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Magboo VPC, Magboo MSA. SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach. ACTA MEDICA PHILIPPINA 2024; 58:67-75. [PMID: 38812768 PMCID: PMC11132284 DOI: 10.47895/amp.vi0.7582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
Background Worldwide, coronary artery disease (CAD) is a leading cause of mortality and morbidity and remains to be a top health priority in many countries. A non-invasive imaging modality for diagnosis of CAD such as single photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI) is usually requested by cardiologists as it displays radiotracer distribution in the heart reflecting myocardial perfusion. The interpretation of SPECT-MPI is done visually by a nuclear medicine physician and is largely dependent on his clinical experience and showing significant inter-observer variability. Objective The aim of the study is to apply a deep learning approach in the classification of SPECT-MPI for perfusion abnormalities using convolutional neural networks (CNN). Methods A publicly available anonymized SPECT-MPI from a machine learning repository (https://www.kaggle.com/selcankaplan/spect-mpi) was used in this study involving 192 patients who underwent stress-test-rest Tc99m MPI. An exploratory approach of CNN hyperparameter selection to search for optimum neural network model was utilized with particular focus on various dropouts (0.2, 0.5, 0.7), batch sizes (8, 16, 32, 64), and number of dense nodes (32, 64, 128, 256). The base CNN model was also compared with the commonly used pre-trained CNNs in medical images such as VGG16, InceptionV3, DenseNet121 and ResNet50. All simulations experiments were performed in Kaggle using TensorFlow 2.6.0., Keras 2.6.0, and Python language 3.7.10. Results The best performing base CNN model with parameters consisting of 0.7 dropout, batch size 8, and 32 dense nodes generated the highest normalized Matthews Correlation Coefficient at 0.909 and obtained 93.75% accuracy, 96.00% sensitivity, 96.00% precision, and 96.00% F1-score. It also obtained higher classification performance as compared to the pre-trained architectures. Conclusions The results suggest that deep learning approaches through the use of CNN models can be deployed by nuclear medicine physicians in their clinical practice to further augment their decision skills in the interpretation of SPECT-MPI tests. These CNN models can also be used as a dependable and valid second opinion that can aid physicians as a decision-support tool as well as serve as teaching or learning materials for the less-experienced physicians particularly those still in their training career. These highlights the clinical utility of deep learning approaches through CNN models in the practice of nuclear cardiology.
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Affiliation(s)
- Vincent Peter C Magboo
- Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila
| | - Ma Sheila A Magboo
- Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila
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Bors S, Abler D, Dietz M, Andrearczyk V, Fageot J, Nicod-Lalonde M, Schaefer N, DeKemp R, Kamani CH, Prior JO, Depeursinge A. Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [ 82Rb] PET for MACE prediction. Sci Rep 2024; 14:9644. [PMID: 38671059 PMCID: PMC11053111 DOI: 10.1038/s41598-024-60095-6] [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: 08/30/2023] [Accepted: 04/18/2024] [Indexed: 04/28/2024] Open
Abstract
Assessing the individual risk of Major Adverse Cardiac Events (MACE) is of major importance as cardiovascular diseases remain the leading cause of death worldwide. Quantitative Myocardial Perfusion Imaging (MPI) parameters such as stress Myocardial Blood Flow (sMBF) or Myocardial Flow Reserve (MFR) constitutes the gold standard for prognosis assessment. We propose a systematic investigation of the value of Artificial Intelligence (AI) to leverage [82 Rb] Silicon PhotoMultiplier (SiPM) PET MPI for MACE prediction. We establish a general pipeline for AI model validation to assess and compare the performance of global (i.e. average of the entire MPI signal), regional (17 segments), radiomics and Convolutional Neural Network (CNN) models leveraging various MPI signals on a dataset of 234 patients. Results showed that all regional AI models significantly outperformed the global model ( p < 0.001 ), where the best AUC of 73.9% (CI 72.5-75.3) was obtained with a CNN model. A regional AI model based on MBF averages from 17 segments fed to a Logistic Regression (LR) constituted an excellent trade-off between model simplicity and performance, achieving an AUC of 73.4% (CI 72.3-74.7). A radiomics model based on intensity features revealed that the global average was the least important feature when compared to other aggregations of the MPI signal over the myocardium. We conclude that AI models can allow better personalized prognosis assessment for MACE.
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Affiliation(s)
- Sacha Bors
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland
- Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Daniel Abler
- Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Matthieu Dietz
- INSERM U1060, CarMeN laboratory, University of Lyon, Lyon, France
| | - Vincent Andrearczyk
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland
- Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Julien Fageot
- AudioVisual Communications Laboratory (LCAV), EPFL, Lausanne, Switzerland
| | - Marie Nicod-Lalonde
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland
- University of Lausanne, Lausanne, Switzerland
| | - Niklaus Schaefer
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland
- University of Lausanne, Lausanne, Switzerland
| | - Robert DeKemp
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada
| | - Christel H Kamani
- Department of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - John O Prior
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland.
- University of Lausanne, Lausanne, Switzerland.
| | - Adrien Depeursinge
- Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital, Lausanne, Switzerland
- Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
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Miller RJH, Slomka PJ. Artificial Intelligence in Nuclear Cardiology: An Update and Future Trends. Semin Nucl Med 2024:S0001-2998(24)00015-1. [PMID: 38521708 DOI: 10.1053/j.semnuclmed.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 02/19/2024] [Indexed: 03/25/2024]
Abstract
Myocardial perfusion imaging (MPI), using either single photon emission computed tomography (SPECT) or positron emission tomography (PET), is one of the most commonly ordered cardiac imaging tests, with prominent clinical roles for disease diagnosis and risk prediction. Artificial intelligence (AI) could potentially play a role in many steps along the typical MPI workflow, from image acquisition through to clinical reporting and risk estimation. AI can be utilized to improve image quality, reducing radiation exposure and image acquisition times. Once images are acquired, AI can help optimize motion correction and image registration during image reconstruction or provide direct image attenuation correction. Utilizing these image sets, AI can segment a number of anatomic features from associated computed tomographic imaging or even generate synthetic attenuation imaging. Lastly, AI may play an important role in disease diagnosis or risk prediction by combining the large number of potentially important clinical, stress, and imaging-related variables. This review will focus on the most recent developments in the field, providing clinicians and researchers with a timely update on the field. Additionally, it will discuss future trends including applications of AI during multiple points of the typical MPI workflow to maximize clinical utility and methods to maximize the information that can be obtained from hybrid imaging.
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Affiliation(s)
- Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA; Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA.
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Berman D, Hunter C, Hossain A, Yao J, Workman E, Guan S, Strickhart L, Beanlands R, Slater D, deKemp RA. Machine and deep learning models for accurate detection of ischemia and scar with myocardial blood flow positron emission tomography imaging. J Nucl Cardiol 2024; 32:101797. [PMID: 38185409 DOI: 10.1016/j.nuclcard.2024.101797] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
BACKGROUND Quantification of myocardial blood flow (MBF) is used for the noninvasive diagnosis of patients with coronary artery disease (CAD). This study compared traditional statistics, machine learning, and deep learning techniques in their ability to diagnose disease using only the rest and stress MBF values. METHODS This study included 3245 rest and stress rubidium-82 positron emission tomography (PET) studies and matching diagnostic labels from perfusion reports. Standard logistic regression, lasso logistic regression, support vector machine, random forest, multilayer perceptron, and dense U-Net were compared for per-patient detection and per-vessel localization of scars and ischemia. RESULTS Receiver-operator characteristic area under the curve (AUC) of machine learning models was significantly higher than those of traditional statistics models for per-patient detection of disease (0.92-0.95 vs. 0.87) but not for per-vessel localization of ischemia or scar. Random forest showed the highest AUC = 0.95 among the different models compared. On the final hold-out set for generalizability, random forest showed an AUC of 0.92 for detection and 0.89 for localization of perfusion abnormalities. CONCLUSIONS For per-vessel localization, simple models trained on segmental data performed similarly to a convolutional neural network trained on polar-map data, highlighting the need to justify the use of complex predictive algorithms through comparison with simpler methods.
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Affiliation(s)
- Daniel Berman
- The MITRE Corporation, 7515 Colshire Drive, McLean, VA 22102, USA
| | - Chad Hunter
- University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, K1Y 4W7, Canada
| | - Alomgir Hossain
- University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, K1Y 4W7, Canada; The Hospital for Sick Children, 555 University Avenue, Toronto, M5G 1X8, Canada
| | - Jason Yao
- University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, K1Y 4W7, Canada
| | - Emily Workman
- The MITRE Corporation, 7515 Colshire Drive, McLean, VA 22102, USA
| | - Steven Guan
- The MITRE Corporation, 7515 Colshire Drive, McLean, VA 22102, USA
| | - Laura Strickhart
- The MITRE Corporation, 7515 Colshire Drive, McLean, VA 22102, USA
| | - Rob Beanlands
- University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, K1Y 4W7, Canada
| | - David Slater
- The MITRE Corporation, 7515 Colshire Drive, McLean, VA 22102, USA
| | - Robert A deKemp
- University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, K1Y 4W7, Canada.
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Wieneke H, Voigt I. Principles of artificial intelligence and its application in cardiovascular medicine. Clin Cardiol 2024; 47:e24148. [PMID: 37721424 PMCID: PMC10766001 DOI: 10.1002/clc.24148] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 08/23/2023] [Accepted: 08/29/2023] [Indexed: 09/19/2023] Open
Abstract
Artificial intelligence (AI) represents a rapidly developing field. Its use can improve diagnosis and therapy in many areas of medicine. Despite this enormous progress, many physicians perceive it as a black box and are skeptical about it. This review will present the basics of machine learning. Different classifications of artificial intelligence, such as supervised versus unsupervised and discriminative versus generative AI, are given. Analogies to human intelligence are discussed as far as algorithms are oriented toward it. In the second step, the most common models like random forest, k-means clustering, convolutional neural network, and transformers will be presented in a way that the underlying idea can be understood. Corresponding medical applications in cardiovascular medicine will be named for all models, respectively. The overview is intended to show that the term artificial intelligence covers a wide range of different concepts. It should help physicians understand the principles of AI to make up one's minds about its application in cardiology. It should also enable them to evaluate results obtained with AI's help critically.
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Affiliation(s)
- Heinrich Wieneke
- Department of Cardiology and Angiology, Contilia Heart and Vascular CenterElisabeth‐Krankenhaus EssenEssenGermany
| | - Ingo Voigt
- Department of Cardiology and Angiology, Contilia Heart and Vascular CenterElisabeth‐Krankenhaus EssenEssenGermany
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Miller RJ. Artificial Intelligence in Nuclear Cardiology. Cardiol Clin 2023; 41:151-161. [PMID: 37003673 DOI: 10.1016/j.ccl.2023.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Artificial intelligence (AI) encompasses a variety of computer algorithms that have a wide range of potential clinical applications in nuclear cardiology. This article will introduce core terminology and concepts for AI including classifications of AI as well as training and testing regimens. We will then highlight the potential role for AI to improve image registration and image quality. Next, we will discuss methods for AI-driven image attenuation correction. Finally, we will review advancements in machine learning and deep-learning applications for disease diagnosis and risk stratification, including efforts to improve clinical translation of this valuable technology with explainable AI models.
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Singh A, Miller RJH, Otaki Y, Kavanagh P, Hauser MT, Tzolos E, Kwiecinski J, Van Kriekinge S, Wei CC, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Huang C, Han D, Dey D, Berman DS, Slomka PJ. Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning. JACC Cardiovasc Imaging 2023; 16:209-220. [PMID: 36274041 PMCID: PMC10980287 DOI: 10.1016/j.jcmg.2022.07.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 06/21/2022] [Accepted: 07/21/2022] [Indexed: 01/19/2023]
Abstract
BACKGROUND Myocardial perfusion imaging (MPI) is frequently used to provide risk stratification, but methods to improve the accuracy of these predictions are needed. OBJECTIVES The authors developed an explainable deep learning (DL) model (HARD MACE [major adverse cardiac events]-DL) for the prediction of death or nonfatal myocardial infarction (MI) and validated its performance in large internal and external testing groups. METHODS Patients undergoing single-photon emission computed tomography MPI were included, with 20,401 patients in the training and internal testing group (5 sites) and 9,019 in the external testing group (2 different sites). HARD MACE-DL uses myocardial perfusion, motion, thickening, and phase polar maps combined with age, sex, and cardiac volumes. The primary outcome was all-cause mortality or nonfatal MI. Prognostic accuracy was evaluated using area under the receiver-operating characteristic curve (AUC). RESULTS During internal testing, patients with normal perfusion and elevated HARD MACE-DL risk were at higher risk than patients with abnormal perfusion and low HARD MACE-DL risk (annualized event rate, 2.9% vs 1.2%; P < 0.001). Patients in the highest quartile of HARD MACE-DL score had an annual rate of death or MI (4.8%) 10-fold higher than patients in the lowest quartile (0.48% per year). In external testing, the AUC for HARD MACE-DL (0.73; 95% CI: 0.71-0.75) was higher than a logistic regression model (AUC: 0.70), stress total perfusion deficit (TPD) (AUC: 0.65), and ischemic TPD (AUC: 0.63; all P < 0.01). Calibration, a measure of how well predicted risk matches actual risk, was excellent in both groups (Brier score, 0.079 for internal and 0.070 for external). CONCLUSIONS The DL model predicts death or MI directly from MPI, by estimating patient-level risk with good calibration and improved accuracy compared with traditional quantitative approaches. The model incorporates mechanisms to explain to the physician which image regions contribute to the adverse event prediction.
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Affiliation(s)
- Ananya Singh
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Robert J H Miller
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Cardiac Sciences, University of Calgary and Libin Cardiovascular Institute, Calgary, Alberta, Canada
| | - Yuka Otaki
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Paul Kavanagh
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Michael T Hauser
- Department of Nuclear Cardiology, Oklahoma Heart Hospital, Oklahoma City, Oklahoma, USA
| | - Evangelos Tzolos
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Jacek Kwiecinski
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland
| | - Serge Van Kriekinge
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Chih-Chun Wei
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Tali Sharir
- Department of Nuclear Cardiology, Assuta Medical Center, Tel Aviv, Israel; Department of Nuclear Cardiology, Ben Gurion University of the Negev, Beer Sheba, Israel
| | - Andrew J Einstein
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, New York, USA; Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, New York, USA
| | - Mathews B Fish
- Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, Oregon, USA
| | - Terrence D Ruddy
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Philipp A Kaufmann
- Division of Cardiac Imaging, Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Albert J Sinusas
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | | | - Sharmila Dorbala
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Marcelo Di Carli
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Joanna X Liang
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Cathleen Huang
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Donghee Han
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Damini Dey
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Daniel S Berman
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Piotr J Slomka
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA.
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Apostolopoulos ID, Papandrianos NI, Feleki A, Moustakidis S, Papageorgiou EI. Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies. EJNMMI Phys 2023; 10:6. [PMID: 36705775 PMCID: PMC9883373 DOI: 10.1186/s40658-022-00522-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 12/19/2022] [Indexed: 01/28/2023] Open
Abstract
Deep learning (DL) has a growing popularity and is a well-established method of artificial intelligence for data processing, especially for images and videos. Its applications in nuclear medicine are broad and include, among others, disease classification, image reconstruction, and image de-noising. Positron emission tomography (PET) and single-photon emission computerized tomography (SPECT) are major image acquisition technologies in nuclear medicine. Though several studies have been conducted to apply DL in many nuclear medicine domains, such as cancer detection and classification, few studies have employed such methods for cardiovascular disease applications. The present paper reviews recent DL approaches focused on cardiac SPECT imaging. Extensive research identified fifty-five related studies, which are discussed. The review distinguishes between major application domains, including cardiovascular disease diagnosis, SPECT attenuation correction, image denoising, full-count image estimation, and image reconstruction. In addition, major findings and dominant techniques employed for the mentioned task are revealed. Current limitations of DL approaches and future research directions are discussed.
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Affiliation(s)
- Ioannis D. Apostolopoulos
- grid.11047.330000 0004 0576 5395Department of Medical Physics, School of Medicine, University of Patras, 26504 Patras, Greece ,grid.410558.d0000 0001 0035 6670Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece
| | - Nikolaos I. Papandrianos
- grid.410558.d0000 0001 0035 6670Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece
| | - Anna Feleki
- grid.410558.d0000 0001 0035 6670Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece
| | - Serafeim Moustakidis
- grid.410558.d0000 0001 0035 6670Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece ,AIDEAS OÜ, 10117 Tallinn, Estonia
| | - Elpiniki I. Papageorgiou
- grid.410558.d0000 0001 0035 6670Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece
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9
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Papandrianos NI, Apostolopoulos ID, Feleki A, Moustakidis S, Kokkinos K, Papageorgiou EI. AI-based classification algorithms in SPECT myocardial perfusion imaging for cardiovascular diagnosis: a review. Nucl Med Commun 2023; 44:1-11. [PMID: 36514926 DOI: 10.1097/mnm.0000000000001634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
In the last few years, deep learning has made a breakthrough and established its position in machine learning classification problems in medical image analysis. Deep learning has recently displayed remarkable applicability in a range of different medical applications, as well as in nuclear cardiology. This paper implements a literature review protocol and reports the latest advances in artificial intelligence (AI)-based classification in SPECT myocardial perfusion imaging in heart disease diagnosis. The representative and most recent works are reported to demonstrate the use of AI and deep learning technologies in medical image analysis in nuclear cardiology for cardiovascular diagnosis. This review also analyses the primary outcomes of the presented research studies and suggests future directions focusing on the explainability of the deployed deep-learning systems in clinical practice.
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Affiliation(s)
| | | | - Anna Feleki
- Department of Energy Systems, University of Thessaly, Larisa, Greece
| | - Serafeim Moustakidis
- Department of Energy Systems, University of Thessaly, Larisa, Greece
- AIDEAS OÜ, Tallinn, Estonia
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10
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Miller RJH, Kuronuma K, Singh A, Otaki Y, Hayes S, Chareonthaitawee P, Kavanagh P, Parekh T, Tamarappoo BK, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Carli MD, Cadet S, Liang JX, Dey D, Berman DS, Slomka PJ. Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging. J Nucl Med 2022; 63:1768-1774. [PMID: 35512997 PMCID: PMC9635672 DOI: 10.2967/jnumed.121.263686] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 04/18/2022] [Indexed: 11/16/2022] Open
Abstract
Artificial intelligence may improve accuracy of myocardial perfusion imaging (MPI) but will likely be implemented as an aid to physician interpretation rather than an autonomous tool. Deep learning (DL) has high standalone diagnostic accuracy for obstructive coronary artery disease (CAD), but its influence on physician interpretation is unknown. We assessed whether access to explainable DL predictions improves physician interpretation of MPI. Methods: We selected a representative cohort of patients who underwent MPI with reference invasive coronary angiography. Obstructive CAD, defined as stenosis ≥50% in the left main artery or ≥70% in other coronary segments, was present in half of the patients. We used an explainable DL model (CAD-DL), which was previously developed in a separate population from different sites. Three physicians interpreted studies first with clinical history, stress, and quantitative perfusion, then with all the data plus the DL results. Diagnostic accuracy was assessed using area under the receiver-operating-characteristic curve (AUC). Results: In total, 240 patients with a median age of 65 y (interquartile range 58-73) were included. The diagnostic accuracy of physician interpretation with CAD-DL (AUC 0.779) was significantly higher than that of physician interpretation without CAD-DL (AUC 0.747, P = 0.003) and stress total perfusion deficit (AUC 0.718, P < 0.001). With matched specificity, CAD-DL had higher sensitivity when operating autonomously compared with readers without DL results (P < 0.001), but not compared with readers interpreting with DL results (P = 0.122). All readers had numerically higher accuracy with CAD-DL, with AUC improvement 0.02-0.05, and interpretation with DL resulted in overall net reclassification improvement of 17.2% (95% CI 9.2%-24.4%, P < 0.001). Conclusion: Explainable DL predictions lead to meaningful improvements in physician interpretation; however, the improvement varied across the readers, reflecting the acceptance of this new technology. This technique could be implemented as an aid to physician diagnosis, improving the diagnostic accuracy of MPI.
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Affiliation(s)
- Robert J H Miller
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
- Department of Cardiac Sciences, University of Calgary and Libin Cardiovascular Institute, Calgary, Alberta, Canada
| | - Keiichiro Kuronuma
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
- Department of Cardiology, Nihon University, Tokyo, Japan
| | - Ananya Singh
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Yuka Otaki
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Sean Hayes
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | | | - Paul Kavanagh
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Tejas Parekh
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Balaji K Tamarappoo
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Tali Sharir
- Department of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, Israel and Ben Gurion University of the Negev, Beer Sheba, Israel
| | - Andrew J Einstein
- Division of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, New York
| | - Mathews B Fish
- Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, Oregon
| | - Terrence D Ruddy
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Philipp A Kaufmann
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland
| | - Albert J Sinusas
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut
| | | | - Sharmila Dorbala
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, Massachusetts
| | - Marcelo Di Carli
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, Massachusetts
| | - Sebastien Cadet
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Joanna X Liang
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Damini Dey
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Daniel S Berman
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Piotr J Slomka
- Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California;
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11
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Wei J, Yang P, Pi Y, Cai H, Jiang L, Xiang Y, Zhao Z, Yi Z. Cross-granularity multi-task network for ischemia diagnosis and defect detection in the myocardial perfusion imaging. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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12
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Miller RJH, Huang C, Liang JX, Slomka PJ. Artificial intelligence for disease diagnosis and risk prediction in nuclear cardiology. J Nucl Cardiol 2022; 29:1754-1762. [PMID: 35508795 DOI: 10.1007/s12350-022-02977-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 03/29/2022] [Indexed: 10/18/2022]
Abstract
Artificial intelligence (AI) techniques have emerged as a highly efficient approach to accurately and rapidly interpret diagnostic imaging and may play a vital role in nuclear cardiology. In nuclear cardiology, there are many clinical, stress, and imaging variables potentially available, which need to be optimally integrated to predict the presence of obstructive coronary artery disease (CAD) or predict the risk of cardiovascular events. In spite of clinical awareness of a large number of potential variables, it is difficult for physicians to integrate multiple features consistently and objectively. Machine learning (ML) is particularly well suited to integrating this vast array of information to provide patient-specific predictions. Deep learning (DL), a branch of ML characterized by a multi-layered convolutional model architecture, can extract information directly from images and identify latent image features associated with a specific prediction. This review will discuss the latest AI applications to disease diagnosis and risk prediction in nuclear cardiology with a focus on potential clinical applications.
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Affiliation(s)
- Robert J H Miller
- Division of Artificial Intelligence in Medicine, Departments of Medicine, Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA
- Department of Cardiac Sciences, University of Calgary and Libin Cardiovascular Institute, Calgary, AB, Canada
| | - Cathleen Huang
- Division of Artificial Intelligence in Medicine, Departments of Medicine, Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA
| | - Joanna X Liang
- Division of Artificial Intelligence in Medicine, Departments of Medicine, Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA
| | - Piotr J Slomka
- Division of Artificial Intelligence in Medicine, Departments of Medicine, Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA.
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13
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An Explainable Classification Method of SPECT Myocardial Perfusion Images in Nuclear Cardiology Using Deep Learning and Grad-CAM. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157592] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Background: This study targets the development of an explainable deep learning methodology for the automatic classification of coronary artery disease, utilizing SPECT MPI images. Deep learning is currently judged as non-transparent due to the model’s complex non-linear structure, and thus, it is considered a «black box», making it hard to gain a comprehensive understanding of its internal processes and explain its behavior. Existing explainable artificial intelligence tools can provide insights into the internal functionality of deep learning and especially of convolutional neural networks, allowing transparency and interpretation. Methods: This study seeks to address the identification of patients’ CAD status (infarction, ischemia or normal) by developing an explainable deep learning pipeline in the form of a handcrafted convolutional neural network. The proposed RGB-CNN model utilizes various pre- and post-processing tools and deploys a state-of-the-art explainability tool to produce more interpretable predictions in decision making. The dataset includes cases from 625 patients as stress and rest representations, comprising 127 infarction, 241 ischemic, and 257 normal cases previously classified by a doctor. The imaging dataset was split into 20% for testing and 80% for training, of which 15% was further used for validation purposes. Data augmentation was employed to increase generalization. The efficacy of the well-known Grad-CAM-based color visualization approach was also evaluated in this research to provide predictions with interpretability in the detection of infarction and ischemia in SPECT MPI images, counterbalancing any lack of rationale in the results extracted by the CNNs. Results: The proposed model achieved 93.3% accuracy and 94.58% AUC, demonstrating efficient performance and stability. Grad-CAM has shown to be a valuable tool for explaining CNN-based judgments in SPECT MPI images, allowing nuclear physicians to make fast and confident judgments by using the visual explanations offered. Conclusions: Prediction results indicate a robust and efficient model based on the deep learning methodology which is proposed for CAD diagnosis in nuclear medicine.
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Prediction of All-Cause Mortality Based on Stress/Rest Myocardial Perfusion Imaging (MPI) Using Deep Learning: A Comparison between Image and Frequency Spectra as Input. J Pers Med 2022; 12:jpm12071105. [PMID: 35887602 PMCID: PMC9322556 DOI: 10.3390/jpm12071105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/02/2022] [Accepted: 07/04/2022] [Indexed: 11/24/2022] Open
Abstract
Background: Cardiovascular management and risk stratification of patients is an important issue in clinics. Patients who have experienced an adverse cardiac event are concerned for their future and want to know the survival probability. Methods: We trained eight state-of-the-art CNN models using polar maps of myocardial perfusion imaging (MPI), gender, lung/heart ratio, and patient age for 5-year survival prediction after an adverse cardiac event based on a cohort of 862 patients who had experienced adverse cardiac events and stress/rest MPIs. The CNN model outcome is to predict a patient’s survival 5 years after a cardiac event, i.e., two classes, either yes or no. Results: The best accuracy of all the CNN prediction models was 0.70 (median value), which resulted from ResNet-50V2, using image as the input in the baseline experiment. All the CNN models had better performance after using frequency spectra as the input. The accuracy increment was about 7~9%. Conclusions: This is the first trial to use pure rest/stress MPI polar maps and limited clinical data to predict patients’ 5-year survival based on CNN models and deep learning. The study shows the feasibility of using frequency spectra rather than images, which might increase the performance of CNNs.
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15
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Papandrianos NI, Feleki A, Papageorgiou EI, Martini C. Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images. J Clin Med 2022; 11:jcm11133918. [PMID: 35807203 PMCID: PMC9267142 DOI: 10.3390/jcm11133918] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/28/2022] [Accepted: 06/29/2022] [Indexed: 12/12/2022] Open
Abstract
(1) Background: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a long-established estimation methodology for medical diagnosis using image classification illustrating conditions in coronary artery disease. For these procedures, convolutional neural networks have proven to be very beneficial in achieving near-optimal accuracy for the automatic classification of SPECT images. (2) Methods: This research addresses the supervised learning-based ideal observer image classification utilizing an RGB-CNN model in heart images to diagnose CAD. For comparison purposes, we employ VGG-16 and DenseNet-121 pre-trained networks that are indulged in an image dataset representing stress and rest mode heart states acquired by SPECT. In experimentally evaluating the method, we explore a wide repertoire of deep learning network setups in conjunction with various robust evaluation and exploitation metrics. Additionally, to overcome the image dataset cardinality restrictions, we take advantage of the data augmentation technique expanding the set into an adequate number. Further evaluation of the model was performed via 10-fold cross-validation to ensure our model's reliability. (3) Results: The proposed RGB-CNN model achieved an accuracy of 91.86%, while VGG-16 and DenseNet-121 reached 88.54% and 86.11%, respectively. (4) Conclusions: The abovementioned experiments verify that the newly developed deep learning models may be of great assistance in nuclear medicine and clinical decision-making.
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Affiliation(s)
- Nikolaos I. Papandrianos
- Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece; (A.F.); (E.I.P.)
- Correspondence: ; Tel.: +30-693-6064613
| | - Anna Feleki
- Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece; (A.F.); (E.I.P.)
| | - Elpiniki I. Papageorgiou
- Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece; (A.F.); (E.I.P.)
| | - Chiara Martini
- Department of Diagnostic, Parma University Hospital, Via Gramsci 14, 43125 Parma, Italy;
- Department of Medicine and Surgery, Section of Radiology, University of Parma, Maggiore Hospital, Via Gramsci 14, 43125 Parma, Italy
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16
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Papandrianos NI, Apostolopoulos ID, Feleki A, Apostolopoulos DJ, Papageorgiou EI. Deep learning exploration for SPECT MPI polar map images classification in coronary artery disease. Ann Nucl Med 2022; 36:823-833. [PMID: 35771376 DOI: 10.1007/s12149-022-01762-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 06/09/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVE The exploration and the implementation of a deep learning method using a state-of-the-art convolutional neural network for the classification of polar maps represent myocardial perfusion for the detection of coronary artery disease. SUBJECTS AND METHODS In the proposed research, the dataset includes stress and rest polar maps in attenuation-corrected (AC) and non-corrected (NAC) format, counting specifically 144 normal and 170 pathological cases. Due to the small number of the dataset, the following methods were implemented: First, transfer learning was conducted using VGG16, which is applied broadly in medical industry. Furthermore, data augmentation was utilized, wherein the images are rotated and flipped for expanding the dataset. Secondly, we evaluated a custom convolutional neural network called RGB CNN, which utilizes fewer parameters and is more lightweight. In addition, we utilized the k-fold validation for evaluating variability and overall performance of the examined model. RESULTS Our RGB CNN model achieved an agreement rating of 92.07% with a loss of 0.2519. The transfer learning technique (VGG16) attained 95.83% accuracy. CONCLUSIONS The proposed model could be an effective tool for medical classification problems, in the case of polar map data acquired from myocardial perfusion images.
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Affiliation(s)
- Nikolaos I Papandrianos
- Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500, Larisa, Greece.
| | | | - Anna Feleki
- Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500, Larisa, Greece
| | | | - Elpiniki I Papageorgiou
- Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500, Larisa, Greece
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17
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Sengupta PP, Tokodi M. Automated Interpretation of Myocardial Perfusion Images: Tell Me AI Where to Look. JACC Cardiovasc Imaging 2022; 15:1103-1106. [PMID: 35680218 DOI: 10.1016/j.jcmg.2022.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 04/21/2022] [Indexed: 11/29/2022]
Affiliation(s)
- Partho P Sengupta
- Division of Cardiovascular Diseases, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA.
| | - Márton Tokodi
- Division of Cardiovascular Diseases, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA; Heart and Vascular Center, Semmelweis University, Budapest, Hungary
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18
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Teuho J, Schultz J, Klén R, Knuuti J, Saraste A, Ono N, Kanaya S. Classification of ischemia from myocardial polar maps in 15O-H 2O cardiac perfusion imaging using a convolutional neural network. Sci Rep 2022; 12:2839. [PMID: 35181681 PMCID: PMC8857225 DOI: 10.1038/s41598-022-06604-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 02/03/2022] [Indexed: 12/02/2022] Open
Abstract
We implemented a two-dimensional convolutional neural network (CNN) for classification of polar maps extracted from Carimas (Turku PET Centre, Finland) software used for myocardial perfusion analysis. 138 polar maps from 15O–H2O stress perfusion study in JPEG format from patients classified as ischemic or non-ischemic based on finding obstructive coronary artery disease (CAD) on invasive coronary artery angiography were used. The CNN was evaluated against the clinical interpretation. The classification accuracy was evaluated with: accuracy (ACC), area under the receiver operating characteristic curve (AUC), F1 score (F1S), sensitivity (SEN), specificity (SPE) and precision (PRE). The CNN had a median ACC of 0.8261, AUC of 0.8058, F1S of 0.7647, SEN of 0.6500, SPE of 0.9615 and PRE of 0.9286. In comparison, clinical interpretation had ACC of 0.8696, AUC of 0.8558, F1S of 0.8333, SEN of 0.7500, SPE of 0.9615 and PRE of 0.9375. The CNN classified only 2 cases differently than the clinical interpretation. The clinical interpretation and CNN had similar accuracy in classifying false positives and true negatives. Classification of ischemia is feasible in 15O–H2O stress perfusion imaging using JPEG polar maps alone with a custom CNN and may be useful for the detection of obstructive CAD.
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Affiliation(s)
- Jarmo Teuho
- Data Science Center, Nara University of Science and Technology, Nara, Japan. .,Turku PET Centre, University of Turku, Turku, Finland. .,Turku PET Centre, Turku University Hospital, Turku, Finland.
| | - Jussi Schultz
- Turku PET Centre, Turku University Hospital, Turku, Finland
| | - Riku Klén
- Turku PET Centre, University of Turku, Turku, Finland.,Turku PET Centre, Turku University Hospital, Turku, Finland
| | - Juhani Knuuti
- Turku PET Centre, University of Turku, Turku, Finland.,Turku PET Centre, Turku University Hospital, Turku, Finland
| | - Antti Saraste
- Turku PET Centre, Turku University Hospital, Turku, Finland.,Heart Centre, Turku University Hospital and University of Turku, Turku, Finland
| | - Naoaki Ono
- Data Science Center, Nara University of Science and Technology, Nara, Japan.,Department of Science and Technology, Nara University of Science and Technology, Nara, Japan
| | - Shigehiko Kanaya
- Department of Science and Technology, Nara University of Science and Technology, Nara, Japan
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Creţu AM, Monti F, Marrone S, Dong X, Bronstein M, de Montjoye YA. Interaction data are identifiable even across long periods of time. Nat Commun 2022; 13:313. [PMID: 35078995 PMCID: PMC8789822 DOI: 10.1038/s41467-021-27714-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Accepted: 11/29/2021] [Indexed: 11/09/2022] Open
Abstract
AbstractFine-grained records of people’s interactions, both offline and online, are collected at large scale. These data contain sensitive information about whom we meet, talk to, and when. We demonstrate here how people’s interaction behavior is stable over long periods of time and can be used to identify individuals in anonymous datasets. Our attack learns the profile of an individual using geometric deep learning and triplet loss optimization. In a mobile phone metadata dataset of more than 40k people, it correctly identifies 52% of individuals based on their 2-hop interaction graph. We further show that the profiles learned by our method are stable over time and that 24% of people are still identifiable after 20 weeks. Our results suggest that people with well-balanced interaction graphs are more identifiable. Applying our attack to Bluetooth close-proximity networks, we show that even 1-hop interaction graphs are enough to identify people more than 26% of the time. Our results provide strong evidence that disconnected and even re-pseudonymized interaction data can be linked together making them personal data under the European Union’s General Data Protection Regulation.
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20
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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: 0] [Impact Index Per Article: 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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21
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Seiffert AP, Gómez-Grande A, Castro-Leal G, Rodríguez A, Palomino-Fernández D, Gómez EJ, Sánchez-González P, Bueno H. An image processing tool for the detection of anthracycline-induced cardiotoxicity by evaluating the myocardial metabolic activity in [ 18F]FDG PET/CT. Int J Comput Assist Radiol Surg 2021; 17:373-383. [PMID: 34698987 PMCID: PMC8784503 DOI: 10.1007/s11548-021-02508-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 09/22/2021] [Indexed: 01/22/2023]
Abstract
Purpose Chemotherapy-induced cardiotoxicity is one of the main complications during and after cancer treatment. While echocardiography is the most used technique in clinical practice to evaluate left ventricular (LV) dysfunction, a multimodal approach is preferred for the early detection of anthracycline-induced cardiotoxicity. In this paper, an image processing tool allowing the qualitative and quantitative analysis of myocardial metabolic activity by [18F]fluorodeoxyglucose (FDG) positron emission tomography computed tomography (PET/CT) images, acquired routinely during and after cancer treatment, is presented. Methods The methodology is based on cardiac single photon emission computed tomography image processing protocols used in clinical practice. LV polar maps are created, and quantitative regional values are calculated. The tool was validated in a study group of 24 patients with Hodgkin or non-Hodgkin lymphoma (HL and NHL, respectively) treated with anthracyclines. Staging, interim and end-of-treatment [18F]FDG PET/CT images were acquired and the presented tool was used to extract the quantitative metrics of LV metabolic activity. Results Results show an overall increase of metabolic activity in the interim PET image acquired while on treatment compared to staging PET, which then decreased in the end-of-treatment scan. Positive correlation coefficients between staging and interim scans, and negative correlation coefficients between interim and end-of-treatment scans also support this finding. Metabolic changes occur predominantly in the septal region. Conclusion The proposed methodology and presented software solution provides the capability to assess quantitatively myocardial metabolism acquired by routine [18F]FDG PET/CT scanning during cancer treatment for evaluating anthracycline-induced cardiotoxicity. The [18F]FDG PET/CT septal-lateral uptake ratio is proposed as a new quantitative measure of myocardial metabolism. Supplementary Information The online version contains supplementary material available at 10.1007/s11548-021-02508-9
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Affiliation(s)
- Alexander P Seiffert
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.
| | - Adolfo Gómez-Grande
- Department of Nuclear Medicine, Hospital Universitario 12 de Octubre, Madrid, Spain.
| | - Gonzalo Castro-Leal
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
| | - Antonia Rodríguez
- Department of Hematology, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - David Palomino-Fernández
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
| | - Enrique J Gómez
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Patricia Sánchez-González
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.,Centro de Investigación Biomédica en Red de Bioingeniería Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Héctor Bueno
- Cardiology Department and Instituto de Investigación Sanitaria (imas12), Hospital Universitario 12 de Octubre, Madrid, Spain.,Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain.,Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain.,Centro de Investigación Biomédica en Red de enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
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22
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Automatic Diagnosis of Coronary Artery Disease in SPECT Myocardial Perfusion Imaging Employing Deep Learning. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11146362] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Focusing on coronary artery disease (CAD) patients, this research paper addresses the problem of automatic diagnosis of ischemia or infarction using single-photon emission computed tomography (SPECT) (Siemens Symbia S Series) myocardial perfusion imaging (MPI) scans and investigates the capabilities of deep learning and convolutional neural networks. Considering the wide applicability of deep learning in medical image classification, a robust CNN model whose architecture was previously determined in nuclear image analysis is introduced to recognize myocardial perfusion images by extracting the insightful features of an image and use them to classify it correctly. In addition, a deep learning classification approach using transfer learning is implemented to classify cardiovascular images as normal or abnormal (ischemia or infarction) from SPECT MPI scans. The present work is differentiated from other studies in nuclear cardiology as it utilizes SPECT MPI images. To address the two-class classification problem of CAD diagnosis, achieving adequate accuracy, simple, fast and efficient CNN architectures were built based on a CNN exploration process. They were then employed to identify the category of CAD diagnosis, presenting its generalization capabilities. The results revealed that the applied methods are sufficiently accurate and able to differentiate the infarction or ischemia from healthy patients (overall classification accuracy = 93.47% ± 2.81%, AUC score = 0.936). To strengthen the findings of this study, the proposed deep learning approaches were compared with other popular state-of-the-art CNN architectures for the specific dataset. The prediction results show the efficacy of new deep learning architecture applied for CAD diagnosis using SPECT MPI scans over the existing ones in nuclear medicine.
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23
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Apostolopoulos ID, Apostolopoulos DI, Spyridonidis TI, Papathanasiou ND, Panayiotakis GS. Multi-input deep learning approach for Cardiovascular Disease diagnosis using Myocardial Perfusion Imaging and clinical data. Phys Med 2021; 84:168-177. [PMID: 33901861 DOI: 10.1016/j.ejmp.2021.04.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 04/02/2021] [Accepted: 04/12/2021] [Indexed: 10/21/2022] Open
Abstract
PURPOSE Accurate detection and treatment of Coronary Artery Disease is mainly based on invasive Coronary Angiography, which could be avoided provided that a robust, non-invasive detection methodology emerged. Despite the progress of computational systems, this remains a challenging issue. The present research investigates Machine Learning and Deep Learning methods in competing with the medical experts' diagnostic yield. Although the highly accurate detection of Coronary Artery Disease, even from the experts, is presently implausible, developing Artificial Intelligence models to compete with the human eye and expertise is the first step towards a state-of-the-art Computer-Aided Diagnostic system. METHODS A set of 566 patient samples is analysed. The dataset contains Polar Maps derived from scintigraphic Myocardial Perfusion Imaging studies, clinical data, and Coronary Angiography results. The latter is considered as reference standard. For the classification of the medical images, the InceptionV3 Convolutional Neural Network is employed, while, for the categorical and continuous features, Neural Networks and Random Forest classifier are proposed. RESULTS The research suggests that an optimal strategy competing with the medical expert's accuracy involves a hybrid multi-input network composed of InceptionV3 and a Random Forest. This method matches the expert's accuracy, which is 79.15% in the particular dataset. CONCLUSION Image classification using deep learning methods can cooperate with clinical data classification methods to enhance the robustness of the predicting model, aiming to compete with the medical expert's ability to identify Coronary Artery Disease subjects, from a large scale patient dataset.
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Affiliation(s)
- Ioannis D Apostolopoulos
- Department of Medical Physics, School of Medicine, University of Patras, GR 265-00 Patras, Greece.
| | | | - Trifon I Spyridonidis
- University Hospital of Patras, Department of Nuclear Medicine, GR 265-00 Patras, Greece
| | | | - George S Panayiotakis
- Department of Medical Physics, School of Medicine, University of Patras, GR 265-00 Patras, Greece
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24
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Slomka PJ, Moody JB, Miller RJH, Renaud JM, Ficaro EP, Garcia EV. Quantitative clinical nuclear cardiology, part 2: Evolving/emerging applications. J Nucl Cardiol 2021; 28:115-127. [PMID: 33067750 DOI: 10.1007/s12350-020-02337-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 07/28/2020] [Indexed: 02/07/2023]
Abstract
Quantitative analysis has been applied extensively to image processing and interpretation in nuclear cardiology to improve disease diagnosis and risk stratification. This is Part 2 of a two-part continuing medical education article, which will review the potential clinical role for emerging quantitative analysis tools. The article will describe advanced methods for quantifying dyssynchrony, ventricular function and perfusion, and hybrid imaging analysis. This article discusses evolving methods to measure myocardial blood flow with positron emission tomography and single-photon emission computed tomography. Novel quantitative assessments of myocardial viability, microcalcification and in patients with cardiac sarcoidosis and cardiac amyloidosis will also be described. Lastly, we will review the potential role for artificial intelligence to improve image analysis, disease diagnosis, and risk prediction. The potential clinical role for all these novel techniques will be highlighted as well as methods to optimize their implementation.
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Affiliation(s)
- Piotr J Slomka
- Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | | | - Robert J H Miller
- Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | | | - Edward P Ficaro
- INVIA Medical Imaging Solutions, Ann Arbor, MI, USA
- Division of Cardiology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Ernest V Garcia
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
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25
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Slomka PJ, Moody JB, Miller RJH, Renaud JM, Ficaro EP, Garcia EV. Quantitative clinical nuclear cardiology, part 2: Evolving/emerging applications. J Nucl Med 2020; 62:168-176. [PMID: 33067339 DOI: 10.2967/jnumed.120.242537] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 07/28/2020] [Indexed: 01/15/2023] Open
Abstract
Quantitative analysis has been applied extensively to image processing and interpretation in nuclear cardiology to improve disease diagnosis and risk stratification. This is Part 2 of a two-part continuing medical education article, which will review the potential clinical role for emerging quantitative analysis tools. The article will describe advanced methods for quantifying dyssynchrony, ventricular function and perfusion, and hybrid imaging analysis. This article discusses evolving methods to measure myocardial blood flow with positron emission tomography and single-photon emission computed tomography. Novel quantitative assessments of myocardial viability, microcalcification and in patients with cardiac sarcoidosis and cardiac amyloidosis will also be described. Lastly, we will review the potential role for artificial intelligence to improve image analysis, disease diagnosis, and risk prediction. The potential clinical role for all these novel techniques will be highlighted as well as methods to optimize their implementation. (J Nucl Cardiol 2020).
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Affiliation(s)
- Piotr J Slomka
- Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA
| | | | - Robert J H Miller
- Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA.,Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | | | - Edward P Ficaro
- INVIA Medical Imaging Solutions, Ann Arbor, MI.,Division of Cardiology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI; and
| | - Ernest V Garcia
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
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26
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Sengupta PP, Shrestha S, Berthon B, Messas E, Donal E, Tison GH, Min JK, D'hooge J, Voigt JU, Dudley J, Verjans JW, Shameer K, Johnson K, Lovstakken L, Tabassian M, Piccirilli M, Pernot M, Yanamala N, Duchateau N, Kagiyama N, Bernard O, Slomka P, Deo R, Arnaout R. Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council. JACC Cardiovasc Imaging 2020; 13:2017-2035. [PMID: 32912474 PMCID: PMC7953597 DOI: 10.1016/j.jcmg.2020.07.015] [Citation(s) in RCA: 111] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 07/15/2020] [Accepted: 07/16/2020] [Indexed: 12/20/2022]
Abstract
Machine learning (ML) has been increasingly used within cardiology, particularly in the domain of cardiovascular imaging. Due to the inherent complexity and flexibility of ML algorithms, inconsistencies in the model performance and interpretation may occur. Several review articles have been recently published that introduce the fundamental principles and clinical application of ML for cardiologists. This paper builds on these introductory principles and outlines a more comprehensive list of crucial responsibilities that need to be completed when developing ML models. This paper aims to serve as a scientific foundation to aid investigators, data scientists, authors, editors, and reviewers involved in machine learning research with the intent of uniform reporting of ML investigations. An independent multidisciplinary panel of ML experts, clinicians, and statisticians worked together to review the theoretical rationale underlying 7 sets of requirements that may reduce algorithmic errors and biases. Finally, the paper summarizes a list of reporting items as an itemized checklist that highlights steps for ensuring correct application of ML models and the consistent reporting of model specifications and results. It is expected that the rapid pace of research and development and the increased availability of real-world evidence may require periodic updates to the checklist.
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Affiliation(s)
- Partho P Sengupta
- West Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia.
| | - Sirish Shrestha
- West Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia
| | - Béatrice Berthon
- Physique pour la Médecine Paris, Inserm U1273, CNRS FRE 2031, ESPCI Paris, PSL Research University, Paris, France
| | - Emmanuel Messas
- Université Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Erwan Donal
- Département de Cardiologie et Maladies Vasculaires, Service de Cardiologie et maladies vasculaires, CHU Rennes, Rennes, France
| | - Geoffrey H Tison
- Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California
| | | | - Jan D'hooge
- Laboratory on Cardiovascular Imaging and Dynamics, Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Jens-Uwe Voigt
- Department of Cardiovascular Science, KU Leuven, Leuven, Belgium; Department of Cardiovascular Diseases, University Hospitals Leuven, Belgium
| | - Joel Dudley
- Department of Genetics and Genomic Sciences and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York; Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Johan W Verjans
- Australian Institute for Machine Learning, University of Adelaide, North Terrace, Adelaide, South Australia, Australia; Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Khader Shameer
- Department of Genetics and Genomic Sciences and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York; Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kipp Johnson
- Department of Genetics and Genomic Sciences and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York; Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Lasse Lovstakken
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Mahdi Tabassian
- Laboratory on Cardiovascular Imaging and Dynamics, Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Marco Piccirilli
- West Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia
| | - Mathieu Pernot
- Physique pour la Médecine Paris, Inserm U1273, CNRS FRE 2031, ESPCI Paris, PSL Research University, Paris, France
| | - Naveena Yanamala
- West Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia
| | - Nicolas Duchateau
- CREATIS, CNRS UMR 5220, INSERM U1206, Université Lyon 1, INSA-LYON, France
| | - Nobuyuki Kagiyama
- West Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia
| | - Olivier Bernard
- CREATIS, CNRS UMR 5220, INSERM U1206, Université Lyon 1, INSA-LYON, France
| | - Piotr Slomka
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Rahul Deo
- Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California
| | - Rima Arnaout
- Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California
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27
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Kaplan Berkaya S, Ak Sivrikoz I, Gunal S. Classification models for SPECT myocardial perfusion imaging. Comput Biol Med 2020; 123:103893. [PMID: 32768042 DOI: 10.1016/j.compbiomed.2020.103893] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 06/26/2020] [Accepted: 06/26/2020] [Indexed: 11/29/2022]
Abstract
OBJECTIVE The main goal of this work is to develop computer-aided classification models for single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) to identify perfusion abnormalities (myocardial ischemia and/or infarction). METHODS Two different classification models, namely, deep learning (DL)-based and knowledge-based, are proposed. The first type of model utilizes transfer learning with pre-trained deep neural networks and a support vector machine classifier with deep and shallow features extracted from those networks. The latter type of model, on the other hand, aims to transform the knowledge of expert readers to appropriate image processing techniques including particular color thresholding, segmentation, feature extraction, and some heuristics. In addition, the summed stress and rest images from 192 patients (age 26-96, average age 61.5, 38% men, and 78% coronary artery disease) were collected to constitute a new dataset. The visual assessment of two expert readers on this dataset is used as a reference standard. The performances of the proposed models were then evaluated according to this standard. RESULTS The maximum accuracy, sensitivity, and specificity values are computed as 94%, 88%, and 100% for the DL-based model and 93%, 100%, and 86% for the knowledge-based model, respectively. CONCLUSION The proposed models provided diagnostic performance close to the level of expert analysis. Therefore, they can aid in clinical decision making for the interpretation of SPECT MPI regarding myocardial ischemia and infarction.
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Affiliation(s)
- Selcan Kaplan Berkaya
- Department of Computer Engineering, Faculty of Engineering, Eskisehir Technical University, Eskisehir, Turkiye.
| | - Ilknur Ak Sivrikoz
- Department of Nuclear Medicine, Faculty of Medicine, Eskisehir Osmangazi University, Eskisehir, Turkiye.
| | - Serkan Gunal
- Department of Computer Engineering, Faculty of Engineering, Eskisehir Technical University, Eskisehir, Turkiye.
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Slomka PJ, Miller RJH, Isgum I, Dey D. Application and Translation of Artificial Intelligence to Cardiovascular Imaging in Nuclear Medicine and Noncontrast CT. Semin Nucl Med 2020; 50:357-366. [DOI: 10.1053/j.semnuclmed.2020.03.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Abstract
Cardiac imaging has a pivotal role in the prevention, diagnosis and treatment of ischaemic heart disease. SPECT is most commonly used for clinical myocardial perfusion imaging, whereas PET is the clinical reference standard for the quantification of myocardial perfusion. MRI does not involve exposure to ionizing radiation, similar to echocardiography, which can be performed at the bedside. CT perfusion imaging is not frequently used but CT offers coronary angiography data, and invasive catheter-based methods can measure coronary flow and pressure. Technical improvements to the quantification of pathophysiological parameters of myocardial ischaemia can be achieved. Clinical consensus recommendations on the appropriateness of each technique were derived following a European quantitative cardiac imaging meeting and using a real-time Delphi process. SPECT using new detectors allows the quantification of myocardial blood flow and is now also suited to patients with a high BMI. PET is well suited to patients with multivessel disease to confirm or exclude balanced ischaemia. MRI allows the evaluation of patients with complex disease who would benefit from imaging of function and fibrosis in addition to perfusion. Echocardiography remains the preferred technique for assessing ischaemia in bedside situations, whereas CT has the greatest value for combined quantification of stenosis and characterization of atherosclerosis in relation to myocardial ischaemia. In patients with a high probability of needing invasive treatment, invasive coronary flow and pressure measurement is well suited to guide treatment decisions. In this Consensus Statement, we summarize the strengths and weaknesses as well as the future technological potential of each imaging modality.
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30
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Slomka P. Leveraging latest computer science tools to advance nuclear cardiology. J Nucl Cardiol 2019; 26:1501-1504. [PMID: 31489585 DOI: 10.1007/s12350-019-01873-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2019] [Accepted: 08/19/2019] [Indexed: 12/23/2022]
Abstract
Nuclear cardiology has unique advantages compared to other modalities, since the image analysis is already much more automated compared to what is currently clinically performed for CT, MR, or echocardiography imaging. The diverse image and clinical data available to assess coronary disease function, perfusion, flow, and associated CT data provide new opportunities, but logistically these additional assessments increase the overall complexity of SPECT/PET reporting, necessitating additional expertise and time. The advances in artificial intelligence software can be leveraged to obtain comprehensive risk predictions and diagnoses from all available data. They will allow nuclear cardiology to retain competitive edge compared to other modalities and improve its overall clinical utility. These tools will enhance diagnosis and risk prediction beyond what is possible by subjective visual analysis and mental integration of data by physicians.
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Affiliation(s)
- Piotr Slomka
- Department of Medicine, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. A047N, Los Angeles, CA, 90048, USA.
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