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Yin K, Chen W, Qin G, Liang J, Bao X, Yu H, Li H, Xu J, Chen X, Wang Y, Savage RH, Schoepf UJ, Mu D, Zhang B. Performance assessment of an artificial intelligence-based coronary artery calcium scoring algorithm in non-gated chest CT scans of different slice thickness. Quant Imaging Med Surg 2024; 14:5708-5720. [PMID: 39144022 PMCID: PMC11320525 DOI: 10.21037/qims-24-247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 07/05/2024] [Indexed: 08/16/2024]
Abstract
Background The coronary artery calcium score (CACS) has been shown to be an independent predictor of cardiovascular events. The traditional coronary artery calcium scoring algorithm has been optimized for electrocardiogram (ECG)-gated images, which are acquired with specific settings and timing. Therefore, if the artificial intelligence-based coronary artery calcium score (AI-CACS) could be calculated from a chest low-dose computed tomography (LDCT) examination, it could be valuable in assessing the risk of coronary artery disease (CAD) in advance, and it could potentially reduce the occurrence of cardiovascular events in patients. This study aimed to assess the performance of an AI-CACS algorithm in non-gated chest scans with three different slice thicknesses (1, 3, and 5 mm). Methods A total of 135 patients who underwent both LDCT of the chest and ECG-gated non-contrast enhanced cardiac CT were prospectively included in this study. The Agatston scores were automatically derived from chest CT images reconstructed at slice thicknesses of 1, 3, and 5 mm using the AI-CACS software. These scores were then compared to those obtained from the ECG-gated cardiac CT data using a conventional semi-automatic method that served as the reference. The correlations between the AI-CACS and electrocardiogram-gated coronary artery calcium score (ECG-CACS) were analyzed, and Bland-Altman plots were used to assess agreement. Risk stratification was based on the calculated CACS, and the concordance rate was determined. Results A total of 112 patients were included in the final analysis. The correlations between the AI-CACS at three different thicknesses (1, 3, and 5 mm) and the ECG-CACS were 0.973, 0.941, and 0.834 (all P<0.01), respectively. The Bland-Altman plots showed mean differences in the AI-CACS for the three thicknesses of -6.5, 15.4, and 53.1, respectively. The risk category agreement for the three AI-CACS groups was 0.868, 0.772, and 0.412 (all P<0.01), respectively. While the concordance rates were 91%, 84.8%, and 62.5%, respectively. Conclusions The AI-based algorithm successfully calculated the CACS from LDCT scans of the chest, demonstrating its utility in risk categorization. Furthermore, the CACS derived from images with a slice thickness of 1 mm was more accurate than those obtained from images with slice thicknesses of 3 and 5 mm.
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Affiliation(s)
- Kejie Yin
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Wenping Chen
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Guochu Qin
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Jing Liang
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Xue Bao
- Department of Cardiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Hongming Yu
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Hui Li
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Jianhua Xu
- Department of Radiology, Yizheng Hospital of Nanjing Drum Tower Hospital Group, Yizheng, China
| | - Xingbiao Chen
- Clinical Science, Philips Healthcare, Shanghai, China
| | - Yujie Wang
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Jiangsu University, Nanjing, China
| | - Rock H. Savage
- Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC, USA
| | - U. Joseph Schoepf
- Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC, USA
| | - Dan Mu
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Radiology, Yizheng Hospital of Nanjing Drum Tower Hospital Group, Yizheng, China
| | - Bing Zhang
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
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Parsa S, Somani S, Dudum R, Jain SS, Rodriguez F. Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time? Curr Atheroscler Rep 2024; 26:263-272. [PMID: 38780665 DOI: 10.1007/s11883-024-01210-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE OF REVIEW This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data. Additionally, it discusses strategies for integrating AI into clinical practice in preventive cardiology. RECENT FINDINGS AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.
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Affiliation(s)
- Shyon Parsa
- Department of Medicine, Stanford University, Stanford, California, USA
| | - Sulaiman Somani
- Department of Medicine, Stanford University, Stanford, California, USA
| | - Ramzi Dudum
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Sneha S Jain
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Fatima Rodriguez
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA.
- Center for Digital Health, Stanford University, Stanford, California, USA.
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3
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Aromiwura AA, Kalra DK. Artificial Intelligence in Coronary Artery Calcium Scoring. J Clin Med 2024; 13:3453. [PMID: 38929986 PMCID: PMC11205094 DOI: 10.3390/jcm13123453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 06/07/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024] Open
Abstract
Cardiovascular disease (CVD), particularly coronary heart disease (CHD), is the leading cause of death in the US, with a high economic impact. Coronary artery calcium (CAC) is a known marker for CHD and a useful tool for estimating the risk of atherosclerotic cardiovascular disease (ASCVD). Although CACS is recommended for informing the decision to initiate statin therapy, the current standard requires a dedicated CT protocol, which is time-intensive and contributes to radiation exposure. Non-dedicated CT protocols can be taken advantage of to visualize calcium and reduce overall cost and radiation exposure; however, they mainly provide visual estimates of coronary calcium and have disadvantages such as motion artifacts. Artificial intelligence is a growing field involving software that independently performs human-level tasks, and is well suited for improving CACS efficiency and repurposing non-dedicated CT for calcium scoring. We present a review of the current studies on automated CACS across various CT protocols and discuss consideration points in clinical application and some barriers to implementation.
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Affiliation(s)
| | - Dinesh K. Kalra
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY 40202, USA
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4
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Nieman K, García-García HM, Hideo-Kajita A, Collet C, Dey D, Pugliese F, Weissman G, Tijssen JGP, Leipsic J, Opolski MP, Ferencik M, Lu MT, Williams MC, Bruining N, Blanco PJ, Maurovich-Horvat P, Achenbach S. Standards for quantitative assessments by coronary computed tomography angiography (CCTA): An expert consensus document of the society of cardiovascular computed tomography (SCCT). J Cardiovasc Comput Tomogr 2024:S1934-5925(24)00341-1. [PMID: 38849237 DOI: 10.1016/j.jcct.2024.05.232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/18/2024] [Accepted: 05/23/2024] [Indexed: 06/09/2024]
Abstract
In current clinical practice, qualitative or semi-quantitative measures are primarily used to report coronary artery disease on cardiac CT. With advancements in cardiac CT technology and automated post-processing tools, quantitative measures of coronary disease severity have become more broadly available. Quantitative coronary CT angiography has great potential value for clinical management of patients, but also for research. This document aims to provide definitions and standards for the performance and reporting of quantitative measures of coronary artery disease by cardiac CT.
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Affiliation(s)
- Koen Nieman
- Stanford University School of Medicine and Cardiovascular Institute, Stanford, CA, United States.
| | - Hector M García-García
- Section of Interventional Cardiology, MedStar Washington Hospital Center, Washington, DC, United States.
| | | | - Carlos Collet
- Onze Lieve Vrouwziekenhuis, Cardiovascular Center Aalst, Aalst, Belgium
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Francesca Pugliese
- NIHR Cardiovascular Biomedical Research Unit at Barts, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London & Department of Cardiology, Barts Health NHS Trust, London, UK
| | - Gaby Weissman
- Section of Interventional Cardiology, MedStar Washington Hospital Center, Washington, DC, United States
| | - Jan G P Tijssen
- Department of Cardiology, Academic Medical Center, Room G4-230, Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands
| | - Jonathon Leipsic
- Department of Radiology and Medicine (Cardiology), University of British Columbia, Vancouver, BC, Canada
| | - Maksymilian P Opolski
- Department of Interventional Cardiology and Angiology, National Institute of Cardiology, Warsaw, Poland
| | - Maros Ferencik
- Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, United States
| | - Michael T Lu
- Cardiovascular Imaging Research Center, Massachusetts General Hospital & Harvard Medical School, Boston, MA, United States
| | - Michelle C Williams
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Nico Bruining
- Department of Cardiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | | | - Pal Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Stephan Achenbach
- Department of Cardiology, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
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Gennari AG, Rossi A, De Cecco CN, van Assen M, Sartoretti T, Giannopoulos AA, Schwyzer M, Huellner MW, Messerli M. Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes. Int J Cardiovasc Imaging 2024; 40:951-966. [PMID: 38700819 PMCID: PMC11147943 DOI: 10.1007/s10554-024-03080-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 03/09/2024] [Indexed: 06/05/2024]
Abstract
Almost 35 years after its introduction, coronary artery calcium score (CACS) not only survived technological advances but became one of the cornerstones of contemporary cardiovascular imaging. Its simplicity and quantitative nature established it as one of the most robust approaches for atherosclerotic cardiovascular disease risk stratification in primary prevention and a powerful tool to guide therapeutic choices. Groundbreaking advances in computational models and computer power translated into a surge of artificial intelligence (AI)-based approaches directly or indirectly linked to CACS analysis. This review aims to provide essential knowledge on the AI-based techniques currently applied to CACS, setting the stage for a holistic analysis of the use of these techniques in coronary artery calcium imaging. While the focus of the review will be detailing the evidence, strengths, and limitations of end-to-end CACS algorithms in electrocardiography-gated and non-gated scans, the current role of deep-learning image reconstructions, segmentation techniques, and combined applications such as simultaneous coronary artery calcium and pulmonary nodule segmentation, will also be discussed.
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Affiliation(s)
- Antonio G Gennari
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Alexia Rossi
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Carlo N De Cecco
- Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Emory University, Atlanta, GA, USA
| | - Marly van Assen
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Emory University, Atlanta, GA, USA
| | - Thomas Sartoretti
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Andreas A Giannopoulos
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
| | - Moritz Schwyzer
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Martin W Huellner
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Michael Messerli
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland.
- University of Zurich, Zurich, Switzerland.
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6
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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] [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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7
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Lin Y, Lin G, Peng MT, Kuo CT, Wan YL, Cherng WJ. The Role of Artificial Intelligence in Coronary Calcium Scoring in Standard Cardiac Computed Tomography and Chest Computed Tomography With Different Reconstruction Kernels. J Thorac Imaging 2024; 39:111-118. [PMID: 37982516 DOI: 10.1097/rti.0000000000000765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
PURPOSE To assess the correlation of coronary calcium score (CS) obtained by artificial intelligence (AI) with those obtained by electrocardiography gated standard cardiac computed tomography (CCT) and nongated chest computed tomography (ChCT) with different reconstruction kernels. PATIENTS AND METHODS Seventy-six patients received standard CCT and ChCT simultaneously. We compared CS obtained in 4 groups: CS CCT , by the traditional method from standard CCT, 25 cm field of view, 3 mm slice thickness, and kernel filter convolution 12 (FC12); CS AICCT , by AI from the standard CCT; CS ChCTsoft , by AI from the non-gated CCT, 40 cm field of view, 3 mm slice thickness, and a soft kernel FC02; and CS ChCTsharp , by AI from CCT image with same parameters for CS ChCTsoft except for using a sharp kernel FC56. Statistical analyses included Spearman rank correlation coefficient (ρ), intraclass correlation (ICC), Bland-Altman plots, and weighted kappa analysis (κ). RESULTS The CS AICCT was consistent with CS CCT (ρ = 0.994 and ICC of 1.00, P < 0.001) with excellent agreement with respect to cardiovascular (CV) risk categories of the Agatston score (κ = 1.000). The correlation between CS ChCTsoft and CS ChCTsharp was good (ρ = 0.912, 0.963 and ICC = 0.929, 0.948, respectively, P < 0.001) with a tendency of underestimation (Bland-Altman mean difference and 95% upper and lower limits of agreements were 329.1 [-798.9 to 1457] and 335.3 [-651.9 to 1322], respectively). The CV risk category agreement between CS ChCTsoft and CS ChCTsharp was moderate (κ = 0.556 and 0.537, respectively). CONCLUSIONS There was an excellent correlation between CS CCT and CS AICCT , with excellent agreement between CV risk categories. There was also a good correlation between CS CCT and CS obtained by ChCT albeit with a tendency for underestimation and moderate accuracy in terms of CV risk assessment.
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Affiliation(s)
- Yenpo Lin
- Department of Medical Imaging and Intervention
| | - Gigin Lin
- Department of Medical Imaging and Intervention
| | | | - Chi-Tai Kuo
- Division of Cardiology, Department of Internal Medicine; Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan City, Taiwan
| | | | - Wen-Jin Cherng
- Division of Cardiology, Department of Internal Medicine; Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan City, Taiwan
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Cundari G, Marchitelli L, Pambianchi G, Catapano F, Conia L, Stancanelli G, Catalano C, Galea N. Imaging biomarkers in cardiac CT: moving beyond simple coronary anatomical assessment. LA RADIOLOGIA MEDICA 2024; 129:380-400. [PMID: 38319493 PMCID: PMC10942914 DOI: 10.1007/s11547-024-01771-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 01/03/2024] [Indexed: 02/07/2024]
Abstract
Cardiac computed tomography angiography (CCTA) is considered the standard non-invasive tool to rule-out obstructive coronary artery disease (CAD). Moreover, several imaging biomarkers have been developed on cardiac-CT imaging to assess global CAD severity and atherosclerotic burden, including coronary calcium scoring, the segment involvement score, segment stenosis score and the Leaman-score. Myocardial perfusion imaging enables the diagnosis of myocardial ischemia and microvascular damage, and the CT-based fractional flow reserve quantification allows to evaluate non-invasively hemodynamic impact of the coronary stenosis. The texture and density of the epicardial and perivascular adipose tissue, the hypodense plaque burden, the radiomic phenotyping of coronary plaques or the fat radiomic profile are novel CT imaging features emerging as biomarkers of inflammation and plaque instability, which may implement the risk stratification strategies. The ability to perform myocardial tissue characterization by extracellular volume fraction and radiomic features appears promising in predicting arrhythmogenic risk and cardiovascular events. New imaging biomarkers are expanding the potential of cardiac CT for phenotyping the individual profile of CAD involvement and opening new frontiers for the practice of more personalized medicine.
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Affiliation(s)
- Giulia Cundari
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Livia Marchitelli
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Giacomo Pambianchi
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Federica Catapano
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, 4, Pieve Emanuele, 20090, Milano, Italy
- Humanitas Research Hospital IRCCS, Via Alessandro Manzoni, 56, Rozzano, 20089, Milano, Italy
| | - Luca Conia
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Giuseppe Stancanelli
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Carlo Catalano
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy
| | - Nicola Galea
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena 324, 00161, Rome, Italy.
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9
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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] [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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Aromiwura AA, Settle T, Umer M, Joshi J, Shotwell M, Mattumpuram J, Vorla M, Sztukowska M, Contractor S, Amini A, Kalra DK. Artificial intelligence in cardiac computed tomography. Prog Cardiovasc Dis 2023; 81:54-77. [PMID: 37689230 DOI: 10.1016/j.pcad.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 09/04/2023] [Indexed: 09/11/2023]
Abstract
Artificial Intelligence (AI) is a broad discipline of computer science and engineering. Modern application of AI encompasses intelligent models and algorithms for automated data analysis and processing, data generation, and prediction with applications in visual perception, speech understanding, and language translation. AI in healthcare uses machine learning (ML) and other predictive analytical techniques to help sort through vast amounts of data and generate outputs that aid in diagnosis, clinical decision support, workflow automation, and prognostication. Coronary computed tomography angiography (CCTA) is an ideal union for these applications due to vast amounts of data generation and analysis during cardiac segmentation, coronary calcium scoring, plaque quantification, adipose tissue quantification, peri-operative planning, fractional flow reserve quantification, and cardiac event prediction. In the past 5 years, there has been an exponential increase in the number of studies exploring the use of AI for cardiac computed tomography (CT) image acquisition, de-noising, analysis, and prognosis. Beyond image processing, AI has also been applied to improve the imaging workflow in areas such as patient scheduling, urgent result notification, report generation, and report communication. In this review, we discuss algorithms applicable to AI and radiomic analysis; we then present a summary of current and emerging clinical applications of AI in cardiac CT. We conclude with AI's advantages and limitations in this new field.
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Affiliation(s)
| | - Tyler Settle
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - Muhammad Umer
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Jonathan Joshi
- Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA
| | - Matthew Shotwell
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Jishanth Mattumpuram
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Mounica Vorla
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Maryta Sztukowska
- Clinical Trials Unit, University of Louisville, Louisville, KY, USA; University of Information Technology and Management, Rzeszow, Poland
| | - Sohail Contractor
- Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA
| | - Amir Amini
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA; Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA
| | - Dinesh K Kalra
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA; Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA.
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11
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Tatsugami F, Nakaura T, Yanagawa M, Fujita S, Kamagata K, Ito R, Kawamura M, Fushimi Y, Ueda D, Matsui Y, Yamada A, Fujima N, Fujioka T, Nozaki T, Tsuboyama T, Hirata K, Naganawa S. Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction. Diagn Interv Imaging 2023; 104:521-528. [PMID: 37407346 DOI: 10.1016/j.diii.2023.06.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/07/2023]
Abstract
Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have shown great potential in enhancing diagnosis and prognosis prediction in patients with cardiovascular disease. Deep learning, a type of machine learning, has revolutionized radiology by enabling automatic feature extraction and learning from large datasets, particularly in image-based applications. Thus, AI-driven techniques have enabled a faster analysis of cardiac CT examinations than when they are analyzed by humans, while maintaining reproducibility. However, further research and validation are required to fully assess the diagnostic performance, radiation dose-reduction capabilities, and clinical correctness of these AI-driven techniques in cardiac CT. This review article presents recent advances of AI in the field of cardiac CT, including deep-learning-based image reconstruction, coronary artery motion correction, automatic calcium scoring, automatic epicardial fat measurement, coronary artery stenosis diagnosis, fractional flow reserve prediction, and prognosis prediction, analyzes current limitations of these techniques and discusses future challenges.
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Affiliation(s)
- Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo Chuo-ku, Kumamoto, 860-8556, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita City, Osaka, 565-0871, Japan
| | - Shohei Fujita
- Departmen of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyoku, Kyoto, 606-8507, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital N15, W5, Kita-Ku, Sapporo 060-8638, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-0016, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita City, Osaka, 565-0871, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15 Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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Tavoosi A, Ihdayhid AR, Konstantopoulos J, Kwok S, Joyner J, Williams MC, Newby DE, Ko B, Dwivedi G, Chow BJW. Evaluation for artificial intelligence-based coronary artery calcification scoring model efficiency and accuracy. J Cardiovasc Comput Tomogr 2023; 17:471-472. [PMID: 37863761 DOI: 10.1016/j.jcct.2023.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/07/2023] [Accepted: 10/13/2023] [Indexed: 10/22/2023]
Affiliation(s)
- Anahita Tavoosi
- University of Ottawa Heart Institute, Department of Medicine (Cardiology), Canada
| | - Abdul Rahman Ihdayhid
- Harry Perkins Institute of Medical Research, Perth, Australia; Curtin Medical School, Curtin University, Perth, Australia; Department of Cardiology, Fiona Stanley Hospital, Perth, Australia
| | | | | | | | - Michelle C Williams
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland, UK
| | - David E Newby
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland, UK
| | - Brian Ko
- Monash Cardiovascular Research Centre, Monash University and Monash Heart, Monash Health, Melbourne, Australia
| | - Girish Dwivedi
- Harry Perkins Institute of Medical Research, Perth, Australia; Department of Cardiology, Fiona Stanley Hospital, Perth, Australia; University of Western Australia, Perth, Australia
| | - Benjamin J W Chow
- University of Ottawa Heart Institute, Department of Medicine (Cardiology), Canada; University of Ottawa, Department of Radiology, Canada.
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13
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Takahashi D, Fujimoto S, Nozaki YO, Kudo A, Kawaguchi YO, Takamura K, Hiki M, Sato E, Tomizawa N, Daida H, Minamino T. Fully automated coronary artery calcium quantification on electrocardiogram-gated non-contrast cardiac computed tomography using deep-learning with novel Heart-labelling method. EUROPEAN HEART JOURNAL OPEN 2023; 3:oead113. [PMID: 38035036 PMCID: PMC10683040 DOI: 10.1093/ehjopen/oead113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/14/2023] [Accepted: 10/26/2023] [Indexed: 12/02/2023]
Abstract
Aims To develop an artificial intelligence (AI)-model which enables fully automated accurate quantification of coronary artery calcium (CAC), using deep learning (DL) on electrocardiogram (ECG)-gated non-contrast cardiac computed tomography (gated CCT) images. Methods and results Retrospectively, 560 gated CCT images (including 60 synthetic images) performed at our institution were used to train AI-model, which can automatically divide heart region into five areas belonging to left main (LM), left anterior descending (LAD), circumflex (LCX), right coronary artery (RCA), and another. Total and vessel-specific CAC score (CACS) in each scan were manually evaluated. AI-model was trained with novel Heart-labelling method via DL according to the manual-derived results. Then, another 409 gated CCT images obtained in our institution were used for model validation. The performance of present AI-model was tested using another external cohort of 400 gated CCT images of Stanford Center for Artificial Intelligence of Medical Imaging by comparing with the ground truth. The overall accuracy of the AI-model for total CACS classification was excellent with Cohen's kappa of k = 0.89 and 0.95 (validation and test, respectively), which surpasses previous research of k = 0.89. Bland-Altman analysis showed little difference in individual total and vessel-specific CACS between AI-derived CACS and ground truth in test cohort (mean difference [95% confidence interval] were 1.5 [-42.6, 45.6], -1.5 [-100.5, 97.5], 6.6 [-60.2, 73.5], 0.96 [-59.2, 61.1], and 7.6 [-134.1, 149.2] for LM, LAD, LCX, RCA, and total CACS, respectively). Conclusion Present Heart-labelling method provides a further improvement in fully automated, total, and vessel-specific CAC quantification on gated CCT.
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Affiliation(s)
- Daigo Takahashi
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Shinichiro Fujimoto
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Yui O Nozaki
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Ayako Kudo
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Yuko O Kawaguchi
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Kazuhisa Takamura
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Makoto Hiki
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Eisuke Sato
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Nobuo Tomizawa
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Hiroyuki Daida
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
| | - Tohru Minamino
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo Bunkyo-ku, Tokyo 113-8421, Japan
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14
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Koponen M, Anwaar W, Sheikh Q, Sadiq F. Use of Artificial Intelligence in Coronary Artery Calcium Scoring. Oman Med J 2023; 38:e543. [PMID: 38053612 PMCID: PMC10694408 DOI: 10.5001/omj.2023.73] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/07/2022] [Indexed: 12/07/2023] Open
Abstract
Coronary artery calcium (CAC) scoring improves traditional risk factor-based coronary heart disease (CHD) risk stratification. Here, the contribution of CAC scoring to a traditional 10-year CHD risk prediction scores and new artificial intelligence methods used to automate CAC scoring were reviewed. Research shows that traditional risk factors tend to overestimate or underestimate the actual risk of CHD, meaning that including CAC score in the risk stratification has potential to reduce over- and undertreatment. The automated CAC scoring methods are shown to be accurate and significantly more time-effective than the commonly used semi-automated method.
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Affiliation(s)
- Mia Koponen
- School of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Waqas Anwaar
- Department of Computing, Shifa School of Computing, Shifa Tameer-e-Millat University, Islamabad, Pakistan
| | - Habib-ur-Rahman3
- School of Medical Sciences, University of Aberdeen, Aberdeen, UK
- Department of Computing, Shifa School of Computing, Shifa Tameer-e-Millat University, Islamabad, Pakistan
- Department of Cardiology, Shifa International Hospital, Islamabad, Pakistan
- Directorate of Research, Shifa Tameer-e-Millat University, Islamabad, Pakistan
| | - Qasim Sheikh
- Department of Computing, Shifa School of Computing, Shifa Tameer-e-Millat University, Islamabad, Pakistan
| | - Fouzia Sadiq
- Directorate of Research, Shifa Tameer-e-Millat University, Islamabad, Pakistan
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15
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Aldana-Bitar J, Cho GW, Anderson L, Karlsberg DW, Manubolu VS, Verghese D, Hussein L, Budoff MJ, Karlsberg RP. Artificial intelligence using a deep learning versus expert computed tomography human reading in calcium score and coronary artery calcium data and reporting system classification. Coron Artery Dis 2023; 34:448-452. [PMID: 37139562 DOI: 10.1097/mca.0000000000001244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
BACKGROUND Artificial intelligence (AI) applied to cardiac imaging may provide improved processing, reading precision and advantages of automation. Coronary artery calcium (CAC) score testing is a standard stratification tool that is rapid and highly reproducible. We analyzed CAC results of 100 studies in order to determine the accuracy and correlation between the AI software (Coreline AVIEW, Seoul, South Korea) and expert level-3 computed tomography (CT) human CAC interpretation and its performance when coronary artery disease data and reporting system (coronary artery calcium data and reporting system) classification is applied. METHODS A total of 100 non-contrast calcium score images were selected by blinded randomization and processed with the AI software versus human level-3 CT reading. The results were compared and the Pearson correlation index was calculated. The CAC-DRS classification system was applied, and the cause of category reclassification was determined using an anatomical qualitative description by the readers. RESULTS The mean age was age 64.5 years, with 48% female. The absolute CAC scores between AI versus human reading demonstrated a highly significant correlation (Pearson coefficient R = 0.996); however, despite these minimal CAC score differences, 14% of the patients had their CAC-DRS category reclassified. The main source of reclassification was observed in CAC-DRS 0-1, where 13 were recategorized, particularly between studies having a CAC Agatston score of 0 versus 1. Qualitative description of the errors showed that the main cause of misclassification was AI underestimation of right coronary calcium, AI overestimation of right ventricle densities and human underestimation of right coronary artery calcium. CONCLUSION Correlation between AI and human values is excellent with absolute numbers. When the CAC-DRS classification system was adopted, there was a strong correlation in the respective categories. Misclassified were predominantly in the category of CAC = 0, most often with minimal values of calcium volume. Additional algorithm optimization with enhanced sensitivity and specificity for low values of calcium volume will be required to enhance AI CAC score utilization for minimal disease. Over a broad range of calcium scores, AI software for calcium scoring had an excellent correlation compared to human expert reading and in rare cases determined calcium missed by human interpretation.
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Affiliation(s)
- Jairo Aldana-Bitar
- Division of Cardiology, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Los Angeles
- Division of Cardiology, Cardiovascular Research Foundation of Southern California, Beverly Hills
| | - Geoffrey W Cho
- Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Lauren Anderson
- Division of Cardiology, Cardiovascular Research Foundation of Southern California, Beverly Hills
| | - Daniel W Karlsberg
- Division of Cardiology, Cardiovascular Research Foundation of Southern California, Beverly Hills
- Division of Cardiology, Princeton Longevity Center, New York, New York
| | - Venkat S Manubolu
- Division of Cardiology, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Los Angeles
| | - Dhiran Verghese
- Division of Cardiology, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Los Angeles
| | - Luay Hussein
- Division of Cardiology, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Los Angeles
| | - Matthew J Budoff
- Division of Cardiology, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Los Angeles
| | - Ronald P Karlsberg
- Division of Cardiology, Cardiovascular Research Foundation of Southern California, Beverly Hills
- Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Division of Cardiology, Cedars - Sinai Smidt Heart Institute, Beverly Hills, California, USA
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16
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Artificial Intelligence in Cardiovascular CT and MR Imaging. Life (Basel) 2023; 13:life13020507. [PMID: 36836864 PMCID: PMC9968221 DOI: 10.3390/life13020507] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 02/06/2023] [Accepted: 02/09/2023] [Indexed: 02/15/2023] Open
Abstract
The technological development of Artificial Intelligence (AI) has grown rapidly in recent years. The applications of AI to cardiovascular imaging are various and could improve the radiologists' workflow, speeding up acquisition and post-processing time, increasing image quality and diagnostic accuracy. Several studies have already proved AI applications in Coronary Computed Tomography Angiography and Cardiac Magnetic Resonance, including automatic evaluation of calcium score, quantification of coronary stenosis and plaque analysis, or the automatic quantification of heart volumes and myocardial tissue characterization. The aim of this review is to summarize the latest advances in the field of AI applied to cardiovascular CT and MR imaging.
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17
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Rijkse E, Roodnat JI, Baart SJ, Bijdevaate DC, Dijkshoorn ML, Kimenai HJAN, van de Wetering J, IJzermans JNM, Minnee RC. Ipsilateral Aorto-Iliac Calcification is Not Directly Associated With eGFR After Kidney Transplantation: A Prospective Cohort Study Analyzed Using a Linear Mixed Model. Transpl Int 2023; 36:10647. [PMID: 36756277 PMCID: PMC9901502 DOI: 10.3389/ti.2023.10647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 01/05/2023] [Indexed: 01/21/2023]
Abstract
Aorto-iliac calcification (AIC) is a well-studied risk factor for post-transplant cardiovascular events and mortality. Its effect on graft function remains unknown. The primary aim of this prospective cohort study was to assess the association between AIC and estimated glomerular filtration rate (eGFR) in the first year post-transplant. Eligibility criteria were: ≥50 years of age or ≥30 years with at least one risk factor for vascular disease. A non-contrast-enhanced CT-scan was performed with quantification of AIC using the modified Agatston score. The association between AIC and eGFR was investigated with a linear mixed model adjusted for predefined variables. One-hundred-and-forty patients were included with a median of 31 (interquartile range 26-39) eGFR measurements per patient. No direct association between AIC and eGFR was found. We observed a significant interaction between follow-up time and ipsilateral AIC, indicating that patients with higher AIC scores had lower eGFR trajectory over time starting 100 days after transplant (p = 0.014). To conclude, severe AIC is not directly associated with lower post-transplant eGFR. The significant interaction indicates that patients with more severe AIC have a lower eGFR trajectory after 100 days in the first year post-transplant.
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Affiliation(s)
- Elsaline Rijkse
- Department of Surgery, Division of HPB and Transplant Surgery, Erasmus MC Transplant Institute, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Joke I. Roodnat
- Department of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC Transplant Institute, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Sara J. Baart
- Department of Biostatistics, Erasmus Medical Center, Rotterdam, Netherlands
| | | | - Marcel L. Dijkshoorn
- Department of Radiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Hendrikus J. A. N. Kimenai
- Department of Surgery, Division of HPB and Transplant Surgery, Erasmus MC Transplant Institute, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Jacqueline van de Wetering
- Department of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC Transplant Institute, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Jan N. M. IJzermans
- Department of Surgery, Division of HPB and Transplant Surgery, Erasmus MC Transplant Institute, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Robert C. Minnee
- Department of Surgery, Division of HPB and Transplant Surgery, Erasmus MC Transplant Institute, Erasmus MC University Medical Center, Rotterdam, Netherlands,*Correspondence: Robert C. Minnee,
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18
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Kampaktsis PN, Emfietzoglou M, Al Shehhi A, Fasoula NA, Bakogiannis C, Mouselimis D, Tsarouchas A, Vassilikos VP, Kallmayer M, Eckstein HH, Hadjileontiadis L, Karlas A. Artificial intelligence in atherosclerotic disease: Applications and trends. Front Cardiovasc Med 2023; 9:949454. [PMID: 36741834 PMCID: PMC9896100 DOI: 10.3389/fcvm.2022.949454] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 12/28/2022] [Indexed: 01/21/2023] Open
Abstract
Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death globally. Increasing amounts of highly diverse ASCVD data are becoming available and artificial intelligence (AI) techniques now bear the promise of utilizing them to improve diagnosis, advance understanding of disease pathogenesis, enable outcome prediction, assist with clinical decision making and promote precision medicine approaches. Machine learning (ML) algorithms in particular, are already employed in cardiovascular imaging applications to facilitate automated disease detection and experts believe that ML will transform the field in the coming years. Current review first describes the key concepts of AI applications from a clinical standpoint. We then provide a focused overview of current AI applications in four main ASCVD domains: coronary artery disease (CAD), peripheral arterial disease (PAD), abdominal aortic aneurysm (AAA), and carotid artery disease. For each domain, applications are presented with refer to the primary imaging modality used [e.g., computed tomography (CT) or invasive angiography] and the key aim of the applied AI approaches, which include disease detection, phenotyping, outcome prediction, and assistance with clinical decision making. We conclude with the strengths and limitations of AI applications and provide future perspectives.
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Affiliation(s)
- Polydoros N. Kampaktsis
- Division of Cardiology, Columbia University Irving Medical Center, New York, NY, United States,*Correspondence: Polydoros N. Kampaktsis,
| | - Maria Emfietzoglou
- Heart Centre, John Radcliffe Hospital, Oxford University Hospitals, NHS Foundation Trust, Oxford, United Kingdom
| | - Aamna Al Shehhi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Nikolina-Alexia Fasoula
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany,School of Medicine, Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), Technical University of Munich, Munich, Germany
| | - Constantinos Bakogiannis
- Third Department of Cardiology, Hippokration University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitrios Mouselimis
- Third Department of Cardiology, Hippokration University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Anastasios Tsarouchas
- Third Department of Cardiology, Hippokration University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilios P. Vassilikos
- Third Department of Cardiology, Hippokration University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Michael Kallmayer
- Department for Vascular and Endovascular Surgery, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Hans-Henning Eckstein
- Department for Vascular and Endovascular Surgery, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany,DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Leontios Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates,Healthcare Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates,Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Angelos Karlas
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany,School of Medicine, Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), Technical University of Munich, Munich, Germany,Department for Vascular and Endovascular Surgery, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany,DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
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19
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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] [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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20
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Wolff J, Matschinske J, Baumgart D, Pytlik A, Keck A, Natarajan A, von Schacky CE, Pauling JK, Baumbach J. Federated machine learning for a facilitated implementation of Artificial Intelligence in healthcare - a proof of concept study for the prediction of coronary artery calcification scores. J Integr Bioinform 2022; 19:jib-2022-0032. [PMID: 36054833 PMCID: PMC9800042 DOI: 10.1515/jib-2022-0032] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/03/2022] [Accepted: 08/11/2022] [Indexed: 01/09/2023] Open
Abstract
The implementation of Artificial Intelligence (AI) still faces significant hurdles and one key factor is the access to data. One approach that could support that is federated machine learning (FL) since it allows for privacy preserving data access. For this proof of concept, a prediction model for coronary artery calcification scores (CACS) has been applied. The FL was trained based on the data in the different institutions, while the centralized machine learning model was trained on one allocation of data. Both algorithms predict patients with risk scores ≥5 based on age, biological sex, waist circumference, dyslipidemia and HbA1c. The centralized model yields a sensitivity of c. 66% and a specificity of c. 70%. The FL slightly outperforms that with a sensitivity of 67% while slightly underperforming it with a specificity of 69%. It could be demonstrated that CACS prediction is feasible via both, a centralized and an FL approach, and that both show very comparable accuracy. In order to increase accuracy, additional and a higher volume of patient data is required and for that FL is utterly necessary. The developed "CACulator" serves as proof of concept, is available as research tool and shall support future research to facilitate AI implementation.
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Affiliation(s)
- Justus Wolff
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Maximus-von-Imhof-Forum 3, 85354Freising, Germany
- Syte – Strategy Institute for Digital Health, Hohe Bleichen 8, 20354Hamburg, Germany
| | - Julian Matschinske
- Chair of Computational Systems Biology, University of Hamburg, Notkestreet 9-11, 22607Hamburg, Germany
| | - Dietrich Baumgart
- Preventicum Essen, Theodor-Althoff-Str. 47 45133Essen, Germany
- Preventicum Duesseldorf, Koenigsallee 11, 40212Duesseldorf, Germany
| | - Anne Pytlik
- Preventicum Essen, Theodor-Althoff-Str. 47 45133Essen, Germany
- Preventicum Duesseldorf, Koenigsallee 11, 40212Duesseldorf, Germany
| | - Andreas Keck
- Syte – Strategy Institute for Digital Health, Hohe Bleichen 8, 20354Hamburg, Germany
| | - Arunakiry Natarajan
- Independent Researcher, Digital Health, Informatics and Data Science, Lower Saxony, Germany
| | - Claudio E. von Schacky
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Ismaningerstr. 22, 81675Munich, Germany
| | - Josch K. Pauling
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Maximus-von-Imhof-Forum 3, 85354Freising, Germany
- LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof-Forum 3, 85354Freising, Germany
| | - Jan Baumbach
- Chair of Computational Systems Biology, University of Hamburg, Notkestreet 9-11, 22607Hamburg, Germany
- Computational BioMedicine Lab, Institute of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, 5230Odense, Denmark
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21
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Muscogiuri G, Volpato V, Cau R, Chiesa M, Saba L, Guglielmo M, Senatieri A, Chierchia G, Pontone G, Dell’Aversana S, Schoepf UJ, Andrews MG, Basile P, Guaricci AI, Marra P, Muraru D, Badano LP, Sironi S. Application of AI in cardiovascular multimodality imaging. Heliyon 2022; 8:e10872. [PMID: 36267381 PMCID: PMC9576885 DOI: 10.1016/j.heliyon.2022.e10872] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/23/2022] [Accepted: 09/27/2022] [Indexed: 12/16/2022] Open
Abstract
Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging.
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Affiliation(s)
- Giuseppe Muscogiuri
- Department of Radiology, Istituto Auxologico Italiano IRCCS, San Luca Hospital, Italy,School of Medicine, University of Milano-Bicocca, Milan, Italy,Corresponding author.
| | - Valentina Volpato
- Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy,IRCCS Ospedale Galeazzi - Sant'Ambrogio, University Cardiology Department, Milan, Italy
| | - Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari, Polo di Monserrato, Cagliari, Italy
| | | | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari, Polo di Monserrato, Cagliari, Italy
| | - Marco Guglielmo
- Department of Cardiology, Division of Heart and Lungs, Utrecht University, Utrecht University Medical Center, Utrecht, the Netherlands
| | | | | | | | - Serena Dell’Aversana
- Department of Radiology, Ospedale S. Maria Delle Grazie - ASL Napoli 2 Nord, Pozzuoli, Italy
| | - U. Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr., Charleston, SC, USA
| | - Mason G. Andrews
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr., Charleston, SC, USA
| | - Paolo Basile
- University Cardiology Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Andrea Igoren Guaricci
- University Cardiology Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Paolo Marra
- Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
| | - Denisa Muraru
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Luigi P. Badano
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Sandro Sironi
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
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22
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Geerlings-Batt J, Sun Z. Evaluation of the Relationship between Left Coronary Artery Bifurcation Angle and Coronary Artery Disease: A Systematic Review. J Clin Med 2022; 11:jcm11175143. [PMID: 36079071 PMCID: PMC9457427 DOI: 10.3390/jcm11175143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/16/2022] [Accepted: 08/30/2022] [Indexed: 11/30/2022] Open
Abstract
Recent studies have suggested a relationship between wide left coronary artery bifurcation (left anterior descending [LAD]-left circumflex [LCx]) angle and coronary artery disease (CAD). Current literature is multifaceted. Different studies have analysed this relationship using computational fluid dynamics, by considering CAD risk factors, and from simple causal-comparative and correlational perspectives. Hence, the purpose of this systematic review was to critically evaluate the current literature and determine whether there is sufficient evidence available to prove the relationship between LAD-LCx angle and CAD. Five electronic databases (ProQuest, Scopus, PubMed, CINAHL Plus with Full Text, and Emcare) were used to locate relevant texts, which were then screened according to predefined eligibility criteria. Thirteen eligible articles were selected for review. Current evidence suggests individuals with a wide LAD-LCx angle experience altered haemodynamics at the bifurcation site compared to those with narrower angles, which likely facilitates a predisposition to developing CAD. However, further research is required to determine causality regarding relationships between LAD-LCx angle and CAD risk factors. Insufficient valid evidence exists to support associations between LAD-LCx angle and degree of coronary stenosis, and future haemodynamic analyses should explore more accurate coronary artery modelling, as well as CAD progression in already stenosed bifurcations.
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23
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Finetuned Super-Resolution Generative Adversarial Network (Artificial Intelligence) Model for Calcium Deblooming in Coronary Computed Tomography Angiography. J Pers Med 2022; 12:jpm12091354. [PMID: 36143139 PMCID: PMC9503533 DOI: 10.3390/jpm12091354] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/17/2022] [Accepted: 08/19/2022] [Indexed: 12/02/2022] Open
Abstract
The purpose of this study was to finetune a deep learning model, real-enhanced super-resolution generative adversarial network (Real-ESRGAN), and investigate its diagnostic value in calcified coronary plaques with the aim of suppressing blooming artifacts for the further improvement of coronary lumen assessment. We finetuned the Real-ESRGAN model and applied it to 50 patients with 184 calcified plaques detected at three main coronary arteries (left anterior descending [LAD], left circumflex [LCx] and right coronary artery [RCA]). Measurements of coronary stenosis were collected from original coronary computed tomography angiography (CCTA) and Real-ESRGAN-processed images, including Real-ESRGAN-high-resolution, Real-ESRGAN-average and Real-ESRGAN-median (Real-ESRGAN-HR, Real-ESRGAN-A and Real-ESRGAN-M) with invasive coronary angiography as the reference. Our results showed specificity and positive predictive value (PPV) of the Real-ESRGAN-processed images were improved at all of the three coronary arteries, leading to significant reduction in the false positive rates when compared to those of the original CCTA images. The specificity and PPV of the Real-ESRGAN-M images were the highest at the RCA level, with values being 80% (95% CI: 64.4%, 90.9%) and 61.9% (95% CI: 45.6%, 75.9%), although the sensitivity was reduced to 81.3% (95% CI: 54.5%, 95.9%) due to false negative results. The corresponding specificity and PPV of the Real-ESRGAN-M images were 51.9 (95% CI: 40.3%, 63.5%) and 31.5% (95% CI: 25.8%, 37.8%) at LAD, 62.5% (95% CI: 40.6%, 81.2%) and 43.8% (95% CI: 30.3%, 58.1%) at LCx, respectively. The area under the receiver operating characteristic curve was also the highest at the RCA with value of 0.76 (95% CI: 0.64, 0.89), 0.84 (95% CI: 0.73, 0.94), 0.85 (95% CI: 0.75, 0.95) and 0.73 (95% CI: 0.58, 0.89), corresponding to original CCTA, Real-ESRGAN-HR, Real-ESRGAN-A and Real-ESRGAN-M images, respectively. This study proves that the finetuned Real-ESRGAN model significantly improves the diagnostic performance of CCTA in assessing calcified plaques.
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24
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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: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [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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25
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Martinez DSL, Noseworthy PA, Akbilgic O, Herrmann J, Ruddy KJ, Hamid A, Maddula R, Singh A, Davis R, Gunturkun F, Jefferies JL, Brown SA. Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2022; 15:100129. [PMID: 35721662 PMCID: PMC9202996 DOI: 10.1016/j.ahjo.2022.100129] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/20/2022] [Accepted: 03/21/2022] [Indexed: 01/21/2023]
Abstract
Cardiovascular disease is a leading cause of death among cancer survivors, second only to cancer recurrence or development of new tumors. Cardio-oncology has therefore emerged as a relatively new specialty focused on prevention and management of cardiovascular consequences of cancer therapies. Yet challenges remain regarding precision and accuracy with predicting individuals at highest risk for cardiotoxicity. Barriers such as access to care also limit screening and early diagnosis to improve prognosis. Thus, developing innovative approaches for prediction and early detection of cardiovascular illness in this population is critical. In this review, we provide an overview of the present state of machine learning applications in cardio-oncology. We begin by outlining some factors that should be considered while utilizing machine learning algorithms. We then examine research in which machine learning has been applied to improve prediction of cardiac dysfunction in cancer survivors. We also highlight the use of artificial intelligence (AI) in conjunction with electrocardiogram (ECG) to predict cardiac malfunction and also atrial fibrillation (AF), and we discuss the potential role of wearables. Additionally, the article summarizes future prospects and critical takeaways for the application of machine learning in cardio-oncology. This study is the first in a series on artificial intelligence in cardio-oncology, and complements our manuscript on echocardiography and other forms of imaging relevant to cancer survivors cared for in cardiology clinical practice.
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Affiliation(s)
- Daniel Sierra-Lara Martinez
- Coronary Care Unit, National Institute of Cardiology/Instituto Nacional de Cardiologia, Ciudad de Mexico, Mexico
| | | | - Oguz Akbilgic
- Department of Health Informatics and Data Science, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, IL, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Wake Forest, NC, USA
| | - Joerg Herrmann
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Ashima Singh
- Institute of Health and Equity, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Robert Davis
- Center for Biomedical Informatics, University of Tennessee Health Sciences Center, USA
| | - Fatma Gunturkun
- Center for Biomedical Informatics, University of Tennessee Health Sciences Center, USA
| | - John L. Jefferies
- Division of Cardiovascular Diseases, University of Tennessee Health Sciences Center, USA
- Department of Epidemiology, St. Jude Children's Research Hospital, USA
| | - Sherry-Ann Brown
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
- Cardio-Oncology Program, Division of Cardiovascular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
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26
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Hong JS, Tzeng YH, Yin WH, Wu KT, Hsu HY, Lu CF, Liu HR, Wu YT. Automated coronary artery calcium scoring using nested U-Net and focal loss. Comput Struct Biotechnol J 2022; 20:1681-1690. [PMID: 35465160 PMCID: PMC9010683 DOI: 10.1016/j.csbj.2022.03.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 03/24/2022] [Accepted: 03/24/2022] [Indexed: 11/28/2022] Open
Abstract
Coronary artery calcium (CAC) is a great risk predictor of the atherosclerotic cardiovascular disease and CAC scores can be used to stratify the risk of heart disease. Current clinical analysis of CAC is performed using onsite semiautomated software. This semiautomated CAC analysis requires experienced radiologists and radiologic technologists and is both demanding and time-consuming. The purpose of this study is to develop a fully automated CAC detection model that can quantify CAC scores. A total of 1,811 cases of cardiac examinations involving contrast-free multidetector computed tomography were retrospectively collected. We divided the database into the Training Data Set, Validation Data Set, Testing Data Set 1, and Testing Data Set 2. The Training, Validation, and Testing Data Set 1 contained cases with clinically detected CAC; Testing Data Set 2 contained those without detected calcium. The intraclass correlation coefficients between the overall standard and model-predicted scores were 1.00 for both the Training Data Set and Testing Data Set 1. In Testing Data Set 2, the model was able to detect clinically undetected cases of mild calcium. The results suggested that the proposed model’s automated detection of CAC was highly consistent with clinical semiautomated CAC analysis. The proposed model demonstrated potential for clinical applications that can improve the quality of CAC risk stratification.
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Affiliation(s)
- Jia-Sheng Hong
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Yun-Hsuan Tzeng
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Division of Advanced Medical Imaging, Health Management Center, Cheng Hsin General Hospital, Taipei 112, Taiwan
| | - Wei-Hsian Yin
- Division of Advanced Medical Imaging, Health Management Center, Cheng Hsin General Hospital, Taipei 112, Taiwan
- Heart Center, Cheng Hsin General Hospital, Taipei 112, Taiwan
| | - Kuan-Ting Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Huan-Yu Hsu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Ho-Ren Liu
- Division of Advanced Medical Imaging, Health Management Center, Cheng Hsin General Hospital, Taipei 112, Taiwan
- Corresponding authors at: Institute of Biophotonics, National Yang Ming Chiao Tung University, No.155, Sec. 2, Linong St., Beitou Dist., Taipei City 112, Taiwan (Y.T. Wu). Health Management Center, Cheng Hsin General Hospital, No. 45, Zhenxing Street, Beitou District, Taipei City, 112, Taiwan (H.R. Liu).
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Corresponding authors at: Institute of Biophotonics, National Yang Ming Chiao Tung University, No.155, Sec. 2, Linong St., Beitou Dist., Taipei City 112, Taiwan (Y.T. Wu). Health Management Center, Cheng Hsin General Hospital, No. 45, Zhenxing Street, Beitou District, Taipei City, 112, Taiwan (H.R. Liu).
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27
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Madan N, Lucas J, Akhter N, Collier P, Cheng F, Guha A, Zhang L, Sharma A, Hamid A, Ndiokho I, Wen E, Garster NC, Scherrer-Crosbie M, Brown SA. Artificial intelligence and imaging: Opportunities in cardio-oncology. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2022; 15:100126. [PMID: 35693323 PMCID: PMC9187287 DOI: 10.1016/j.ahjo.2022.100126] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/20/2022] [Accepted: 03/21/2022] [Indexed: 12/29/2022]
Abstract
Cardiovascular disease is a leading cause of death in cancer survivors. It is critical to apply new predictive and early diagnostic methods in this population, as this can potentially inform cardiovascular treatment and surveillance decision-making. We discuss the application of artificial intelligence (AI) technologies to cardiovascular imaging in cardio-oncology, with a particular emphasis on prevention and targeted treatment of a variety of cardiovascular conditions in cancer patients. Recently, the use of AI-augmented cardiac imaging in cardio-oncology is gaining traction. A large proportion of cardio-oncology patients are screened and followed using left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), currently obtained using echocardiography. This use will continue to increase with new cardiotoxic cancer treatments. AI is being tested to increase precision, throughput, and accuracy of LVEF and GLS, guide point-of-care image acquisition, and integrate imaging and clinical data to optimize the prediction and detection of cardiac dysfunction. The application of AI to cardiovascular magnetic resonance imaging (CMR), computed tomography (CT; especially coronary artery calcium or CAC scans), single proton emission computed tomography (SPECT) and positron emission tomography (PET) imaging acquisition is also in early stages of analysis for prediction and assessment of cardiac tumors and cardiovascular adverse events in patients treated for childhood or adult cancer. The opportunities for application of AI in cardio-oncology imaging are promising, and if availed, will improve clinical practice and benefit patient care.
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Affiliation(s)
- Nidhi Madan
- Division of Cardiology, Rush University Medical Center, Chicago, IL, USA
| | | | - Nausheen Akhter
- Division of Cardiology, Northwestern University, Chicago, IL, USA
| | - Patrick Collier
- Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Miller Family Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Avirup Guha
- Harrington Heart and Vascular Institute, Cleveland, OH, USA
| | - Lili Zhang
- Cardio-Oncology Program, Division of Cardiology, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Abhinav Sharma
- Division of Cardiovascular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Imeh Ndiokho
- Medical College of Wisconsin, Milwaukee, WI, USA
| | - Ethan Wen
- Medical College of Wisconsin, Milwaukee, WI, USA
| | - Noelle C. Garster
- Division of Cardiovascular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Sherry-Ann Brown
- Cardio-Oncology Program, Division of Cardiovascular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
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28
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Current and Future Applications of Artificial Intelligence in Coronary Artery Disease. Healthcare (Basel) 2022; 10:healthcare10020232. [PMID: 35206847 PMCID: PMC8872080 DOI: 10.3390/healthcare10020232] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/19/2022] [Accepted: 01/24/2022] [Indexed: 02/07/2023] Open
Abstract
Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.
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29
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Winkelmann MT, Jacoby J, Schwemmer C, Faby S, Krumm P, Artzner C, Bongers MN. Fully Automated Artery-Specific Calcium Scoring Based on Machine Learning in Low-Dose Computed Tomography Screening. ROFO-FORTSCHR RONTG 2022; 194:763-770. [PMID: 35081651 DOI: 10.1055/a-1717-2703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
PURPOSE Evaluation of machine learning-based fully automated artery-specific coronary artery calcium (CAC) scoring software, using semi-automated software as a reference. METHODS A total of 505 patients underwent non-contrast-enhanced calcium scoring computed tomography (CSCT). Automated, machine learning-based software quantified the Agatston score (AS), volume score (VS), and mass score (MS) of each coronary artery [right coronary artery (RCA), left main (LM), circumflex (CX) and left anterior descending (LAD)]. Identified CAC of readers who annotated the data with semi-automated software served as a reference standard. Statistics included comparisons of evaluation time, agreement of identified CAC, and comparisons of the AS, VS, and MS of the reference standard and the fully automated algorithm. RESULTS The machine learning-based software correlated strongly with the reference standard for the AS, VS, and MS (Spearman's rho > 0.969) (p < 0.001), with excellent agreement (ICC > 0.919) (p < 0.001). The mean assessment time of the reference standard was 59 seconds (IQR 39-140) and that of the automated algorithm was 5.9 seconds (IQR 3.9-16) (p < 0.001). The Bland-Altman plots mean difference and 1.96 upper and lower limits of agreement for all arteries combined were: AS 0.996 (1.33 to 0.74), VS 0.995 (1.40 to 0.71), and MS 0.995 (1.35 to 0.74). The mean bias was minimal: 0.964-1.0429. Risk class assignment showed high accuracy for the AS in total (weighed κ = 0.99) and for each individual artery (κ = 0.96-0.99) with corresponding correct risk group assignment in 497 of 505 patients (98.4 %). CONCLUSION The fully automated artery-specific coronary calcium scoring algorithm is a time-saving procedure and shows excellent correlation and agreement compared with the clinically established semi-automated approach. KEY POINTS · Very high correlation and agreement between fully automatic and semi-automatic calcium scoring software.. · Less time-consuming than conventional semi-automatic methods.. · Excellent tool for artery-specific calcium scoring in a clinical setting.. CITATION FORMAT · Winkelmann MT, Jacoby J, Schwemmer C et al. Fully Automated Artery-Specific Calcium Scoring Based on Machine Learning in Low-Dose Computed Tomography Screening. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1717-2703.
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Affiliation(s)
- Moritz T Winkelmann
- Department for Diagnostic and Interventional Radiology, Eberhard Karls Universitat Tubingen, Tuebingen, Germany
| | - Johann Jacoby
- Institute of Clinical Epidemiology and Applied Biometry, Eberhard Karls Universitat Tubingen, Tuebingen, Germany
| | - Chris Schwemmer
- Siemens Healthcare GmbH, Forchheim, Siemens Healthcare GmbH, Forchheim, Germany
| | - Sebastian Faby
- Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany
| | - Patrick Krumm
- Department for Diagnostic and Interventional Radiology, Eberhard Karls Universitat Tubingen, Tuebingen, Germany
| | - Christoph Artzner
- Department for Diagnostic and Interventional Radiology, Eberhard Karls Universitat Tubingen, Tuebingen, Germany
| | - Malte N Bongers
- Department for Diagnostic and Interventional Radiology, Eberhard Karls Universitat Tubingen, Tuebingen, Germany
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30
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Infante T, Cavaliere C, Punzo B, Grimaldi V, Salvatore M, Napoli C. Radiogenomics and Artificial Intelligence Approaches Applied to Cardiac Computed Tomography Angiography and Cardiac Magnetic Resonance for Precision Medicine in Coronary Heart Disease: A Systematic Review. Circ Cardiovasc Imaging 2021; 14:1133-1146. [PMID: 34915726 DOI: 10.1161/circimaging.121.013025] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The risk of coronary heart disease (CHD) clinical manifestations and patient management is estimated according to risk scores accounting multifactorial risk factors, thus failing to cover the individual cardiovascular risk. Technological improvements in the field of medical imaging, in particular, in cardiac computed tomography angiography and cardiac magnetic resonance protocols, laid the development of radiogenomics. Radiogenomics aims to integrate a huge number of imaging features and molecular profiles to identify optimal radiomic/biomarker signatures. In addition, supervised and unsupervised artificial intelligence algorithms have the potential to combine different layers of data (imaging parameters and features, clinical variables and biomarkers) and elaborate complex and specific CHD risk models allowing more accurate diagnosis and reliable prognosis prediction. Literature from the past 5 years was systematically collected from PubMed and Scopus databases, and 60 studies were selected. We speculated the applicability of radiogenomics and artificial intelligence through the application of machine learning algorithms to identify CHD and characterize atherosclerotic lesions and myocardial abnormalities. Radiomic features extracted by cardiac computed tomography angiography and cardiac magnetic resonance showed good diagnostic accuracy for the identification of coronary plaques and myocardium structure; on the other hand, few studies exploited radiogenomics integration, thus suggesting further research efforts in this field. Cardiac computed tomography angiography resulted the most used noninvasive imaging modality for artificial intelligence applications. Several studies provided high performance for CHD diagnosis, classification, and prognostic assessment even though several efforts are still needed to validate and standardize algorithms for CHD patient routine according to good medical practice.
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Affiliation(s)
- Teresa Infante
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy (T.I., C.N.)
| | | | - Bruna Punzo
- IRCCS SDN, Naples, Italy (C.C., B.P., V.G., M.S., C.N.)
| | | | | | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy (T.I., C.N.).,IRCCS SDN, Naples, Italy (C.C., B.P., V.G., M.S., C.N.)
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Xu J, Liu J, Guo N, Chen L, Song W, Guo D, Zhang Y, Fang Z. Performance of artificial intelligence-based coronary artery calcium scoring in non-gated chest CT. Eur J Radiol 2021; 145:110034. [PMID: 34837795 DOI: 10.1016/j.ejrad.2021.110034] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 10/17/2021] [Accepted: 10/22/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To evaluate the risk category performance of artificial intelligence-based coronary artery calcium score (AI-CACS) software used in non-gated chest computed tomography (CT) on three types of CT machines, considering the manual method as the standard. METHODS A total of 901 patients who underwent both chest CT and electrocardiogram (ECG)-gated non-contrast-enhanced cardiac CT with the same equipment within a 3-month period were enrolled in the study. AI-CACS software was based on a deep learning algorithm and was trained on multi-vendor, multi-scanner, and multi-hospital anonymized data from the chest CT database. The AI-CACS was automatically obtained from chest CT data by the AI-CACS software, while the manual CACS was obtained from cardiac CT data by the manual method. The correlation of the AI-CACS and manual CACS, concordance rate and kappa value of the risk categories determined by the two methods were calculated. The chi-square test was used to evaluate the differences in risk categories among the three types of CT machines from different manufacturers. The risk category performance of the AI-CACS for dichotomous risk categories bounded by 0, 100 and 400 was assessed. RESULTS The correlation of the AI-CACS with the manual CACS was ρ = 0.893 (p < 0.001). The Bland-Altman plot (AI-CACS minus manual CACS) showed a mean difference of -27.2 and 95% limits of agreement of -290.0 to 235.6. The agreement of risk categories for the CACS was kappa (κ) = 0.679 (p < 0.001), and the concordance rate was 80.6%. The risk categories determined by the AI-CACS software on three types of CT machines were not significantly different (p = 0.7543). As dichotomous risk categories bounded by 0, 100 and 400, the accuracy, kappa value, and area under the curve of the AI-CACS were 88.6% vs. 92.9% vs. 97.9%, 0.77 vs. 0.77 vs. 0.83, and 0.885 vs. 0.964 vs. 0.981, respectively. CONCLUSIONS There was good correlation and agreement between the AI-CACS and manual CACS in terms of the risk category. It is feasible to obtain the CACS using AI software based on non-gated chest CT data in a short time without increasing the radiation dose or economic burden. The AI-CACS software algorithm has good clinical universality and can be applied to CT machines from different manufacturers.
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Affiliation(s)
- Jie Xu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, No.74 Linjiang Rd, Yuzhong District, 400010 Chongqing, China
| | - Jia Liu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, No.74 Linjiang Rd, Yuzhong District, 400010 Chongqing, China
| | - Ning Guo
- ShuKun (BeiJing) Technology Co., Ltd., Jinhui Bd, Qiyang Rd, 100000 Beijing, China
| | - Linli Chen
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, No.74 Linjiang Rd, Yuzhong District, 400010 Chongqing, China
| | - Weixiang Song
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, No.74 Linjiang Rd, Yuzhong District, 400010 Chongqing, China
| | - Dajing Guo
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, No.74 Linjiang Rd, Yuzhong District, 400010 Chongqing, China
| | - Yu Zhang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, No.74 Linjiang Rd, Yuzhong District, 400010 Chongqing, China.
| | - Zheng Fang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, No.74 Linjiang Rd, Yuzhong District, 400010 Chongqing, China.
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Maragna R, Giacari CM, Guglielmo M, Baggiano A, Fusini L, Guaricci AI, Rossi A, Rabbat M, Pontone G. Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management. Front Cardiovasc Med 2021; 8:736223. [PMID: 34631834 PMCID: PMC8493089 DOI: 10.3389/fcvm.2021.736223] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 08/25/2021] [Indexed: 12/14/2022] Open
Abstract
Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both diagnosis and risk stratification of acute and chronic CAD. For example, the role of Coronary Computed Tomography Angiography (CCTA) has become increasingly important to rule out CAD according to the latest guidelines. These changes and others will likely increase the request for appropriate imaging tests in the future. In this setting, artificial intelligence (AI) will play a pivotal role in echocardiography, CCTA, cardiac magnetic resonance and nuclear imaging, making multimodality imaging more efficient and reliable for clinicians, as well as more sustainable for healthcare systems. Furthermore, AI can assist clinicians in identifying early predictors of adverse outcome that human eyes cannot see in the fog of “big data.” AI algorithms applied to multimodality imaging will play a fundamental role in the management of patients with suspected or established CAD. This study aims to provide a comprehensive overview of current and future AI applications to the field of multimodality imaging of ischemic heart disease.
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Affiliation(s)
- Riccardo Maragna
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Carlo Maria Giacari
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Marco Guglielmo
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Andrea Baggiano
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy.,Department of Clinical Sciences and Community Health, Cardiovascular Section, University of Milan, Milan, Italy
| | - Laura Fusini
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Andrea Igoren Guaricci
- Department of Emergency and Organ Transplantation, Institute of Cardiovascular Disease, University Hospital Policlinico of Bari, Bari, Italy
| | - Alexia Rossi
- Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.,Center for Molecular Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - Mark Rabbat
- Department of Medicine and Radiology, Division of Cardiology, Loyola University of Chicago, Chicago, IL, United States.,Department of Medicine, Division of Cardiology, Edward Hines Jr. VA Hospital, Hines, IL, United States
| | - Gianluca Pontone
- Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
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Woo M, Devane AM, Lowe SC, Lowther EL, Gimbel RW. Deep learning for semi-automated unidirectional measurement of lung tumor size in CT. Cancer Imaging 2021; 21:43. [PMID: 34162439 PMCID: PMC8220702 DOI: 10.1186/s40644-021-00413-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 06/09/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Performing Response Evaluation Criteria in Solid Tumor (RECISTS) measurement is a non-trivial task requiring much expertise and time. A deep learning-based algorithm has the potential to assist with rapid and consistent lesion measurement. PURPOSE The aim of this study is to develop and evaluate deep learning (DL) algorithm for semi-automated unidirectional CT measurement of lung lesions. METHODS This retrospective study included 1617 lung CT images from 8 publicly open datasets. A convolutional neural network was trained using 1373 training and validation images annotated by two radiologists. Performance of the DL algorithm was evaluated 244 test images annotated by one radiologist. DL algorithm's measurement consistency with human radiologist was evaluated using Intraclass Correlation Coefficient (ICC) and Bland-Altman plotting. Bonferroni's method was used to analyze difference in their diagnostic behavior, attributed by tumor characteristics. Statistical significance was set at p < 0.05. RESULTS The DL algorithm yielded ICC score of 0.959 with human radiologist. Bland-Altman plotting suggested 240 (98.4 %) measurements realized within the upper and lower limits of agreement (LOA). Some measurements outside the LOA revealed difference in clinical reasoning between DL algorithm and human radiologist. Overall, the algorithm marginally overestimated the size of lesion by 2.97 % compared to human radiologists. Further investigation indicated tumor characteristics may be associated with the DL algorithm's diagnostic behavior of over or underestimating the lesion size compared to human radiologist. CONCLUSIONS The DL algorithm for unidirectional measurement of lung tumor size demonstrated excellent agreement with human radiologist.
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Affiliation(s)
- MinJae Woo
- Department of Public Health Sciences, Clemson University, 501 Edwards Hall, Clemson, SC, 29634, USA
| | - A Michael Devane
- Department of Radiology, Prisma Health System, 200 Patewood Drive, Greenville, SC, 29615, USA
| | - Steven C Lowe
- Department of Radiology, Prisma Health System, 200 Patewood Drive, Greenville, SC, 29615, USA
| | - Ervin L Lowther
- Department of Radiology, Prisma Health System, 200 Patewood Drive, Greenville, SC, 29615, USA
| | - Ronald W Gimbel
- Department of Public Health Sciences, Clemson University, 501 Edwards Hall, Clemson, SC, 29634, USA.
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Lee SY, Kim TH, Han K, Shin JM, Kim JY, Kim D, Park CH. Feasibility of Coronary Artery Calcium Scoring on Dual-Energy Chest Computed Tomography: A Prospective Comparison with Electrocardiogram-Gated Calcium Score Computed Tomography. J Clin Med 2021; 10:jcm10040653. [PMID: 33567707 PMCID: PMC7915048 DOI: 10.3390/jcm10040653] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 02/02/2021] [Accepted: 02/03/2021] [Indexed: 11/16/2022] Open
Abstract
Rationale and Objectives: This study aimed to evaluate the feasibility of assessment using the coronary artery calcium score (CACS) in dual-energy chest computed tomography (CT). Materials and Methods: We prospectively enrolled 30 patients (19 male, 11 female; mean age, 63.73 ± 9.40 years) who clinically required contrast-enhanced chest CT. The patients underwent electrocardiogram-gated cardiac calcium-scoring CT with a slice thickness of 2.5 mm followed by a sequentially non-gated contrast-enhanced dual-energy chest CT using 140/80 fast kVp switching technology with slice thicknesses of 1.25 mm and 2.5 mm. Virtual unenhanced (VUE) images were then reconstructed from the dual-energy CT using the material suppressed iodine (MSI) technique. Results: The mean heart rates were 63.33 ± 12.01 beats per minute. The mean CACS on the coronary calcium-scoring CT was 361.1 ± 435.5, and CACSs of the VUE images were 76.8 ± 128.6 (2.5 mm slice) and 108.7 ± 165.1 (1.25 mm slice). The correlation coefficients of CACS between the coronary calcium-scoring CT with the VUE 2.5 mm and 1.25 mm images were 0.888 and 0.904, respectively. The inter-observer agreements for the calcium score measurement between the calcium-scoring CT, VUE 2.5 mm, and VUE 1.25 mm were 1.000, 0.999, and 1.000, respectively. Conclusions: In conclusion, assessment of CACS using dual-energy chest CT might be feasible when using MSI virtual unenhanced dual-energy chest CT images with a slice thickness of 1.25 mm.
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Affiliation(s)
- Sun Yong Lee
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea; (S.Y.L.); (T.H.K.); (J.M.S.); (J.Y.K.); (D.K.)
| | - Tae Hoon Kim
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea; (S.Y.L.); (T.H.K.); (J.M.S.); (J.Y.K.); (D.K.)
| | - Kyunghwa Han
- Department of Radiology and The Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Korea;
| | - Jae Min Shin
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea; (S.Y.L.); (T.H.K.); (J.M.S.); (J.Y.K.); (D.K.)
| | - Ji Young Kim
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea; (S.Y.L.); (T.H.K.); (J.M.S.); (J.Y.K.); (D.K.)
| | - Daein Kim
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea; (S.Y.L.); (T.H.K.); (J.M.S.); (J.Y.K.); (D.K.)
| | - Chul Hwan Park
- Department of Radiology and The Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea; (S.Y.L.); (T.H.K.); (J.M.S.); (J.Y.K.); (D.K.)
- Correspondence: ; Tel.: +82-2-2019-3510; Fax: +82-2-3462-5472
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Obisesan OH, Osei AD, Uddin SI, Dzaye O, Blaha MJ. An Update on Coronary Artery Calcium Interpretation at Chest and Cardiac CT. Radiol Cardiothorac Imaging 2021; 3:e200484. [PMID: 33778659 PMCID: PMC7977732 DOI: 10.1148/ryct.2021200484] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/17/2020] [Accepted: 12/23/2020] [Indexed: 11/11/2022]
Abstract
Coronary artery calcium (CAC) is a marker of overall coronary atherosclerotic burden in an individual. As such, it is an important tool in cardiovascular risk stratification and preventive treatment of asymptomatic patients with unclear cardiovascular disease risk. Several guidelines have recommended the use of CAC testing in shared decision making between the clinician and patient. With recent updates in clinical management guidelines and broad recommendations for CAC, there is a need for concise updated information on CAC interpretation on traditional electrocardiographically gated scans and nongated thoracic scans. Important points to report when interpreting CAC scans include: the absolute Agatston score and the age, sex, and race-specific CAC percentile; general recommendations on time-to-rescan for individuals with a CAC score of 0; the number of vessels with CAC; the presence of CAC in the left main coronary artery; and specific highlighting of individuals with very high CAC scores of greater than 1000. When risk factor information is available, the 10-year coronary heart disease risk can also be easily assessed using the free online Multi-Ethnic Study of Atherosclerosis risk score calculator. Recent improvements in standardizing the reporting of CAC findings across gated and nongated studies, such as the CAC Data and Reporting System, show promise for improving the widespread clinical value of CAC in clinical practice. © RSNA, 2021.
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Affiliation(s)
- Olufunmilayo H. Obisesan
- From the Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, 733 N Broadway, Baltimore, MD 21205 (O.H.O., A.D.O., S.M.I.U., O.D., M.J.B.); American Heart Association Tobacco Regulation and Addiction Center, Dallas, Tex (O.H.O., A.D.O., S.M.I.U., M.J.B.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (O.D.)
| | - Albert D. Osei
- From the Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, 733 N Broadway, Baltimore, MD 21205 (O.H.O., A.D.O., S.M.I.U., O.D., M.J.B.); American Heart Association Tobacco Regulation and Addiction Center, Dallas, Tex (O.H.O., A.D.O., S.M.I.U., M.J.B.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (O.D.)
| | - S.M. Iftekhar Uddin
- From the Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, 733 N Broadway, Baltimore, MD 21205 (O.H.O., A.D.O., S.M.I.U., O.D., M.J.B.); American Heart Association Tobacco Regulation and Addiction Center, Dallas, Tex (O.H.O., A.D.O., S.M.I.U., M.J.B.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (O.D.)
| | - Omar Dzaye
- From the Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, 733 N Broadway, Baltimore, MD 21205 (O.H.O., A.D.O., S.M.I.U., O.D., M.J.B.); American Heart Association Tobacco Regulation and Addiction Center, Dallas, Tex (O.H.O., A.D.O., S.M.I.U., M.J.B.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (O.D.)
| | - Michael J. Blaha
- From the Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, 733 N Broadway, Baltimore, MD 21205 (O.H.O., A.D.O., S.M.I.U., O.D., M.J.B.); American Heart Association Tobacco Regulation and Addiction Center, Dallas, Tex (O.H.O., A.D.O., S.M.I.U., M.J.B.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (O.D.)
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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 RESEARCH INTERNATIONAL 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] [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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Javor D, Kaplan H, Kaplan A, Puchner SB, Krestan C, Baltzer P. Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography. Eur J Radiol 2020; 133:109402. [PMID: 33190102 PMCID: PMC7641539 DOI: 10.1016/j.ejrad.2020.109402] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 10/13/2020] [Accepted: 11/02/2020] [Indexed: 12/26/2022]
Abstract
INTRODUCTION Computed Tomography is an essential diagnostic tool in the management of COVID-19. Considering the large amount of examinations in high case-load scenarios, an automated tool could facilitate and save critical time in the diagnosis and risk stratification of the disease. METHODS A novel deep learning derived machine learning (ML) classifier was developed using a simplified programming approach and an open source dataset consisting of 6868 chest CT images from 418 patients which was split into training and validation subsets. The diagnostic performance was then evaluated and compared to experienced radiologists on an independent testing dataset. Diagnostic performance metrics were calculated using Receiver Operating Characteristics (ROC) analysis. Operating points with high positive (>10) and low negative (<0.01) likelihood ratios to stratify the risk of COVID-19 being present were identified and validated. RESULTS The model achieved an overall accuracy of 0.956 (AUC) on an independent testing dataset of 90 patients. Both rule-in and rule out thresholds were identified and tested. At the rule-in operating point, sensitivity and specificity were 84.4 % and 93.3 % and did not differ from both radiologists (p > 0.05). At the rule-out threshold, sensitivity (100 %) and specificity (60 %) differed significantly from the radiologists (p < 0.05). Likelihood ratios and a Fagan nomogram provide prevalence independent test performance estimates. CONCLUSION Accurate diagnosis of COVID-19 using a basic deep learning approach is feasible using open-source CT image data. In addition, the machine learning classifier provided validated rule-in and rule-out criteria could be used to stratify the risk of COVID-19 being present.
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Affiliation(s)
- D Javor
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - H Kaplan
- Deepinsights Study Group for Artificial Intelligence, Vienna, Austria
| | - A Kaplan
- Deepinsights Study Group for Artificial Intelligence, Vienna, Austria
| | - S B Puchner
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
| | - C Krestan
- Department of Radiology, Sozialmedizinisches Zentrum Süd - Kaiser-Franz-Josef Spital, Vienna, Austria
| | - P Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
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Artificial Intelligence in Cardiac CT: Automated Calcium Scoring and Plaque Analysis. CURRENT CARDIOVASCULAR IMAGING REPORTS 2020. [DOI: 10.1007/s12410-020-09549-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Abstract
Artificial intelligence (AI) is entering the clinical arena, and in the early stage, its implementation will be focused on the automatization tasks, improving diagnostic accuracy and reducing reading time. Many studies investigate the potential role of AI to support cardiac radiologist in their day-to-day tasks, assisting in segmentation, quantification, and reporting tasks. In addition, AI algorithms can be also utilized to optimize image reconstruction and image quality. Since these algorithms will play an important role in the field of cardiac radiology, it is increasingly important for radiologists to be familiar with the potential applications of AI. The main focus of this article is to provide an overview of cardiac-related AI applications for CT and MRI studies, as well as non-imaging-based applications for reporting and image optimization.
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Guo Y, Hao Z, Zhao S, Gong J, Yang F. Artificial Intelligence in Health Care: Bibliometric Analysis. J Med Internet Res 2020; 22:e18228. [PMID: 32723713 PMCID: PMC7424481 DOI: 10.2196/18228] [Citation(s) in RCA: 123] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 04/22/2020] [Accepted: 05/14/2020] [Indexed: 02/06/2023] Open
Abstract
Background As a critical driving power to promote health care, the health care–related artificial intelligence (AI) literature is growing rapidly. Objective The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care–related AI publications. Methods The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software. Results The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019. Conclusions This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care–related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.
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Affiliation(s)
- Yuqi Guo
- School of Social Work, University of North Carolina at Charlotte, Charlotte, NC, United States
| | - Zhichao Hao
- School of Social Work, The University of Alabama, Tuscaloosa, AL, United States
| | - Shichong Zhao
- Social Welfare Program, School of Public Administration, Dongbei University of Finance and Economics, Dalian, China
| | - Jiaqi Gong
- Department of Information Systems, University of Maryland, Baltimore, MD, United States
| | - Fan Yang
- Social Welfare Program, School of Public Administration, Dongbei University of Finance and Economics, Dalian, China
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Lee H, Martin S, Burt JR, Bagherzadeh PS, Rapaka S, Gray HN, Leonard TJ, Schwemmer C, Schoepf UJ. Machine Learning and Coronary Artery Calcium Scoring. Curr Cardiol Rep 2020; 22:90. [PMID: 32647932 DOI: 10.1007/s11886-020-01337-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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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Waltz J, Kocher M, Kahn J, Dirr M, Burt JR. The Future of Concurrent Automated Coronary Artery Calcium Scoring on Screening Low-Dose Computed Tomography. Cureus 2020; 12:e8574. [PMID: 32670710 PMCID: PMC7358941 DOI: 10.7759/cureus.8574] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 06/11/2020] [Indexed: 12/19/2022] Open
Abstract
Low-dose computed tomography (LDCT) has been extensively validated for lung cancer screening in selected patient populations. Additionally, the use of gated cardiac CT to assess coronary artery calcium (CAC) burden has been validated to determine a patient's risk for major cardiovascular adverse events. This is typically performed by calculating an Agatston score based on density and overall burden of calcified plaque within the coronary arteries. Patients that qualify for LDCT for lung cancer screening commonly share major risk factors for coronary artery disease and would frequently benefit from an additional gated cardiac CT for the assessment of CAC. Given the widespread use of LDCT for lung cancer screening, we evaluated current literature regarding the use of non-gated chest CT, specifically LDCT, for the detection and grading of coronary artery calcifications. Additionally, given the evolving and increasing use of artificial intelligence (AI) in the interpretation of radiologic studies, current literature for the use of AI in CAC assessment was reviewed. We reviewed primary scientific literature dating up to April 2020 using Pubmed and Google Scholar, with the search terms low dose CT, lung cancer screening, coronary artery calcium, EKG/cardiac gated CT, deep learning, machine learning, and AI. These publications were then independently evaluated by each member of our team. Overall, there was a consensus within these papers that LDCT for lung cancer screening plays a role in the evaluation of CAC. Most studies note the inherent problems with the evaluation of the density of coronary calcifications on LDCT to give an accurate numeric calcium or Agatston score. The current method of evaluating CAC on LDCT involves using a qualitative categorical system (none, mild, moderate, or severe). When performed by cardiac imaging experts, this method broadly correlates with traditional CAC score groups (0, 1 to 100, 101 to 400, and > 400). Furthermore, given the high sensitivity of a properly protocolled LDCT for coronary calcium, a negative study for CAC precludes the need for a dedicated gated CT assessment. However, qualitative methods are not as accurate or reproducible when performed by general radiologists. The implementation of AI in the LDCT screening process has the potential to give a quantifiable and reproducible numeric value to the calcium score, based on whole heart volume scoring of calcium. This more closely aligns with the Agatston score and serves as a better guide for treatment and risk assessment using current guidelines. We conclude that CAC should be assessed on all LDCT performed for lung cancer screening and that a qualitative categorical scoring system should be provided in the impression for each patient. Early studies involving AI for the assessment of CAC are promising, but more extensive studies are needed before a final recommendation for its use can be given. The implementation of an accurate, automated AI CAC assessment tool would improve radiologist compliance and ease of overall workflow. Ultimately, the potential end result would be improved turnaround time, better patient outcomes, and reduced healthcare costs by maximizing preventative care in this high-risk population.
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Affiliation(s)
- Jeffrey Waltz
- Diagnostic Radiology, Medical University of South Carolina, Charleston, USA
| | - Madison Kocher
- Radiology, Medical University of South Carolina, Charleston, USA
| | - Jacob Kahn
- Radiology, Medical University of South Carolina, Charleston, USA
| | - McKenzie Dirr
- Radiology, Medical University of South Carolina, Charleston, USA
| | - Jeremy R Burt
- Radiology, Medical University of South Carolina, Charleston, USA
- Cardiothoracic Imaging, Medical University of South Carolina, Charleston, USA
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