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Doolub G, Khurshid S, Theriault-Lauzier P, Nolin Lapalme A, Tastet O, So D, Labrecque Langlais E, Cobin D, Avram R. Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges. Can J Cardiol 2024; 40:1813-1827. [PMID: 38901544 DOI: 10.1016/j.cjca.2024.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 05/29/2024] [Accepted: 06/12/2024] [Indexed: 06/22/2024] Open
Abstract
This article reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes in the face of the global burden of cardiovascular diseases. It explores how AI algorithms can rapidly and accurately process data for the prediction and diagnosis of acute cardiac conditions. The review examines AI's impact on patient health across various diagnostic tools such as echocardiography, electrocardiography, coronary angiography, cardiac computed tomography, and magnetic resonance imaging, discusses the regulatory landscape for AI in health care, and categorises AI algorithms by their risk levels. Furthermore, it addresses the challenges of data quality, generalisability, bias, transparency, and regulatory considerations, underscoring the necessity for inclusive data and robust validation processes. The review concludes with future perspectives on integrating AI into clinical workflows and the ongoing need for research, regulation, and innovation to harness AI's full potential in improving acute cardiac care.
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Affiliation(s)
- Gemina Doolub
- Department of Medicine, Montréal Heart Institute, Université de Montréal, Montréal, Québec, Canada
| | - Shaan Khurshid
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | - Alexis Nolin Lapalme
- Department of Medicine, Montréal Heart Institute, Université de Montréal, Montréal, Québec, Canada; Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada; Mila-Québec AI Institute, Montréal, Québec, Canada
| | - Olivier Tastet
- Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada
| | - Derek So
- University of Ottawa, Heart Institute, Ottawa, Ontario, Canada
| | | | - Denis Cobin
- Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada
| | - Robert Avram
- Department of Medicine, Montréal Heart Institute, Université de Montréal, Montréal, Québec, Canada; Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada.
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Wang Y, Wang M, Yuan M, Peng W. The value of CCTA combined with machine learning for predicting angina pectoris in the anomalous origin of the right coronary artery. Biomed Eng Online 2024; 23:95. [PMID: 39267079 PMCID: PMC11391755 DOI: 10.1186/s12938-024-01286-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Accepted: 08/27/2024] [Indexed: 09/14/2024] Open
Abstract
BACKGROUND Anomalous origin of coronary artery is a common coronary artery anatomy anomaly. The anomalous origin of the coronary artery may lead to problems such as narrowing of the coronary arteries at the beginning of the coronary arteries and abnormal alignment, which may lead to myocardial ischemia due to the compression of the coronary arteries. Clinical symptoms include chest tightness and dyspnea, with angina pectoris as a common symptom that can be life-threatening. Timely and accurate diagnosis of anomalous coronary artery origin is of great importance. Coronary computed tomography angiography (CCTA) can provide detailed information on the characteristics of coronary arteries. Therefore, we combined CCTA and artificial intelligence (AI) technology to analyze the CCTA image features and clinical features of patients with anomalous origin of the right coronary artery to predict angina pectoris and the relevance of different features to angina pectoris. METHODS In this retrospective analysis, we compiled data on 15 characteristics from 126 patients diagnosed with anomalous right coronary artery origins. The dataset encompassed both CCTA imaging attributes, such as the positioning of the right coronary artery orifices and the alignment of coronary arteries, and clinical parameters including gender and age. To identify the most salient features, we employed the Chi-square feature selection method, which filters features based on their statistical significance. We then focused on features yielding a Chi-square score exceeding a threshold of 1, thereby narrowing down the selection to seven key variables, including cardiac function and gender. Subsequently, we evaluated seven classifiers known for their efficacy in classification tasks. Through rigorous training and testing, we conducted a comparative analysis to identify the top three classifiers with the highest accuracy rates. RESULTS The top three classifiers in this study are Support Vector Machine (SVM), Ensemble Learning (EL), and Kernel Approximation Classifier. Among the SVM, EL and Kernel Approximation Classifier-based classifiers, the best performance is achieved for linear SVM, optimizable Ensembles Learning and SVM kernel, respectively. And the corresponding accuracy is 75.7%, 75.7%, and 73.0%, respectively. The AUC values are 0.77, 0.80, and 0.75, respectively. CONCLUSIONS Machine learning (ML) models can predict angina pectoris caused by the origin anomalous of the right coronary artery, providing valuable auxiliary diagnostic information for clinicians and serving as a warning to clinicians. It is hoped that timely intervention and treatment can be realized to avoid serious consequences such as myocardial infarction.
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Affiliation(s)
- Ying Wang
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
- School of Sports and Health, Shanghai University of Sport, Shanghai, China
| | - MengXing Wang
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Mingyuan Yuan
- Department of Radiology, Affiliated Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China.
| | - Wenxian Peng
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China.
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Yang Y, Zhou Z, Zhang N, Wang R, Gao Y, Ran X, Sun Z, Zhang H, Yang G, Song X, Xu L. Performance of artificial intelligence in detecting the chronic total occlusive lesions of coronary artery based on coronary computed tomographic angiography. Cardiovasc Diagn Ther 2024; 14:655-667. [PMID: 39263478 PMCID: PMC11384454 DOI: 10.21037/cdt-23-407] [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: 10/21/2023] [Accepted: 04/19/2024] [Indexed: 09/13/2024]
Abstract
Background Coronary chronic total occlusion (CTO) increases the risk of developing major adverse cardiovascular events (MACE) and cardiogenic shock. Coronary computed tomography angiography (CCTA) is a safe, noninvasive method to diagnose CTO lesions. With the development of artificial intelligence (AI), AI has been broadly applied in cardiovascular images, but AI-based detection of CTO lesions from CCTA images is difficult. We aim to evaluate the performance of AI in detecting the CTO lesions of coronary arteries based on CCTA images. Methods We retrospectively and consecutively enrolled patients with 50% stenosis, 50-99% stenosis, and CTO lesions who received CCTA scans between June 2021 and June 2022 in Beijing Anzhen Hospital. Four-fifths of them were randomly assigned to the training dataset, while the rest (1/5) were randomly assigned to the testing dataset. Performance of the AI-assisted CCTA (CCTA-AI) in detecting the CTO lesions was evaluated through sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic analysis. With invasive coronary angiography as the reference, the diagnostic performance of AI method and manual method was compared. Results A total of 537 patients with 1,569 stenotic lesions (including 672 lesions with <50% stenosis, 493 lesions with 50-99% stenosis, and 404 CTO lesions) were enrolled in our study. CCTA-AI saved 75% of the time in post-processing and interpreting the CCTA images when compared to the manual method (116±15 vs. 472±45 seconds). In the testing dataset, the accuracy of CCTA-AI in detecting CTO lesions was 86.2% (79.0%, 90.3%), with the area under the curve of 0.874. No significant difference was found in detecting CTO lesions between AI and manual methods (P=0.53). Conclusions AI can automatically detect CTO lesions based on CCTA images, with high diagnostic accuracy and efficiency.
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Affiliation(s)
- Yanying Yang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
- Department of Radiology, Beijing Geriatric Hospital, Beijing, China
| | - Zhen Zhou
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Nan Zhang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Rui Wang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Yifeng Gao
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Xiaowei Ran
- Shukun (Beijing) Technology Co., Ltd., Beijing, China
| | - Zhonghua Sun
- Discipline of Medical Radiation Science, Curtin Medical School, Curtin University, Perth, Australia
- Curtin Health Innovation Research Institute (CHIRI), Curtin University, Perth, Australia
| | - Heye Zhang
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Guang Yang
- Bioengineering Department and Imperial-X, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, UK
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Xiantao Song
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Lei Xu
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
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Thribhuvan Reddy D, Grewal I, García Pinzon LF, Latchireddy B, Goraya S, Ali Alansari B, Gadwal A. The Role of Artificial Intelligence in Healthcare: Enhancing Coronary Computed Tomography Angiography for Coronary Artery Disease Management. Cureus 2024; 16:e61523. [PMID: 38957241 PMCID: PMC11218716 DOI: 10.7759/cureus.61523] [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] [Accepted: 06/02/2024] [Indexed: 07/04/2024] Open
Abstract
This review aims to explore the potential of artificial intelligence (AI) in coronary CT angiography (CCTA), a key tool for diagnosing coronary artery disease (CAD). Because CAD is still a major cause of death worldwide, effective and accurate diagnostic methods are required to identify and manage the condition. CCTA certainly is a noninvasive alternative for diagnosing CAD, but it requires a large amount of data as input. We intend to discuss the idea of incorporating AI into CCTA, which enhances its diagnostic accuracy and operational efficiency. Using such AI technologies as machine learning (ML) and deep learning (DL) tools, CCTA images are automated to perfection and the analysis is significantly refined. It enables the characterization of a plaque, assesses the severity of the stenosis, and makes more accurate risk stratifications than traditional methods, with pinpoint accuracy. Automating routine tasks through AI-driven CCTA will reduce the radiologists' workload considerably, which is a standard benefit of such technologies. More importantly, it would enable radiologists to allocate more time and expertise to complex cases, thereby improving overall patient care. However, the field of AI in CCTA is not without its challenges, which include data protection, algorithm transparency, as well as criteria for standardization encoding. Despite such obstacles, it appears that the integration of AI technology into CCTA in the future holds great promise for keeping CAD itself in check, thereby aiding the fight against this disease and begetting better clinical outcomes and more optimized modes of healthcare. Future research on AI algorithms for CCTA, making ethical use of AI, and thereby overcoming the technical and clinical barriers to widespread adoption of this new tool, will hopefully pave the way for profound AI-driven transformations in healthcare.
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Affiliation(s)
| | - Inayat Grewal
- Department of Medicine, Government Medical College and Hospital, Chandigarh, IND
| | | | | | - Simran Goraya
- Department of Medicine, Kharkiv National Medical University, Kharkiv, UKR
| | | | - Aishwarya Gadwal
- Department of Radiodiagnosis, St. John's Medical College and Hospital, Bengaluru, IND
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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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Sandeep B, Liu X, Huang X, Wang X, Mao L, Xiao Z. Feasibility of artificial intelligence its current status, clinical applications, and future direction in cardiovascular disease. Curr Probl Cardiol 2024; 49:102349. [PMID: 38103818 DOI: 10.1016/j.cpcardiol.2023.102349] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
In routine clinical practice, the diagnosis and treatment of cardiovascular disease (CVD) rely on data in a variety of formats. These formats comprise invasive angiography, laboratory data, non-invasive imaging diagnostics, and patient history. Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. In cardiovascular medicine, artificial intelligence (AI) algorithms have been used to discover novel genotypes and phenotypes in established diseases enhance patient care, enable cost effectiveness, and lower readmission and mortality rates. AI will lead to a paradigm change toward precision cardiovascular medicine in the near future. The promise application of AI in cardiovascular medicine is immense; however, failure to recognize and ignorance of the challenges may overshadow its potential clinical impact. AI can facilitate every stage in cardiology in the imaging process, from acquisition and reconstruction, to segmentation, measurement, interpretation, and subsequent clinical pathways. Along with new possibilities, new threats arise, acknowledging and understanding them is as important as understanding the machine learning (ML) methodology itself. Therefore, attention is also paid to the current opinions and guidelines regarding the validation and safety of AI. This paper provides a outline for clinicians on relevant aspects of AI and machine learning, selection of applications and methods in cardiology to date, and identifies how cardiovascular medicine could incorporate AI in the future. With progress continuing in this emerging technology, the impact for cardiovascular medicine is highlighted to provide insight for the practicing clinician and to identify potential patient benefits.
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Affiliation(s)
- Bhushan Sandeep
- Department of Cardio-Thoracic Surgery, Chengdu Second People's Hospital, Chengdu, Sichuan 610017, China.
| | - Xian Liu
- Department of Cardio-Thoracic Surgery, Chengdu Second People's Hospital, Chengdu, Sichuan 610017, China
| | - Xin Huang
- Department of Anesthesiology, West China Hospital of Medicine, Sichuan University, Chengdu, Sichuan 610017, China
| | - Xiaowei Wang
- Department of Cardio-Thoracic Surgery, Chengdu Second People's Hospital, Chengdu, Sichuan 610017, China
| | - Long Mao
- Department of Cardio-Thoracic Surgery, Chengdu Second People's Hospital, Chengdu, Sichuan 610017, China
| | - Zongwei Xiao
- Department of Cardio-Thoracic Surgery, Chengdu Second People's Hospital, Chengdu, Sichuan 610017, China
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Sun Z, Silberstein J, Vaccarezza M. Cardiovascular Computed Tomography in the Diagnosis of Cardiovascular Disease: Beyond Lumen Assessment. J Cardiovasc Dev Dis 2024; 11:22. [PMID: 38248892 PMCID: PMC10816599 DOI: 10.3390/jcdd11010022] [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: 11/22/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 01/23/2024] Open
Abstract
Cardiovascular CT is being widely used in the diagnosis of cardiovascular disease due to the rapid technological advancements in CT scanning techniques. These advancements include the development of multi-slice CT, from early generation to the latest models, which has the capability of acquiring images with high spatial and temporal resolution. The recent emergence of photon-counting CT has further enhanced CT performance in clinical applications, providing improved spatial and contrast resolution. CT-derived fractional flow reserve is superior to standard CT-based anatomical assessment for the detection of lesion-specific myocardial ischemia. CT-derived 3D-printed patient-specific models are also superior to standard CT, offering advantages in terms of educational value, surgical planning, and the simulation of cardiovascular disease treatment, as well as enhancing doctor-patient communication. Three-dimensional visualization tools including virtual reality, augmented reality, and mixed reality are further advancing the clinical value of cardiovascular CT in cardiovascular disease. With the widespread use of artificial intelligence, machine learning, and deep learning in cardiovascular disease, the diagnostic performance of cardiovascular CT has significantly improved, with promising results being presented in terms of both disease diagnosis and prediction. This review article provides an overview of the applications of cardiovascular CT, covering its performance from the perspective of its diagnostic value based on traditional lumen assessment to the identification of vulnerable lesions for the prediction of disease outcomes with the use of these advanced technologies. The limitations and future prospects of these technologies are also discussed.
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Affiliation(s)
- Zhonghua Sun
- Curtin Medical School, Curtin University, Perth, WA 6102, Australia; (J.S.); (M.V.)
- Curtin Health Innovation Research Institute (CHIRI), Curtin University, Perth, WA 6102, Australia
| | - Jenna Silberstein
- Curtin Medical School, Curtin University, Perth, WA 6102, Australia; (J.S.); (M.V.)
| | - Mauro Vaccarezza
- Curtin Medical School, Curtin University, Perth, WA 6102, Australia; (J.S.); (M.V.)
- Curtin Health Innovation Research Institute (CHIRI), Curtin University, Perth, WA 6102, Australia
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Lee DY, Chang CC, Ko CF, Lee YH, Tsai YL, Chou RH, Chang TY, Guo SM, Huang PH. Artificial intelligence evaluation of coronary computed tomography angiography for coronary stenosis classification and diagnosis. Eur J Clin Invest 2024; 54:e14089. [PMID: 37668089 DOI: 10.1111/eci.14089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 08/14/2023] [Accepted: 08/29/2023] [Indexed: 09/06/2023]
Abstract
BACKGROUND Ruling out obstructive coronary artery disease (CAD) using coronary computed tomography angiography (CCTA) is time-consuming and challenging. This study developed a deep learning (DL) model to assist in detecting obstructive CAD on CCTA to streamline workflows. METHODS In total, 2929 DICOM files and 7945 labels were extracted from curved planar reformatted CCTA images. A modified Inception V3 model was adopted. To validate the artificial intelligence (AI) model, two cardiologists labelled and adjudicated the classification of coronary stenosis on CCTA. The model was trained to differentiate the coronary artery into binary stenosis classifications <50% and ≥50% stenosis. Using the quantitative coronary angiography (QCA) consensus results as a reference standard, the performance of the AI model and CCTA radiology readers was compared by calculating Cohen's kappa coefficients at patient and vessel levels. The net reclassification index was used to evaluate the net benefit of the DL model. RESULTS The diagnostic accuracy of the AI model was 92.3% and 88.4% at the patient and vessel levels, respectively. Compared with CCTA radiology readers, the AI model had a better agreement for binary stenosis classification at both patient and vessel levels (Cohen kappa coefficient: .79 vs. .39 and .77 vs. .40, p < .0001). The AI model also exhibited significantly improved model discrimination and reclassification (Net reclassification index = .350; Z = 4.194; p < .001). CONCLUSIONS The developed AI model identified obstructive CAD, and the model results correlated well with QCA results. Incorporating the model into the reporting system of CCTA may improve workflows.
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Affiliation(s)
- Dan-Ying Lee
- Department of Internal Medicine, Division of Cardiology, Taipei Veterans General Hospital, Taipei City, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
- Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei City, Taiwan
| | - Chun-Chin Chang
- Department of Internal Medicine, Division of Cardiology, Taipei Veterans General Hospital, Taipei City, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
- Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei City, Taiwan
| | - Chieh-Fu Ko
- Institute of Medical Informatics, National Cheng Kung University, Tainan City, Taiwan
| | - Yin-Hao Lee
- Department of Internal Medicine, Division of Cardiology, Taipei Veterans General Hospital, Taipei City, Taiwan
- Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei City, Taiwan
- Department of Medicine, Division of Cardiology, Taipei City Hospital, Taipei City, Taiwan
| | - Yi-Lin Tsai
- Department of Internal Medicine, Division of Cardiology, Taipei Veterans General Hospital, Taipei City, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
- Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei City, Taiwan
| | - Ruey-Hsing Chou
- Department of Internal Medicine, Division of Cardiology, Taipei Veterans General Hospital, Taipei City, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
- Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei City, Taiwan
- Department of Critical Care Medicine, Taipei Veterans General Hospital, Taipei City, Taiwan
| | - Ting-Yung Chang
- Department of Internal Medicine, Division of Cardiology, Taipei Veterans General Hospital, Taipei City, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
- Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei City, Taiwan
| | - Shu-Mei Guo
- Institute of Medical Informatics, National Cheng Kung University, Tainan City, Taiwan
| | - Po-Hsun Huang
- Department of Internal Medicine, Division of Cardiology, Taipei Veterans General Hospital, Taipei City, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
- Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei City, Taiwan
- Department of Critical Care Medicine, Taipei Veterans General Hospital, Taipei City, Taiwan
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Chen Q, Zhou F, Xie G, Tang CX, Gao X, Zhang Y, Yin X, Xu H, Zhang LJ. Advances in Artificial Intelligence-Assisted Coronary Computed Tomographic Angiography for Atherosclerotic Plaque Characterization. Rev Cardiovasc Med 2024; 25:27. [PMID: 39077649 PMCID: PMC11262402 DOI: 10.31083/j.rcm2501027] [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: 07/08/2023] [Revised: 09/01/2023] [Accepted: 09/13/2023] [Indexed: 07/31/2024] Open
Abstract
Coronary artery disease is a leading cause of death worldwide. Major adverse cardiac events are associated not only with coronary luminal stenosis but also with atherosclerotic plaque components. Coronary computed tomography angiography (CCTA) enables non-invasive evaluation of atherosclerotic plaque along the entire coronary tree. However, precise and efficient assessment of plaque features on CCTA is still a challenge for physicians in daily practice. Artificial intelligence (AI) refers to algorithms that can simulate intelligent human behavior to improve clinical work efficiency. Recently, cardiovascular imaging has seen remarkable advancements with the use of AI. AI-assisted CCTA has the potential to facilitate the clinical workflow, offer objective and repeatable quantitative results, accelerate the interpretation of reports, and guide subsequent treatment. Several AI algorithms have been developed to provide a comprehensive assessment of atherosclerotic plaques. This review serves to highlight the cutting-edge applications of AI-assisted CCTA in atherosclerosis plaque characterization, including detecting obstructive plaques, assessing plaque volumes and vulnerability, monitoring plaque progression, and providing risk assessment. Finally, this paper discusses the current problems and future directions for implementing AI in real-world clinical settings.
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Affiliation(s)
- Qian Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, China
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, 210002 Nanjing, Jiangsu, China
| | - Fan Zhou
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, 210002 Nanjing, Jiangsu, China
| | - Guanghui Xie
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, China
| | - Chun Xiang Tang
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, 210002 Nanjing, Jiangsu, China
| | - Xiaofei Gao
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, China
| | - Yamei Zhang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, China
| | - Hui Xu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006 Nanjing, Jiangsu, China
| | - Long Jiang Zhang
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, 210002 Nanjing, Jiangsu, China
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Unlu O, Fahed AC. Machine Learning in Invasive and Noninvasive Coronary Angiography. Curr Atheroscler Rep 2023; 25:1025-1033. [PMID: 38095805 DOI: 10.1007/s11883-023-01178-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/21/2023] [Indexed: 01/06/2024]
Abstract
PURPOSE OF REVIEW The objective of this review is to shed light on the transformative potential of machine learning (ML) in coronary angiography. We aim to understand existing developments in using ML for coronary angiography and discuss broader implications for the future of coronary angiography and cardiovascular medicine. RECENT FINDINGS The developments in invasive and noninvasive imaging have revolutionized diagnosis and treatment of coronary artery disease (CAD). However, CAD remains underdiagnosed and undertreated. ML has emerged as a powerful tool to further improve image analysis, hemodynamic assessment, lesion detection, and predictive modeling. These advancements have enabled more accurate identification of CAD, streamlined workflows, reduced the need for invasive diagnostic procedures, and improved the diagnostic value of invasive procedures when they are needed. Further integration of ML with coronary angiography will advance the prevention, diagnosis, and treatment of CAD. The integration of ML with coronary angiography is ushering in a new era in cardiovascular medicine. We highlight five use cases to leverage ML in coronary angiography: (1) improvement of quality and efficacy, (2) characterization of plaque, (3) hemodynamic assessment, (4) prediction of future outcomes, and (5) diagnosis of non-atherosclerotic coronary disease.
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Affiliation(s)
- Ozan Unlu
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Clinical Informatics, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Cardiovascular Disease Initiative and ML for Health, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Akl C Fahed
- Cardiovascular Disease Initiative and ML for Health, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street CPZN 3.128, Boston, MA, 02114, USA.
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11
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Ramasamy A, Sokooti H, Zhang X, Tzorovili E, Bajaj R, Kitslaar P, Broersen A, Amersey R, Jain A, Ozkor M, Reiber JHC, Dijkstra J, Serruys PW, Moon JC, Mathur A, Baumbach A, Torii R, Pugliese F, Bourantas CV. Novel near-infrared spectroscopy-intravascular ultrasound-based deep-learning methodology for accurate coronary computed tomography plaque quantification and characterization. EUROPEAN HEART JOURNAL OPEN 2023; 3:oead090. [PMID: 37908441 PMCID: PMC10615127 DOI: 10.1093/ehjopen/oead090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/16/2023] [Accepted: 08/17/2023] [Indexed: 11/02/2023]
Abstract
Aims Coronary computed tomography angiography (CCTA) is inferior to intravascular imaging in detecting plaque morphology and quantifying plaque burden. We aim to, for the first time, train a deep-learning (DL) methodology for accurate plaque quantification and characterization in CCTA using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS). Methods and results Seventy patients were prospectively recruited who underwent CCTA and NIRS-IVUS imaging. Corresponding cross sections were matched using an in-house developed software, and the estimations of NIRS-IVUS for the lumen, vessel wall borders, and plaque composition were used to train a convolutional neural network in 138 vessels. The performance was evaluated in 48 vessels and compared against the estimations of NIRS-IVUS and the conventional CCTA expert analysis. Sixty-four patients (186 vessels, 22 012 matched cross sections) were included. Deep-learning methodology provided estimations that were closer to NIRS-IVUS compared with the conventional approach for the total atheroma volume (ΔDL-NIRS-IVUS: -37.8 ± 89.0 vs. ΔConv-NIRS-IVUS: 243.3 ± 183.7 mm3, variance ratio: 4.262, P < 0.001) and percentage atheroma volume (-3.34 ± 5.77 vs. 17.20 ± 7.20%, variance ratio: 1.578, P < 0.001). The DL methodology detected lesions more accurately than the conventional approach (Area under the curve (AUC): 0.77 vs. 0.67, P < 0.001) and quantified minimum lumen area (ΔDL-NIRS-IVUS: -0.35 ± 1.81 vs. ΔConv-NIRS-IVUS: 1.37 ± 2.32 mm2, variance ratio: 1.634, P < 0.001), maximum plaque burden (4.33 ± 11.83% vs. 5.77 ± 16.58%, variance ratio: 2.071, P = 0.004), and calcific burden (-51.2 ± 115.1 vs. -54.3 ± 144.4, variance ratio: 2.308, P < 0.001) more accurately than conventional approach. The DL methodology was able to segment a vessel on CCTA in 0.3 s. Conclusions The DL methodology developed for CCTA analysis from co-registered NIRS-IVUS and CCTA data enables rapid and accurate assessment of lesion morphology and is superior to expert analysts (Clinicaltrials.gov: NCT03556644).
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Affiliation(s)
- Anantharaman Ramasamy
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, Mile End Road, London E1 4NS, UK
| | | | - Xiaotong Zhang
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Evangelia Tzorovili
- Pragmatic Clinical Trials Unit, Centre for Evaluation and Methods, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Retesh Bajaj
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, Mile End Road, London E1 4NS, UK
| | - Pieter Kitslaar
- Medis Medical Imaging Systems, Leiden, The Netherlands
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Alexander Broersen
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Rajiv Amersey
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
| | - Ajay Jain
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
| | - Mick Ozkor
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
| | - Johan H C Reiber
- Medis Medical Imaging Systems, Leiden, The Netherlands
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jouke Dijkstra
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Patrick W Serruys
- Faculty of Medicine, National Heart and Lung Institute, Imperial College London, Cale Street, London SW3 6LY, UK
- Department of Cardiology, National University of Ireland, Galway, Ireland
| | - James C Moon
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Institute of Cardiovascular Sciences, University College London, Gower Street, London WC1E 6BT, UK
| | - Anthony Mathur
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, Mile End Road, London E1 4NS, UK
| | - Andreas Baumbach
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, Mile End Road, London E1 4NS, UK
| | - Ryo Torii
- Department of Mechanical Engineering, University College London, Torrington Place, London WC1E 7JE, UK
| | - Francesca Pugliese
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, Mile End Road, London E1 4NS, UK
| | - Christos V Bourantas
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, Mile End Road, London E1 4NS, UK
- Institute of Cardiovascular Sciences, University College London, Gower Street, London WC1E 6BT, UK
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12
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Tahir AM, Mutlu O, Bensaali F, Ward R, Ghareeb AN, Helmy SMHA, Othman KT, Al-Hashemi MA, Abujalala S, Chowdhury MEH, Alnabti ARDMH, Yalcin HC. Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes. J Clin Med 2023; 12:4774. [PMID: 37510889 PMCID: PMC10381346 DOI: 10.3390/jcm12144774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/08/2023] [Accepted: 04/10/2023] [Indexed: 07/30/2023] Open
Abstract
Aortic valve defects are among the most prevalent clinical conditions. A severely damaged or non-functioning aortic valve is commonly replaced with a bioprosthetic heart valve (BHV) via the transcatheter aortic valve replacement (TAVR) procedure. Accurate pre-operative planning is crucial for a successful TAVR outcome. Assessment of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid-solid interaction (FSI) analysis offer a solution that has been increasingly utilized to evaluate BHV mechanics and dynamics. However, the high computational costs and the complex operation of computational modeling hinder its application. Recent advancements in the deep learning (DL) domain can offer a real-time surrogate that can render hemodynamic parameters in a few seconds, thus guiding clinicians to select the optimal treatment option. Herein, we provide a comprehensive review of classical computational modeling approaches, medical imaging, and DL approaches for planning and outcome assessment of TAVR. Particularly, we focus on DL approaches in previous studies, highlighting the utilized datasets, deployed DL models, and achieved results. We emphasize the critical challenges and recommend several future directions for innovative researchers to tackle. Finally, an end-to-end smart DL framework is outlined for real-time assessment and recommendation of the best BHV design for TAVR. Ultimately, deploying such a framework in future studies will support clinicians in minimizing risks during TAVR therapy planning and will help in improving patient care.
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Affiliation(s)
- Anas M Tahir
- Electrical and Computer Engineering Department, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Biomedical Research Center, Qatar University, Doha 2713, Qatar
| | - Onur Mutlu
- Biomedical Research Center, Qatar University, Doha 2713, Qatar
| | - Faycal Bensaali
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Rabab Ward
- Electrical and Computer Engineering Department, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Abdel Naser Ghareeb
- Heart Hospital, Hamad Medical Corporation, Doha 3050, Qatar
- Faculty of Medicine, Al Azhar University, Cairo 11884, Egypt
| | - Sherif M H A Helmy
- Noninvasive Cardiology Section, Cardiology Department, Heart Hospital, Hamad Medical Corporation, Doha 3050, Qatar
| | | | - Mohammed A Al-Hashemi
- Noninvasive Cardiology Section, Cardiology Department, Heart Hospital, Hamad Medical Corporation, Doha 3050, Qatar
| | | | | | | | - Huseyin C Yalcin
- Biomedical Research Center, Qatar University, Doha 2713, Qatar
- Department of Biomedical Science, College of Health Sciences, QU Health, Qatar University, Doha 2713, Qatar
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13
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Joshi M, Melo DP, Ouyang D, Slomka PJ, Williams MC, Dey D. Current and Future Applications of Artificial Intelligence in Cardiac CT. Curr Cardiol Rep 2023; 25:109-117. [PMID: 36708505 DOI: 10.1007/s11886-022-01837-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/10/2022] [Indexed: 01/29/2023]
Abstract
PURPOSE OF REVIEW In this review, we aim to summarize state-of-the-art artificial intelligence (AI) approaches applied to cardiovascular CT and their future implications. RECENT FINDINGS Recent studies have shown that deep learning networks can be applied for rapid automated segmentation of coronary plaque from coronary CT angiography, with AI-enabled measurement of total plaque volume predicting future heart attack. AI has also been applied to automate assessment of coronary artery calcium on cardiac and ungated chest CT and to automate the measurement of epicardial fat. Additionally, AI-based prediction models integrating clinical and imaging parameters have been shown to improve prediction of cardiac events compared to traditional risk scores. Artificial intelligence applications have been applied in all aspects of cardiovascular CT - in image acquisition, reconstruction and denoising, segmentation and quantitative analysis, diagnosis and decision assistance and to integrate prognostic risk from clinical data and images. Further incorporation of artificial intelligence in cardiovascular imaging holds important promise to enhance cardiovascular CT as a precision medicine tool.
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Affiliation(s)
- Mugdha Joshi
- Department of Medicine, Stanford Healthcare, Palo Alto, CA, USA
| | - Diana Patricia Melo
- Division of Cardiovascular Medicine, Stanford Healthcare, Palo Alto, CA, USA
| | - David Ouyang
- Cedars-Sinai Medical Center, Smidt Heart Institute, Los Angeles, CA, USA
| | - Piotr J Slomka
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Damini Dey
- Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, 116 N Robertson Boulevard, Los Angeles, CA, 90048, USA.
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14
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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: 6] [Impact Index Per Article: 6.0] [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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15
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Kunchur NN, Mostaço-Guidolin LB. Development of an image classification pipeline for atherosclerotic plaques assessment using supervised machine learning. BMC Bioinformatics 2022; 23:542. [DOI: 10.1186/s12859-022-05059-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 11/16/2022] [Indexed: 12/15/2022] Open
Abstract
Abstract
Background
During atherosclerosis, the narrowing of the arterial lumen is observed through the accumulation of bio compounds and the formation of plaque within artery walls. A non-linear optical imaging modality (NLOM), coherent anti-stokes Raman scattering (CARS) microscopy, can be used to image lipid-rich structures commonly found in atherosclerotic plaques. By matching the lipid’s molecular vibrational frequencies (CH bonds), it is possible to map the accumulation of lipid-rich structures without the need for exogenous labelling and/or processing of the samples. CARS allows for the visualization of the morphological features of plaque. In combination with supervised machine learning, CARS imaged morphological features can be used to characterize the progression of atherosclerotic plaques.
Results
Based on a set of label-free CARS images of atherosclerotic plaques (i.e. foam cell clusters) from a Watanabe heritable hyperlipidemic rabbit model, we developed an automated pipeline to classify atherosclerotic lesions based on their major morphological features. Our method uses image preprocessing to first improve the quality of the CARS-imaged plaque, followed by the segmentation of the plaque using Otsu thresholding, marker-controlled watershed, K-means segmentation and a novel independent foam cell thresholding segmentation. To define relevant morphological features, 27 quantitative features were extracted and further refined by a novel coefficient of variation feature refinement method in accordance with filter-type feature selection. Refined morphological features were supplied into three supervised machine learning algorithms; K-nearest neighbour, support vector machine and decision tree classifier. The classification pipeline showcased the ability to exploit relevant plaque morphological features to accurately classify 3 pre-defined stages of atherosclerosis: early fatty streak development (EFS) and advancing atheroma (AA) with a greater than 85% class accuracy
Conclusions
Through the combination of CARS microscopy and computational methods, a powerful classification tool was developed to identify the progression of atherosclerotic plaque in an automated manner. Using a curated dataset, the classification pipeline demonstrated the ability to differentiate between EFS, EF and AA. Thus, presenting the opportunity to classify the onset of atherosclerosis at an earlier stage of development
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16
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Alskaf E, Dutta U, Scannell CM, Chiribiri A. Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis. JOURNAL OF MEDICAL ARTIFICIAL INTELLIGENCE 2022; 5:11. [PMID: 36861064 PMCID: PMC7614252 DOI: 10.21037/jmai-22-36] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Background The application of deep learning on medical imaging is growing in prevalence in the recent literature. One of the most studied areas is coronary artery disease (CAD). Imaging of coronary artery anatomy is fundamental, which has led to a high number of publications describing a variety of techniques. The aim of this systematic review is to review the evidence behind the accuracy of deep learning applications in coronary anatomy imaging. Methods The search for the relevant studies, which applied deep learning on coronary anatomy imaging, was performed in a systematic approach on MEDLINE and EMBASE databases, followed by reviewing of abstracts and full texts. The data from the final studies was retrieved using data extraction forms. A meta-analysis was performed on a subgroup of studies, which looked at fractional flow reserve (FFR) prediction. Heterogeneity was tested using tau2, I2 and Q tests. Finally, a risk of bias was performed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS) approach. Results A total of 81 studies met the inclusion criteria. The most common imaging modality was coronary computed tomography angiography (CCTA) (58%) and the most common deep learning method was convolutional neural network (CNN) (52%). The majority of studies demonstrated good performance metrics. The most common outputs were focused on coronary artery segmentation, clinical outcome prediction, coronary calcium quantification and FFR prediction, and most studies reported area under the curve (AUC) of ≥80%. The pooled diagnostic odds ratio (DOR) derived from 8 studies looking at FFR prediction using CCTA was 12.5 using the Mantel-Haenszel (MH) method. There was no significant heterogeneity amongst studies according to Q test (P=0.2496). Conclusions Deep learning has been used in many applications on coronary anatomy imaging, most of which are yet to be externally validated and prepared for clinical use. The performance of deep learning, especially CNN models, proved to be powerful and some applications have already translated into medical practice, such as computed tomography (CT)-FFR. These applications have the potential to translate technology into better care of CAD patients.
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Affiliation(s)
- Ebraham Alskaf
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Utkarsh Dutta
- GKT School of Medical Education, King’s College London, London, UK
| | - Cian M. Scannell
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK,Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Amedeo Chiribiri
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
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17
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Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA). Diagnostics (Basel) 2022; 12:diagnostics12071660. [PMID: 35885564 PMCID: PMC9318450 DOI: 10.3390/diagnostics12071660] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 06/23/2022] [Accepted: 07/01/2022] [Indexed: 12/24/2022] Open
Abstract
Radiomics is the process of extracting useful quantitative features of high-dimensional data that allows for automated disease classification, including atherosclerotic disease. Hence, this study aimed to quantify and extract the radiomic features from Coronary Computed Tomography Angiography (CCTA) images and to evaluate the performance of automated machine learning (AutoML) model in classifying the atherosclerotic plaques. In total, 202 patients who underwent CCTA examination at Institut Jantung Negara (IJN) between September 2020 and May 2021 were selected as they met the inclusion criteria. Three primary coronary arteries were segmented on axial sectional images, yielding a total of 606 volume of interest (VOI). Subsequently, the first order, second order, and shape order of radiomic characteristics were extracted for each VOI. Model 1, Model 2, Model 3, and Model 4 were constructed using AutoML-based Tree-Pipeline Optimization Tools (TPOT). The heatmap confusion matrix, recall (sensitivity), precision (PPV), F1 score, accuracy, receiver operating characteristic (ROC), and area under the curve (AUC) were analysed. Notably, Model 1 with the first-order features showed superior performance in classifying the normal coronary arteries (F1 score: 0.88; Inverse F1 score: 0.94), as well as in classifying the calcified (F1 score: 0.78; Inverse F1 score: 0.91) and mixed plaques (F1 score: 0.76; Inverse F1 score: 0.86). Moreover, Model 2 consisting of second-order features was proved useful, specifically in classifying the non-calcified plaques (F1 score: 0.63; Inverse F1 score: 0.92) which are a key point for prediction of cardiac events. Nevertheless, Model 3 comprising the shape-based features did not contribute to the classification of atherosclerotic plaques. Overall, TPOT shown promising capabilities in terms of finding the best pipeline and tailoring the model using CCTA-based radiomic datasets.
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18
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Liao J, Huang L, Qu M, Chen B, Wang G. Artificial Intelligence in Coronary CT Angiography: Current Status and Future Prospects. Front Cardiovasc Med 2022; 9:896366. [PMID: 35783834 PMCID: PMC9247240 DOI: 10.3389/fcvm.2022.896366] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/18/2022] [Indexed: 12/28/2022] Open
Abstract
Coronary heart disease (CHD) is the leading cause of mortality in the world. Early detection and treatment of CHD are crucial. Currently, coronary CT angiography (CCTA) has been the prior choice for CHD screening and diagnosis, but it cannot meet the clinical needs in terms of examination quality, the accuracy of reporting, and the accuracy of prognosis analysis. In recent years, artificial intelligence (AI) has developed rapidly in the field of medicine; it played a key role in auxiliary diagnosis, disease mechanism analysis, and prognosis assessment, including a series of studies related to CHD. In this article, the application and research status of AI in CCTA were summarized and the prospects of this field were also described.
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Affiliation(s)
- Jiahui Liao
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
- School of Biomedical Engineering, Guangzhou Xinhua University, Guangzhou, China
| | - Lanfang Huang
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Meizi Qu
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Binghui Chen
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
- *Correspondence: Binghui Chen
| | - Guojie Wang
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
- Guojie Wang
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19
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Lu H, Yao Y, Wang L, Yan J, Tu S, Xie Y, He W. Research Progress of Machine Learning and Deep Learning in Intelligent Diagnosis of the Coronary Atherosclerotic Heart Disease. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3016532. [PMID: 35516452 PMCID: PMC9064517 DOI: 10.1155/2022/3016532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 02/27/2022] [Accepted: 03/04/2022] [Indexed: 11/17/2022]
Abstract
The coronary atherosclerotic heart disease is a common cardiovascular disease with high morbidity, disability, and societal burden. Early, precise, and comprehensive diagnosis of the coronary atherosclerotic heart disease is of great significance. The rise of artificial intelligence technologies, represented by machine learning and deep learning, provides new methods to address the above issues. In recent years, artificial intelligence has achieved an extraordinary progress in multiple aspects of coronary atherosclerotic heart disease diagnosis, including the construction of intelligent diagnostic models based on artificial intelligence algorithms, applications of artificial intelligence algorithms in coronary angiography, coronary CT angiography, intravascular imaging, cardiac magnetic resonance, and functional parameters. This paper presents a comprehensive review of the technical background and current state of research on the application of artificial intelligence in the diagnosis of the coronary atherosclerotic heart disease and analyzes recent challenges and perspectives in this field.
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Affiliation(s)
- Haoxuan Lu
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
| | - Yudong Yao
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
| | - Li Wang
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
| | - Jianing Yan
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
| | - Shuangshuang Tu
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
| | - Yanqing Xie
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
| | - Wenming He
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
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20
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Pontone G, Rossi A, Guglielmo M, Dweck MR, Gaemperli O, Nieman K, Pugliese F, Maurovich-Horvat P, Gimelli A, Cosyns B, Achenbach S. Clinical applications of cardiac computed tomography: a consensus paper of the European Association of Cardiovascular Imaging-part II. Eur Heart J Cardiovasc Imaging 2022; 23:e136-e161. [PMID: 35175348 PMCID: PMC8944330 DOI: 10.1093/ehjci/jeab292] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 12/28/2021] [Indexed: 11/12/2022] Open
Abstract
Cardiac computed tomography (CT) was initially developed as a non-invasive diagnostic tool to detect and quantify coronary stenosis. Thanks to the rapid technological development, cardiac CT has become a comprehensive imaging modality which offers anatomical and functional information to guide patient management. This is the second of two complementary documents endorsed by the European Association of Cardiovascular Imaging aiming to give updated indications on the appropriate use of cardiac CT in different clinical scenarios. In this article, emerging CT technologies and biomarkers, such as CT-derived fractional flow reserve, perfusion imaging, and pericoronary adipose tissue attenuation, are described. In addition, the role of cardiac CT in the evaluation of atherosclerotic plaque, cardiomyopathies, structural heart disease, and congenital heart disease is revised.
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Affiliation(s)
- Gianluca Pontone
- Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
| | - Alexia Rossi
- Department of Nuclear Medicine, University Hospital, Zurich, Switzerland
- Center for Molecular Cardiology, University of Zurich, Zurich, Switzerland
| | - Marco Guglielmo
- Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
| | - Marc R Dweck
- Centre for Cardiovascular Sciences, University of Edinburgh, Edinburgh, UK
| | | | - Koen Nieman
- Department of Radiology and Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Francesca Pugliese
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Pal Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Alessia Gimelli
- Fondazione CNR/Regione Toscana “Gabriele Monasterio”, Pisa, Italy
| | - Bernard Cosyns
- Department of Cardiology, CHVZ (Centrum voor Hart en Vaatziekten), ICMI (In Vivo Cellular and Molecular Imaging) Laboratory, Universitair ziekenhuis Brussel, Brussel, Belgium
| | - Stephan Achenbach
- Department of Cardiology, Friedrich-Alexander-University of Erlangen, Erlangen, Germany
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21
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Covas P, De Guzman E, Barrows I, Bradley AJ, Choi BG, Krepp JM, Lewis JF, Katz R, Tracy CM, Zeman RK, Earls JP, Choi AD. Artificial Intelligence Advancements in the Cardiovascular Imaging of Coronary Atherosclerosis. Front Cardiovasc Med 2022; 9:839400. [PMID: 35387447 PMCID: PMC8977643 DOI: 10.3389/fcvm.2022.839400] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 02/03/2022] [Indexed: 12/03/2022] Open
Abstract
Coronary artery disease is a leading cause of death worldwide. There has been a myriad of advancements in the field of cardiovascular imaging to aid in diagnosis, treatment, and prevention of coronary artery disease. The application of artificial intelligence in medicine, particularly in cardiovascular medicine has erupted in the past decade. This article serves to highlight the highest yield articles within cardiovascular imaging with an emphasis on coronary CT angiography methods for % stenosis evaluation and atherosclerosis quantification for the general cardiologist. The paper finally discusses the evolving paradigm of implementation of artificial intelligence in real world practice.
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Affiliation(s)
- Pedro Covas
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Eison De Guzman
- Department of Internal Medicine, The George Washington University School of Medicine, Washington, DC, United States
| | - Ian Barrows
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Andrew J. Bradley
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Brian G. Choi
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
- Department of Radiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Joseph M. Krepp
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Jannet F. Lewis
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Richard Katz
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Cynthia M. Tracy
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Robert K. Zeman
- Department of Radiology, The George Washington University School of Medicine, Washington, DC, United States
| | - James P. Earls
- Department of Radiology, The George Washington University School of Medicine, Washington, DC, United States
| | - Andrew D. Choi
- Division of Cardiology, The George Washington University School of Medicine, Washington, DC, United States
- Department of Radiology, The George Washington University School of Medicine, Washington, DC, United States
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22
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Paixão GMDM, Santos BC, Araujo RMD, Ribeiro MH, Moraes JLD, Ribeiro AL. Machine Learning na Medicina: Revisão e Aplicabilidade. Arq Bras Cardiol 2022; 118:95-102. [PMID: 35195215 PMCID: PMC8959062 DOI: 10.36660/abc.20200596] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 12/02/2020] [Indexed: 01/04/2023] Open
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23
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Mostafa A, Ghanem AM, El-Shatoury M, Basha T. Improved Centerline Extraction in Fully Automated Coronary Ostium Localization and Centerline Extraction Framework using Deep Learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3846-3849. [PMID: 34892073 DOI: 10.1109/embc46164.2021.9629655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Coronary artery extraction in cardiac CT angiography (CCTA) image volume is a necessary step for any quantitative assessment of stenoses and atherosclerotic plaque. In this work, we propose a fully automated workflow that depends on convolutional networks to extract the centerlines of the coronary arteries from CCTA image volumes, starting from identifying the ostium points and then tracking the vessel till its end based on its radius and direction. First, a regression U-Net is employed to identify the ostium points in the image volume, then these points are fed to an orientation and radius predictor CNN model to track and extract each artery till its end point. Our results show that an average of 96% of the ostium points were identified and located within less than 5mm from their true location. The coronary arteries centerlines extraction was performed with high accuracy and lower number of training parameters making it suitable for real clinical applications and continuous learning.
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24
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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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25
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Lauzier PT, Avram R, Dey D, Slomka P, Afilalo J, Chow BJ. The evolving role of artificial intelligence in cardiac image analysis. Can J Cardiol 2021; 38:214-224. [PMID: 34619340 DOI: 10.1016/j.cjca.2021.09.030] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/28/2021] [Accepted: 09/28/2021] [Indexed: 12/13/2022] Open
Abstract
Research in artificial intelligence (AI) have progressed over the last decade. The field of cardiac imaging has seen significant developments using newly developed deep learning methods for automated image analysis and AI tools for disease detection and prognostication. This review article is aimed at those without special background in AI. We review AI concepts and we survey the growing contemporary applications of AI for image analysis in echocardiography, nuclear cardiology, cardiac computed tomography, cardiac magnetic resonance, and invasive angiography.
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Affiliation(s)
| | - Robert Avram
- University of Ottawa Heart Institute, Ottawa, ON, Canada; Montreal Heart Institute, Montreal, QC, Canada
| | - Damini Dey
- Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr Slomka
- Cedars-Sinai Medical Center, Los Angeles, CA, USA
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26
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Li Y, Jia K, Jia Y, Yang Y, Yao Y, Chen M, Peng Y. Understanding the predictive value and methods of risk assessment based on coronary computed tomographic angiography in populations with coronary artery disease: a review. PRECISION CLINICAL MEDICINE 2021; 4:192-203. [PMID: 35693218 PMCID: PMC8982592 DOI: 10.1093/pcmedi/pbab018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 07/23/2021] [Accepted: 07/23/2021] [Indexed: 02/05/2023] Open
Abstract
Risk assessment in coronary artery disease plays an essential role in the early identification of high-risk patients. However, conventional invasive imaging procedures all require long intraprocedural times and high costs. The rapid development of coronary computed tomographic angiography (CCTA) and related image processing technology has facilitated the formulation of noninvasive approaches to perform comprehensive evaluations. Evidence has shown that CCTA has outstanding performance in identifying the degree of stenosis, plaque features, and functional reserve. Moreover, advancements in radiomics and machine learning allow more comprehensive interpretations of CCTA images. This paper reviews conventional as well as novel diagnostic and risk assessment tools based on CCTA.
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Affiliation(s)
- Yiming Li
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Kaiyu Jia
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yuheng Jia
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yong Yang
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yijun Yao
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Mao Chen
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yong Peng
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
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27
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Oikonomou EK, Siddique M, Antoniades C. Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease. Cardiovasc Res 2021; 116:2040-2054. [PMID: 32090243 DOI: 10.1093/cvr/cvaa021] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 11/29/2019] [Accepted: 01/23/2020] [Indexed: 12/23/2022] Open
Abstract
ABSTRACT Rapid technological advances in non-invasive imaging, coupled with the availability of large data sets and the expansion of computational models and power, have revolutionized the role of imaging in medicine. Non-invasive imaging is the pillar of modern cardiovascular diagnostics, with modalities such as cardiac computed tomography (CT) now recognized as first-line options for cardiovascular risk stratification and the assessment of stable or even unstable patients. To date, cardiovascular imaging has lagged behind other fields, such as oncology, in the clinical translational of artificial intelligence (AI)-based approaches. We hereby review the current status of AI in non-invasive cardiovascular imaging, using cardiac CT as a running example of how novel machine learning (ML)-based radiomic approaches can improve clinical care. The integration of ML, deep learning, and radiomic methods has revealed direct links between tissue imaging phenotyping and tissue biology, with important clinical implications. More specifically, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging and CT, as well as lessons that can be learned from other areas. Finally, we propose a scientific framework in order to ensure the clinical and scientific validity of future studies in this novel, yet highly promising field. Still in its infancy, AI-based cardiovascular imaging has a lot to offer to both the patients and their doctors as it catalyzes the transition towards a more precise phenotyping of cardiovascular disease.
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Affiliation(s)
- Evangelos K Oikonomou
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.,Department of Internal Medicine, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT, USA
| | - Musib Siddique
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.,Caristo Diagnostics Ltd., Oxford, UK
| | - Charalambos Antoniades
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.,Oxford Centre of Research Excellence, British Heart Foundation, Oxford, UK.,Oxford Biomedical Research Centre, National Institute of Health Research, Oxford, UK
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28
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Zhang ZZ, Guo Y, Hou Y. Artificial intelligence in coronary computed tomography angiography. Artif Intell Med Imaging 2021; 2:73-85. [DOI: 10.35711/aimi.v2.i3.73] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/20/2021] [Accepted: 07/02/2021] [Indexed: 02/06/2023] Open
Abstract
Coronary computed tomography angiography (CCTA) is recommended as a frontline diagnostic tool in the non-invasive assessment of patients with suspected coronary artery disease (CAD) and cardiovascular risk stratification. To date, artificial intelligence (AI) techniques have brought major changes in the way that we make individualized decisions for patients with CAD. Applications of AI in CCTA have produced improvements in many aspects, including assessment of stenosis degree, determination of plaque type, identification of high-risk plaque, quantification of coronary artery calcium score, diagnosis of myocardial infarction, estimation of computed tomography-derived fractional flow reserve, left ventricular myocardium analysis, perivascular adipose tissue analysis, prognosis of CAD, and so on. The purpose of this review is to provide a comprehensive overview of current status of AI in CCTA.
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Affiliation(s)
- Zhe-Zhe Zhang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
| | - Yan Guo
- GE Healthcare, Beijing 100176, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
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29
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Cau R, Flanders A, Mannelli L, Politi C, Faa G, Suri JS, Saba L. Artificial intelligence in computed tomography plaque characterization: A review. Eur J Radiol 2021; 140:109767. [PMID: 34000598 DOI: 10.1016/j.ejrad.2021.109767] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 04/23/2021] [Accepted: 04/29/2021] [Indexed: 12/12/2022]
Abstract
Cardiovascular disease (CVD) is associated with high mortality around the world. Prevention and early diagnosis are key targets in reducing the socio-economic burden of CVD. Artificial intelligence (AI) has experienced a steady growth due to technological innovations that have to lead to constant development. Several AI algorithms have been applied to various aspects of CVD in order to improve the quality of image acquisition and reconstruction and, at the same time adding information derived from the images to create strong predictive models. In computed tomography angiography (CTA), AI can offer solutions for several parts of plaque analysis, including an automatic assessment of the degree of stenosis and characterization of plaque morphology. A growing body of evidence demonstrates a correlation between some type of plaques, so-called high-risk plaque or vulnerable plaque, and cardiovascular events, independent of the degree of stenosis. The radiologist must apprehend and participate actively in developing and implementing AI in current clinical practice. In this current overview on the existing AI literature, we describe the strengths, limitations, recent applications, and promising developments of employing AI to plaque characterization with CT.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato (Cagliari), 09045, Italy
| | - Adam Flanders
- Thomas Jefferson University, 1020 Walnut Street, Philadelphia, PA, United States
| | | | - Carola Politi
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato (Cagliari), 09045, Italy
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria (AOU) di Cagliari, University Hospital San Giovanni di Dio, Cagliari, Italy; Proteomic Laboratory - European Center for Brain Research, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Jasjit S Suri
- Stroke Diagnosis and Monitoring Division ATHEROPOINT LLC, Roseville, CA USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato (Cagliari), 09045, Italy.
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30
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Lin A, Kolossváry M, Motwani M, Išgum I, Maurovich-Horvat P, Slomka PJ, Dey D. Artificial Intelligence in Cardiovascular Imaging for Risk Stratification in Coronary Artery Disease. Radiol Cardiothorac Imaging 2021; 3:e200512. [PMID: 33778661 DOI: 10.1148/ryct.2021200512] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 12/24/2020] [Accepted: 01/04/2021] [Indexed: 12/12/2022]
Abstract
Artificial intelligence (AI) describes the use of computational techniques to perform tasks that normally require human cognition. Machine learning and deep learning are subfields of AI that are increasingly being applied to cardiovascular imaging for risk stratification. Deep learning algorithms can accurately quantify prognostic biomarkers from image data. Additionally, conventional or AI-based imaging parameters can be combined with clinical data using machine learning models for individualized risk prediction. The aim of this review is to provide a comprehensive review of state-of-the-art AI applications across various noninvasive imaging modalities (coronary artery calcium scoring CT, coronary CT angiography, and nuclear myocardial perfusion imaging) for the quantification of cardiovascular risk in coronary artery disease. © RSNA, 2021.
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Affiliation(s)
- Andrew Lin
- Biomedical Imaging Research Institute (A.L., D.D.) and Artificial Intelligence in Medicine Program (P.J.S.), Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA 90048; MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center (M.K., P.M.H.), and Medical Imaging Centre (P.M.H.), Semmelweis University, Budapest, Hungary; Manchester Heart Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, England (M.M.); and Department of Biomedical Engineering and Physics, Department of Radiology and Nuclear Medicine, and Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, the Netherlands (I.I.)
| | - Márton Kolossváry
- Biomedical Imaging Research Institute (A.L., D.D.) and Artificial Intelligence in Medicine Program (P.J.S.), Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA 90048; MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center (M.K., P.M.H.), and Medical Imaging Centre (P.M.H.), Semmelweis University, Budapest, Hungary; Manchester Heart Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, England (M.M.); and Department of Biomedical Engineering and Physics, Department of Radiology and Nuclear Medicine, and Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, the Netherlands (I.I.)
| | - Manish Motwani
- Biomedical Imaging Research Institute (A.L., D.D.) and Artificial Intelligence in Medicine Program (P.J.S.), Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA 90048; MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center (M.K., P.M.H.), and Medical Imaging Centre (P.M.H.), Semmelweis University, Budapest, Hungary; Manchester Heart Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, England (M.M.); and Department of Biomedical Engineering and Physics, Department of Radiology and Nuclear Medicine, and Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, the Netherlands (I.I.)
| | - Ivana Išgum
- Biomedical Imaging Research Institute (A.L., D.D.) and Artificial Intelligence in Medicine Program (P.J.S.), Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA 90048; MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center (M.K., P.M.H.), and Medical Imaging Centre (P.M.H.), Semmelweis University, Budapest, Hungary; Manchester Heart Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, England (M.M.); and Department of Biomedical Engineering and Physics, Department of Radiology and Nuclear Medicine, and Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, the Netherlands (I.I.)
| | - Pál Maurovich-Horvat
- Biomedical Imaging Research Institute (A.L., D.D.) and Artificial Intelligence in Medicine Program (P.J.S.), Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA 90048; MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center (M.K., P.M.H.), and Medical Imaging Centre (P.M.H.), Semmelweis University, Budapest, Hungary; Manchester Heart Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, England (M.M.); and Department of Biomedical Engineering and Physics, Department of Radiology and Nuclear Medicine, and Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, the Netherlands (I.I.)
| | - Piotr J Slomka
- Biomedical Imaging Research Institute (A.L., D.D.) and Artificial Intelligence in Medicine Program (P.J.S.), Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA 90048; MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center (M.K., P.M.H.), and Medical Imaging Centre (P.M.H.), Semmelweis University, Budapest, Hungary; Manchester Heart Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, England (M.M.); and Department of Biomedical Engineering and Physics, Department of Radiology and Nuclear Medicine, and Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, the Netherlands (I.I.)
| | - Damini Dey
- Biomedical Imaging Research Institute (A.L., D.D.) and Artificial Intelligence in Medicine Program (P.J.S.), Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA 90048; MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center (M.K., P.M.H.), and Medical Imaging Centre (P.M.H.), Semmelweis University, Budapest, Hungary; Manchester Heart Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, England (M.M.); and Department of Biomedical Engineering and Physics, Department of Radiology and Nuclear Medicine, and Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, the Netherlands (I.I.)
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31
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Liu H, Wingert A, Wang J, Zhang J, Wang X, Sun J, Chen F, Khalid SG, Jiang J, Zheng D. Extraction of Coronary Atherosclerotic Plaques From Computed Tomography Imaging: A Review of Recent Methods. Front Cardiovasc Med 2021; 8:597568. [PMID: 33644127 PMCID: PMC7903898 DOI: 10.3389/fcvm.2021.597568] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 01/18/2021] [Indexed: 12/21/2022] Open
Abstract
Background: Atherosclerotic plaques are the major cause of coronary artery disease (CAD). Currently, computed tomography (CT) is the most commonly applied imaging technique in the diagnosis of CAD. However, the accurate extraction of coronary plaque geometry from CT images is still challenging. Summary of Review: In this review, we focused on the methods in recent studies on the CT-based coronary plaque extraction. According to the dimension of plaque extraction method, the studies were categorized into two-dimensional (2D) and three-dimensional (3D) ones. In each category, the studies were analyzed in terms of data, methods, and evaluation. We summarized the merits and limitations of current methods, as well as the future directions for efficient and accurate extraction of coronary plaques using CT imaging. Conclusion: The methodological innovations are important for more accurate CT-based assessment of coronary plaques in clinical applications. The large-scale studies, de-blooming algorithms, more standardized datasets, and more detailed classification of non-calcified plaques could improve the accuracy of coronary plaque extraction from CT images. More multidimensional geometric parameters can be derived from the 3D geometry of coronary plaques. Additionally, machine learning and automatic 3D reconstruction could improve the efficiency of coronary plaque extraction in future studies.
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Affiliation(s)
- Haipeng Liu
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom.,Faculty of Health, Education, Medicine, and Social Care, Anglia Ruskin University, Chelmsford, United Kingdom
| | - Aleksandra Wingert
- Faculty of Health, Education, Medicine, and Social Care, Anglia Ruskin University, Chelmsford, United Kingdom
| | - Jian'an Wang
- Department of Cardiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Jucheng Zhang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Xinhong Wang
- Department of Radiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Jianzhong Sun
- Department of Radiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Syed Ghufran Khalid
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
| | - Jun Jiang
- Department of Cardiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
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32
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Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography. J Thorac Imaging 2021; 35 Suppl 1:S49-S57. [PMID: 32168163 DOI: 10.1097/rti.0000000000000491] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
PURPOSE The purpose of this study was to evaluate the accuracy of a novel fully automated deep learning (DL) algorithm implementing a recurrent neural network (RNN) with long short-term memory (LSTM) for the detection of coronary artery calcium (CAC) from coronary computed tomography angiography (CCTA) data. MATERIALS AND METHODS Under an IRB waiver and in HIPAA compliance, a total of 194 patients who had undergone CCTA were retrospectively included. Two observers independently evaluated the image quality and recorded the presence of CAC in the right (RCA), the combination of left main and left anterior descending (LM-LAD), and left circumflex (LCx) coronary arteries. Noncontrast CACS scans were allowed to be used in cases of uncertainty. Heart and coronary artery centerline detection and labeling were automatically performed. Presence of CAC was assessed by a RNN-LSTM. The algorithm's overall and per-vessel sensitivity, specificity, and diagnostic accuracy were calculated. RESULTS CAC was absent in 84 and present in 110 patients. As regards CCTA, the median subjective image quality, signal-to-noise ratio, and contrast-to-noise ratio were 3.0, 13.0, and 11.4. A total of 565 vessels were evaluated. On a per-vessel basis, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 93.1% (confidence interval [CI], 84.3%-96.7%), 82.76% (CI, 74.6%-89.4%), and 86.7% (CI, 76.8%-87.9%), respectively, for the RCA, 93.1% (CI, 86.4%-97.7%), 95.5% (CI, 88.77%-98.75%), and 94.2% (CI. 90.2%-94.6%), respectively, for the LM-LAD, and 89.9% (CI, 80.2%-95.8%), 90.0% (CI, 83.2%-94.7%), and 89.9% (CI, 85.0%-94.1%), respectively, for the LCx. The overall sensitivity, specificity, and diagnostic accuracy were 92.1% (CI, 92.1%-95.2%), 88.9% (CI. 84.9%-92.1%), and 90.3% (CI, 88.0%-90.0%), respectively. When accounting for image quality, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 76.2%, 87.5%, and 82.2%, respectively, for poor-quality data sets and 93.3%, 89.2% and 90.9%, respectively, when data sets rated adequate or higher were combined. CONCLUSION The proposed RNN-LSTM demonstrated high diagnostic accuracy for the detection of CAC from CCTA.
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Abstract
PURPOSE OF REVIEW Artificial intelligence is a broad set of sophisticated computer-based statistical tools that have become widely available. Cardiovascular medicine with its large data repositories, need for operational efficiency and growing focus on precision care is set to be transformed by artificial intelligence. Applications range from new pathophysiologic discoveries to decision support for individual patient care to optimization of system-wide logistical processes. RECENT FINDINGS Machine learning is the dominant form of artificial intelligence wherein complex statistical algorithms 'learn' by deducing patterns in datasets. Supervised machine learning uses classified large data to train an algorithm to accurately predict the outcome, whereas in unsupervised machine learning, the algorithm uncovers mathematical relationships within unclassified data. Artificial multilayered neural networks or deep learning is one of the most successful tools. Artificial intelligence has demonstrated superior efficacy in disease phenomapping, early warning systems, risk prediction, automated processing and interpretation of imaging, and increasing operational efficiency. SUMMARY Artificial intelligence demonstrates the ability to learn through assimilation of large datasets to unravel complex relationships, discover prior unfound pathophysiological states and develop predictive models. Artificial intelligence needs widespread exploration and adoption for large-scale implementation in cardiovascular practice.
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Affiliation(s)
- Sagar Ranka
- Department of Cardiovascular Medicine, The University of Kansas, Health System, Kansas City, Kansas, USA
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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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Al'Aref SJ, Anchouche K, Singh G, Slomka PJ, Kolli KK, Kumar A, Pandey M, Maliakal G, van Rosendael AR, Beecy AN, Berman DS, Leipsic J, Nieman K, Andreini D, Pontone G, Schoepf UJ, Shaw LJ, Chang HJ, Narula J, Bax JJ, Guan Y, Min JK. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J 2020; 40:1975-1986. [PMID: 30060039 DOI: 10.1093/eurheartj/ehy404] [Citation(s) in RCA: 233] [Impact Index Per Article: 58.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 05/29/2018] [Accepted: 07/06/2018] [Indexed: 12/19/2022] Open
Abstract
Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field.
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Affiliation(s)
- Subhi J Al'Aref
- Department of Radiology, NewYork-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Khalil Anchouche
- Department of Radiology, NewYork-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Gurpreet Singh
- Department of Radiology, NewYork-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Piotr J Slomka
- Departments of Imaging and Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Kranthi K Kolli
- Department of Radiology, NewYork-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Amit Kumar
- Department of Radiology, NewYork-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Mohit Pandey
- Department of Radiology, NewYork-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Gabriel Maliakal
- Department of Radiology, NewYork-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Alexander R van Rosendael
- Department of Radiology, NewYork-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Ashley N Beecy
- Department of Radiology, NewYork-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Daniel S Berman
- Departments of Imaging and Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jonathan Leipsic
- Departments of Medicine and Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Koen Nieman
- Departments of Cardiology and Radiology, Stanford University School of Medicine and Cardiovascular Institute, Stanford, CA, USA
| | | | | | - U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science and Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC, USA
| | - Leslee J Shaw
- Department of Radiology, NewYork-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Hyuk-Jae Chang
- Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea
| | - Jagat Narula
- Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jeroen J Bax
- Department of Cardiology, Heart Lung Center, Leiden University Medical Center, Leiden, The Netherlands
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - James K Min
- Department of Radiology, NewYork-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
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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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The correlation of deep learning-based CAD-RADS evaluated by coronary computed tomography angiography with breast arterial calcification on mammography. Sci Rep 2020; 10:11532. [PMID: 32661231 PMCID: PMC7359346 DOI: 10.1038/s41598-020-68378-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 06/18/2020] [Indexed: 12/16/2022] Open
Abstract
This study sought to evaluate the association of breast arterial calcification (BAC) on breast screening mammography with the Coronary Artery Disease-Reporting and Data System (CAD-RADS) based on Deep Learning-coronary computed tomography angiography (CCTA). This prospective single institution study included asymptomatic women over 40 who underwent CCTA and breast cancer screening mammography between July 2018 and April 2019. CAD-RADS was scored based on Deep Learning (DL). Mammograms were assessed visually for the presence of BAC. A total of 213 patients were included in the analysis. In comparison to the low CAD-RADS (CAD-RADS < 3) group, the high CAD-RADS (CAD-RADS ≥ 3) group, more often had a history of hypertension (P = 0.036), diabetes (P = 0.017), and chronic kidney disease (P = 0.006). They also had a significantly higher level of LDL-C (P = 0.024), while HDL-C was lower than in the low CAD-RADS group (P = 0.003). BAC was also significantly higher in the high CAD-RADS group (P = 0.002). In multivariate analysis, the presence of BAC [odd ratio (OR) 10.22, 95% CI 2.86–36.49, P < 0.001] maintained a significant associations with CAD-RADS after adjustment by meaningful variable. The same tendency was also found after adjustment by all covariates. There was a significant correlation between the severities of CAD detected by DL based CCTA and BAC in women undergoing breast screening mammography. BAC may be used as an additional diagnostic tool to predict the severity of CAD in this population.
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Ischemia and outcome prediction by cardiac CT based machine learning. Int J Cardiovasc Imaging 2020; 36:2429-2439. [DOI: 10.1007/s10554-020-01929-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 06/26/2020] [Indexed: 12/30/2022]
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Zhao FJ, Fan SQ, Ren JF, von Deneen KM, He XW, Chen XL. Machine learning for diagnosis of coronary artery disease in computed tomography angiography: A survey. Artif Intell Med Imaging 2020; 1:31-39. [DOI: 10.35711/aimi.v1.i1.31] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 06/12/2020] [Accepted: 06/16/2020] [Indexed: 02/06/2023] Open
Abstract
Coronary artery disease (CAD) has become a major illness endangering human health. It mainly manifests as atherosclerotic plaques, especially vulnerable plaques without obvious symptoms in the early stage. Once a rupture occurs, it will lead to severe coronary stenosis, which in turn may trigger a major adverse cardiovascular event. Computed tomography angiography (CTA) has become a standard diagnostic tool for early screening of coronary plaque and stenosis due to its advantages in high resolution, noninvasiveness, and three-dimensional imaging. However, manual examination of CTA images by radiologists has been proven to be tedious and time-consuming, which might also lead to intra- and interobserver errors. Nowadays, many machine learning algorithms have enabled the (semi-)automatic diagnosis of CAD by extracting quantitative features from CTA images. This paper provides a survey of these machine learning algorithms for the diagnosis of CAD in CTA images, including coronary artery extraction, coronary plaque detection, vulnerable plaque identification, and coronary stenosis assessment. Most included articles were published within this decade and are found in the Web of Science. We wish to give readers a glimpse of the current status, challenges, and perspectives of these machine learning-based analysis methods for automatic CAD diagnosis.
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Affiliation(s)
- Feng-Jun Zhao
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
- Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Si-Qi Fan
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
- Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Jing-Fang Ren
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
- Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Karen M von Deneen
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an 710126, Shaanxi Province, China
| | - Xiao-Wei He
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
- Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Xue-Li Chen
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an 710126, Shaanxi Province, China
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Chen M, Wang X, Hao G, Cheng X, Ma C, Guo N, Hu S, Tao Q, Yao F, Hu C. Diagnostic performance of deep learning-based vascular extraction and stenosis detection technique for coronary artery disease. Br J Radiol 2020; 93:20191028. [PMID: 32101464 DOI: 10.1259/bjr.20191028] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVE To investigate the diagnostic performance of deep learning (DL)-based vascular extraction and stenosis detection technology in assessing coronary artery disease (CAD). METHODS The diagnostic performance of DL technology was evaluated by retrospective analysis of coronary computed tomography angiography in 124 suspected CAD patients, using invasive coronary angiography as reference standard. Lumen diameter stenosis ≥50% was considered obstructive, and the diagnostic performances were evaluated at per-patient, per-vessel and per-segment levels. The diagnostic performances between DL model and reader model were compared by the areas under the receiver operating characteristics curves (AUCs). RESULTS In patient-based analysis, AUC of 0.78 was obtained by DL model to detect obstructive CAD [sensitivity of 94%, specificity of 63%, positive predictive value of 94%, and negative predictive value of 59%], While AUC by reader model was 0.74 (sensitivity of 97%, specificity of 50%, positive predictive value of 93%, negative predictive value of 73%). In vessel-based analysis, the AUCs of DL model and reader model were 0.87 and 0.89 respectively. In segment-based analysis, the AUCs of 0.84 and 0.89 were obtained by DL model and reader model respectively. It took 0.47 min to analyze all segments per patient by DL model, which is significantly less than reader model (29.65 min) (p < 0.001). CONCLUSION The DL technology can accurately and effectively identify obstructive CAD, with less time-consuming, and it could be a reliable diagnostic tool to detect CAD. ADVANCES IN KNOWLEDGE The DL technology has valuable prospect with the diagnostic ability to detect CAD.
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Affiliation(s)
- Meng Chen
- Department of Radiology, The First Affiliated Hospital of Soochow University, NO.899 Pinghai Road, Gusu District, Suzhou, Jiangsu, China.,Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu, China
| | - Ximing Wang
- Department of Radiology, The First Affiliated Hospital of Soochow University, NO.899 Pinghai Road, Gusu District, Suzhou, Jiangsu, China.,Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu, China
| | - Guangyu Hao
- Department of Radiology, The First Affiliated Hospital of Soochow University, NO.899 Pinghai Road, Gusu District, Suzhou, Jiangsu, China.,Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu, China
| | - Xujie Cheng
- Department of Cardiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Chune Ma
- ShuKun (BeiJing) Technology Co., Ltd., Jinhui Bd, Qiyang Rd, Beijing, China
| | - Ning Guo
- ShuKun (BeiJing) Technology Co., Ltd., Jinhui Bd, Qiyang Rd, Beijing, China
| | - Su Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, NO.899 Pinghai Road, Gusu District, Suzhou, Jiangsu, China.,Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu, China
| | - Qing Tao
- Department of Radiology, The First Affiliated Hospital of Soochow University, NO.899 Pinghai Road, Gusu District, Suzhou, Jiangsu, China.,Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu, China
| | - Feirong Yao
- Department of Radiology, The First Affiliated Hospital of Soochow University, NO.899 Pinghai Road, Gusu District, Suzhou, Jiangsu, China.,Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu, China
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, NO.899 Pinghai Road, Gusu District, Suzhou, Jiangsu, China.,Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu, China
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Lim LJ, Tison GH, Delling FN. Artificial Intelligence in Cardiovascular Imaging. Methodist Debakey Cardiovasc J 2020; 16:138-145. [PMID: 32670474 PMCID: PMC7350824 DOI: 10.14797/mdcj-16-2-138] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
The number of cardiovascular imaging studies is growing exponentially, and so is the need to improve clinical workflow efficiency and avoid missed diagnoses. With the availability and use of large datasets, artificial intelligence (AI) has the potential to improve patient care at every stage of the imaging chain. Current literature indicates that in the short-term, AI has the capacity to reduce human error and save time in the clinical workflow through automated segmentation of cardiac structures. In the future, AI may expand the informational value of diagnostic images based on images alone or a combination of images and clinical variables, thus facilitating disease detection, prognosis, and decision making. This review describes the role of AI, specifically machine learning, in multimodality imaging, including echocardiography, nuclear imaging, computed tomography, and cardiac magnetic resonance, and highlights current uses of AI as well as potential challenges to its widespread implementation.
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Affiliation(s)
- Lisa J. Lim
- UNIVERSITY OF CALIFORNIA SAN FRANCISCO, SAN FRANCISCO, CALIFORNIA
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Muscogiuri G, Chiesa M, Trotta M, Gatti M, Palmisano V, Dell'Aversana S, Baessato F, Cavaliere A, Cicala G, Loffreno A, Rizzon G, Guglielmo M, Baggiano A, Fusini L, Saba L, Andreini D, Pepi M, Rabbat MG, Guaricci AI, De Cecco CN, Colombo G, Pontone G. Performance of a deep learning algorithm for the evaluation of CAD-RADS classification with CCTA. Atherosclerosis 2019; 294:25-32. [PMID: 31945615 DOI: 10.1016/j.atherosclerosis.2019.12.001] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 12/01/2019] [Accepted: 12/06/2019] [Indexed: 12/22/2022]
Abstract
BACKGROUND AND AIMS Artificial intelligence (AI) is increasing its role in diagnosis of patients with suspicious coronary artery disease. The aim of this manuscript is to develop a deep convolutional neural network (CNN) to classify coronary computed tomography angiography (CCTA) in the correct Coronary Artery Disease Reporting and Data System (CAD-RADS) category. METHODS Two hundred eighty eight patients who underwent clinically indicated CCTA were included in this single-center retrospective study. The CCTAs were stratified by CAD-RADS scores by expert readers and considered as reference standard. A deep CNN was designed and tested on the CCTA dataset and compared to on-site reading. The deep CNN analyzed the diagnostic accuracy of the following three Models based on CAD-RADS classification: Model A (CAD-RADS 0 vs CAD-RADS 1-2 vs CAD-RADS 3,4,5), Model 1 (CAD-RADS 0 vs CAD-RADS>0), Model 2 (CAD-RADS 0-2 vs CAD-RADS 3-5). Time of analysis for both physicians and CNN were recorded. RESULTS Model A showed a sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 47%, 74%, 77%, 46% and 60%, respectively. Model 1 showed a sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 66%, 91%, 92%, 63%, 86%, respectively. Conversely, Model 2 demonstrated the following sensitivity, specificity, negative predictive value, positive predictive value and accuracy: 82%, 58%, 74%, 69%, 71%, respectively. Time of analysis was significantly lower using CNN as compared to on-site reading (530.5 ± 179.1 vs 104.3 ± 1.4 sec, p=0.01) CONCLUSIONS: Deep CNN yielded accurate automated classification of patients with CAD-RADS.
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Affiliation(s)
| | | | - Michela Trotta
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Marco Gatti
- Department of Surgical Sciences, Radiology Institute, University of Turin, Turin, Italy
| | - Vitanio Palmisano
- Department of Medical Imaging, University of Cagliari, Monserrato, Italy
| | - Serena Dell'Aversana
- Department of Advanced Biomedical Sciences, University of Naples "Federico II,", Naples, Italy
| | - Francesca Baessato
- Section of Cardiology, Department of Medicine, University of Verona, Verona, Italy
| | - Annachiara Cavaliere
- Department of Medicine, Institute of Radiology, University of Padova, Padua, Italy
| | - Gloria Cicala
- Section of Radiology, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | | | - Giulia Rizzon
- Department of Medicine, Institute of Radiology, University of Padova, Padua, Italy
| | | | | | - Laura Fusini
- Centro Cardiologico Monzino, IRCCS, Milan, Italy
| | - Luca Saba
- Department of Medical Imaging, University of Cagliari, Monserrato, Italy
| | - Daniele Andreini
- Centro Cardiologico Monzino, IRCCS, Milan, Italy; Department of Cardiovascular Sciences and Community Health, University of Milan, Italy
| | - Mauro Pepi
- Centro Cardiologico Monzino, IRCCS, Milan, Italy
| | - Mark G Rabbat
- Loyola University of Chicago, Chicago, IL, USA; Edward Hines Jr. VA Hospital, Hines, IL, USA
| | - Andrea I Guaricci
- Institute of Cardiovascular Disease, Department of Emergency and Organ Transplantation, University Hospital "Policlinico Consorziale" of Bari, Bari, Italy
| | - Carlo N De Cecco
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
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Hampe N, Wolterink JM, van Velzen SGM, Leiner T, Išgum I. Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey. Front Cardiovasc Med 2019; 6:172. [PMID: 32039237 PMCID: PMC6988816 DOI: 10.3389/fcvm.2019.00172] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 11/12/2019] [Indexed: 01/10/2023] Open
Abstract
Cardiac computed tomography (CT) allows rapid visualization of the heart and coronary arteries with high spatial resolution. However, analysis of cardiac CT scans for manifestation of coronary artery disease is time-consuming and challenging. Machine learning (ML) approaches have the potential to address these challenges with high accuracy and consistent performance. In this mini review, we present a survey of the literature on ML-based analysis of coronary artery disease in cardiac CT. We summarize ML methods for detection and characterization of atherosclerotic plaque as well as anatomically and functionally significant coronary artery stenosis.
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Affiliation(s)
- Nils Hampe
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Jelmer M Wolterink
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Sanne G M van Velzen
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands.,Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
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Seetharam K, Shrestha S, Sengupta PP. Artificial Intelligence in Cardiovascular Medicine. CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2019; 21:25. [PMID: 31089906 PMCID: PMC7561035 DOI: 10.1007/s11936-019-0728-1] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
PURPOSE OF REVIEW The ripples of artificial intelligence are being felt in various sectors of human life. Machine learning, a subset of artificial intelligence, extracts information from large databases of information and is gaining traction in various fields of cardiology. In this review, we highlight noteworthy examples of machine learning utilization in echocardiography, nuclear cardiology, computed tomography, and magnetic resonance imaging over the past year. RECENT FINDINGS In the past year, machine learning (ML) has expanded its boundaries in cardiology with several positive results. Some studies have integrated clinical and imaging information to further augment the accuracy of these ML algorithms. All the studies mentioned in this review have clearly demonstrated superior results of ML in relation to conventional approaches for identifying obstructions or predicting major adverse events in reference to conventional approaches. As the influx of data arriving from gradually evolving technologies in health care and wearable devices continues to be more complex, ML may serve as the bridge to transcend the gap between health care and patients in the future. In order to facilitate a seamless transition between both, a few issues must be resolved for a successful implementation of ML in health care.
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Affiliation(s)
- Karthik Seetharam
- WVU Heart & Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | - Sirish Shrestha
- WVU Heart & Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | - Partho P Sengupta
- WVU Heart & Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA.
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Ghanem AM, Hamimi AH, Matta JR, Carass A, Elgarf RM, Gharib AM, Abd-Elmoniem KZ. Automatic Coronary Wall and Atherosclerotic Plaque Segmentation from 3D Coronary CT Angiography. Sci Rep 2019; 9:47. [PMID: 30631101 PMCID: PMC6328572 DOI: 10.1038/s41598-018-37168-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 11/25/2018] [Indexed: 12/11/2022] Open
Abstract
Coronary plaque burden measured by coronary computerized tomography angiography (CCTA), independent of stenosis, is a significant independent predictor of coronary heart disease (CHD) events and mortality. Hence, it is essential to develop comprehensive CCTA plaque quantification beyond existing subjective plaque volume or stenosis scoring methods. The purpose of this study is to develop a framework for automated 3D segmentation of CCTA vessel wall and quantification of atherosclerotic plaque, independent of the amount of stenosis, along with overcoming challenges caused by poor contrast, motion artifacts, severe stenosis, and degradation of image quality. Vesselness, region growing, and two sequential level sets are employed for segmenting the inner and outer wall to prevent artifact-defective segmentation. Lumen and vessel boundaries are joined to create the coronary wall. Curved multiplanar reformation is used to straighten the segmented lumen and wall using lumen centerline. In-vivo evaluation included CCTA stenotic and non-stenotic plaques from 41 asymptomatic subjects with 122 plaques of different characteristics against the individual and consensus of expert readers. Results demonstrate that the framework segmentation performed robustly by providing a reliable working platform for accelerated, objective, and reproducible atherosclerotic plaque characterization beyond subjective assessment of stenosis; can be potentially applicable for monitoring response to therapy.
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Affiliation(s)
- Ahmed M Ghanem
- The Biomedical and Metabolic Imaging Branch, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Ahmed H Hamimi
- The Biomedical and Metabolic Imaging Branch, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Jatin R Matta
- The Biomedical and Metabolic Imaging Branch, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Aaron Carass
- The Image Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Reham M Elgarf
- The Biomedical and Metabolic Imaging Branch, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Ahmed M Gharib
- The Biomedical and Metabolic Imaging Branch, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Khaled Z Abd-Elmoniem
- The Biomedical and Metabolic Imaging Branch, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA.
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Slomka P. Hybrid quantitative imaging: Will it enter clinical practice? J Nucl Cardiol 2018; 25:1387-1389. [PMID: 28390041 DOI: 10.1007/s12350-017-0868-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Accepted: 02/16/2017] [Indexed: 11/26/2022]
Affiliation(s)
- Piotr Slomka
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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Juarez-Orozco LE, Martinez-Manzanera O, Nesterov SV, Kajander S, Knuuti J. The machine learning horizon in cardiac hybrid imaging. Eur J Hybrid Imaging 2018. [DOI: 10.1186/s41824-018-0033-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
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Singh G, Al’Aref SJ, Van Assen M, Kim TS, van Rosendael A, Kolli KK, Dwivedi A, Maliakal G, Pandey M, Wang J, Do V, Gummalla M, De Cecco CN, Min JK. Machine learning in cardiac CT: Basic concepts and contemporary data. J Cardiovasc Comput Tomogr 2018; 12:192-201. [DOI: 10.1016/j.jcct.2018.04.010] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Accepted: 04/27/2018] [Indexed: 01/16/2023]
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50
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Slomka PJ. Machine learning for predicting death and heart attacks from CCTA. J Cardiovasc Comput Tomogr 2018; 12:210-211. [DOI: 10.1016/j.jcct.2018.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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