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Omaygenc MO, Kadoya Y, Small GR, Chow BJW. Cardiac CT: Competition, complimentary or confounder. J Med Imaging Radiat Sci 2024; 55:S31-S38. [PMID: 38433089 DOI: 10.1016/j.jmir.2024.01.005] [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: 12/18/2023] [Revised: 01/17/2024] [Accepted: 01/22/2024] [Indexed: 03/05/2024]
Abstract
Coronary CT angiography (CCTA) has been gradually adopted into clinical practice over the last two decades. CCTA has high diagnostic accuracy, prognostic value, and unique features such as assessment of plaque composition. CCTA-derived functional assessment techniques such as fractional flow reserve and CT perfusion are also available and can increase the diagnostic specificity of the modality. These properties propound CCTA as a competitor of functional testing in diagnosis of obstructive CAD, however, utilizing CCTA in a concomitant fashion to potentiate the performance of the latter can lead to better patient care and may provide more accurate prognostic information. Although multiple diagnostic challenges such as evaluation of calcified segments, stents, and small distal vessels still exist, the technologic developments in hardware as well as growing incorporation of artificial intelligence to daily practice are all set to augment the diagnostic and prognostic role of CCTA in cardiovascular disorders.
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Affiliation(s)
- Mehmet Onur Omaygenc
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada.
| | - Yoshito Kadoya
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada
| | - Gary Robert Small
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada
| | - Benjamin Joe Wade Chow
- Department of Medicine (Cardiology), University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada; Department of Radiology, University of Ottawa, Ottawa, Canada
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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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Lee E, Amadi C, Williams MC, Agarwal PP. Coronary Artery Disease: Role of Computed Tomography and Recent Advances. Radiol Clin North Am 2024; 62:385-398. [PMID: 38553176 DOI: 10.1016/j.rcl.2023.12.017] [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
In this review, the authors summarize the role of coronary computed tomography angiography and coronary artery calcium scoring in different clinical presentations of chest pain and preventative care and discuss future directions and new technologies such as pericoronary fat inflammation and the growing footprint of artificial intelligence in cardiovascular medicine.
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Affiliation(s)
- Elizabeth Lee
- Department of Radiology, Michigan Medicine, 1500 East Medical Center Drive, TC B1-148, Ann Arbor, MI 48109-5030, USA.
| | - Chiemezie Amadi
- Department of Radiology, Michigan Medicine, 1500 Medical Center Drive, Room 5481, Ann Arbor, MI 48109-5868, USA
| | - Michelle C Williams
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, The Queen's Medical Research Institute, Edinburg BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Prachi P Agarwal
- Department of Radiology, Division of Cardiothoracic Radiology, Michigan Medicine, 1500 East Medical Center Drive SPC 5868, Ann Arbor, MI 48109, USA
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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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