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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Parvathy G, Kamaraj B, Sah B, Maheshwari A, Alexander A, Dixit V, Mumtaz H, Saqib M. Emerging artificial intelligence-aided diagnosis and management methods for ischemic strokes and vascular occlusions: A comprehensive review. World Neurosurg X 2024; 22:100303. [PMID: 38510336 PMCID: PMC10951088 DOI: 10.1016/j.wnsx.2024.100303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 02/21/2024] [Indexed: 03/22/2024] Open
Abstract
Large-vessel occlusion (LVO) stroke is a promising field for the use of AI, especially machine learning (ML) because optimal results are highly dependent on timely diagnosis, communication, and treatment. In order to better understand the current state of artificial intelligence (AI) in relation to LVO strokes, its efficacy, and potential future applications, we searched relevant literature to perform a comprehensive evaluation of the topic. The databases PubMed, Embase, and Scopus were extensively searched for this review. Studies were then screened using title and abstract criteria and duplicate studies were excluded. By using pre-established inclusion and exclusion criteria, it was decided whether or not to include full-text papers in the final analysis. The studies were analyzed, and the relevant information was retrieved. In recognizing LVO on computed tomography, ML approaches were very accurate. There is a shortage of AI applications for thrombectomy patient selection, despite the fact that certain research accurately evaluates individual patient eligibility for endovascular therapy. Machine learning algorithms may reasonably predict clinical and angiographic outcomes as well as associated factors. AI has shown promise in the diagnosis and treatment of people who have just suffered a stroke. However, the usefulness of AI in management and forecasting remains restricted, necessitating more studies into machine learning applications that can guide decision making in the future.
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Abdelrahman K, Shiyovich A, Huck DM, Berman AN, Weber B, Gupta S, Cardoso R, Blankstein R. Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification. Diagnostics (Basel) 2024; 14:125. [PMID: 38248002 PMCID: PMC10814920 DOI: 10.3390/diagnostics14020125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/25/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024] Open
Abstract
Coronary artery calcium (CAC) is a marker of coronary atherosclerosis, and the presence and severity of CAC have been shown to be powerful predictors of future cardiovascular events. Due to its value in risk discrimination and reclassification beyond traditional risk factors, CAC has been supported by recent guidelines, particularly for the purposes of informing shared decision-making regarding the use of preventive therapies. In addition to dedicated ECG-gated CAC scans, the presence and severity of CAC can also be accurately estimated on non-contrast chest computed tomography scans performed for other clinical indications. However, the presence of such "incidental" CAC is rarely reported. Advances in artificial intelligence have now enabled automatic CAC scoring for both cardiac and non-cardiac CT scans. Various AI approaches, from rule-based models to machine learning algorithms and deep learning, have been applied to automate CAC scoring. Convolutional neural networks, a deep learning technique, have had the most successful approach, with high agreement with manual scoring demonstrated in multiple studies. Such automated CAC measurements may enable wider and more accurate detection of CAC from non-gated CT studies, thus improving the efficiency of healthcare systems to identify and treat previously undiagnosed coronary artery disease.
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Affiliation(s)
| | | | | | | | | | | | | | - Ron Blankstein
- Departments of Medicine (Cardiovascular Division) and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
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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 DOI: 10.1177/17539447241249650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [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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Muscogiuri E, van Assen M, Tessarin G, Razavi A, Schwemmer C, Schoebinger M, Wels M, Rapaka S, Fung GSK, Stillman AE, De Cecco CN. Validation of a convolutional neural network algorithm for calcium score quantification using a multivendor dataset. J Cardiovasc Comput Tomogr 2023; 17:473-475. [PMID: 37945453 PMCID: PMC10908358 DOI: 10.1016/j.jcct.2023.10.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/07/2023] [Accepted: 10/26/2023] [Indexed: 11/12/2023]
Affiliation(s)
- Emanuele Muscogiuri
- Department of Radiology and Imaging Sciences, Emory University Hospital | Emory Healthcare, Inc., Atlanta, GA, USA; Thoracic Imaging Division, Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | - Marly van Assen
- Department of Radiology and Imaging Sciences, Emory University Hospital | Emory Healthcare, Inc., Atlanta, GA, USA
| | - Giovanni Tessarin
- Department of Radiology and Imaging Sciences, Emory University Hospital | Emory Healthcare, Inc., Atlanta, GA, USA; Department of Medicine-DIMED, Institute of Radiology, University of Padova, Padua, Italy; Department of Radiology, Ca' Foncello General Hospital, Treviso, Italy
| | - Alexander Razavi
- Department of Cardiology, Emory University Hospital | Emory Healthcare, Inc., Atlanta, GA, USA
| | - Chris Schwemmer
- Computed Tomography, Siemens Healthineers, Forchheim, Germany
| | - Max Schoebinger
- Computed Tomography, Siemens Healthineers, Forchheim, Germany
| | - Michael Wels
- Computed Tomography, Siemens Healthineers, Forchheim, Germany
| | | | | | - Arthur E Stillman
- Department of Radiology and Imaging Sciences, Emory University Hospital | Emory Healthcare, Inc., Atlanta, GA, USA
| | - Carlo N De Cecco
- Department of Radiology and Imaging Sciences, Emory University Hospital | Emory Healthcare, Inc., Atlanta, GA, USA.
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Yamaoka T, Watanabe S. Artificial intelligence in coronary artery calcium measurement: Barriers and solutions for implementation into daily practice. Eur J Radiol 2023; 164:110855. [PMID: 37167685 DOI: 10.1016/j.ejrad.2023.110855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/29/2023] [Accepted: 04/28/2023] [Indexed: 05/13/2023]
Abstract
Coronary artery calcification (CAC) measurement is a valuable predictor of cardiovascular risk. However, its measurement can be time-consuming and complex, thus driving the desire for artificial intelligence (AI)-based approaches. The aim of this review is to explore the current status of CAC volume measurement using AI-based systems for the automated prediction of cardiovascular events. We also make proposals for the implementation of these systems into clinical practice. Research to date on applying AI to CAC scoring has shown the potential for automation and risk stratification, and, overall, efficacy and a high level of agreement with categorisation by trained clinicians have been demonstrated. However, research in this field has not been uniform or directed. One contributing factor may be a lack of integration and communication between computer scientists and cardiologists. Clinicians, institutions, and organisations should work together towards applying this technology to improve processes, preserve healthcare resources, and improve patient outcomes.
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Affiliation(s)
- Toshihide Yamaoka
- Department of Diagnostic Imaging and Interventional Radiology, Kyoto Katsura Hospital, Japan.
| | - Sachika Watanabe
- Department of Diagnostic Imaging and Interventional Radiology, Kyoto Katsura Hospital, Japan
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Shimoyama T, Gaj S, Nakamura K, Kovi S, Man S, Uchino K. Quantitative CTA vascular calcification, atherosclerosis burden, and stroke mechanism in patients with ischemic stroke. J Neurol Sci 2023; 449:120667. [PMID: 37148773 DOI: 10.1016/j.jns.2023.120667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 04/23/2023] [Accepted: 04/27/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND Vascular calcification is recognized as the advanced stage of atherosclerosis burden. We hypothesized that vascular calcium quantification in CT angiography (CTA) would be helpful to differentiate large artery atherosclerosis (LAA) from other stroke etiology in patients with ischemic stroke. METHODS We studied 375 acute ischemic stroke patients (200 males, mean age 69.9 years) who underwent complete CTA images of the aortic arch, neck, and head. The automatic artery and calcification segmentation method measured calcification volumes in the intracranial internal carotid artery (ICA), cervical carotid artery, and aortic arch using deep-learning U-net model and region-grow algorithms. We investigated the correlations and patterns of vascular calcification in the different vessel beds among stroke etiology by age category (young: <65 years, intermediate: 65-74 years, older ≥75 years). RESULTS Ninety-five (25.3%) were diagnosed with LAA according to TOAST criteria. Median calcification volumes were higher by increasing the age category in each vessel bed. One-way ANOVA with Bonferroni correction showed calcification volumes in all vessel beds were significantly higher in LAA compared with other stroke subtypes in the younger subgroup. Calcification volumes were independently associated with LAA in intracranial ICA (OR; 2.89, 95% CI 1.56-5.34, P = .001), cervical carotid artery (OR; 3.40, 95% CI 1.94-5.94, P < .001) and aorta (OR; 1.69, 95%CI 1.01-2.80, P = .044) in younger subsets. By contrast, the intermediate and older subsets did not show a significant relationship between calcification volumes and stroke subtypes. CONCLUSION Atherosclerosis calcium volumes in major vessels were significantly higher in LAA compared to non-LAA stroke in younger age.
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Affiliation(s)
- Takashi Shimoyama
- Cerebrovascular Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Sibaji Gaj
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Kunio Nakamura
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Shivakrishna Kovi
- Cerebrovascular Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Shumei Man
- Cerebrovascular Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Ken Uchino
- Cerebrovascular Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Lanzafame LRM, Bucolo GM, Muscogiuri G, Sironi S, Gaeta M, Ascenti G, Booz C, Vogl TJ, Blandino A, Mazziotti S, D'Angelo T. Artificial Intelligence in Cardiovascular CT and MR Imaging. Life (Basel) 2023; 13. [PMID: 36836864 DOI: 10.3390/life13020507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [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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Suh YJ, Kim C, Lee JG, Oh H, Kang H, Kim YH, Yang DH. Fully automatic coronary calcium scoring in non-ECG-gated low-dose chest CT: comparison with ECG-gated cardiac CT. Eur Radiol 2023; 33:1254-1265. [PMID: 36098798 DOI: 10.1007/s00330-022-09117-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 07/05/2022] [Accepted: 08/15/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVES To validate an artificial intelligence (AI)-based fully automatic coronary artery calcium (CAC) scoring system on non-electrocardiogram (ECG)-gated low-dose chest computed tomography (LDCT) using multi-institutional datasets with manual CAC scoring as the reference standard. METHODS This retrospective study included 452 subjects from three academic institutions, who underwent both ECG-gated calcium scoring computed tomography (CSCT) and LDCT scans. For all CSCT and LDCT scans, automatic CAC scoring (CAC_auto) was performed using AI-based software, and manual CAC scoring (CAC_man) was set as the reference standard. The reliability and agreement of CAC_auto was evaluated and compared with that of CAC_man using intraclass correlation coefficients (ICCs) and Bland-Altman plots. The reliability between CAC_auto and CAC_man for CAC severity categories was analyzed using weighted kappa (κ) statistics. RESULTS CAC_auto on CSCT and LDCT yielded a high ICC (0.998, 95% confidence interval (CI) 0.998-0.999 and 0.989, 95% CI 0.987-0.991, respectively) and a mean difference with 95% limits of agreement of 1.3 ± 37.1 and 0.8 ± 75.7, respectively. CAC_auto achieved excellent reliability for CAC severity (κ = 0.918-0.972) on CSCT and good to excellent but heterogenous reliability among datasets (κ = 0.748-0.924) on LDCT. CONCLUSIONS The application of an AI-based automatic CAC scoring software to LDCT shows good to excellent reliability in CAC score and CAC severity categorization in multi-institutional datasets; however, the reliability varies among institutions. KEY POINTS • AI-based automatic CAC scoring on LDCT shows excellent reliability with manual CAC scoring in multi-institutional datasets. • The reliability for CAC score-based severity categorization varies among datasets. • Automatic scoring for LDCT shows a higher false-positive rate than automatic scoring for CSCT, and most common causes of a false-positive are image noise and artifacts for both CSCT and LDCT.
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Affiliation(s)
- Young Joo Suh
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Cherry Kim
- Department of Radiology, Korea University Ansan Hospital, Ansan, South Korea
| | - June-Goo Lee
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Hongmin Oh
- Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Heejun Kang
- Divison of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Young-Hak Kim
- Divison of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Dong Hyun Yang
- Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
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Kim SY, Suh YJ, Kim NY, Lee S, Nam K, Kim J, Kim H, Lee H, Han K, Yong HS. A Modified Length-Based Grading Method for Assessing Coronary Artery Calcium Severity on Non-Electrocardiogram-Gated Chest Computed Tomography: A Multiple-Observer Study. Korean J Radiol 2023; 24:284-293. [PMID: 36996903 PMCID: PMC10067688 DOI: 10.3348/kjr.2022.0826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 12/19/2022] [Accepted: 02/04/2023] [Indexed: 03/29/2023] Open
Abstract
OBJECTIVE To validate a simplified ordinal scoring method, referred to as modified length-based grading, for assessing coronary artery calcium (CAC) severity on non-electrocardiogram (ECG)-gated chest computed tomography (CT). MATERIALS AND METHODS This retrospective study enrolled 120 patients (mean age ± standard deviation [SD], 63.1 ± 14.5 years; male, 64) who underwent both non-ECG-gated chest CT and ECG-gated cardiac CT between January 2011 and December 2021. Six radiologists independently assessed CAC severity on chest CT using two scoring methods (visual assessment and modified length-based grading) and categorized the results as none, mild, moderate, or severe. The CAC category on cardiac CT assessed using the Agatston score was used as the reference standard. Agreement among the six observers for CAC category classification was assessed using Fleiss kappa statistics. Agreement between CAC categories on chest CT obtained using either method and the Agatston score categories on cardiac CT was assessed using Cohen's kappa. The time taken to evaluate CAC grading was compared between the observers and two grading methods. RESULTS For differentiation of the four CAC categories, interobserver agreement was moderate for visual assessment (Fleiss kappa, 0.553 [95% confidence interval {CI}: 0.496-0.610]) and good for modified length-based grading (Fleiss kappa, 0.695 [95% CI: 0.636-0.754]). The modified length-based grading demonstrated better agreement with the reference standard categorization with cardiac CT than visual assessment (Cohen's kappa, 0.565 [95% CI: 0.511-0.619 for visual assessment vs. 0.695 [95% CI: 0.638-0.752] for modified length-based grading). The overall time for evaluating CAC grading was slightly shorter in visual assessment (mean ± SD, 41.8 ± 38.9 s) than in modified length-based grading (43.5 ± 33.2 s) (P < 0.001). CONCLUSION The modified length-based grading worked well for evaluating CAC on non-ECG-gated chest CT with better interobserver agreement and agreement with cardiac CT than visual assessment.
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Affiliation(s)
- Suh Young Kim
- Department of Radiology, Gangneung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Korea
- Department of Medicine, Yonsei University Graduate School, College of Medicine, Seoul, Korea
| | - Young Joo Suh
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
| | - Na Young Kim
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Suji Lee
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Kyungsun Nam
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jeongyun Kim
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hwan Kim
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hyunji Lee
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hwan Seok Yong
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
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Ihdayhid AR, Lan NSR, Williams M, Newby D, Flack J, Kwok S, Joyner J, Gera S, Dembo L, Adler B, Ko B, Chow BJW, Dwivedi G. Evaluation of an artificial intelligence coronary artery calcium scoring model from computed tomography. Eur Radiol 2023; 33:321-329. [PMID: 35986771 PMCID: PMC9755106 DOI: 10.1007/s00330-022-09028-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 06/07/2022] [Accepted: 07/13/2022] [Indexed: 11/24/2022]
Abstract
OBJECTIVES Coronary artery calcium (CAC) scores derived from computed tomography (CT) scans are used for cardiovascular risk stratification. Artificial intelligence (AI) can assist in CAC quantification and potentially reduce the time required for human analysis. This study aimed to develop and evaluate a fully automated model that identifies and quantifies CAC. METHODS Fully convolutional neural networks for automated CAC scoring were developed and trained on 2439 cardiac CT scans and validated using 771 scans. The model was tested on an independent set of 1849 cardiac CT scans. Agatston CAC scores were further categorised into five risk categories (0, 1-10, 11-100, 101-400, and > 400). Automated scores were compared to the manual reference standard (level 3 expert readers). RESULTS Of 1849 scans used for model testing (mean age 55.7 ± 10.5 years, 49% males), the automated model detected the presence of CAC in 867 (47%) scans compared with 815 (44%) by human readers (p = 0.09). CAC scores from the model correlated very strongly with the manual score (Spearman's r = 0.90, 95% confidence interval [CI] 0.89-0.91, p < 0.001 and intraclass correlation coefficient = 0.98, 95% CI 0.98-0.99, p < 0.001). The model classified 1646 (89%) into the same risk category as human observers. The Bland-Altman analysis demonstrated little difference (1.69, 95% limits of agreement: -41.22, 44.60) and there was almost excellent agreement (Cohen's κ = 0.90, 95% CI 0.88-0.91, p < 0.001). Model analysis time was 13.1 ± 3.2 s/scan. CONCLUSIONS This artificial intelligence-based fully automated CAC scoring model shows high accuracy and low analysis times. Its potential to optimise clinical workflow efficiency and patient outcomes requires evaluation. KEY POINTS • Coronary artery calcium (CAC) scores are traditionally assessed using cardiac computed tomography and require manual input by human operators to identify calcified lesions. • A novel artificial intelligence (AI)-based model for fully automated CAC scoring was developed and tested on an independent dataset of computed tomography scans, showing very high levels of correlation and agreement with manual measurements as a reference standard. • AI has the potential to assist in the identification and quantification of CAC, thereby reducing the time required for human analysis.
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Affiliation(s)
- Abdul Rahman Ihdayhid
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia.
- Harry Perkins Institute of Medical Research, Curtin University, Perth, Australia.
| | - Nick S R Lan
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia
- Harry Perkins Institute of Medical Research, University of Western Australia, Perth, Australia
| | - Michelle Williams
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland, UK
| | - David Newby
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland, UK
| | | | | | | | - Sahil Gera
- Harry Perkins Institute of Medical Research, University of Western Australia, Perth, Australia
| | - Lawrence Dembo
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia
- Envision Medical Imaging, Perth, Australia
| | | | - Brian Ko
- Monash Cardiovascular Research Centre, Monash University and MonashHeart, Monash Health, Melbourne, Australia
| | | | - Girish Dwivedi
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia.
- Harry Perkins Institute of Medical Research, University of Western Australia, Perth, Australia.
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13
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Zhai Z, van Velzen SGM, Lessmann N, Planken N, Leiner T, Išgum I. Learning coronary artery calcium scoring in coronary CTA from non-contrast CT using unsupervised domain adaptation. Front Cardiovasc Med 2022; 9:981901. [PMID: 36172575 PMCID: PMC9510682 DOI: 10.3389/fcvm.2022.981901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/11/2022] [Indexed: 11/21/2022] Open
Abstract
Deep learning methods have demonstrated the ability to perform accurate coronary artery calcium (CAC) scoring. However, these methods require large and representative training data hampering applicability to diverse CT scans showing the heart and the coronary arteries. Training methods that accurately score CAC in cross-domain settings remains challenging. To address this, we present an unsupervised domain adaptation method that learns to perform CAC scoring in coronary CT angiography (CCTA) from non-contrast CT (NCCT). To address the domain shift between NCCT (source) domain and CCTA (target) domain, feature distributions are aligned between two domains using adversarial learning. A CAC scoring convolutional neural network is divided into a feature generator that maps input images to features in the latent space and a classifier that estimates predictions from the extracted features. For adversarial learning, a discriminator is used to distinguish the features between source and target domains. Hence, the feature generator aims to extract features with aligned distributions to fool the discriminator. The network is trained with adversarial loss as the objective function and a classification loss on the source domain as a constraint for adversarial learning. In the experiments, three data sets were used. The network is trained with 1,687 labeled chest NCCT scans from the National Lung Screening Trial. Furthermore, 200 labeled cardiac NCCT scans and 200 unlabeled CCTA scans were used to train the generator and the discriminator for unsupervised domain adaptation. Finally, a data set containing 313 manually labeled CCTA scans was used for testing. Directly applying the CAC scoring network trained on NCCT to CCTA led to a sensitivity of 0.41 and an average false positive volume 140 mm3/scan. The proposed method improved the sensitivity to 0.80 and reduced average false positive volume of 20 mm3/scan. The results indicate that the unsupervised domain adaptation approach enables automatic CAC scoring in contrast enhanced CT while learning from a large and diverse set of CT scans without contrast. This may allow for better utilization of existing annotated data sets and extend the applicability of automatic CAC scoring to contrast-enhanced CT scans without the need for additional manual annotations. The code is publicly available at https://github.com/qurAI-amsterdam/CACscoringUsingDomainAdaptation.
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Affiliation(s)
- Zhiwei Zhai
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, Netherlands
- Faculty of Science, Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
- *Correspondence: Zhiwei Zhai
| | - Sanne G. M. van Velzen
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, Netherlands
- Faculty of Science, Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Amsterdam, Netherlands
| | - Nikolas Lessmann
- Diagnostic Image Analysis Group, Radboud University Medical Center Nijmegen, Nijmegen, Netherlands
| | - Nils Planken
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, Netherlands
| | - Tim Leiner
- Department of Radiology, Utrecht University Medical Center, University of Utrecht, Utrecht, Netherlands
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, Netherlands
- Faculty of Science, Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Amsterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, Netherlands
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14
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Chen X, Lei Y, Su J, Yang H, Ni W, Yu J, Gu Y, Mao Y. A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges. Curr Neuropharmacol 2022; 20:1359-1382. [PMID: 34749621 PMCID: PMC9881077 DOI: 10.2174/1570159x19666211108141446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/07/2021] [Accepted: 10/10/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND A variety of emerging medical imaging technologies based on artificial intelligence have been widely applied in many diseases, but they are still limitedly used in the cerebrovascular field even though the diseases can lead to catastrophic consequences. OBJECTIVE This work aims to discuss the current challenges and future directions of artificial intelligence technology in cerebrovascular diseases through reviewing the existing literature related to applications in terms of computer-aided detection, prediction and treatment of cerebrovascular diseases. METHODS Based on artificial intelligence applications in four representative cerebrovascular diseases including intracranial aneurysm, arteriovenous malformation, arteriosclerosis and moyamoya disease, this paper systematically reviews studies published between 2006 and 2021 in five databases: National Center for Biotechnology Information, Elsevier Science Direct, IEEE Xplore Digital Library, Web of Science and Springer Link. And three refinement steps were further conducted after identifying relevant literature from these databases. RESULTS For the popular research topic, most of the included publications involved computer-aided detection and prediction of aneurysms, while studies about arteriovenous malformation, arteriosclerosis and moyamoya disease showed an upward trend in recent years. Both conventional machine learning and deep learning algorithms were utilized in these publications, but machine learning techniques accounted for a larger proportion. CONCLUSION Algorithms related to artificial intelligence, especially deep learning, are promising tools for medical imaging analysis and will enhance the performance of computer-aided detection, prediction and treatment of cerebrovascular diseases.
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Affiliation(s)
- Xi Chen
- School of Information Science and Technology, Fudan University, Shanghai, China; ,These authors contributed equally to this work
| | - Yu Lei
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China,These authors contributed equally to this work
| | - Jiabin Su
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Heng Yang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Wei Ni
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai, China; ,Address correspondence to these authors at the School of Information Science and Technology, Fudan University, Shanghai 200433, China; Tel: +86 021 65643202; Fax: +86 021 65643202; E-mail: Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai 200040, China; Tel: +86 021 52889999; Fax: +86 021 62489191; E-mail:
| | - Yuxiang Gu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China,Address correspondence to these authors at the School of Information Science and Technology, Fudan University, Shanghai 200433, China; Tel: +86 021 65643202; Fax: +86 021 65643202; E-mail: Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai 200040, China; Tel: +86 021 52889999; Fax: +86 021 62489191; E-mail:
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
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15
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Koulaouzidis G, Jadczyk T, Iakovidis DK, Koulaouzidis A, Bisnaire M, Charisopoulou D. Artificial Intelligence in Cardiology-A Narrative Review of Current Status. J Clin Med 2022; 11:jcm11133910. [PMID: 35807195 PMCID: PMC9267740 DOI: 10.3390/jcm11133910] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 06/17/2022] [Accepted: 06/20/2022] [Indexed: 02/01/2023] Open
Abstract
Artificial intelligence (AI) is an integral part of clinical decision support systems (CDSS), offering methods to approximate human reasoning and computationally infer decisions. Such methods are generally based on medical knowledge, either directly encoded with rules or automatically extracted from medical data using machine learning (ML). ML techniques, such as Artificial Neural Networks (ANNs) and support vector machines (SVMs), are based on mathematical models with parameters that can be optimally tuned using appropriate algorithms. The ever-increasing computational capacity of today’s computer systems enables more complex ML systems with millions of parameters, bringing AI closer to human intelligence. With this objective, the term deep learning (DL) has been introduced to characterize ML based on deep ANN (DNN) architectures with multiple layers of artificial neurons. Despite all of these promises, the impact of AI in current clinical practice is still limited. However, this could change shortly, as the significantly increased papers in AI, machine learning and deep learning in cardiology show. We highlight the significant achievements of recent years in nearly all areas of cardiology and underscore the mounting evidence suggesting how AI will take a central stage in the field.
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Affiliation(s)
- George Koulaouzidis
- Department of Biochemical Sciences, Pomeranian Medical University (PMU), 70-204 Szczecin, Poland;
| | - Tomasz Jadczyk
- Division of Cardiology and Structural Heart Diseases, Medical University of Silesia, 40-551 Katowice, Poland;
- International Clinical Research Center, St. Anne’s University Hospital Brno, 656 91 Brno, Czech Republic
| | - Dimitris K. Iakovidis
- Department of Computer Science and Biomedical Informatics, University of Thessaly, 40500 Lamia, Greece;
| | - Anastasios Koulaouzidis
- Department of Social Medicine & Public Health, Pomeranian Medical University (PMU), 70-204 Szczecin, Poland
- Department of Medicine, OUH Svendborg Sygehus, 5700 Svendborg, Denmark
- Surgical Research Unit, Odense University Hospital, 5000 Odense, Denmark
- Department of Clinical Research, University of Southern Denmark (SDU), 5000 Odense, Denmark
- Correspondence:
| | - Marc Bisnaire
- Cardiology Research and Scientific Advancements, UVA Research, Toronto, ON L3R 3Z3, Canada;
| | - Dafni Charisopoulou
- Academic Centre for Congenital Heart Disease, 6500 HB Nijmegen, The Netherlands;
- Amalia Children’s Hospital, Radboud University Medical Centre, 6525 GA Nijmegen, The Netherlands
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16
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Adikari D, Gharleghi R, Zhang S, Jorm L, Sowmya A, Moses D, Ooi SY, Beier S. A new and automated risk prediction of coronary artery disease using clinical endpoints and medical imaging-derived patient-specific insights: protocol for the retrospective GeoCAD cohort study. BMJ Open 2022; 12:e054881. [PMID: 35725256 PMCID: PMC9214399 DOI: 10.1136/bmjopen-2021-054881] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
INTRODUCTION Coronary artery disease (CAD) is the leading cause of death worldwide. More than a quarter of cardiovascular events are unexplained by current absolute cardiovascular disease risk calculators, and individuals without clinical risk factors have been shown to have worse outcomes. The 'anatomy of risk' hypothesis recognises that adverse anatomical features of coronary arteries enhance atherogenic haemodynamics, which in turn mediate the localisation and progression of plaques. We propose a new risk prediction method predicated on CT coronary angiography (CTCA) data and state-of-the-art machine learning methods based on a better understanding of anatomical risk for CAD. This may open new pathways in the early implementation of personalised preventive therapies in susceptible individuals as a potential key in addressing the growing burden of CAD. METHODS AND ANALYSIS GeoCAD is a retrospective cohort study in 1000 adult patients who have undergone CTCA for investigation of suspected CAD. It is a proof-of-concept study to test the hypothesis that advanced image-derived patient-specific data can accurately predict long-term cardiovascular events. The objectives are to (1) profile CTCA images with respect to variations in anatomical shape and associated haemodynamic risk expressing, at least in part, an individual's CAD risk, (2) develop a machine-learning algorithm for the rapid assessment of anatomical risk directly from unprocessed CTCA images and (3) to build a novel CAD risk model combining traditional risk factors with these novel anatomical biomarkers to provide a higher accuracy CAD risk prediction tool. ETHICS AND DISSEMINATION The study protocol has been approved by the St Vincent's Hospital Human Research Ethics Committee, Sydney-2020/ETH02127 and the NSW Population and Health Service Research Ethics Committee-2021/ETH00990. The project outcomes will be published in peer-reviewed and biomedical journals, scientific conferences and as a higher degree research thesis.
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Affiliation(s)
- Dona Adikari
- Faculty of Medicine, The University of New South Wales, Sydney, New South Wales, Australia
- Cardiology Department, The Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Ramtin Gharleghi
- School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, New South Wales, Australia
| | - Shisheng Zhang
- School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, New South Wales, Australia
| | - Louisa Jorm
- Centre for Big Data Research in Health, The University of New South Wales, Sydney, New South Wales, Australia
| | - Arcot Sowmya
- School of Computer Science and Engineering, The University of New South Wales, Sydney, New South Wales, Australia
| | - Daniel Moses
- School of Computer Science and Engineering, The University of New South Wales, Sydney, New South Wales, Australia
- Department of Medical Imaging, The Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Sze-Yuan Ooi
- Faculty of Medicine, The University of New South Wales, Sydney, New South Wales, Australia
- Cardiology Department, The Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Susann Beier
- School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, New South Wales, Australia
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17
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Qian T. Evaluation of the Effect of Refined Nursing Intervention on Coronary CT Imaging Microscopy. Scanning 2022; 2022:4870548. [PMID: 35800208 PMCID: PMC9203219 DOI: 10.1155/2022/4870548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/20/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
The aim is to study the benefits of using advanced medical services for coronary CT angiography. From August 2019 to August 2020, 50 patients who underwent CT angiography were selected and divided into control groups and study groups, with 25 patients in each group. The monitoring team provides basic support, and the training team provides maximum support. Experimental results showed that the positive microscopic image of the study group after the intervention was better than that of the control group, and the stress score was lower than that of the control group (P < 0.05 s and30.73 ± 9.57 min) and are short (58.32 ± 13.15seconds and53.17 ± 11.84minutes) between control groups, with significant differences. The significance is (P < 0.05. Patient care is relevant to patients with coronary CT angiography, which has been shown to improve heart rate, reduce stress, improve microscopic imaging, and provide relevant liver function tests. It is recommended to promote the show.
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Affiliation(s)
- Tao Qian
- Huashan Hospital, Fudan University, Shanghai 201100, China
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18
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Winkel DJ, Suryanarayana VR, Ali AM, Görich J, Buß SJ, Mendoza A, Schwemmer C, Sharma P, Schoepf UJ, Rapaka S. Deep learning for vessel-specific coronary artery calcium scoring: validation on a multi-centre dataset. Eur Heart J Cardiovasc Imaging 2022; 23:846-854. [PMID: 34322693 DOI: 10.1093/ehjci/jeab119] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 05/26/2021] [Indexed: 12/24/2022] Open
Abstract
AIMS To present and validate a fully automated, deep learning (DL)-based branch-wise coronary artery calcium (CAC) scoring algorithm on a multi-centre dataset. METHODS AND RESULTS We retrospectively included 1171 patients referred for a CAC computed tomography examination. Total CAC scores for each case were manually evaluated by a human reader. Next, each dataset was fully automatically evaluated by the DL-based software solution with output of the total CAC score and sub-scores per coronary artery (CA) branch [right coronary artery (RCA), left main (LM), left anterior descending (LAD), and circumflex (CX)]. Three readers independently manually scored the CAC for all CA branches for 300 cases from a single centre and formed the consensus using a majority vote rule, serving as the reference standard. Established CAC cut-offs for the total Agatston score were used for risk group assignments. The performance of the algorithm was evaluated using metrics for risk class assignment based on total Agatston score, and unweighted Cohen's Kappa for branch label assignment. The DL-based software solution yielded a class accuracy of 93% (1085/1171) with a sensitivity, specificity, and accuracy of detecting non-zero coronary calcium being 97%, 93%, and 95%. The overall accuracy of the algorithm for branch label classification was 94% (LM: 89%, LAD: 91%, CX: 93%, RCA: 100%) with a Cohen's kappa of k = 0.91. CONCLUSION Our results demonstrate that fully automated total and vessel-specific CAC scoring is feasible using a DL-based algorithm. There was a high agreement with the manually assessed total CAC from a multi-centre dataset and the vessel-specific scoring demonstrated consistent and reproducible results.
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Affiliation(s)
- David J Winkel
- Department of Radiology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland.,Siemens Healthineers, 755 College Rd E, 08540 Princeton, NJ, USA
| | | | - A Mohamed Ali
- Siemens Healthcare Private Limited, Unit No. 9A, 9th Floor, North Tower, Mumbai 400079, India
| | - Johannes Görich
- Das Radiologische Zentrum - Radiology Center, Sinsheim-Eberbach-Walldorf-Heidelberg, Germany
| | - Sebastian Johannes Buß
- Das Radiologische Zentrum - Radiology Center, Sinsheim-Eberbach-Walldorf-Heidelberg, Germany
| | - Axel Mendoza
- Siemens Healthineers, 755 College Rd E, 08540 Princeton, NJ, USA
| | - Chris Schwemmer
- Siemens Healthineers, Siemensstrasse 1, 91301 Forchheim, Germany
| | - Puneet Sharma
- Siemens Healthineers, 755 College Rd E, 08540 Princeton, NJ, USA
| | - U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, 29425 Charleston, SC, USA
| | - Saikiran Rapaka
- Siemens Healthineers, 755 College Rd E, 08540 Princeton, NJ, USA
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19
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Guidi L, Lareyre F, Chaudhuri A, Cong Duy L, Adam C, Carrier M, Réda HK, Elixène JB, Raffort J. Automatic measurement of vascular calcifications in patients with aorto-iliac occlusive disease to predict the risk of re-intervention after endovascular repair. Ann Vasc Surg 2022; 83:10-19. [PMID: 35271959 DOI: 10.1016/j.avsg.2022.02.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 02/03/2022] [Accepted: 02/05/2022] [Indexed: 11/26/2022]
Abstract
OBJECTIVE There is currently a lack of consensus and tools to easily measure vascular calcification using computed tomography angiography (CTA). The aim of this study was to develop a fully automatic software to measure calcifications and to evaluate the interest as predictive factor in patients with aorto-iliac occlusive disease. METHODS This study retrospectively included 171 patients who had endovascular repair of an aorto-iliac occlusive lesion at the University Hospital of Nice between January 2011 and December 2019. Calcifications volumes were measured from CT-angiography (CTA) using an automatic method consisting in 3 sequential steps: image pre-processing, lumen segmentation using expert system and deep learning algorithms and segmentation of calcifications. Calcification volumes were measured in the infrarenal abdominal aorta and the iliac arterial segments, corresponding to the common and the external iliac arteries. RESULTS Among 171 patients included with a mean age of 65 years, the revascularization was performed on the native external and internal iliac arteries in respectively: 83 patients (48.5%); 107 (62.3%) and 7 (4.1%). The mean volumes of calcifications were 2759 mm3 in the infrarenal abdominal aorta, 1821 mm3 and 1795 mm3 in the right and left iliac arteries. For a mean follow up of 39 months, TLR was performed in 55 patients (32.2%). These patients had higher volume of calcifications in the right and left iliac arteries, compared with patients who did not have a re-intervention (2274 mm3 vs 1606 mm3, p=0.0319 and 2278 vs 1567 mm3, p=0.0213). CONCLUSION The development of a fully automatic software would be useful to facilitate the measurement of vascular calcifications and possibly better inform the prognosis of patients.
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Affiliation(s)
- Lucas Guidi
- Department of Vascular Surgery, University Hospital of Nice, France
| | - Fabien Lareyre
- Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, France; Université Côte d'Azur, Inserm U1065, C3M, Nice, France.
| | - Arindam Chaudhuri
- Bedfordshire-Milton Keynes Vascular Centre, Bedfordshire Hospitals NHS Foundation Trust, Bedford, UK
| | - Lê Cong Duy
- Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, France; Université Côte d'Azur, Inserm U1065, C3M, Nice, France
| | - Cédric Adam
- Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec, Université Paris-Saclay, France
| | - Marion Carrier
- Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec, Université Paris-Saclay, France
| | | | | | - Juliette Raffort
- Université Côte d'Azur, Inserm U1065, C3M, Nice, France; Clinical Chemistry Laboratory, University Hospital of Nice, France; Institute 3IA Côte d'Azur, Université Côte d'Azur, France
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21
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Azhar AZ, Rai D, Bandyopadhyay D, Rzechorzek W, Akhtar T, Aronow WS, Ranjan P. Use of coronary artery calcium and coronary tomography angiography in the evaluation of ischemic heart disease. Hosp Pract (1995) 2022; 50:9-16. [PMID: 35037541 DOI: 10.1080/21548331.2022.2030630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Over the years, significant technological advances have been made in the field of cardiac CT imaging which has led to the widespread use of the modality in the evaluation of ischemic and structural heart disease. The advent of newer scanning techniques has led to a reduction in scanning time as well as a reduction in the radiation and contrast media dose required - making these scans both convenient and safer to perform. Research has shown that coronary CT angiography has a high negative predictive value in the evaluation of patients with coronary artery disease. There is more recent evidence that coronary CTA has a positive impact on clinical outcomes as well. In this review article, we discuss the clinical applications of coronary CTA in the evaluation of patients with stable ischemic heart disease, the most recent studies evaluating the efficacy and limitations of the modality, the role of coronary calcium in cardiovascular risk prediction in asymptomatic patients and the future applications of the modality.
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Affiliation(s)
| | - Devesh Rai
- Department of Cardiology, Rochester General Hospital, Rochester, NY, USA
| | | | - Wojciech Rzechorzek
- Department of Cardiology, Westchester Medical Center at New York Medical College, Valhalla, NY, USA
| | - Tauseef Akhtar
- Medicine, John's Hopkins University School of Medicine, Baltimore, MD, USA
| | - Wilbert S Aronow
- Department of Cardiology, Westchester Medical Center at New York Medical College, Valhalla, NY, USA
| | - Pragya Ranjan
- Department of Cardiology, Westchester Medical Center at New York Medical College, Valhalla, NY, USA
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22
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Cainzos-Achirica M, Anugula D, Mszar R, Grandhi G, Patel KV, Bittencourt MS, Blankstein R, Blaha MJ, Blumenthal RS, Ray KK, Bhatt DL, Nasir K. Rationale and pathways forward in the implementation of coronary artery calcium-based enrichment of randomized trials. Am Heart J 2022; 243:54-65. [PMID: 34587511 DOI: 10.1016/j.ahj.2021.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 09/15/2021] [Indexed: 11/24/2022]
Abstract
The Food and Drug Administration recommends prognostic enrichment of randomized controlled trials (RCTs), aimed at restricting the study population to participants most likely to have events and therefore derive benefit from a given intervention. The coronary artery calcium (CAC) score is powerful discriminator of cardiovascular risk, and in this review we discuss how CAC may be used to augment widely used prognostic enrichment paradigms of RCTs of add-on therapies in primary prevention. We describe recent studies in this space, with special attention to the ability of CAC to further stratify risk among guideline-recommended candidates for add-on risk-reduction therapies. Given the potential benefits in terms of sample size, cost reduction, and overall RCT feasibility of a CAC-based enrichment strategy, we discuss approaches that may help maximize its advantages while minimizing logistical barriers and other challenges. Specifically, use of already existing CAC data to avoid the need to re-scan participants with previously documented high CAC scores, use of increasingly available, large clinical CAC databases to facilitate the identification of potential RCT participants, and implementation of machine learning approaches to measure CAC in existing computed tomography images performed for other purposes, will most likely boost the implementation of a CAC-based enrichment paradigm in future RCTs.
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Wang Y, Liu N, Yang M, Tian Z, Dong H, Lu Y, Zou D. Application and Prospect of Postmortem Imaging Technology in Forensic Cardiac Pathology: A Systemic Review. J Forensic Sci Med 2022. [DOI: 10.4103/jfsm.jfsm_129_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
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Lee S, Rim B, Jou SS, Gil HW, Jia X, Lee A, Hong M. Deep-Learning-Based Coronary Artery Calcium Detection from CT Image. Sensors (Basel) 2021; 21:s21217059. [PMID: 34770366 PMCID: PMC8588163 DOI: 10.3390/s21217059] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 10/19/2021] [Accepted: 10/20/2021] [Indexed: 11/16/2022]
Abstract
One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-one, and check the exact range. In this paper, three CNN models are applied for 1200 normal cardiovascular CT images, and 1200 CT images in which calcium is present in the cardiovascular system. We conduct the experimental test by classifying the CT image data into the original coronary artery calcium score CT images containing the entire rib cage, the cardiac segmented images that cut out only the heart region, and cardiac cropped images that are created by using the cardiac images that are segmented into nine sub-parts and enlarged. As a result of the experimental test to determine the presence of calcium in a given CT image using Inception Resnet v2, VGG, and Resnet 50 models, the highest accuracy of 98.52% was obtained when cardiac cropped image data was applied using the Resnet 50 model. Therefore, in this paper, it is expected that through further research, both the simple presence of calcium and the automation of the calcium analysis score for each coronary artery calcium score CT will become possible.
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Affiliation(s)
- Sungjin Lee
- Department of Software Convergence, Soonchunhyang University, Asan 31538, Korea; (S.L.); (B.R.)
| | - Beanbonyka Rim
- Department of Software Convergence, Soonchunhyang University, Asan 31538, Korea; (S.L.); (B.R.)
| | - Sung-Shick Jou
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea; (S.-S.J.); (H.-W.G.)
| | - Hyo-Wook Gil
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea; (S.-S.J.); (H.-W.G.)
| | - Xibin Jia
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
| | - Ahyoung Lee
- Department of Computer Science, Kennesaw State University, Marietta, GA 30144, USA;
| | - Min Hong
- Department of Computer Software Engineering, Soonchunhyang University, Asan 31538, Korea
- Correspondence:
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Lauzier PT, Avram R, Dey D, Slomka P, Afilalo J, Chow BJ. The evolving role of artificial intelligence in cardiac image analysis. Can J Cardiol 2021; 38:214-224. [PMID: 34619340 DOI: 10.1016/j.cjca.2021.09.030] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/28/2021] [Accepted: 09/28/2021] [Indexed: 12/13/2022] Open
Abstract
Research in artificial intelligence (AI) have progressed over the last decade. The field of cardiac imaging has seen significant developments using newly developed deep learning methods for automated image analysis and AI tools for disease detection and prognostication. This review article is aimed at those without special background in AI. We review AI concepts and we survey the growing contemporary applications of AI for image analysis in echocardiography, nuclear cardiology, cardiac computed tomography, cardiac magnetic resonance, and invasive angiography.
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Affiliation(s)
| | - Robert Avram
- University of Ottawa Heart Institute, Ottawa, ON, Canada; Montreal Heart Institute, Montreal, QC, Canada
| | - Damini Dey
- Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr Slomka
- Cedars-Sinai Medical Center, Los Angeles, CA, USA
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Abstract
Research into artificial intelligence (AI) has made tremendous progress over the past decade. In particular, the AI-powered analysis of images and signals has reached human-level performance in many applications owing to the efficiency of modern machine learning methods, in particular deep learning using convolutional neural networks. Research into the application of AI to medical imaging is now very active, especially in the field of cardiovascular imaging because of the challenges associated with acquiring and analysing images of this dynamic organ. In this Review, we discuss the clinical questions in cardiovascular imaging that AI can be used to address and the principal methodological AI approaches that have been developed to solve the related image analysis problems. Some approaches are purely data-driven and rely mainly on statistical associations, whereas others integrate anatomical and physiological information through additional statistical, geometric and biophysical models of the human heart. In a structured manner, we provide representative examples of each of these approaches, with particular attention to the underlying computational imaging challenges. Finally, we discuss the remaining limitations of AI approaches in cardiovascular imaging (such as generalizability and explainability) and how they can be overcome.
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Affiliation(s)
| | | | - Hubert Cochet
- IHU Liryc, CHU Bordeaux, Université Bordeaux, Inserm 1045, Pessac, France
| | - Pierre Jaïs
- IHU Liryc, CHU Bordeaux, Université Bordeaux, Inserm 1045, Pessac, France
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Yang DH. Application of Artificial Intelligence to Cardiovascular Computed Tomography. Korean J Radiol 2021; 22:1597-1608. [PMID: 34402240 PMCID: PMC8484158 DOI: 10.3348/kjr.2020.1314] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 03/26/2021] [Accepted: 05/14/2021] [Indexed: 11/15/2022] Open
Abstract
Cardiovascular computed tomography (CT) is among the most active fields with ongoing technical innovation related to image acquisition and analysis. Artificial intelligence can be incorporated into various clinical applications of cardiovascular CT, including imaging of the heart valves and coronary arteries, as well as imaging to evaluate myocardial function and congenital heart disease. This review summarizes the latest research on the application of deep learning to cardiovascular CT. The areas covered range from image quality improvement to automatic analysis of CT images, including methods such as calcium scoring, image segmentation, and coronary artery evaluation.
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Affiliation(s)
- Dong Hyun Yang
- Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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Lee JG, Kim H, Kang H, Koo HJ, Kang JW, Kim YH, Yang DH. Fully Automatic Coronary Calcium Score Software Empowered by Artificial Intelligence Technology: Validation Study Using Three CT Cohorts. Korean J Radiol 2021; 22:1764-1776. [PMID: 34402248 PMCID: PMC8546141 DOI: 10.3348/kjr.2021.0148] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/26/2021] [Accepted: 05/13/2021] [Indexed: 11/26/2022] Open
Abstract
Objective This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. Materials and Methods We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1–10, 11–100, 101–400, > 400) was evaluated. Results In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and false-positive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). Conclusion The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.
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Affiliation(s)
- June Goo Lee
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - HeeSoo Kim
- Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Heejun Kang
- Divison of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hyun Jung Koo
- Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Joon Won Kang
- Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Young Hak Kim
- Divison of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| | - Dong Hyun Yang
- Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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Cau R, Flanders A, Mannelli L, Politi C, Faa G, Suri JS, Saba L. Artificial intelligence in computed tomography plaque characterization: A review. Eur J Radiol 2021; 140:109767. [PMID: 34000598 DOI: 10.1016/j.ejrad.2021.109767] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 04/23/2021] [Accepted: 04/29/2021] [Indexed: 12/12/2022]
Abstract
Cardiovascular disease (CVD) is associated with high mortality around the world. Prevention and early diagnosis are key targets in reducing the socio-economic burden of CVD. Artificial intelligence (AI) has experienced a steady growth due to technological innovations that have to lead to constant development. Several AI algorithms have been applied to various aspects of CVD in order to improve the quality of image acquisition and reconstruction and, at the same time adding information derived from the images to create strong predictive models. In computed tomography angiography (CTA), AI can offer solutions for several parts of plaque analysis, including an automatic assessment of the degree of stenosis and characterization of plaque morphology. A growing body of evidence demonstrates a correlation between some type of plaques, so-called high-risk plaque or vulnerable plaque, and cardiovascular events, independent of the degree of stenosis. The radiologist must apprehend and participate actively in developing and implementing AI in current clinical practice. In this current overview on the existing AI literature, we describe the strengths, limitations, recent applications, and promising developments of employing AI to plaque characterization with CT.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato (Cagliari), 09045, Italy
| | - Adam Flanders
- Thomas Jefferson University, 1020 Walnut Street, Philadelphia, PA, United States
| | | | - Carola Politi
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato (Cagliari), 09045, Italy
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria (AOU) di Cagliari, University Hospital San Giovanni di Dio, Cagliari, Italy; Proteomic Laboratory - European Center for Brain Research, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Jasjit S Suri
- Stroke Diagnosis and Monitoring Division ATHEROPOINT LLC, Roseville, CA USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato (Cagliari), 09045, Italy.
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Weikert T, Rapaka S, Grbic S, Re T, Chaganti S, Winkel DJ, Anastasopoulos C, Niemann T, Wiggli BJ, Bremerich J, Twerenbold R, Sommer G, Comaniciu D, Sauter AW. Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings. Korean J Radiol 2021; 22:994-1004. [PMID: 33686818 PMCID: PMC8154782 DOI: 10.3348/kjr.2020.0994] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 12/21/2020] [Accepted: 12/23/2020] [Indexed: 11/15/2022] Open
Abstract
Objective To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. Materials and Methods All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. Results While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79–0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77–0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85–0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66–0.88). Conclusion Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.
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Affiliation(s)
- Thomas Weikert
- Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland.
| | | | - Sasa Grbic
- Siemens Healthineers, Princeton, NJ, USA
| | - Thomas Re
- Siemens Healthineers, Princeton, NJ, USA
| | | | - David J Winkel
- Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland
| | | | - Tilo Niemann
- Department of Radiology, Kantonsspital Baden, Baden, Switzerland
| | - Benedikt J Wiggli
- Department of Infectious Diseases & Infection Control, Kantonsspital Baden, Baden, Switzerland
| | - Jens Bremerich
- Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Raphael Twerenbold
- Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Gregor Sommer
- Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland
| | | | - Alexander W Sauter
- Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland
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Tesche C, Brandt V. Calling for a New Framingham: Machine Learning in Cardiovascular Risk Assessment-The Key for Improved Outcome Prediction? JACC Cardiovasc Imaging 2021; 14:626-8. [PMID: 33582058 DOI: 10.1016/j.jcmg.2020.12.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 12/21/2020] [Accepted: 12/22/2020] [Indexed: 11/21/2022]
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Zeleznik R, Foldyna B, Eslami P, Weiss J, Alexander I, Taron J, Parmar C, Alvi RM, Banerji D, Uno M, Kikuchi Y, Karady J, Zhang L, Scholtz JE, Mayrhofer T, Lyass A, Mahoney TF, Massaro JM, Vasan RS, Douglas PS, Hoffmann U, Lu MT, Aerts HJWL. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nat Commun 2021; 12:715. [PMID: 33514711 PMCID: PMC7846726 DOI: 10.1038/s41467-021-20966-2] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 01/05/2021] [Indexed: 11/30/2022] Open
Abstract
Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT. As we evaluate in 20,084 individuals from distinct asymptomatic (Framingham Heart Study, NLST) and stable and acute chest pain (PROMISE, ROMICAT-II) cohorts, the automated score is a strong predictor of cardiovascular events, independent of risk factors (multivariable-adjusted hazard ratios up to 4.3), shows high correlation with manual quantification, and robust test-retest reliability. Our results demonstrate the clinical value of a deep learning system for the automated prediction of cardiovascular events. Implementation into clinical practice would address the unmet need of automating proven imaging biomarkers to guide management and improve population health.
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Affiliation(s)
- Roman Zeleznik
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Borek Foldyna
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Parastou Eslami
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jakob Weiss
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Ivanov Alexander
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jana Taron
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Chintan Parmar
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Raza M Alvi
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Dahlia Banerji
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mio Uno
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yasuka Kikuchi
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Cause of Death Investigation, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Julia Karady
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Lili Zhang
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jan-Erik Scholtz
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Thomas Mayrhofer
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany
| | - Asya Lyass
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Taylor F Mahoney
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Joseph M Massaro
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Ramachandran S Vasan
- National Heart, Lung, and Blood Institute and Boston University, Framingham Heart Study, Framingham, MA, USA
- Departments of Cardiology and Preventive Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Pamela S Douglas
- Department of Medicine, Division of Cardiology, Duke University School of Medicine, Duke Clinical Research Institute, Durham, NC, USA
| | - Udo Hoffmann
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael T Lu
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands.
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Kapoor K, Cainzos-Achirica M, Nasir K. The evolving role of coronary artery calcium in preventive cardiology 30 years after the Agatston score. Curr Opin Cardiol 2020; 35:500-7. [PMID: 32649358 DOI: 10.1097/HCO.0000000000000771] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
PURPOSE OF REVIEW On the brink of the 30th anniversary of the Agatston score we summarize the epidemiological data that shaped the recommendations relevant to coronary artery calcium (CAC) included in the 2018/2019 US and European guidelines for the primary prevention of atherosclerotic cardiovascular disease (ASCVD). We also discuss the implications of novel CAC research conducted in asymptomatic populations within the past 2 years. RECENT FINDINGS Based on a wealth of observational evidence, CAC has emerged as a mainstay in personalized risk assessment and is now endorsed as a class IIa tool in both US and European guidelines. In the past 2 years, data supporting the prognostic power of CAC has kept mounting, with longer term follow-up data now available. CAC has been evaluated in a variety of patient populations including individuals with severe hypercholesterolemia, diabetes mellitus and younger adults with family history of ASCVD, in all of whom it may be able to inform a more personalized management. Novel CAC scoring approaches are also discussed. SUMMARY Despite a strong endorsement in recent guidelines, active research in the last 2 years has provided further insights on the potential utility of CAC in informing a more individualized preventive management in broader populations.
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Muscogiuri G, Van Assen M, Tesche C, De Cecco CN, Chiesa M, Scafuri S, Guglielmo M, Baggiano A, Fusini L, Guaricci AI, Rabbat MG, Pontone G. Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis. Biomed Res Int 2020; 2020:6649410. [PMID: 33381570 PMCID: PMC7762640 DOI: 10.1155/2020/6649410] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 11/30/2020] [Accepted: 12/09/2020] [Indexed: 12/20/2022]
Abstract
Cardiac computed tomography angiography (CCTA) is widely used as a diagnostic tool for evaluation of coronary artery disease (CAD). Despite the excellent capability to rule-out CAD, CCTA may overestimate the degree of stenosis; furthermore, CCTA analysis can be time consuming, often requiring advanced postprocessing techniques. In consideration of the most recent ESC guidelines on CAD management, which will likely increase CCTA volume over the next years, new tools are necessary to shorten reporting time and improve the accuracy for the detection of ischemia-inducing coronary lesions. The application of artificial intelligence (AI) may provide a helpful tool in CCTA, improving the evaluation and quantification of coronary stenosis, plaque characterization, and assessment of myocardial ischemia. Furthermore, in comparison with existing risk scores, machine-learning algorithms can better predict the outcome utilizing both imaging findings and clinical parameters. Medical AI is moving from the research field to daily clinical practice, and with the increasing number of CCTA examinations, AI will be extensively utilized in cardiac imaging. This review is aimed at illustrating the state of the art in AI-based CCTA applications and future clinical scenarios.
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Affiliation(s)
| | - Marly Van Assen
- Division of Cardiothoracic Imaging, Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
| | - Christian Tesche
- Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany
- Department of Internal Medicine, St. Johannes-Hospital, Dortmund, Germany
| | - Carlo N. De Cecco
- Division of Cardiothoracic Imaging, Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
| | | | - Stefano Scafuri
- Division of Interventional Structural Cardiology, Cardiothoracovascular Department, Careggi University Hospital, Florence, Italy
| | | | | | - Laura Fusini
- Centro Cardiologico Monzino, IRCCS, Milan, Italy
| | - Andrea I. Guaricci
- Institute of Cardiovascular Disease, Department of Emergency and Organ Transplantation, University Hospital “Policlinico Consorziale” of Bari, Bari, Italy
| | - Mark G. Rabbat
- Loyola University of Chicago, Chicago, IL, USA
- Edward Hines Jr. VA Hospital, Hines, IL, USA
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Lee H, Emrich T, Schoepf UJ, Brandt V, Leonard TJ, Gray HN, Giovagnoli VM, Dargis DM, Burt JR, Tesche C. Artificial Intelligence in Cardiac CT: Automated Calcium Scoring and Plaque Analysis. Curr Cardiovasc Imaging Rep 2020; 13. [DOI: 10.1007/s12410-020-09549-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Abstract
PURPOSE OF REVIEW To summarize current artificial intelligence (AI)-based applications for coronary artery calcium scoring (CACS) and their potential clinical impact. RECENT FINDINGS Recent evolution of AI-based technologies in medical imaging has accelerated progress in CACS performed in diverse types of CT examinations, providing promising results for future clinical application in this field. CACS plays a key role in risk stratification of coronary artery disease (CAD) and patient management. Recent emergence of AI algorithms, particularly deep learning (DL)-based applications, have provided considerable progress in CACS. Many investigations have focused on the clinical role of DL models in CACS and showed excellent agreement between those algorithms and manual scoring, not only in dedicated coronary calcium CT but also in coronary CT angiography (CCTA), low-dose chest CT, and standard chest CT. Therefore, the potential of AI-based CACS may become more influential in the future.
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Affiliation(s)
- Heon Lee
- Department of Radiology, Soonchunhyang University Hospital Bucheon, 170 Jomaru-ro, Bucheon, 14584, Republic of Korea
| | - Simon Martin
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC, 29425, USA
| | - Jeremy R Burt
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC, 29425, USA
| | | | - Saikiran Rapaka
- Siemens Healthcare GmbH, Siemensstr. 3, 91301, Forchheim, Germany
| | - Hunter N Gray
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC, 29425, USA
| | - Tyler J Leonard
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC, 29425, USA
| | - Chris Schwemmer
- Siemens Healthcare GmbH, Siemensstr. 3, 91301, Forchheim, Germany
| | - U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC, 29425, USA.
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Brandt V, Emrich T, Schoepf UJ, Dargis DM, Bayer RR, De Cecco CN, Tesche C. Ischemia and outcome prediction by cardiac CT based machine learning. Int J Cardiovasc Imaging 2020; 36:2429-39. [DOI: 10.1007/s10554-020-01929-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 06/26/2020] [Indexed: 12/30/2022]
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Lin A, Kolossváry M, Išgum I, Maurovich-Horvat P, Slomka PJ, Dey D. Artificial intelligence: improving the efficiency of cardiovascular imaging. Expert Rev Med Devices 2020; 17:565-577. [PMID: 32510252 PMCID: PMC7382901 DOI: 10.1080/17434440.2020.1777855] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 06/01/2020] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Artificial intelligence (AI) describes the use of computational techniques to mimic human intelligence. In healthcare, this typically involves large medical datasets being used to predict a diagnosis, identify new disease genotypes or phenotypes, or guide treatment strategies. Noninvasive imaging remains a cornerstone for the diagnosis, risk stratification, and management of patients with cardiovascular disease. AI can facilitate every stage of the imaging process, from acquisition and reconstruction, to segmentation, measurement, interpretation, and subsequent clinical pathways. AREAS COVERED In this paper, we review state-of-the-art AI techniques and their current applications in cardiac imaging, and discuss the future role of AI as a precision medicine tool. EXPERT OPINION Cardiovascular medicine is primed for scalable AI applications which can interpret vast amounts of clinical and imaging data in greater depth than ever before. AI-augmented medical systems have the potential to improve workflow and provide reproducible and objective quantitative results which can inform clinical decisions. In the foreseeable future, AI may work in the background of cardiac image analysis software and routine clinical reporting, automatically collecting data and enabling real-time diagnosis and risk stratification.
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Affiliation(s)
- Andrew Lin
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Márton Kolossváry
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Amsterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, Amsterdam, Netherlands
| | - Pál Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Piotr J Slomka
- Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
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Schoepf UJ, Abadia AF. Greasing the Skids: Deep Learning for Fully Automated Quantification of Epicardial Fat. Radiol Artif Intell 2019; 1:e190140. [PMID: 33937806 DOI: 10.1148/ryai.2019190140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 08/14/2019] [Indexed: 12/15/2022]
Affiliation(s)
- U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Town, 25 Courtenay Dr, MSC 226, Charleston, SC, 29425
| | - Andres F Abadia
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Town, 25 Courtenay Dr, MSC 226, Charleston, SC, 29425
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