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Fitian AI, Shieh MC, Gimnich OA, Belousova T, Taylor AA, Ballantyne CM, Bismuth J, Shah DJ, Brunner G. Contrast-Enhanced Magnetic Resonance Imaging Based T1 Mapping and Extracellular Volume Fractions Are Associated with Peripheral Artery Disease. J Cardiovasc Dev Dis 2024; 11:181. [PMID: 38921681 PMCID: PMC11203653 DOI: 10.3390/jcdd11060181] [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: 03/29/2024] [Revised: 05/28/2024] [Accepted: 06/09/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND Extracellular volume fraction (ECV), measured with contrast-enhanced magnetic resonance imaging (CE-MRI), has been utilized to study myocardial fibrosis, but its role in peripheral artery disease (PAD) remains unknown. We hypothesized that T1 mapping and ECV differ between PAD patients and matched controls. METHODS AND RESULTS A total of 37 individuals (18 PAD patients and 19 matched controls) underwent 3.0T CE-MRI. Skeletal calf muscle T1 mapping was performed before and after gadolinium contrast with a motion-corrected modified look-locker inversion recovery (MOLLI) pulse sequence. T1 values were calculated with a three-parameter Levenberg-Marquardt curve fitting algorithm. ECV and T1 maps were quantified in five calf muscle compartments (anterior [AM], lateral [LM], and deep posterior [DM] muscle groups; soleus [SM] and gastrocnemius [GM] muscles). Averaged peak blood pool T1 values were obtained from the posterior and anterior tibialis and peroneal arteries. T1 values and ECV are heterogeneous across calf muscle compartments. Native peak T1 values of the AM, LM, and DM were significantly higher in PAD patients compared to controls (all p < 0.028). ECVs of the AM and SM were significantly higher in PAD patients compared to controls (AM: 26.4% (21.2, 31.6) vs. 17.3% (10.2, 25.1), p = 0.046; SM: 22.7% (19.5, 27.8) vs. 13.8% (10.2, 19.1), p = 0.020). CONCLUSIONS Native peak T1 values across all five calf muscle compartments, and ECV fractions of the anterior muscle group and the soleus muscle were significantly elevated in PAD patients compared with matched controls. Non-invasive T1 mapping and ECV quantification may be of interest for the study of PAD.
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Affiliation(s)
- Asem I. Fitian
- Penn State Heart and Vascular Institute, College of Medicine, Pennsylvania State University, Hershey, PA 17033, USA
| | - Michael C. Shieh
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Olga A. Gimnich
- Penn State Heart and Vascular Institute, College of Medicine, Pennsylvania State University, Hershey, PA 17033, USA
| | - Tatiana Belousova
- Methodist DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX 77030, USA
| | - Addison A. Taylor
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Michael E DeBakey VA Medical Center, Houston, TX 77030, USA
| | - Christie M. Ballantyne
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jean Bismuth
- Division of Vascular Surgery, University of South Florida Health Morsani School of Medicine, Tampa, FL 33620, USA
| | - Dipan J. Shah
- Methodist DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX 77030, USA
| | - Gerd Brunner
- Penn State Heart and Vascular Institute, College of Medicine, Pennsylvania State University, Hershey, PA 17033, USA
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
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2
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Kresoja KP, Unterhuber M, Wachter R, Thiele H, Lurz P. A cardiologist's guide to machine learning in cardiovascular disease prognosis prediction. Basic Res Cardiol 2023; 118:10. [PMID: 36939941 PMCID: PMC10027799 DOI: 10.1007/s00395-023-00982-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/21/2023] [Accepted: 02/26/2023] [Indexed: 03/21/2023]
Abstract
A modern-day physician is faced with a vast abundance of clinical and scientific data, by far surpassing the capabilities of the human mind. Until the last decade, advances in data availability have not been accompanied by analytical approaches. The advent of machine learning (ML) algorithms might improve the interpretation of complex data and should help to translate the near endless amount of data into clinical decision-making. ML has become part of our everyday practice and might even further change modern-day medicine. It is important to acknowledge the role of ML in prognosis prediction of cardiovascular disease. The present review aims on preparing the modern physician and researcher for the challenges that ML might bring, explaining basic concepts but also caveats that might arise when using these methods. Further, a brief overview of current established classical and emerging concepts of ML disease prediction in the fields of omics, imaging and basic science is presented.
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Affiliation(s)
- Karl-Patrik Kresoja
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany
| | - Matthias Unterhuber
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany
| | - Rolf Wachter
- Department of Cardiology, University Hospital Leipzig, Leipzig, Germany
- Clinic for Cardiology and Pneumology, University Medicine Göttingen, Göttingen, Germany
- German Cardiovascular Research Center (DZHK), Partner Site Göttingen, Göttingen, Germany
| | - Holger Thiele
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany.
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany.
| | - Philipp Lurz
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany.
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany.
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3
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Muscogiuri G, Volpato V, Cau R, Chiesa M, Saba L, Guglielmo M, Senatieri A, Chierchia G, Pontone G, Dell’Aversana S, Schoepf UJ, Andrews MG, Basile P, Guaricci AI, Marra P, Muraru D, Badano LP, Sironi S. Application of AI in cardiovascular multimodality imaging. Heliyon 2022; 8:e10872. [PMID: 36267381 PMCID: PMC9576885 DOI: 10.1016/j.heliyon.2022.e10872] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/23/2022] [Accepted: 09/27/2022] [Indexed: 12/16/2022] Open
Abstract
Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging.
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Affiliation(s)
- Giuseppe Muscogiuri
- Department of Radiology, Istituto Auxologico Italiano IRCCS, San Luca Hospital, Italy,School of Medicine, University of Milano-Bicocca, Milan, Italy,Corresponding author.
| | - Valentina Volpato
- Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy,IRCCS Ospedale Galeazzi - Sant'Ambrogio, University Cardiology Department, Milan, Italy
| | - Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari, Polo di Monserrato, Cagliari, Italy
| | | | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari, Polo di Monserrato, Cagliari, Italy
| | - Marco Guglielmo
- Department of Cardiology, Division of Heart and Lungs, Utrecht University, Utrecht University Medical Center, Utrecht, the Netherlands
| | | | | | | | - Serena Dell’Aversana
- Department of Radiology, Ospedale S. Maria Delle Grazie - ASL Napoli 2 Nord, Pozzuoli, Italy
| | - U. Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr., Charleston, SC, USA
| | - Mason G. Andrews
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr., Charleston, SC, USA
| | - Paolo Basile
- University Cardiology Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Andrea Igoren Guaricci
- University Cardiology Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Paolo Marra
- Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
| | - Denisa Muraru
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Luigi P. Badano
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Sandro Sironi
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
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4
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Zeng D, Zeng C, Zeng Z, Li S, Deng Z, Chen S, Bian Z, Ma J. Basis and current state of computed tomography perfusion imaging: a review. Phys Med Biol 2022; 67. [PMID: 35926503 DOI: 10.1088/1361-6560/ac8717] [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: 11/17/2021] [Accepted: 08/04/2022] [Indexed: 12/30/2022]
Abstract
Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.
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Affiliation(s)
- Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Cuidie Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhixiong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Sui Li
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhen Deng
- Department of Neurology, Nanfang Hospital, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Sijin Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
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5
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Muscogiuri G, Chiesa M, Baggiano A, Spadafora P, De Santis R, Guglielmo M, Scafuri S, Fusini L, Mushtaq S, Conte E, Annoni A, Formenti A, Mancini ME, Ricci F, Ariano FP, Spiritigliozzi L, Babbaro M, Mollace R, Maragna R, Giacari CM, Andreini D, Guaricci AI, Colombo GI, Rabbat MG, Pepi M, Sardanelli F, Pontone G. Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion. Eur J Nucl Med Mol Imaging 2022; 49:3119-3128. [PMID: 35194673 DOI: 10.1007/s00259-022-05732-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 02/12/2022] [Indexed: 12/30/2022]
Abstract
PURPOSE To evaluate the diagnostic accuracy of a deep learning (DL) algorithm predicting hemodynamically significant coronary artery disease (CAD) by using a rest dataset of myocardial computed tomography perfusion (CTP) as compared to invasive evaluation. METHODS One hundred and twelve consecutive symptomatic patients scheduled for clinically indicated invasive coronary angiography (ICA) underwent CCTA plus static stress CTP and ICA with invasive fractional flow reserve (FFR) for stenoses ranging between 30 and 80%. Subsequently, a DL algorithm for the prediction of significant CAD by using the rest dataset (CTP-DLrest) and stress dataset (CTP-DLstress) was developed. The diagnostic accuracy for identification of significant CAD using CCTA, CCTA + CTP stress, CCTA + CTP-DLrest, and CCTA + CTP-DLstress was measured and compared. The time of analysis for CTP stress, CTP-DLrest, and CTP-DLStress was recorded. RESULTS Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and area under the curve (AUC) of CCTA alone and CCTA + CTPStress were 100%, 33%, 100%, 54%, 63%, 67% and 86%, 89%, 89%, 86%, 88%, 87%, respectively. Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and AUC of CCTA + DLrest and CCTA + DLstress were 100%, 72%, 100%, 74%, 84%, 96% and 93%, 83%, 94%, 81%, 88%, 98%, respectively. All CCTA + CTP stress, CCTA + CTP-DLRest, and CCTA + CTP-DLStress significantly improved detection of hemodynamically significant CAD compared to CCTA alone (p < 0.01). Time of CTP-DL was significantly lower as compared to human analysis (39.2 ± 3.2 vs. 379.6 ± 68.0 s, p < 0.001). CONCLUSION Evaluation of myocardial ischemia using a DL approach on rest CTP datasets is feasible and accurate. This approach may be a useful gatekeeper prior to CTP stress..
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Affiliation(s)
| | - Mattia Chiesa
- Centro Cardiologico Monzino, IRCCS, Milan, Italy.,Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, 20133, Milan, Italy
| | | | - Pierino Spadafora
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Rossella De Santis
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | | | | | - Laura Fusini
- Centro Cardiologico Monzino, IRCCS, Milan, Italy
| | | | | | | | | | | | | | | | | | | | | | | | | | - Daniele Andreini
- Centro Cardiologico Monzino, IRCCS, Milan, Italy.,Department of Clinical Sciences and Community Health, Cardiovascular Section, University of Milan, Milan, Italy
| | - Andrea Igoren Guaricci
- Department of Emergency and Organ Transplantation, Institute of Cardiovascular Disease, 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
| | - Mauro Pepi
- Centro Cardiologico Monzino, IRCCS, Milan, Italy
| | - Francesco Sardanelli
- Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, 20133, Milan, Italy.,Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
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6
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Cardiovascular Computed Tomography in Pediatric Congenital Heart Disease: A State of the Art Review. J Cardiovasc Comput Tomogr 2022; 16:467-482. [DOI: 10.1016/j.jcct.2022.04.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 04/27/2022] [Accepted: 04/28/2022] [Indexed: 01/04/2023]
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7
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Deep learning applications in myocardial perfusion imaging, a systematic review and meta-analysis. INFORMATICS IN MEDICINE UNLOCKED 2022; 32:101055. [PMID: 36187893 PMCID: PMC9514037 DOI: 10.1016/j.imu.2022.101055] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/10/2022] [Accepted: 08/12/2022] [Indexed: 11/22/2022] Open
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8
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Wang Y, Liu N, Yang M, Tian Z, Dong H, Lu Y, Zou D. Application and Prospect of Postmortem Imaging Technology in Forensic Cardiac Pathology: A Systemic Review. JOURNAL OF FORENSIC SCIENCE AND MEDICINE 2022. [DOI: 10.4103/jfsm.jfsm_129_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
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9
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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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10
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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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11
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Mohd Faizal AS, Thevarajah TM, Khor SM, Chang SW. A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 207:106190. [PMID: 34077865 DOI: 10.1016/j.cmpb.2021.106190] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 05/10/2021] [Indexed: 06/12/2023]
Abstract
Cardiovascular disease (CVD) is the leading cause of death worldwide and is a global health issue. Traditionally, statistical models are used commonly in the risk prediction and assessment of CVD. However, the adoption of artificial intelligent (AI) approach is rapidly taking hold in the current era of technology to evaluate patient risks and predict the outcome of CVD. In this review, we outline various conventional risk scores and prediction models and do a comparison with the AI approach. The strengths and limitations of both conventional and AI approaches are discussed. Besides that, biomarker discovery related to CVD are also elucidated as the biomarkers can be used in the risk stratification as well as early detection of the disease. Moreover, problems and challenges involved in current CVD studies are explored. Lastly, future prospects of CVD risk prediction and assessment in the multi-modality of big data integrative approaches are proposed.
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Affiliation(s)
- Aizatul Shafiqah Mohd Faizal
- Bioinformatics Programme, Institute of Biological Science, Faculty of Science, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - T Malathi Thevarajah
- Department of Pathology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Sook Mei Khor
- Department of Chemistry, Faculty of Science, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Siow-Wee Chang
- Bioinformatics Programme, Institute of Biological Science, Faculty of Science, Universiti Malaya, Kuala Lumpur 50603, Malaysia.
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12
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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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Clinical Implications of Machine Learning, Artificial Intelligence, and Radiomics in Cardiac Imaging. CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2020. [DOI: 10.1007/s11936-020-00838-6] [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: 10/23/2022]
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14
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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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15
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Kwan AC, McElhinney PA, Tamarappoo BK, Cadet S, Hurtado C, Miller RJH, Han D, Otaki Y, Eisenberg E, Ebinger JE, Slomka PJ, Cheng VY, Berman DS, Dey D. Prediction of revascularization by coronary CT angiography using a machine learning ischemia risk score. Eur Radiol 2020; 31:1227-1235. [PMID: 32880697 DOI: 10.1007/s00330-020-07142-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 05/25/2020] [Accepted: 08/03/2020] [Indexed: 01/05/2023]
Abstract
OBJECTIVES The machine learning ischemia risk score (ML-IRS) is a machine learning-based algorithm designed to identify hemodynamically significant coronary disease using quantitative coronary computed tomography angiography (CCTA). The purpose of this study was to examine whether the ML-IRS can predict revascularization in patients referred for invasive coronary angiography (ICA) after CCTA. METHODS This study was a post hoc analysis of a prospective dual-center registry of sequential patients undergoing CCTA followed by ICA within 3 months, referred from inpatient, outpatient, and emergency department settings (n = 352, age 63 ± 10 years, 68% male). The primary outcome was revascularization by either percutaneous coronary revascularization or coronary artery bypass grafting. Blinded readers performed semi-automated quantitative coronary plaque analysis. The ML-IRS was automatically computed. Relationships between clinical risk factors, coronary plaque features, and ML-IRS with revascularization were examined. RESULTS The study cohort consisted of 352 subjects with 1056 analyzable vessels. The ML-IRS ranged between 0 and 81% with a median of 18.7% (6.4-34.8). Revascularization was performed in 26% of vessels. Vessels receiving revascularization had higher ML-IRS (33.6% (21.1-55.0) versus 13.0% (4.5-29.1), p < 0.0001), as well as higher contrast density difference, and total, non-calcified, calcified, and low-density plaque burden. ML-IRS, when added to a traditional risk model based on clinical data and stenosis to predict revascularization, resulted in increased area under the curve from 0.69 (95% CI: 0.65-0.72) to 0.78 (95% CI: 0.75-0.81) (p < 0.0001), with an overall continuous net reclassification improvement of 0.636 (95% CI: 0.503-0.769; p < 0.0001). CONCLUSIONS ML-IRS from quantitative coronary CT angiography improved the prediction of future revascularization and can potentially identify patients likely to receive revascularization if referred to cardiac catheterization. KEY POINTS • Machine learning ischemia risk from quantitative coronary CT angiography was significantly higher in patients who received revascularization versus those who did not receive revascularization. • The machine learning ischemia risk score was significantly higher in patients with invasive fractional flow ≤ 0.8 versus those with > 0.8. • The machine learning ischemia risk score improved the prediction of future revascularization significantly when added to a standard prediction model including stenosis.
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Affiliation(s)
- Alan C Kwan
- Departments of Imaging, Medicine, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA, 90048, USA
| | - Priscilla A McElhinney
- Departments of Imaging, Medicine, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA, 90048, USA
| | - Balaji K Tamarappoo
- Departments of Imaging, Medicine, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA, 90048, USA
| | - Sebastien Cadet
- Departments of Imaging, Medicine, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA, 90048, USA
| | - Cecilia Hurtado
- Departments of Imaging, Medicine, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA, 90048, USA
| | - Robert J H Miller
- Departments of Imaging, Medicine, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA, 90048, USA.,Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Donghee Han
- Departments of Imaging, Medicine, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA, 90048, USA
| | - Yuka Otaki
- Departments of Imaging, Medicine, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA, 90048, USA
| | - Evann Eisenberg
- Departments of Imaging, Medicine, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA, 90048, USA
| | - Joseph E Ebinger
- Departments of Imaging, Medicine, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA, 90048, USA
| | - Piotr J Slomka
- Departments of Imaging, Medicine, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA, 90048, USA
| | - Victor Y Cheng
- Department of Cardiology and Cardiovascular Imaging, Minneapolis Heart Institute, Minneapolis, MN, USA.,Oklahoma Heart Institute, Tulsa, OK, USA
| | - Daniel S Berman
- Departments of Imaging, Medicine, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA, 90048, USA
| | - Damini Dey
- Departments of Imaging, Medicine, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA, 90048, USA.
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16
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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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17
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He X, Guo BJ, Lei Y, Wang T, Fu Y, Curran WJ, Zhang LJ, Liu T, Yang X. Automatic segmentation and quantification of epicardial adipose tissue from coronary computed tomography angiography. Phys Med Biol 2020; 65:095012. [PMID: 32182595 DOI: 10.1088/1361-6560/ab8077] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Epicardial adipose tissue (EAT) is a visceral fat deposit, that's known for its association with factors, such as obesity, diabetes mellitus, age, and hypertension. Segmentation of the EAT in a fast and reproducible way is important for the interpretation of its role as an independent risk marker intricate. However, EAT has a variable distribution, and various diseases may affect the volume of the EAT, which can increase the complexity of the already time-consuming manual segmentation work. We propose a 3D deep attention U-Net method to automatically segment the EAT from coronary computed tomography angiography (CCTA). Five-fold cross-validation and hold-out experiments were used to evaluate the proposed method through a retrospective investigation of 200 patients. The automatically segmented EAT volume was compared with physician-approved clinical contours. Quantitative metrics used were the Dice similarity coefficient (DSC), sensitivity, specificity, Jaccard index (JAC), Hausdorff distance (HD), mean surface distance (MSD), residual mean square distance (RMSD), and the center of mass distance (CMD). For cross-validation, the median DSC, sensitivity, and specificity were 92.7%, 91.1%, and 95.1%, respectively, with JAC, HD, CMD, MSD, and RMSD are 82.9% ± 8.8%, 3.77 ± 1.86 mm, 1.98 ± 1.50 mm, 0.37 ± 0.24 mm, and 0.65 ± 0.37 mm, respectively. For the hold-out test, the accuracy of the proposed method remained high. We developed a novel deep learning-based approach for the automated segmentation of the EAT on CCTA images. We demonstrated the high accuracy of the proposed learning-based segmentation method through comparison with ground truth contour of 200 clinical patient cases using 8 quantitative metrics, Pearson correlation, and Bland-Altman analysis. Our automatic EAT segmentation results show the potential of the proposed method to be used in computer-aided diagnosis of coronary artery diseases (CADs) in clinical settings.
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Affiliation(s)
- Xiuxiu He
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America. Co-first author
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18
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Applications of artificial intelligence in multimodality cardiovascular imaging: A state-of-the-art review. Prog Cardiovasc Dis 2020; 63:367-376. [DOI: 10.1016/j.pcad.2020.03.003] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 03/08/2020] [Indexed: 02/06/2023]
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19
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Zreik M, van Hamersvelt RW, Khalili N, Wolterink JM, Voskuil M, Viergever MA, Leiner T, Isgum I. Deep Learning Analysis of Coronary Arteries in Cardiac CT Angiography for Detection of Patients Requiring Invasive Coronary Angiography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1545-1557. [PMID: 31725371 DOI: 10.1109/tmi.2019.2953054] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment. This is typically established through fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA). We present a method for automatic and non-invasive detection of patients requiring ICA, employing deep unsupervised analysis of complete coronary arteries in cardiac CT angiography (CCTA) images. We retrospectively collected CCTA scans of 187 patients, 137 of them underwent invasive FFR measurement in 192 different coronary arteries. These FFR measurements served as a reference standard for the functional significance of the coronary stenosis. The centerlines of the coronary arteries were extracted and used to reconstruct straightened multi-planar reformatted (MPR) volumes. To automatically identify arteries with functionally significant stenosis that require ICA, each MPR volume was encoded into a fixed number of encodings using two disjoint 3D and 1D convolutional autoencoders performing spatial and sequential encodings, respectively. Thereafter, these encodings were employed to classify arteries using a support vector machine classifier. The detection of coronary arteries requiring invasive evaluation, evaluated using repeated cross-validation experiments, resulted in an area under the receiver operating characteristic curve of 0.81 ± 0.02 on the artery-level, and 0.87 ± 0.02 on the patient-level. The results demonstrate the feasibility of automatic non-invasive detection of patients that require ICA and possibly subsequent coronary artery intervention. This could potentially reduce the number of patients that unnecessarily undergo ICA.
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20
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Machine Learning and Deep Neural Networks Applications in Computed Tomography for Coronary Artery Disease and Myocardial Perfusion. J Thorac Imaging 2020; 35 Suppl 1:S58-S65. [DOI: 10.1097/rti.0000000000000490] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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21
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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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22
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Leiner T, Takx RAP. Predicting the Need for Revascularization in Stable Coronary Artery Disease: Protons or Photons? JACC Cardiovasc Imaging 2019; 13:1005-1007. [PMID: 31607672 DOI: 10.1016/j.jcmg.2019.09.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 09/05/2019] [Accepted: 09/10/2019] [Indexed: 11/29/2022]
Affiliation(s)
- Tim Leiner
- Department of Radiology, University Medical Center of Utrecht, Utrecht, the Netherlands.
| | - Richard A P Takx
- Department of Radiology, University Medical Center of Utrecht, Utrecht, the Netherlands
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23
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Kopanitsa G, Dudchenko A, Ganzinger M. Machine Learning Algorithms in Cardiology Domain: A Systematic Review (Preprint). JMIR Med Inform 2019. [DOI: 10.2196/14784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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24
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Applications of Artificial Intelligence in Ophthalmology: General Overview. J Ophthalmol 2018; 2018:5278196. [PMID: 30581604 PMCID: PMC6276430 DOI: 10.1155/2018/5278196] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2018] [Revised: 10/06/2018] [Accepted: 10/17/2018] [Indexed: 12/26/2022] Open
Abstract
With the emergence of unmanned plane, autonomous vehicles, face recognition, and language processing, the artificial intelligence (AI) has remarkably revolutionized our lifestyle. Recent studies indicate that AI has astounding potential to perform much better than human beings in some tasks, especially in the image recognition field. As the amount of image data in imaging center of ophthalmology is increasing dramatically, analyzing and processing these data is in urgent need. AI has been tried to apply to decipher medical data and has made extraordinary progress in intelligent diagnosis. In this paper, we presented the basic workflow for building an AI model and systematically reviewed applications of AI in the diagnosis of eye diseases. Future work should focus on setting up systematic AI platforms to diagnose general eye diseases based on multimodal data in the real world.
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25
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van Hamersvelt RW, Zreik M, Voskuil M, Viergever MA, Išgum I, Leiner T. Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis. Eur Radiol 2018; 29:2350-2359. [PMID: 30421020 PMCID: PMC6443613 DOI: 10.1007/s00330-018-5822-3] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 08/23/2018] [Accepted: 10/02/2018] [Indexed: 01/17/2023]
Abstract
OBJECTIVES To evaluate the added value of deep learning (DL) analysis of the left ventricular myocardium (LVM) in resting coronary CT angiography (CCTA) over determination of coronary degree of stenosis (DS), for identification of patients with functionally significant coronary artery stenosis. METHODS Patients who underwent CCTA prior to an invasive fractional flow reserve (FFR) measurement were retrospectively selected. Highest DS from CCTA was used to classify patients as having non-significant (≤ 24% DS), intermediate (25-69% DS), or significant stenosis (≥ 70% DS). Patients with intermediate stenosis were referred for fully automatic DL analysis of the LVM. The DL algorithm characterized the LVM, and likely encoded information regarding shape, texture, contrast enhancement, and more. Based on these encodings, features were extracted and patients classified as having a non-significant or significant stenosis. Diagnostic performance of the combined method was evaluated and compared to DS evaluation only. Functionally significant stenosis was defined as FFR ≤ 0.8 or presence of angiographic high-grade stenosis (≥ 90% DS). RESULTS The final study population consisted of 126 patients (77% male, 59 ± 9 years). Eighty-one patients (64%) had a functionally significant stenosis. The proposed method resulted in improved discrimination (AUC = 0.76) compared to classification based on DS only (AUC = 0.68). Sensitivity and specificity were 92.6% and 31.1% for DS only (≥ 50% indicating functionally significant stenosis), and 84.6% and 48.4% for the proposed method. CONCLUSION The combination of DS with DL analysis of the LVM in intermediate-degree coronary stenosis may result in improved diagnostic performance for identification of patients with functionally significant coronary artery stenosis. KEY POINTS • Assessment of degree of coronary stenosis on CCTA has consistently high sensitivity and negative predictive value, but has limited specificity for identifying the functional significance of a stenosis. • Deep learning algorithms are able to learn complex patterns and relationships directly from the images without prior specification of which image features represent presence of disease, and thereby may be more sensitive to subtle changes in the LVM caused by functionally significant stenosis. • Addition of deep learning analysis of the left ventricular myocardium to the evaluation of degree of coronary artery stenosis improves diagnostic performance and increases specificity of resting CCTA. This could potentially decrease the number of patients undergoing invasive coronary angiography.
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Affiliation(s)
- Robbert W van Hamersvelt
- Department of Radiology, University Medical Center Utrecht, Utrecht University, P.O. Box 85500, 3508 GA, Utrecht, the Netherlands.
| | - Majd Zreik
- Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Michiel Voskuil
- Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Max A Viergever
- Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Ivana Išgum
- Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht, Utrecht University, P.O. Box 85500, 3508 GA, Utrecht, the Netherlands
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26
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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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27
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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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28
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Selvarajah A, Bennamoun M, Playford D, Chow BJW, Dwivedi G. Application of Artificial Intelligence in Coronary Computed Tomography Angiography. CURRENT CARDIOVASCULAR IMAGING REPORTS 2018. [DOI: 10.1007/s12410-018-9453-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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29
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Han D, Lee JH, Rizvi A, Gransar H, Baskaran L, Schulman-Marcus J, Ó Hartaigh B, Lin FY, Min JK. Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: A machine learning approach. J Nucl Cardiol 2018; 25:223-233. [PMID: 28303473 DOI: 10.1007/s12350-017-0834-y] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Accepted: 01/31/2017] [Indexed: 10/20/2022]
Abstract
BACKGROUND Evaluation of resting myocardial computed tomography perfusion (CTP) by coronary CT angiography (CCTA) might serve as a useful addition for determining coronary artery disease. We aimed to evaluate the incremental benefit of resting CTP over coronary stenosis for predicting ischemia using a computational algorithm trained by machine learning methods. METHODS 252 patients underwent CCTA and invasive fractional flow reserve (FFR). CT stenosis was classified as 0%, 1-30%, 31-49%, 50-70%, and >70% maximal stenosis. Significant ischemia was defined as invasive FFR < 0.80. Resting CTP analysis was performed using a gradient boosting classifier for supervised machine learning. RESULTS On a per-patient basis, accuracy, sensitivity, specificity, positive predictive, and negative predictive values according to resting CTP when added to CT stenosis (>70%) for predicting ischemia were 68.3%, 52.7%, 84.6%, 78.2%, and 63.0%, respectively. Compared with CT stenosis [area under the receiver operating characteristic curve (AUC): 0.68, 95% confidence interval (CI) 0.62-0.74], the addition of resting CTP appeared to improve discrimination (AUC: 0.75, 95% CI 0.69-0.81, P value .001) and reclassification (net reclassification improvement: 0.52, P value < .001) of ischemia. CONCLUSIONS The addition of resting CTP analysis acquired from machine learning techniques may improve the predictive utility of significant ischemia over coronary stenosis.
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Affiliation(s)
- Donghee Han
- Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY, USA
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Ji Hyun Lee
- Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY, USA
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Asim Rizvi
- Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY, USA
| | - Heidi Gransar
- Department of Imaging, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Lohendran Baskaran
- Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY, USA
| | - Joshua Schulman-Marcus
- Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY, USA
- Division of Cardiology, Albany Medical College, Albany, NY, USA
| | - Bríain Ó Hartaigh
- Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY, USA
| | - Fay Y Lin
- Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY, USA
| | - James K Min
- Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY, USA.
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Shameer K, Johnson KW, Glicksberg BS, Dudley JT, Sengupta PP. Machine learning in cardiovascular medicine: are we there yet? Heart 2018; 104:1156-1164. [PMID: 29352006 DOI: 10.1136/heartjnl-2017-311198] [Citation(s) in RCA: 233] [Impact Index Per Article: 38.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 12/19/2017] [Accepted: 12/21/2017] [Indexed: 12/11/2022] Open
Abstract
Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine.
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Affiliation(s)
- Khader Shameer
- Departments of Medical Informatics and Research Informatics, Northwell Health, Great Neck, New York, USA.,Institute for Next Generation Healthcare, Mount Sinai Health System, New York City, New York, USA.,Icahn Institute for Genomics and Multiscale Biology, Mount Sinai Health System, New York City, New York, USA.,Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York City, New York, USA.,Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, New York, USA.,Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, NY, USA
| | - Kipp W Johnson
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York City, New York, USA.,Icahn Institute for Genomics and Multiscale Biology, Mount Sinai Health System, New York City, New York, USA.,Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York City, New York, USA.,Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, New York, USA
| | - Benjamin S Glicksberg
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York City, New York, USA.,Icahn Institute for Genomics and Multiscale Biology, Mount Sinai Health System, New York City, New York, USA.,Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York City, New York, USA.,Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, New York, USA.,Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, California, USA
| | - Joel T Dudley
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York City, New York, USA.,Icahn Institute for Genomics and Multiscale Biology, Mount Sinai Health System, New York City, New York, USA.,Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York City, New York, USA.,Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, New York, USA
| | - Partho P Sengupta
- Division of Cardiology, West Virginia Heart and Vascular Institute, Morgantown, West Virginia, USA
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Zreik M, Lessmann N, van Hamersvelt RW, Wolterink JM, Voskuil M, Viergever MA, Leiner T, Išgum I. Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis. Med Image Anal 2017; 44:72-85. [PMID: 29197253 DOI: 10.1016/j.media.2017.11.008] [Citation(s) in RCA: 106] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Revised: 11/16/2017] [Accepted: 11/20/2017] [Indexed: 12/11/2022]
Abstract
In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The study includes consecutively acquired CCTA scans of 166 patients who underwent invasive FFR measurements. To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages. First, the LV myocardium is segmented using a multiscale convolutional neural network (CNN). To characterize the segmented LV myocardium, it is subsequently encoded using unsupervised convolutional autoencoder (CAE). As ischemic changes are expected to appear locally, the LV myocardium is divided into a number of spatially connected clusters, and statistics of the encodings are computed as features. Thereafter, patients are classified according to the presence of functionally significant stenosis using an SVM classifier based on the extracted features. Quantitative evaluation of LV myocardium segmentation in 20 images resulted in an average Dice coefficient of 0.91 and an average mean absolute distance between the segmented and reference LV boundaries of 0.7 mm. Twenty CCTA images were used to train the LV myocardium encoder. Classification of patients was evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation experiments and resulted in an area under the receiver operating characteristic curve of 0.74 ± 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the corresponding specificity was 0.77, 0.71 and 0.59, respectively. The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally significant coronary artery stenosis. This might reduce the number of patients undergoing unnecessary invasive FFR measurements.
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Affiliation(s)
- Majd Zreik
- Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
| | - Nikolas Lessmann
- Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
| | - Robbert W van Hamersvelt
- Department of Radiology, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
| | - Jelmer M Wolterink
- Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
| | - Michiel Voskuil
- Department of Cardiology, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
| | - Max A Viergever
- Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
| | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
| | - Ivana Išgum
- Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
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Henglin M, Stein G, Hushcha PV, Snoek J, Wiltschko AB, Cheng S. Machine Learning Approaches in Cardiovascular Imaging. Circ Cardiovasc Imaging 2017; 10:CIRCIMAGING.117.005614. [PMID: 28956772 DOI: 10.1161/circimaging.117.005614] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Cardiovascular imaging technologies continue to increase in their capacity to capture and store large quantities of data. Modern computational methods, developed in the field of machine learning, offer new approaches to leveraging the growing volume of imaging data available for analyses. Machine learning methods can now address data-related problems ranging from simple analytic queries of existing measurement data to the more complex challenges involved in analyzing raw images. To date, machine learning has been used in 2 broad and highly interconnected areas: automation of tasks that might otherwise be performed by a human and generation of clinically important new knowledge. Most cardiovascular imaging studies have focused on task-oriented problems, but more studies involving algorithms aimed at generating new clinical insights are emerging. Continued expansion in the size and dimensionality of cardiovascular imaging databases is driving strong interest in applying powerful deep learning methods, in particular, to analyze these data. Overall, the most effective approaches will require an investment in the resources needed to appropriately prepare such large data sets for analyses. Notwithstanding current technical and logistical challenges, machine learning and especially deep learning methods have much to offer and will substantially impact the future practice and science of cardiovascular imaging.
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Affiliation(s)
- Mir Henglin
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.).
| | - Gillian Stein
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.)
| | - Pavel V Hushcha
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.)
| | - Jasper Snoek
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.)
| | - Alexander B Wiltschko
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.)
| | - Susan Cheng
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.)
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Landreth SP, Spearman JV. Machine Learning in Cardiac CT. CURRENT RADIOLOGY REPORTS 2017. [DOI: 10.1007/s40134-017-0241-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Sengupta PP, Huang YM, Bansal M, Ashrafi A, Fisher M, Shameer K, Gall W, Dudley JT. Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy. Circ Cardiovasc Imaging 2017; 9:CIRCIMAGING.115.004330. [PMID: 27266599 DOI: 10.1161/circimaging.115.004330] [Citation(s) in RCA: 129] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Accepted: 04/27/2016] [Indexed: 01/19/2023]
Abstract
BACKGROUND Associating a patient's profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography data sets derived from patients with known constrictive pericarditis and restrictive cardiomyopathy. METHODS AND RESULTS Clinical and echocardiographic data of 50 patients with constrictive pericarditis and 44 with restrictive cardiomyopathy were used for developing an associative memory classifier-based machine-learning algorithm. The speckle tracking echocardiography data were normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve of the associative memory classifier was evaluated for differentiating constrictive pericarditis from restrictive cardiomyopathy. Using only speckle tracking echocardiography variables, associative memory classifier achieved a diagnostic area under the curve of 89.2%, which improved to 96.2% with addition of 4 echocardiographic variables. In comparison, the area under the curve of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63.7%, respectively. Furthermore, the associative memory classifier demonstrated greater accuracy and shorter learning curves than other machine-learning approaches, with accuracy asymptotically approaching 90% after a training fraction of 0.3 and remaining flat at higher training fractions. CONCLUSIONS This study demonstrates feasibility of a cognitive machine-learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine-learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience.
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Affiliation(s)
- Partho P Sengupta
- From the Zena and Michael A. Wiener Cardiovascular Institute and the Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, NY (P.P.S., M.B.); Saffron Technology, Inc, Cary, NC (Y.-M.H., A.A., M.F., W.G.); and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY (K.S., J.T.D.).
| | - Yen-Min Huang
- From the Zena and Michael A. Wiener Cardiovascular Institute and the Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, NY (P.P.S., M.B.); Saffron Technology, Inc, Cary, NC (Y.-M.H., A.A., M.F., W.G.); and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY (K.S., J.T.D.)
| | - Manish Bansal
- From the Zena and Michael A. Wiener Cardiovascular Institute and the Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, NY (P.P.S., M.B.); Saffron Technology, Inc, Cary, NC (Y.-M.H., A.A., M.F., W.G.); and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY (K.S., J.T.D.)
| | - Ali Ashrafi
- From the Zena and Michael A. Wiener Cardiovascular Institute and the Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, NY (P.P.S., M.B.); Saffron Technology, Inc, Cary, NC (Y.-M.H., A.A., M.F., W.G.); and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY (K.S., J.T.D.)
| | - Matt Fisher
- From the Zena and Michael A. Wiener Cardiovascular Institute and the Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, NY (P.P.S., M.B.); Saffron Technology, Inc, Cary, NC (Y.-M.H., A.A., M.F., W.G.); and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY (K.S., J.T.D.)
| | - Khader Shameer
- From the Zena and Michael A. Wiener Cardiovascular Institute and the Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, NY (P.P.S., M.B.); Saffron Technology, Inc, Cary, NC (Y.-M.H., A.A., M.F., W.G.); and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY (K.S., J.T.D.)
| | - Walt Gall
- From the Zena and Michael A. Wiener Cardiovascular Institute and the Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, NY (P.P.S., M.B.); Saffron Technology, Inc, Cary, NC (Y.-M.H., A.A., M.F., W.G.); and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY (K.S., J.T.D.)
| | - Joel T Dudley
- From the Zena and Michael A. Wiener Cardiovascular Institute and the Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, NY (P.P.S., M.B.); Saffron Technology, Inc, Cary, NC (Y.-M.H., A.A., M.F., W.G.); and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY (K.S., J.T.D.)
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Xiong G, Sun P, Zhou H, Ha S, Hartaigh BO, Truong QA, Min JK. Comprehensive Modeling and Visualization of Cardiac Anatomy and Physiology from CT Imaging and Computer Simulations. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2017; 23:1014-1028. [PMID: 26863663 PMCID: PMC4975682 DOI: 10.1109/tvcg.2016.2520946] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
In clinical cardiology, both anatomy and physiology are needed to diagnose cardiac pathologies. CT imaging and computer simulations provide valuable and complementary data for this purpose. However, it remains challenging to gain useful information from the large amount of high-dimensional diverse data. The current tools are not adequately integrated to visualize anatomic and physiologic data from a complete yet focused perspective. We introduce a new computer-aided diagnosis framework, which allows for comprehensive modeling and visualization of cardiac anatomy and physiology from CT imaging data and computer simulations, with a primary focus on ischemic heart disease. The following visual information is presented: (1) Anatomy from CT imaging: geometric modeling and visualization of cardiac anatomy, including four heart chambers, left and right ventricular outflow tracts, and coronary arteries; (2) Function from CT imaging: motion modeling, strain calculation, and visualization of four heart chambers; (3) Physiology from CT imaging: quantification and visualization of myocardial perfusion and contextual integration with coronary artery anatomy; (4) Physiology from computer simulation: computation and visualization of hemodynamics (e.g., coronary blood velocity, pressure, shear stress, and fluid forces on the vessel wall). Substantially, feedback from cardiologists have confirmed the practical utility of integrating these features for the purpose of computer-aided diagnosis of ischemic heart disease.
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