AI)-based tool by comparing the quantified stenosis severity at coronary CT angiography (CCTA) with a reference standard derived from invasive quantitative coronary angiography (QCA). Materials and Methods This secondary, post hoc analysis included 120 participants (mean age, 59.7 years ± 10.8 [SD]; 73 [60.8%] men, 47 [39.2%] women) from three large clinical trials (AFFECTS, P3, REFINE) who underwent CCTA and invasive coronary angiography with QCA. Quantitative analysis of coronary stenosis severity at CCTA was performed using an AI-based coronary stenosis quantification (AI-CSQ) software service. Blinded comparison between QCA and AI-CSQ was measured on a per-vessel and per-patient basis. Results The per-vessel AI-CSQ diagnostic sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 80%, 88%, 86%, 65%, and 94%, respectively, for diameter stenosis (DS) 50% or greater; and 78%, 92%, 91%, 47%, and 98%, respectively, for DS 70% or greater. The areas under the receiver operating characteristic curve (AUCs) to predict DS of 50% or greater and 70% or greater on a per-vessel basis were 0.92 (95% CI: 0.88, 0.95; P < .001) and 0.93 (95% CI: 0.89, 0.97; P < .001), respectively. The AUCs to predict DS of 50% or greater and 70% or greater on a per-patient basis were 0.93 (95% CI: 0.88, 0.97; P < .001) and 0.88 (95% CI: 0.81, 0.94; P < .001), respectively. Conclusion AI-CSQ at CCTA demonstrated a high diagnostic performance compared with QCA both on a per-patient and per-vessel basis, with high sensitivity for stenosis detection. Keywords: CT Angiography, Cardiac, Coronary Arteries Supplemental material is available for this article. Published under a CC BY 4.0 license.
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Affiliation(s)
- James Dundas
- From the Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada (J.D., J.A.L., P.B., G.T.); Cardiovascular Translational Laboratory, Centre for Heart Lung Innovation & Providence Research, Vancouver, British Columbia, Canada (S.S.); HeartFlow, Mountain View, Calif (P.M., N.N., S.M.); Centre for Heart Valve Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada (D.M., J.S.); Interventional Cardiology Department, Ramsay Générale de Santé, Institut Cardiovasculaire Paris Sud, Massy, France (M.A.); OLV Clinic, Cardiovascular Center Aalst, Aalst, Belgium (C.C., B.d.B.); and Department of Cardiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland (O.M., G.T.)
| | - Jonathon A Leipsic
- From the Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada (J.D., J.A.L., P.B., G.T.); Cardiovascular Translational Laboratory, Centre for Heart Lung Innovation & Providence Research, Vancouver, British Columbia, Canada (S.S.); HeartFlow, Mountain View, Calif (P.M., N.N., S.M.); Centre for Heart Valve Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada (D.M., J.S.); Interventional Cardiology Department, Ramsay Générale de Santé, Institut Cardiovasculaire Paris Sud, Massy, France (M.A.); OLV Clinic, Cardiovascular Center Aalst, Aalst, Belgium (C.C., B.d.B.); and Department of Cardiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland (O.M., G.T.)
| | - Stephanie Sellers
- From the Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada (J.D., J.A.L., P.B., G.T.); Cardiovascular Translational Laboratory, Centre for Heart Lung Innovation & Providence Research, Vancouver, British Columbia, Canada (S.S.); HeartFlow, Mountain View, Calif (P.M., N.N., S.M.); Centre for Heart Valve Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada (D.M., J.S.); Interventional Cardiology Department, Ramsay Générale de Santé, Institut Cardiovasculaire Paris Sud, Massy, France (M.A.); OLV Clinic, Cardiovascular Center Aalst, Aalst, Belgium (C.C., B.d.B.); and Department of Cardiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland (O.M., G.T.)
| | - Philipp Blanke
- From the Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada (J.D., J.A.L., P.B., G.T.); Cardiovascular Translational Laboratory, Centre for Heart Lung Innovation & Providence Research, Vancouver, British Columbia, Canada (S.S.); HeartFlow, Mountain View, Calif (P.M., N.N., S.M.); Centre for Heart Valve Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada (D.M., J.S.); Interventional Cardiology Department, Ramsay Générale de Santé, Institut Cardiovasculaire Paris Sud, Massy, France (M.A.); OLV Clinic, Cardiovascular Center Aalst, Aalst, Belgium (C.C., B.d.B.); and Department of Cardiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland (O.M., G.T.)
| | - Patricia Miranda
- From the Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada (J.D., J.A.L., P.B., G.T.); Cardiovascular Translational Laboratory, Centre for Heart Lung Innovation & Providence Research, Vancouver, British Columbia, Canada (S.S.); HeartFlow, Mountain View, Calif (P.M., N.N., S.M.); Centre for Heart Valve Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada (D.M., J.S.); Interventional Cardiology Department, Ramsay Générale de Santé, Institut Cardiovasculaire Paris Sud, Massy, France (M.A.); OLV Clinic, Cardiovascular Center Aalst, Aalst, Belgium (C.C., B.d.B.); and Department of Cardiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland (O.M., G.T.)
| | - Nicholas Ng
- From the Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada (J.D., J.A.L., P.B., G.T.); Cardiovascular Translational Laboratory, Centre for Heart Lung Innovation & Providence Research, Vancouver, British Columbia, Canada (S.S.); HeartFlow, Mountain View, Calif (P.M., N.N., S.M.); Centre for Heart Valve Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada (D.M., J.S.); Interventional Cardiology Department, Ramsay Générale de Santé, Institut Cardiovasculaire Paris Sud, Massy, France (M.A.); OLV Clinic, Cardiovascular Center Aalst, Aalst, Belgium (C.C., B.d.B.); and Department of Cardiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland (O.M., G.T.)
| | - Sarah Mullen
- From the Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada (J.D., J.A.L., P.B., G.T.); Cardiovascular Translational Laboratory, Centre for Heart Lung Innovation & Providence Research, Vancouver, British Columbia, Canada (S.S.); HeartFlow, Mountain View, Calif (P.M., N.N., S.M.); Centre for Heart Valve Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada (D.M., J.S.); Interventional Cardiology Department, Ramsay Générale de Santé, Institut Cardiovasculaire Paris Sud, Massy, France (M.A.); OLV Clinic, Cardiovascular Center Aalst, Aalst, Belgium (C.C., B.d.B.); and Department of Cardiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland (O.M., G.T.)
| | - David Meier
- From the Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada (J.D., J.A.L., P.B., G.T.); Cardiovascular Translational Laboratory, Centre for Heart Lung Innovation & Providence Research, Vancouver, British Columbia, Canada (S.S.); HeartFlow, Mountain View, Calif (P.M., N.N., S.M.); Centre for Heart Valve Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada (D.M., J.S.); Interventional Cardiology Department, Ramsay Générale de Santé, Institut Cardiovasculaire Paris Sud, Massy, France (M.A.); OLV Clinic, Cardiovascular Center Aalst, Aalst, Belgium (C.C., B.d.B.); and Department of Cardiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland (O.M., G.T.)
| | - Mariama Akodad
- From the Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada (J.D., J.A.L., P.B., G.T.); Cardiovascular Translational Laboratory, Centre for Heart Lung Innovation & Providence Research, Vancouver, British Columbia, Canada (S.S.); HeartFlow, Mountain View, Calif (P.M., N.N., S.M.); Centre for Heart Valve Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada (D.M., J.S.); Interventional Cardiology Department, Ramsay Générale de Santé, Institut Cardiovasculaire Paris Sud, Massy, France (M.A.); OLV Clinic, Cardiovascular Center Aalst, Aalst, Belgium (C.C., B.d.B.); and Department of Cardiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland (O.M., G.T.)
| | - Janarthanan Sathananthan
- From the Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada (J.D., J.A.L., P.B., G.T.); Cardiovascular Translational Laboratory, Centre for Heart Lung Innovation & Providence Research, Vancouver, British Columbia, Canada (S.S.); HeartFlow, Mountain View, Calif (P.M., N.N., S.M.); Centre for Heart Valve Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada (D.M., J.S.); Interventional Cardiology Department, Ramsay Générale de Santé, Institut Cardiovasculaire Paris Sud, Massy, France (M.A.); OLV Clinic, Cardiovascular Center Aalst, Aalst, Belgium (C.C., B.d.B.); and Department of Cardiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland (O.M., G.T.)
| | - Carlos Collet
- From the Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada (J.D., J.A.L., P.B., G.T.); Cardiovascular Translational Laboratory, Centre for Heart Lung Innovation & Providence Research, Vancouver, British Columbia, Canada (S.S.); HeartFlow, Mountain View, Calif (P.M., N.N., S.M.); Centre for Heart Valve Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada (D.M., J.S.); Interventional Cardiology Department, Ramsay Générale de Santé, Institut Cardiovasculaire Paris Sud, Massy, France (M.A.); OLV Clinic, Cardiovascular Center Aalst, Aalst, Belgium (C.C., B.d.B.); and Department of Cardiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland (O.M., G.T.)
| | - Bernard de Bruyne
- From the Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada (J.D., J.A.L., P.B., G.T.); Cardiovascular Translational Laboratory, Centre for Heart Lung Innovation & Providence Research, Vancouver, British Columbia, Canada (S.S.); HeartFlow, Mountain View, Calif (P.M., N.N., S.M.); Centre for Heart Valve Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada (D.M., J.S.); Interventional Cardiology Department, Ramsay Générale de Santé, Institut Cardiovasculaire Paris Sud, Massy, France (M.A.); OLV Clinic, Cardiovascular Center Aalst, Aalst, Belgium (C.C., B.d.B.); and Department of Cardiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland (O.M., G.T.)
| | - Olivier Muller
- From the Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada (J.D., J.A.L., P.B., G.T.); Cardiovascular Translational Laboratory, Centre for Heart Lung Innovation & Providence Research, Vancouver, British Columbia, Canada (S.S.); HeartFlow, Mountain View, Calif (P.M., N.N., S.M.); Centre for Heart Valve Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada (D.M., J.S.); Interventional Cardiology Department, Ramsay Générale de Santé, Institut Cardiovasculaire Paris Sud, Massy, France (M.A.); OLV Clinic, Cardiovascular Center Aalst, Aalst, Belgium (C.C., B.d.B.); and Department of Cardiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland (O.M., G.T.)
| | - Georgios Tzimas
- From the Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada (J.D., J.A.L., P.B., G.T.); Cardiovascular Translational Laboratory, Centre for Heart Lung Innovation & Providence Research, Vancouver, British Columbia, Canada (S.S.); HeartFlow, Mountain View, Calif (P.M., N.N., S.M.); Centre for Heart Valve Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada (D.M., J.S.); Interventional Cardiology Department, Ramsay Générale de Santé, Institut Cardiovasculaire Paris Sud, Massy, France (M.A.); OLV Clinic, Cardiovascular Center Aalst, Aalst, Belgium (C.C., B.d.B.); and Department of Cardiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland (O.M., G.T.)
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21
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Nakanishi R, Okubo R, Sobue Y, Kaneko U, Sato H, Fujimoto S, Nozaki Y, Kajiya T, Miyoshi T, Ichikawa K, Abe M, Kitagawa T, Ikenaga H, Osawa K, Saji M, Iguchi N, Nakazawa G, Takahashi K, Ijich T, Mikamo H, Kurata A, Moroi M, Iijima R, Malkasian S, Crabtree T, Chamie D, Alexandra LJ, Min JK, Earls JP, Matsuo H. Rationale and design of the INVICTUS Registry: (Multicenter Registry of Invasive and Non-Invasive imaging modalities to compare Coronary Computed Tomography Angiography, Intravascular Ultrasound and Optical Coherence Tomography for the determination of Severity, Volume and Type of coronary atherosclerosiS). J Cardiovasc Comput Tomogr 2023; 17:401-406. [PMID: 37679247 DOI: 10.1016/j.jcct.2023.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 08/01/2023] [Accepted: 08/21/2023] [Indexed: 09/09/2023]
Abstract
BACKGROUND Coronary CT angiography (CCTA) is a first-line noninvasive imaging modality for evaluating coronary artery disease (CAD). Recent advances in CCTA technology enabled semi-automated detection of coronary arteries and atherosclerosis. However, there have been to date no large-scale validation studies of automated assessment of coronary atherosclerosis phenotype and coronary artery dimensions by artificial intelligence (AI) compared to current standard invasive imaging. METHODS INVICTUS registry is a multicenter, retrospective, and prospective study designed to evaluate the dimensions of coronary arteries, as well as the characteristic, volume, and phenotype of coronary atherosclerosis by CCTA, compared with the invasive imaging modalities including intravascular ultrasound (IVUS), near-infrared spectroscopy (NIRS)-IVUS and optical coherence tomography (OCT). All patients clinically underwent both CCTA and invasive imaging modalities within three months. RESULTS Patients data are sent to the core-laboratories to analyze for stenosis severity, plaque characteristics and volume. The variables for CCTA are measured using an AI-based automated software and assessed independently with the variables measured at the imaging core laboratories for IVUS, NIRS-IVUS, and OCT in a blind fashion. CONCLUSION The INVICTUS registry will provide new insights into the diagnostic value of CCTA for determining coronary atherosclerosis phenotype and coronary artery dimensions compared to IVUS, NIRS-IVUS, and OCT. Our findings will potentially shed new light on precision medicine informed by an AI-based coronary CTA assessment of coronary atherosclerosis burden, composition, and severity. (ClinicalTrials.gov: NCT04066062).
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Affiliation(s)
- Rine Nakanishi
- Department of Cardiovascular Medicine, Toho University Graduate School of Medicine, Toho University Omori Medical Center, Tokyo, Japan.
| | - Ryo Okubo
- Division of Cardiovascular Medicine, Department of Internal Medicine, Toho University Faculty of Medicine, Toho University Omori Medical Center, Tokyo, Japan
| | - Yoshihiro Sobue
- Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan
| | | | - Hideyuki Sato
- Edogawa Hospital Tokyo, Japan; Department of Cardiovascular Biology and Medicine, Juntendo University, Graduate School of Medicine, Tokyo, Japan
| | - Shinichiro Fujimoto
- Department of Cardiovascular Biology and Medicine, Juntendo University, Graduate School of Medicine, Tokyo, Japan
| | - Yui Nozaki
- Department of Cardiovascular Biology and Medicine, Juntendo University, Graduate School of Medicine, Tokyo, Japan
| | | | - Toru Miyoshi
- Department of Cardiovascular Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Graduate School of Medicine, Okayama, Japan
| | - Keishi Ichikawa
- Department of Cardiovascular Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Graduate School of Medicine, Okayama, Japan
| | | | - Toshiro Kitagawa
- Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan
| | - Hiroki Ikenaga
- Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan
| | - Kazuhiro Osawa
- Department of General Internal Medicine 3, Kawasaki Medical School General Medical Center, Okayama, Japan; Okayama Red-Cross Hospital, Okayama, Japan
| | - Mike Saji
- Division of Cardiovascular Medicine, Department of Internal Medicine, Toho University Faculty of Medicine, Toho University Omori Medical Center, Tokyo, Japan; Department of Cardiology, Sakakibara Heart Institute, Tokyo, Japan
| | | | - Gaku Nakazawa
- Department of Cardiology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Kuniaki Takahashi
- Department of Cardiology, Tokai University, School of Medicine, Kanagawa, Japan
| | - Takeshi Ijich
- Department of Cardiology, Tokai University, School of Medicine, Kanagawa, Japan
| | - Hiroshi Mikamo
- Department of Cardiology, Toho University Sakura Medical Center, Chiba, Japan
| | - Akira Kurata
- Department of Cardiology, Shikoku Cancer Center, Ehime, Japan; Department of Radiology, Ehime University Graduate School of Medicine, Ehime, Japan
| | - Masao Moroi
- Department of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Raisuke Iijima
- Department of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | | | | | - Daniel Chamie
- Cardiovascular Medicine, Yale School of Medicine, CT, USA
| | | | | | - James P Earls
- Cleerly Inc., CO, USA; George Washington University School of Medicine and Health Sciences, Washington DC, USA
| | - Hitoshi Matsuo
- Department of Cardiovascular Medicine, Toho University Graduate School of Medicine, Toho University Omori Medical Center, Tokyo, Japan
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22
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Samant S, Bakhos JJ, Wu W, Zhao S, Kassab GS, Khan B, Panagopoulos A, Makadia J, Oguz UM, Banga A, Fayaz M, Glass W, Chiastra C, Burzotta F, LaDisa JF, Iaizzo P, Murasato Y, Dubini G, Migliavacca F, Mickley T, Bicek A, Fontana J, West NEJ, Mortier P, Boyers PJ, Gold JP, Anderson DR, Tcheng JE, Windle JR, Samady H, Jaffer FA, Desai NR, Lansky A, Mena-Hurtado C, Abbott D, Brilakis ES, Lassen JF, Louvard Y, Stankovic G, Serruys PW, Velazquez E, Elias P, Bhatt DL, Dangas G, Chatzizisis YS. Artificial Intelligence, Computational Simulations, and Extended Reality in Cardiovascular Interventions. JACC Cardiovasc Interv 2023; 16:2479-2497. [PMID: 37879802 DOI: 10.1016/j.jcin.2023.07.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 07/11/2023] [Accepted: 07/13/2023] [Indexed: 10/27/2023]
Abstract
Artificial intelligence, computational simulations, and extended reality, among other 21st century computational technologies, are changing the health care system. To collectively highlight the most recent advances and benefits of artificial intelligence, computational simulations, and extended reality in cardiovascular therapies, we coined the abbreviation AISER. The review particularly focuses on the following applications of AISER: 1) preprocedural planning and clinical decision making; 2) virtual clinical trials, and cardiovascular device research, development, and regulatory approval; and 3) education and training of interventional health care professionals and medical technology innovators. We also discuss the obstacles and constraints associated with the application of AISER technologies, as well as the proposed solutions. Interventional health care professionals, computer scientists, biomedical engineers, experts in bioinformatics and visualization, the device industry, ethics committees, and regulatory agencies are expected to streamline the use of AISER technologies in cardiovascular interventions and medicine in general.
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Affiliation(s)
- Saurabhi Samant
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Jules Joel Bakhos
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Wei Wu
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Shijia Zhao
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Ghassan S Kassab
- California Medical Innovations Institute, San Diego, California, USA
| | - Behram Khan
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Anastasios Panagopoulos
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Janaki Makadia
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Usama M Oguz
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Akshat Banga
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Muhammad Fayaz
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - William Glass
- Interprofessional Experiential Center for Enduring Learning, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Claudio Chiastra
- PoliTo(BIO)Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
| | - Francesco Burzotta
- Department of Cardiovascular Sciences, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - John F LaDisa
- Departments of Biomedical Engineering and Pediatrics - Division of Cardiology, Herma Heart Institute, Children's Wisconsin and the Medical College of Wisconsin, and the MARquette Visualization Lab, Marquette University, Milwaukee, Wisconsin, USA
| | - Paul Iaizzo
- Visible Heart Laboratories, Department of Surgery, University of Minnesota, Minnesota, USA
| | - Yoshinobu Murasato
- Department of Cardiology, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan
| | - Gabriele Dubini
- Department of Chemistry, Materials and Chemical Engineering 'Giulio Natta', Politecnico di Milano, Milan, Italy
| | - Francesco Migliavacca
- Department of Chemistry, Materials and Chemical Engineering 'Giulio Natta', Politecnico di Milano, Milan, Italy
| | | | - Andrew Bicek
- Boston Scientific Inc, Marlborough, Massachusetts, USA
| | | | | | | | - Pamela J Boyers
- Interprofessional Experiential Center for Enduring Learning, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Jeffrey P Gold
- Interprofessional Experiential Center for Enduring Learning, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Daniel R Anderson
- Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - James E Tcheng
- Cardiovascular Division, Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina, USA
| | - John R Windle
- Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Habib Samady
- Georgia Heart Institute, Gainesville, Georgia, USA
| | - Farouc A Jaffer
- Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Nihar R Desai
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Alexandra Lansky
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Carlos Mena-Hurtado
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Dawn Abbott
- Cardiovascular Institute, Warren Alpert Medical School at Brown University, Providence, Rhode Island, USA
| | - Emmanouil S Brilakis
- Center for Advanced Coronary Interventions, Minneapolis Heart Institute, Minneapolis, Minnesota, USA
| | - Jens Flensted Lassen
- Department of Cardiology B, Odense University Hospital, Odense, Syddanmark, Denmark
| | - Yves Louvard
- Institut Cardiovasculaire Paris Sud, Massy, France
| | - Goran Stankovic
- Department of Cardiology, Clinical Center of Serbia, Belgrade, Serbia
| | - Patrick W Serruys
- Department of Cardiology, National University of Ireland, Galway, Galway, Ireland
| | - Eric Velazquez
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Pierre Elias
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, New York, New York, USA
| | - Deepak L Bhatt
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - George Dangas
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yiannis S Chatzizisis
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA.
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23
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Karlsberg D, Steyer H, Fisher R, Crabtree T, Min JK, Earls JP, Rumberger J. Impact of visceral fat on coronary artery disease as defined by quantitative computed tomography angiography. Obesity (Silver Spring) 2023; 31:2460-2466. [PMID: 37559558 DOI: 10.1002/oby.23804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/18/2023] [Accepted: 04/23/2023] [Indexed: 08/11/2023]
Abstract
OBJECTIVE Obesity is associated with all-cause mortality and cardiovascular disease (CVD). Visceral fat (VF) is an important CVD risk metric given its independent correlation with myocardial infarction and stroke. This study aims to clarify the relationship between the presence and severity of VF with the presence and severity of coronary artery plaque. METHODS In 145 consecutive asymptomatic patients, atherosclerosis imaging-quantitative computed tomography was performed for total plaque volume (TPV) and percentage atheroma volume, as well as the volume of noncalcified plaque (NCP), calcified plaque, and low-density NCP (LD-NCP), diameter stenosis, and vascular remodeling. This study also included VF analysis and subcutaneous fat analysis, recording of outer waist circumference, and percentage body fat analysis. RESULTS The mean age of the patients was 56.1 [SD 8.5] years, and 84.0% were male. Measures of visceral adiposity (mean [SD, Q1-Q3 thresholds]) included estimated body fat, 28.7% (9.0%, 24.1%-33.0%); VF, 169.8 cm2 (92.3, 102.0-219.0 cm2 ); and subcutaneous fat, 223.6 mm2 (114.2, 142.5-288.0 mm2 ). The Spearman correlation coefficients of VF and plaque volume included TPV 0.22 (p = 0.0074), calcified plaque 0.12 (p = 0.62), NCP 0.25 (p = 0.0023), and LD-NCP 0.37 (p < 0.0001). There was a progression of the median coronary plaque volume for each quartile of VF including TPV (Q1: 19.8, Q2: 48.1, Q3: 86.4, and Q4: 136.6 mm3 [p = 0.0098]), NCP (Q1: 15.7, Q2: 35.4, Q3: 86.4, and Q4: 136.6 mm3 [p = 0.0032]), and LD-NCP (Q1: 0.6, Q2: 0.81, Q3: 2.0, and Q4: 5.0 mm3 [p < 0.0001]). CONCLUSIONS These findings demonstrate progression with regard to VF and TPV, NCP volume, and LD-NCP volume. Notably, there was a progression of VF and amount of LD-NCP, which is known to be high risk for future cardiovascular events. A consistent progression may indicate the future utility of VF in CVD risk stratification.
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Affiliation(s)
- Daniel Karlsberg
- The Princeton Longevity Center, New York, New York, USA
- Department of Medicine, Leon H. Charney Division of Cardiology, New York University Langone Health, New York, New York, USA
- Cardiovascular Research Foundation of Southern California, Beverly Hills, California, USA
| | - Henry Steyer
- University of Southern California, Los Angeles, California, USA
| | - Rebecca Fisher
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | - James P Earls
- Cleerly, Inc., New York, New York, USA
- George Washington University School of Medicine, Washington, DC, USA
| | - John Rumberger
- The Princeton Longevity Center, New York, New York, USA
- Department of Medicine, Leon H. Charney Division of Cardiology, New York University Langone Health, New York, New York, USA
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24
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Jonas R, Patel T, Crabtree TR, Jennings RS, Heo R, Park HB, Marques H, Chang HJ, Stuijfzand WJ, van Rosendael AR, Choi JH, Doh JH, Her AY, Koo BK, Nam CW, Shin SH, Cole J, Gimelli A, Khan MA, Lu B, Gao Y, Nabi F, Al-Mallah MH, Nakazato R, Schoepf UJ, Driessen RS, Bom MJ, Thompson RC, Jang JJ, Ridner M, Rowan C, Avelar E, Généreux P, Knaapen P, de Waard GA, Pontone G, Andreini D, Bax JJ, Choi AD, Earls JP, Hoffmann U, Min JK, Villines TC. Relation of Gender to Atherosclerotic Plaque Characteristics by Differing Angiographic Stenosis Severity. Am J Cardiol 2023; 204:276-283. [PMID: 37562193 DOI: 10.1016/j.amjcard.2023.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 06/24/2023] [Accepted: 07/05/2023] [Indexed: 08/12/2023]
Abstract
It is unknown whether gender influences the atherosclerotic plaque characteristics (APCs) of lesions of varying angiographic stenosis severity. This study evaluated the imaging data of 303 symptomatic patients from the derivation arm of the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial, all of whom underwent coronary computed tomographic angiography and clinically indicated nonemergent invasive coronary angiography upon study enrollment. Index tests were interpreted by 2 blinded core laboratories, one of which performed quantitative coronary computed tomographic angiography using an artificial intelligence application to characterize and quantify APCs, including percent atheroma volume (PAV), low-density noncalcified plaque (LD-NCP), noncalcified plaque (NCP), calcified plaque (CP), lesion length, positive arterial remodeling, and high-risk plaque (a combination of LD-NCP and positive remodeling ≥1.10); the other classified lesions as obstructive (≥50% diameter stenosis) or nonobstructive (<50% diameter stenosis) based on quantitative invasive coronary angiography. The relation between APCs and angiographic stenosis was further examined by gender. The mean age of the study cohort was 64.4 ± 10.2 years (29.0% female). In patients with obstructive disease, men had more LD-NCP PAV (0.5 ± 0.4 vs 0.3 ± 0.8, p = 0.03) and women had more CP PAV (11.7 ± 1.6 vs 8.0 ± 0.8, p = 0.04). Obstructive lesions had more NCP PAV compared with their nonobstructive lesions in both genders, however, obstructive lesions in women also demonstrated greater LD-NCP PAV (0.4 ± 0.5 vs 1.0 ± 1.8, p = 0.03), and CP PAV (17.4 ± 16.5 vs 25.9 ± 18.7, p = 0.03) than nonobstructive lesions. Comparing the composition of obstructive lesions by gender, women had more CP PAV (26.3 ± 3.4 vs 15.8 ± 1.5, p = 0.005) whereas men had more NCP PAV (33.0 ± 1.6 vs 26.7 ± 2.5, p = 0.04). Men had more LD-NCP PAV in nonobstructive lesions compared with women (1.2 ± 0.2 vs 0.6 ± 0.2, p = 0.02). In conclusion, there are gender-specific differences in plaque composition based on stenosis severity.
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Affiliation(s)
- Rebecca Jonas
- Department of Internal Medicine, Yale New Haven Hospital, New Haven, Connecticut.
| | - Toral Patel
- Department of Cardiology and Advanced Cardiac Imaging, Centra Heart and Vascular Institute, Lynchburg, Virginia
| | | | | | - Ran Heo
- Division of Cardiology, Department of Internal Medicine, Hanyang University Medical Center, Seoul, Korea College of Medicine, Hanyang University, Seoul, Korea
| | - Hyung-Bok Park
- Division of Cardiology, Department of Internal Medicine, International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon, South Korea
| | - Hugo Marques
- Faculdade de Medicina da Universidade Católica Portuguesa, Lisboa, Portugal
| | - Hyuk-Jae Chang
- Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea
| | - Wijnand J Stuijfzand
- Amsterdam University Medical Center, VU University Medical Center, Amsterdam, The Netherlands
| | | | - Jung Hyun Choi
- Department of Cardiology, Pusan National University Hospital, Busan, South Korea
| | - Joon-Hyung Doh
- Division of Cardiology, Inje University Ilsan Paik Hospital, Goyang, South Korea
| | - Ae-Young Her
- Division of Cardiology, Department of Internal Medicine, Kangwon National University, College of Medicine, Kangwon National University School of Medicine, Chuncheon, South Korea
| | - Bon-Kwon Koo
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Chang-Wook Nam
- Department of Cardiology, Cardiovascular Center, Keimyung University Dongsan Hospital, Daegu, South Korea
| | - Sang-Hoon Shin
- Division of Cardiology, Department of Internal Medicine, Ewha Women's University Seoul Hospital, Seoul, South Korea
| | - Jason Cole
- Mobile Cardiology Associates, Mobile, Alabama
| | - Alessia Gimelli
- Department of Imaging, Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | | | - Bin Lu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Beijing, China
| | - Yang Gao
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Beijing, China
| | - Faisal Nabi
- Department of Cardiology, Houston Methodist Hospital, Houston, Texas
| | - Mouaz H Al-Mallah
- Department of Cardiology, Houston Methodist Hospital, Houston, Texas
| | - Ryo Nakazato
- Cardiovascular Center, St. Luke's International Hospital, Tokyo, Japan
| | - U Joseph Schoepf
- Department of Cardiology, Department of Radiology, Medical University of South Carolina, Charleston, South Carolina
| | - Roel S Driessen
- Amsterdam University Medical Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Michiel J Bom
- Amsterdam University Medical Center, VU University Medical Center, Amsterdam, The Netherlands
| | | | - James J Jang
- Kaiser Permanente San Jose Medical Center, San Jose, California
| | | | - Chris Rowan
- Renown Heart and Vascular Institute, Reno, Nevada
| | - Erick Avelar
- Oconee Heart and Vascular Center at St Mary's Hospital, Athens, Georgia
| | - Philippe Généreux
- Gagnon Cardiovascular Institute at Morristown Medical Center, Morristown, New Jersey
| | - Paul Knaapen
- Amsterdam University Medical Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Guus A de Waard
- Amsterdam University Medical Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Gianluca Pontone
- Department of University Cardiology and Cardiac Imaging, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ospedale Galeazzi Sant'Ambrogio, Milan, Italy; Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Daniele Andreini
- Department of University Cardiology and Cardiac Imaging, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ospedale Galeazzi Sant'Ambrogio, Milan, Italy; Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Jeroen J Bax
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Andrew D Choi
- Department of Radiology, Division of Cardiology, The George Washington University School of Medicine, Washington, District of Columbia
| | | | | | | | - Todd C Villines
- Department of Medicine, Division of Cardiovascular Medicine, University of Virginia School of Medicine, Charlottesville, Virginia
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25
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Choi AD. CT-FFR: Real-World Questions, and the New CAD Imaging Triple Aim. JACC Cardiovasc Imaging 2023; 16:1066-1068. [PMID: 37269266 DOI: 10.1016/j.jcmg.2023.03.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 03/30/2023] [Indexed: 06/05/2023]
Affiliation(s)
- Andrew D Choi
- Division of Cardiology and Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA.
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26
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Bienstock S, Lin F, Blankstein R, Leipsic J, Cardoso R, Ahmadi A, Gelijns A, Patel K, Baldassarre LA, Hadley M, LaRocca G, Sanz J, Narula J, Chandrashekhar YS, Shaw LJ, Fuster V. Advances in Coronary Computed Tomographic Angiographic Imaging of Atherosclerosis for Risk Stratification and Preventive Care. JACC Cardiovasc Imaging 2023; 16:1099-1115. [PMID: 37178070 DOI: 10.1016/j.jcmg.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 01/04/2023] [Accepted: 02/01/2023] [Indexed: 05/15/2023]
Abstract
The diagnostic evaluation of coronary artery disease is undergoing a dramatic transformation with a new focus on atherosclerotic plaque. This review details the evidence needed for effective risk stratification and targeted preventive care based on recent advances in automated measurement of atherosclerosis from coronary computed tomography angiography (CTA). To date, research findings support that automated stenosis measurement is reasonably accurate, but evidence on variability by location, artery size, or image quality is unknown. The evidence for quantification of atherosclerotic plaque is unfolding, with strong concordance reported between coronary CTA and intravascular ultrasound measurement of total plaque volume (r >0.90). Statistical variance is higher for smaller plaque volumes. Limited data are available on how technical or patient-specific factors result in measurement variability by compositional subgroups. Coronary artery dimensions vary by age, sex, heart size, coronary dominance, and race and ethnicity. Accordingly, quantification programs excluding smaller arteries affect accuracy for women, patients with diabetes, and other patient subsets. Evidence is unfolding that quantification of atherosclerotic plaque is useful to enhance risk prediction, yet more evidence is required to define high-risk patients across varied populations and to determine whether such information is incremental to risk factors or currently used coronary computed tomography techniques (eg, coronary artery calcium scoring or visual assessment of plaque burden or stenosis). In summary, there is promise for the utility of coronary CTA quantification of atherosclerosis, especially if it can lead to targeted and more intensive cardiovascular prevention, notably for those patients with nonobstructive coronary artery disease and high-risk plaque features. The new quantification techniques available to imagers must not only provide sufficient added value to improve patient care, but also add minimal and reasonable cost to alleviate the financial burden on our patients and the health care system.
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Affiliation(s)
- Solomon Bienstock
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Fay Lin
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ron Blankstein
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jonathon Leipsic
- University of British Columbia, Vancouver, British Columbia, Canada
| | - Rhanderson Cardoso
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Amir Ahmadi
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Annetine Gelijns
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Krishna Patel
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Lauren A Baldassarre
- Department of Cardiovascular Medicine and Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Michael Hadley
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Gina LaRocca
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Javier Sanz
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jagat Narula
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Leslee J Shaw
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
| | - Valentin Fuster
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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27
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Meah MN, Wereski R, Bularga A, van Beek EJR, Dweck MR, Mills NL, Newby DE, Dey D, Williams MC, Lee KK. Coronary low-attenuation plaque and high-sensitivity cardiac troponin. Heart 2023; 109:702-709. [PMID: 36631142 PMCID: PMC10357930 DOI: 10.1136/heartjnl-2022-321867] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 11/23/2022] [Indexed: 01/13/2023] Open
Abstract
OBJECTIVE In patients with acute chest pain who have had myocardial infarction excluded, plasma cardiac troponin I concentrations ≥5 ng/L are associated with risk of future adverse cardiovascular events. We aim to evaluate the association between cardiac troponin and coronary plaque composition in such patients. METHODS In a prespecified secondary analysis of a prospective cohort study, blinded quantitative plaque analysis was performed on 242 CT coronary angiograms of patients with acute chest pain in whom myocardial infarction was excluded. Patients were stratified by peak plasma cardiac troponin I concentration ≥5 ng/L or <5 ng/L. Associations were assessed using univariable and multivariable logistic regression analyses. RESULTS The cohort was predominantly middle-aged (62±12 years) men (69%). Patients with plasma cardiac troponin I concentration ≥5 ng/L (n=161) had a higher total (median 33% (IQR 0-47) vs 0% (IQR 0-33)), non-calcified (27% (IQR 0-37) vs 0% (IQR 0-28)), calcified (2% (IQR 0-8) vs 0% (IQR 0-3)) and low-attenuation (1% (IQR 0-3) vs 0% (IQR 0-1)) coronary plaque burden compared with those with concentrations <5 ng/L (n=81; p≤0.001 for all). Low-attenuation plaque burden was independently associated with plasma cardiac troponin I concentration ≥5 ng/L after adjustment for clinical characteristics (adjusted OR per doubling 1.62 (95% CI 1.17 to 2.32), p=0.005) or presence of any visible coronary artery disease (adjusted OR per doubling 1.57 (95% CI 1.07 to 2.37), p=0.026). CONCLUSION In patients with acute chest pain but without myocardial infarction, plasma cardiac troponin I concentrations ≥5 ng/L are associated with greater burden of low-attenuation coronary plaque.
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Affiliation(s)
- Mohammed N Meah
- British Heart Foundation Centre for Cardiovascular Science, Edinburgh, UK
| | - Ryan Wereski
- British Heart Foundation Centre for Cardiovascular Science, Edinburgh, UK
| | - Anda Bularga
- British Heart Foundation Centre for Cardiovascular Science, Edinburgh, UK
| | - Edwin J R van Beek
- British Heart Foundation Centre for Cardiovascular Science, Edinburgh, UK
- Edinburgh Imaging Facility, Queens Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Marc R Dweck
- British Heart Foundation Centre for Cardiovascular Science, Edinburgh, UK
| | - Nicholas L Mills
- British Heart Foundation Centre for Cardiovascular Science, Edinburgh, UK
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - David E Newby
- British Heart Foundation Centre for Cardiovascular Science, Edinburgh, UK
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | | | - Kuan Ken Lee
- British Heart Foundation Centre for Cardiovascular Science, Edinburgh, UK
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28
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Serruys PW, Kotoku N, Nørgaard BL, Garg S, Nieman K, Dweck MR, Bax JJ, Knuuti J, Narula J, Perera D, Taylor CA, Leipsic JA, Nicol ED, Piazza N, Schultz CJ, Kitagawa K, Bruyne BD, Collet C, Tanaka K, Mushtaq S, Belmonte M, Dudek D, Zlahoda-Huzior A, Tu S, Wijns W, Sharif F, Budoff MJ, Mey JD, Andreini D, Onuma Y. Computed tomographic angiography in coronary artery disease. EUROINTERVENTION 2023; 18:e1307-e1327. [PMID: 37025086 PMCID: PMC10071125 DOI: 10.4244/eij-d-22-00776] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 11/14/2022] [Indexed: 04/05/2023]
Abstract
Coronary computed tomographic angiography (CCTA) is becoming the first-line investigation for establishing the presence of coronary artery disease and, with fractional flow reserve (FFRCT), its haemodynamic significance. In patients without significant epicardial obstruction, its role is either to rule out atherosclerosis or to detect subclinical plaque that should be monitored for plaque progression/regression following prevention therapy and provide risk classification. Ischaemic non-obstructive coronary arteries are also expected to be assessed by non-invasive imaging, including CCTA. In patients with significant epicardial obstruction, CCTA can assist in planning revascularisation by determining the disease complexity, vessel size, lesion length and tissue composition of the atherosclerotic plaque, as well as the best fluoroscopic viewing angle; it may also help in selecting adjunctive percutaneous devices (e.g., rotational atherectomy) and in determining the best landing zone for stents or bypass grafts.
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Affiliation(s)
| | - Nozomi Kotoku
- Department of Cardiology, University of Galway, Galway, Ireland
| | - Bjarne L Nørgaard
- Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark
| | - Scot Garg
- Department of Cardiology, Royal Blackburn Hospital, Blackburn, UK
| | - Koen Nieman
- Department of Radiology and Division of Cardiovascular Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Marc R Dweck
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Jeroen J Bax
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Heart Center, Turku University Hospital and University of Turku, Turku, Finland
| | - Juhani Knuuti
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Heart Center, Turku University Hospital and University of Turku, Turku, Finland
| | | | - Divaka Perera
- School of Cardiovascular Medicine and Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, UK
| | | | - Jonathon A Leipsic
- Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Edward D Nicol
- Royal Brompton Hospital, London, UK
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Nicolo Piazza
- Department of Medicine, Division of Cardiology, McGill University Health Center, Montreal, Quebec, Canada
| | - Carl J Schultz
- Division of Internal Medicine, Medical School, University of Western Australia, Perth, WA, Australia
- Department of Cardiology, Royal Perth Hospital, Perth, WA, Australia
| | - Kakuya Kitagawa
- Department of Advanced Diagnostic Imaging, Mie University Graduate School of Medicine, Mie, Japan
| | - Bernard De Bruyne
- Cardiovascular Center Aalst, OLV-Clinic, Aalst, Belgium
- Department of Cardiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Carlos Collet
- Cardiovascular Center Aalst, OLV-Clinic, Aalst, Belgium
| | - Kaoru Tanaka
- Department of Radiology, Universitair Ziekenhuis Brussel, VUB, Brussels, Belgium
| | | | | | - Darius Dudek
- Szpital Uniwersytecki w Krakowie, Krakow, Poland
| | - Adriana Zlahoda-Huzior
- Digital Innovations & Robotics Hub, Krakow, Poland
- Department of Measurement and Electronics, AGH University of Science and Technology, Krakow, Poland
| | - Shengxian Tu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - William Wijns
- Department of Cardiology, University of Galway, Galway, Ireland
- The Lambe Institute for Translational Medicine, The Smart Sensors Laboratory and CURAM, Galway, University of Galway, Galway, Ireland
| | - Faisal Sharif
- Department of Cardiology, University of Galway, Galway, Ireland
| | - Matthew J Budoff
- Division of Cardiology, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Johan de Mey
- Department of Radiology, Universitair Ziekenhuis Brussel, VUB, Brussels, Belgium
| | - Daniele Andreini
- Division of Cardiology and Cardiac Imaging, IRCCS Galeazzi Sant'Ambrogio, Milan, Italy
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Yoshinobu Onuma
- Department of Cardiology, University of Galway, Galway, Ireland
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29
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Kim Y, Choi AD, Telluri A, Lipkin I, Bradley AJ, Sidahmed A, Jonas R, Andreini D, Bathina R, Baggiano A, Cerci R, Choi EY, Choi JH, Choi SY, Chung N, Cole J, Doh JH, Ha SJ, Her AY, Kepka C, Kim JY, Kim JW, Kim SW, Kim W, Pontone G, Villines TC, Cho I, Danad I, Heo R, Lee SE, Lee JH, Park HB, Sung JM, Crabtree T, Earls JP, Min JK, Chang HJ. Atherosclerosis Imaging Quantitative Computed Tomography (AI-QCT) to guide referral to invasive coronary angiography in the randomized controlled CONSERVE trial. Clin Cardiol 2023; 46:477-483. [PMID: 36847047 PMCID: PMC10189079 DOI: 10.1002/clc.23995] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 01/25/2023] [Indexed: 03/01/2023] Open
Abstract
AIMS We compared diagnostic performance, costs, and association with major adverse cardiovascular events (MACE) of clinical coronary computed tomography angiography (CCTA) interpretation versus semiautomated approach that use artificial intelligence and machine learning for atherosclerosis imaging-quantitative computed tomography (AI-QCT) for patients being referred for nonemergent invasive coronary angiography (ICA). METHODS CCTA data from individuals enrolled into the randomized controlled Computed Tomographic Angiography for Selective Cardiac Catheterization trial for an American College of Cardiology (ACC)/American Heart Association (AHA) guideline indication for ICA were analyzed. Site interpretation of CCTAs were compared to those analyzed by a cloud-based software (Cleerly, Inc.) that performs AI-QCT for stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque. CCTA interpretation and AI-QCT guided findings were related to MACE at 1-year follow-up. RESULTS Seven hundred forty-seven stable patients (60 ± 12.2 years, 49% women) were included. Using AI-QCT, 9% of patients had no CAD compared with 34% for clinical CCTA interpretation. Application of AI-QCT to identify obstructive coronary stenosis at the ≥50% and ≥70% threshold would have reduced ICA by 87% and 95%, respectively. Clinical outcomes for patients without AI-QCT-identified obstructive stenosis was excellent; for 78% of patients with maximum stenosis < 50%, no cardiovascular death or acute myocardial infarction occurred. When applying an AI-QCT referral management approach to avoid ICA in patients with <50% or <70% stenosis, overall costs were reduced by 26% and 34%, respectively. CONCLUSIONS In stable patients referred for ACC/AHA guideline-indicated nonemergent ICA, application of artificial intelligence and machine learning for AI-QCT can significantly reduce ICA rates and costs with no change in 1-year MACE.
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Affiliation(s)
- Yumin Kim
- The George Washington University School of Medicine, Washington, District of Columbia, USA
| | - Andrew D Choi
- The George Washington University School of Medicine, Washington, District of Columbia, USA
| | - Anha Telluri
- The George Washington University School of Medicine, Washington, District of Columbia, USA
| | - Isabella Lipkin
- The George Washington University School of Medicine, Washington, District of Columbia, USA
| | - Andrew J Bradley
- The George Washington University School of Medicine, Washington, District of Columbia, USA
| | - Alfateh Sidahmed
- The George Washington University School of Medicine, Washington, District of Columbia, USA
| | - Rebecca Jonas
- Jefferson Medical Institute, Philadelphia, Pennsylvania, USA
| | | | - Ravi Bathina
- CARE Hospital and FACTS Foundation, Hyderabad, India
| | | | | | | | | | - So-Yeon Choi
- Ajou University Hospital, Gyeonggi-do, South Korea
| | - Namsik Chung
- Severance Cardiovascular Hospital, Yonsei University Health System, Seoul, South Korea
| | - Jason Cole
- Cardiology Associates of Mobile, Mobile, Alabama, USA
| | - Joon-Hyung Doh
- Inje University, Ilsan Paik Hospital, Gyeonggi-do, South Korea
| | - Sang-Jin Ha
- Gangneung Asan Hospital, Gangwon-do, South Korea
| | - Ae-Young Her
- Kangwon National University Hospital, Gangwon-do, South Korea
| | - Cezary Kepka
- National Institute of Cardiology, Warsaw, Poland
| | | | - Jin Won Kim
- Korea University Guro Hospital, Seoul, South Korea
| | | | - Woong Kim
- Yeungnam University Hospital, Daegu, South Korea
| | | | - Todd C Villines
- University of Virginia Medical Center, Charlottesville, Virginia, USA
| | - Iksung Cho
- Chung-Ang University Hospital, Seoul, South Korea
| | | | - Ran Heo
- Hanyang University, Hanyang University Medical Center, Seoul, South Korea
| | - Sang-Eun Lee
- Myongji Hospital, Seonam University College of Medicine, Gyeonggi-do, South Korea
| | - Ji Hyun Lee
- Severance Cardiovascular Hospital, Yonsei University Health System, Seoul, South Korea
| | - Hyung-Bok Park
- Myongji Hospital, Seonam University College of Medicine, Gyeonggi-do, South Korea.,International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon, South Korea
| | - Ji-Min Sung
- Jefferson Medical Institute, Philadelphia, Pennsylvania, USA
| | | | - James P Earls
- The George Washington University School of Medicine, Washington, District of Columbia, USA.,Cleerly Inc, New York, New York, USA
| | | | - Hyuk-Jae Chang
- Severance Cardiovascular Hospital, Yonsei University Health System, Seoul, South Korea
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30
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Baeßler B, Götz M, Antoniades C, Heidenreich JF, Leiner T, Beer M. Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective. Front Cardiovasc Med 2023; 10:1120361. [PMID: 36873406 PMCID: PMC9978503 DOI: 10.3389/fcvm.2023.1120361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 01/25/2023] [Indexed: 02/18/2023] Open
Abstract
Coronary computed tomography angiography (CCTA) is increasingly the cornerstone in the management of patients with chronic coronary syndromes. This fact is reflected by current guidelines, which show a fundamental shift towards non-invasive imaging - especially CCTA. The guidelines for acute and stable coronary artery disease (CAD) of the European Society of Cardiology from 2019 and 2020 emphasize this shift. However, to fulfill this new role, a broader availability in adjunct with increased robustness of data acquisition and speed of data reporting of CCTA is needed. Artificial intelligence (AI) has made enormous progress for all imaging methodologies concerning (semi)-automatic tools for data acquisition and data post-processing, with outreach toward decision support systems. Besides onco- and neuroimaging, cardiac imaging is one of the main areas of application. Most current AI developments in the scenario of cardiac imaging are related to data postprocessing. However, AI applications (including radiomics) for CCTA also should enclose data acquisition (especially the fact of dose reduction) and data interpretation (presence and extent of CAD). The main effort will be to integrate these AI-driven processes into the clinical workflow, and to combine imaging data/results with further clinical data, thus - beyond the diagnosis of CAD- enabling prediction and forecast of morbidity and mortality. Furthermore, data fusing for therapy planning (e.g., invasive angiography/TAVI planning) will be warranted. The aim of this review is to present a holistic overview of AI applications in CCTA (including radiomics) under the umbrella of clinical workflows and clinical decision-making. The review first summarizes and analyzes applications for the main role of CCTA, i.e., to non-invasively rule out stable coronary artery disease. In the second step, AI applications for additional diagnostic purposes, i.e., to improve diagnostic power (CAC = coronary artery classifications), improve differential diagnosis (CT-FFR and CT perfusion), and finally improve prognosis (again CAC plus epi- and pericardial fat analysis) are reviewed.
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Affiliation(s)
- Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Michael Götz
- Division of Experimental Radiology, Department for Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Charalambos Antoniades
- British Heart Foundation Chair of Cardiovascular Medicine, Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom
| | - Julius F. Heidenreich
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Tim Leiner
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Meinrad Beer
- Department for Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
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31
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van Assen M, Razavi AC, Whelton SP, De Cecco CN. Artificial intelligence in cardiac imaging: where we are and what we want. Eur Heart J 2023; 44:541-543. [PMID: 36527291 DOI: 10.1093/eurheartj/ehac700] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
- Marly van Assen
- Department of Radiology and Imaging Sciences, Emory University, 1365 Clifton Road NE, Atlanta, GA 30322, USA.,Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Emory University, 2015 Uppergate Dr, Atlanta, GA 30307, USA
| | - Alexander C Razavi
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Emory University, 2015 Uppergate Dr, Atlanta, GA 30307, USA.,Department of Medicine, Emory University, 2015 Uppergate Dr, Atlanta, GA 30307, USA
| | - Seamus P Whelton
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, 733 N Broadway, Baltimore, MD 21205, USA
| | - Carlo N De Cecco
- Department of Radiology and Imaging Sciences, Emory University, 1365 Clifton Road NE, Atlanta, GA 30322, USA.,Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Emory University, 2015 Uppergate Dr, Atlanta, GA 30307, USA
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32
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Artificial Intelligence in Cardiovascular CT and MR Imaging. Life (Basel) 2023; 13:life13020507. [PMID: 36836864 PMCID: PMC9968221 DOI: 10.3390/life13020507] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 02/06/2023] [Accepted: 02/09/2023] [Indexed: 02/15/2023] Open
Abstract
The technological development of Artificial Intelligence (AI) has grown rapidly in recent years. The applications of AI to cardiovascular imaging are various and could improve the radiologists' workflow, speeding up acquisition and post-processing time, increasing image quality and diagnostic accuracy. Several studies have already proved AI applications in Coronary Computed Tomography Angiography and Cardiac Magnetic Resonance, including automatic evaluation of calcium score, quantification of coronary stenosis and plaque analysis, or the automatic quantification of heart volumes and myocardial tissue characterization. The aim of this review is to summarize the latest advances in the field of AI applied to cardiovascular CT and MR imaging.
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33
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Nicol ED. Machine Learning Assessment of CAD: A Giant Leap or a Small Step for Coronary CTA? JACC Cardiovasc Imaging 2023; 16:206-208. [PMID: 36754477 DOI: 10.1016/j.jcmg.2022.12.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 12/30/2022] [Indexed: 02/09/2023]
Affiliation(s)
- Edward D Nicol
- Department of Cardiovascular CT, Royal Brompton Hospital, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College, London, United Kingdom.
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34
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Hays AG, Choi AD, Lopez-Mattei J, Mukherjee M. Editorial: Multimodality imaging in cardiomyopathy. Front Cardiovasc Med 2023; 9:1082023. [PMID: 36712276 PMCID: PMC9875132 DOI: 10.3389/fcvm.2022.1082023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 12/28/2022] [Indexed: 01/13/2023] Open
Affiliation(s)
- Allison G. Hays
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Andrew D. Choi
- Division of Cardiology, Department of Radiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Juan Lopez-Mattei
- Heart and Vascular Institute, Lee Health Hospital, Fort Myers, FL, United States
| | - Monica Mukherjee
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States,*Correspondence: Monica Mukherjee ✉
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35
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Motwani M, Williams MC, Nieman K, Choi AD. Great debates in cardiac computed tomography: OPINION: "Artificial intelligence is key to the future of CCTA - The great hope". J Cardiovasc Comput Tomogr 2023; 17:18-21. [PMID: 35945132 DOI: 10.1016/j.jcct.2022.07.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/05/2022] [Accepted: 07/16/2022] [Indexed: 10/17/2022]
Affiliation(s)
- Manish Motwani
- Manchester Heart Institute, Manchester University NHS Foundation Trust, UK; Institute of Cardiovascular Science, University of Manchester, UK
| | - Michelle C Williams
- Center for Cardiovascular Sciences, The University of Edinburgh, Edinburgh, UK
| | - Koen Nieman
- Departments of Cardiovascular Medicine and Radiology, Stanford University, Stanford, CA, USA
| | - Andrew D Choi
- Division of Cardiology and Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA.
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36
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Nurmohamed NS, Cantlay C, Sidahmed A, Choi AD. Refining Cardiovascular Risk: Looking Beneath the Calcium Surface. Arq Bras Cardiol 2022; 119:921-922. [PMID: 36541986 DOI: 10.36660/abc.20220763] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Affiliation(s)
- Nick S Nurmohamed
- Divisão de Cardiologia, The George Washington University School of Medicine, Washington, DC - EUA.,Departamento de Cardiologia, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam - Holanda.,Departamento de Medicina Vascular, Amsterdam UMC, University of Amsterdam, Amsterdam - Holanda
| | - Catherine Cantlay
- Divisão de Cardiologia, The George Washington University School of Medicine, Washington, DC - EUA
| | - Alfateh Sidahmed
- Divisão de Cardiologia, The George Washington University School of Medicine, Washington, DC - EUA
| | - Andrew D Choi
- Divisão de Cardiologia, The George Washington University School of Medicine, Washington, DC - EUA
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37
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Current Concepts and Future Applications of Non-Invasive Functional and Anatomical Evaluation of Coronary Artery Disease. Life (Basel) 2022; 12:life12111803. [PMID: 36362957 PMCID: PMC9696378 DOI: 10.3390/life12111803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 10/31/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
Over the last decades, significant advances have been achieved in the treatment of coronary artery disease (CAD). Proper non-invasive diagnosis and appropriate management based on functional information and the extension of ischemia or viability remain the cornerstone in the fight against adverse CAD events. Stress echocardiography and single photon emission computed tomography are often used for the evaluation of ischemia. Advancements in non-invasive imaging modalities such as computed tomography (CT) coronary angiography and cardiac magnetic resonance imaging (MRI) have not only allowed non-invasive imaging of coronary artery lumen but also provide additional functional information. Other characteristics regarding the plaque morphology can be further evaluated with the latest modalities achieving a morpho-functional evaluation of CAD. Advances in the utilization of positron emission tomography (PET), as well as software advancements especially regarding cardiac CT, may provide additional prognostic information to a more evidence-based treatment decision. Since the armamentarium on non-invasive imaging modalities has evolved, the knowledge of the capabilities and limitations of each imaging modality should be evaluated in a case-by-case basis to achieve the best diagnosis and treatment decision. In this review article, we present the most recent advances in the noninvasive anatomical and functional evaluation of CAD.
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38
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Muscogiuri G, Volpato V, Cau R, Chiesa M, Saba L, Guglielmo M, Senatieri A, Chierchia G, Pontone G, Dell’Aversana S, Schoepf UJ, Andrews MG, Basile P, Guaricci AI, Marra P, Muraru D, Badano LP, Sironi S. Application of AI in cardiovascular multimodality imaging. Heliyon 2022; 8:e10872. [PMID: 36267381 PMCID: PMC9576885 DOI: 10.1016/j.heliyon.2022.e10872] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/23/2022] [Accepted: 09/27/2022] [Indexed: 12/16/2022] Open
Abstract
Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging.
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Affiliation(s)
- Giuseppe Muscogiuri
- Department of Radiology, Istituto Auxologico Italiano IRCCS, San Luca Hospital, Italy,School of Medicine, University of Milano-Bicocca, Milan, Italy,Corresponding author.
| | - Valentina Volpato
- Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy,IRCCS Ospedale Galeazzi - Sant'Ambrogio, University Cardiology Department, Milan, Italy
| | - Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari, Polo di Monserrato, Cagliari, Italy
| | | | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari, Polo di Monserrato, Cagliari, Italy
| | - Marco Guglielmo
- Department of Cardiology, Division of Heart and Lungs, Utrecht University, Utrecht University Medical Center, Utrecht, the Netherlands
| | | | | | | | - Serena Dell’Aversana
- Department of Radiology, Ospedale S. Maria Delle Grazie - ASL Napoli 2 Nord, Pozzuoli, Italy
| | - U. Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr., Charleston, SC, USA
| | - Mason G. Andrews
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr., Charleston, SC, USA
| | - Paolo Basile
- University Cardiology Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Andrea Igoren Guaricci
- University Cardiology Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Paolo Marra
- Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
| | - Denisa Muraru
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Luigi P. Badano
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Sandro Sironi
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
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39
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Muscogiuri G, Guaricci AI, Soldato N, Cau R, Saba L, Siena P, Tarsitano MG, Giannetta E, Sala D, Sganzerla P, Gatti M, Faletti R, Senatieri A, Chierchia G, Pontone G, Marra P, Rabbat MG, Sironi S. Multimodality Imaging of Sudden Cardiac Death and Acute Complications in Acute Coronary Syndrome. J Clin Med 2022; 11:jcm11195663. [PMID: 36233531 PMCID: PMC9573273 DOI: 10.3390/jcm11195663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/07/2022] [Accepted: 09/22/2022] [Indexed: 11/23/2022] Open
Abstract
Sudden cardiac death (SCD) is a potentially fatal event usually caused by a cardiac arrhythmia, which is often the result of coronary artery disease (CAD). Up to 80% of patients suffering from SCD have concomitant CAD. Arrhythmic complications may occur in patients with acute coronary syndrome (ACS) before admission, during revascularization procedures, and in hospital intensive care monitoring. In addition, about 20% of patients who survive cardiac arrest develop a transmural myocardial infarction (MI). Prevention of ACS can be evaluated in selected patients using cardiac computed tomography angiography (CCTA), while diagnosis can be depicted using electrocardiography (ECG), and complications can be evaluated with cardiac magnetic resonance (CMR) and echocardiography. CCTA can evaluate plaque, burden of disease, stenosis, and adverse plaque characteristics, in patients with chest pain. ECG and echocardiography are the first-line tests for ACS and are affordable and useful for diagnosis. CMR can evaluate function and the presence of complications after ACS, such as development of ventricular thrombus and presence of myocardial tissue characterization abnormalities that can be the substrate of ventricular arrhythmias.
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Affiliation(s)
- Giuseppe Muscogiuri
- Department of Radiology, Istituto Auxologico Italiano IRCCS, San Luca Hospital, Piazzale Brescia 20, 20149 Milan, Italy
- School of Medicine, University of Milano-Bicocca, 20126 Milan, Italy
- Correspondence:
| | - Andrea Igoren Guaricci
- University Cardiology Unit, Department of Interdisciplinary Medicine, University of Bari, 70121 Bari, Italy
| | - Nicola Soldato
- University Cardiology Unit, Department of Interdisciplinary Medicine, University of Bari, 70121 Bari, Italy
| | - Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, 09124 Cagliari, Italy
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, 09124 Cagliari, Italy
| | - Paola Siena
- University Cardiology Unit, Department of Interdisciplinary Medicine, University of Bari, 70121 Bari, Italy
| | - Maria Grazia Tarsitano
- Department of Medical and Surgical Science, University Magna Grecia, 88100 Catanzaro, Italy
| | - Elisa Giannetta
- Department of Experimental Medicine, Sapienza University of Rome, Viale Regina Elena, 324, 00161 Rome, Italy
| | - Davide Sala
- Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, 20149 Milan, Italy
| | - Paolo Sganzerla
- Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, 20149 Milan, Italy
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, University of Turin, 10124 Turin, Italy
| | - Riccardo Faletti
- Radiology Unit, Department of Surgical Sciences, University of Turin, 10124 Turin, Italy
| | - Alberto Senatieri
- School of Medicine, University of Milano-Bicocca, 20126 Milan, Italy
| | | | | | - Paolo Marra
- School of Medicine, University of Milano-Bicocca, 20126 Milan, Italy
- Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
| | - Mark G. Rabbat
- Division of Cardiology, Loyola University of Chicago, Chicago, IL 60611, USA
- Edward Hines Jr. VA Hospital, Hines, IL 60141, USA
| | - Sandro Sironi
- School of Medicine, University of Milano-Bicocca, 20126 Milan, Italy
- Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
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Jonas RA, Weerakoon S, Fisher R, Griffin WF, Kumar V, Rahban H, Marques H, Karlsberg RP, Jennings RS, Crabtree TR, Choi AD, Earls JP. Interobserver variability among expert readers quantifying plaque volume and plaque characteristics on coronary CT angiography: a CLARIFY trial sub-study. Clin Imaging 2022; 91:19-25. [PMID: 35986973 DOI: 10.1016/j.clinimag.2022.08.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/21/2022] [Accepted: 08/07/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND The difference between expert level (L3) reader and artificial intelligence (AI) performance for quantifying coronary plaque and plaque components is unknown. OBJECTIVE This study evaluates the interobserver variability among expert readers for quantifying the volume of coronary plaque and plaque components on coronary computed tomographic angiography (CCTA) using an artificial intelligence enabled quantitative CCTA analysis software as a reference (AI-QCT). METHODS This study uses CCTA imaging obtained from 232 patients enrolled in the CLARIFY (CT EvaLuation by ARtificial Intelligence For Atherosclerosis, Stenosis and Vascular MorphologY) study. Readers quantified overall plaque volume and the % breakdown of noncalcified plaque (NCP) and calcified plaque (CP) on a per vessel basis. Readers categorized high risk plaque (HRP) based on the presence of low-attenuation-noncalcified plaque (LA-NCP) and positive remodeling (PR; ≥1.10). All CCTAs were analyzed by an FDA-cleared software service that performs AI-driven plaque characterization and quantification (AI-QCT) for comparison to L3 readers. Reader generated analyses were compared among readers and to AI-QCT generated analyses. RESULTS When evaluating plaque volume on a per vessel basis, expert readers achieved moderate to high interobserver consistency with an intra-class correlation coefficient of 0.78 for a single reader score and 0.91 for mean scores. There was a moderate trend between readers 1, 2, and 3 and AI with spearman coefficients of 0.70, 0.68 and 0.74, respectively. There was high discordance between readers and AI plaque component analyses. When quantifying %NCP v. %CP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.23, 0.34 and 0.24, respectively, compared to AI with a spearman coefficient of 0.38, 0.51, and 0.60, respectively. The intra-class correlation coefficient among readers for plaque composition assessment was 0.68. With respect to HRP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.22, 0.26, and 0.17, respectively, and a spearman coefficient of 0.36, 0.35, and 0.44, respectively. CONCLUSION Expert readers performed moderately well quantifying total plaque volumes with high consistency. However, there was both significant interobserver variability and high discordance with AI-QCT when quantifying plaque composition.
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Affiliation(s)
- Rebecca A Jonas
- Department of Internal Medicine, Thomas Jefferson University Medical Center, Philadelphia, PA, USA
| | - Shaneke Weerakoon
- Department of Cardiology, The George Washington University School of Medicine, Washington, DC, USA
| | - Rebecca Fisher
- Icahn School of Medicine at Mount Sinai, NY, New York, USA
| | - William F Griffin
- Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA
| | - Vishak Kumar
- Department of Cardiology, The George Washington University School of Medicine, Washington, DC, USA
| | - Habib Rahban
- Cardiovascular Research Foundation of Southern California, Beverly Hills, CA, USA
| | - Hugo Marques
- Centro Hospitalar Universitario de Lisboa Central - Servico de Radiologia do Hospital de Santa Marta, Lisboa, Portugal; Nova Medical School - Faculdade de Ciencias Medicas -, Lisboa, Portugal
| | - Ronald P Karlsberg
- Cardiovascular Research Foundation of Southern California, Beverly Hills, CA, USA
| | | | | | - Andrew D Choi
- Department of Cardiology, The George Washington University School of Medicine, Washington, DC, USA; Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA
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Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA). Diagnostics (Basel) 2022; 12:diagnostics12071660. [PMID: 35885564 PMCID: PMC9318450 DOI: 10.3390/diagnostics12071660] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 06/23/2022] [Accepted: 07/01/2022] [Indexed: 12/24/2022] Open
Abstract
Radiomics is the process of extracting useful quantitative features of high-dimensional data that allows for automated disease classification, including atherosclerotic disease. Hence, this study aimed to quantify and extract the radiomic features from Coronary Computed Tomography Angiography (CCTA) images and to evaluate the performance of automated machine learning (AutoML) model in classifying the atherosclerotic plaques. In total, 202 patients who underwent CCTA examination at Institut Jantung Negara (IJN) between September 2020 and May 2021 were selected as they met the inclusion criteria. Three primary coronary arteries were segmented on axial sectional images, yielding a total of 606 volume of interest (VOI). Subsequently, the first order, second order, and shape order of radiomic characteristics were extracted for each VOI. Model 1, Model 2, Model 3, and Model 4 were constructed using AutoML-based Tree-Pipeline Optimization Tools (TPOT). The heatmap confusion matrix, recall (sensitivity), precision (PPV), F1 score, accuracy, receiver operating characteristic (ROC), and area under the curve (AUC) were analysed. Notably, Model 1 with the first-order features showed superior performance in classifying the normal coronary arteries (F1 score: 0.88; Inverse F1 score: 0.94), as well as in classifying the calcified (F1 score: 0.78; Inverse F1 score: 0.91) and mixed plaques (F1 score: 0.76; Inverse F1 score: 0.86). Moreover, Model 2 consisting of second-order features was proved useful, specifically in classifying the non-calcified plaques (F1 score: 0.63; Inverse F1 score: 0.92) which are a key point for prediction of cardiac events. Nevertheless, Model 3 comprising the shape-based features did not contribute to the classification of atherosclerotic plaques. Overall, TPOT shown promising capabilities in terms of finding the best pipeline and tailoring the model using CCTA-based radiomic datasets.
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Accuracy Transcends Simplicity in Coronary Atherosclerosis Imaging. J Am Coll Cardiol 2022; 79:e487. [PMID: 35710198 DOI: 10.1016/j.jacc.2022.03.386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 03/21/2022] [Indexed: 11/21/2022]
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43
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The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography. Clin Imaging 2022; 84:149-158. [DOI: 10.1016/j.clinimag.2022.01.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 11/23/2022]
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