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Hussain D, Al-Masni MA, Aslam M, Sadeghi-Niaraki A, Hussain J, Gu YH, Naqvi RA. Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches: methods, applications and limitations. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024:XST230429. [PMID: 38701131 DOI: 10.3233/xst-230429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
BACKGROUND The emergence of deep learning (DL) techniques has revolutionized tumor detection and classification in medical imaging, with multimodal medical imaging (MMI) gaining recognition for its precision in diagnosis, treatment, and progression tracking. OBJECTIVE This review comprehensively examines DL methods in transforming tumor detection and classification across MMI modalities, aiming to provide insights into advancements, limitations, and key challenges for further progress. METHODS Systematic literature analysis identifies DL studies for tumor detection and classification, outlining methodologies including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. Integration of multimodality imaging enhances accuracy and robustness. RESULTS Recent advancements in DL-based MMI evaluation methods are surveyed, focusing on tumor detection and classification tasks. Various DL approaches, including CNNs, YOLO, Siamese Networks, Fusion-Based Models, Attention-Based Models, and Generative Adversarial Networks, are discussed with emphasis on PET-MRI, PET-CT, and SPECT-CT. FUTURE DIRECTIONS The review outlines emerging trends and future directions in DL-based tumor analysis, aiming to guide researchers and clinicians toward more effective diagnosis and prognosis. Continued innovation and collaboration are stressed in this rapidly evolving domain. CONCLUSION Conclusions drawn from literature analysis underscore the efficacy of DL approaches in tumor detection and classification, highlighting their potential to address challenges in MMI analysis and their implications for clinical practice.
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Affiliation(s)
- Dildar Hussain
- Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Republic of Korea
| | - Mohammed A Al-Masni
- Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Republic of Korea
| | - Muhammad Aslam
- Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Republic of Korea
| | - Abolghasem Sadeghi-Niaraki
- Department of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea
| | - Jamil Hussain
- Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Republic of Korea
| | - Yeong Hyeon Gu
- Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Republic of Korea
| | - Rizwan Ali Naqvi
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, Republic of Korea
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Cau R, Pisu F, Suri JS, Montisci R, Gatti M, Mannelli L, Gong X, Saba L. Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes. Diagnostics (Basel) 2024; 14:156. [PMID: 38248033 PMCID: PMC11154548 DOI: 10.3390/diagnostics14020156] [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: 12/12/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 01/23/2024] Open
Abstract
Artificial intelligence (AI) is rapidly being applied to the medical field, especially in the cardiovascular domain. AI approaches have demonstrated their applicability in the detection, diagnosis, and management of several cardiovascular diseases, enhancing disease stratification and typing. Cardiomyopathies are a leading cause of heart failure and life-threatening ventricular arrhythmias. Identifying the etiologies is fundamental for the management and diagnostic pathway of these heart muscle diseases, requiring the integration of various data, including personal and family history, clinical examination, electrocardiography, and laboratory investigations, as well as multimodality imaging, making the clinical diagnosis challenging. In this scenario, AI has demonstrated its capability to capture subtle connections from a multitude of multiparametric datasets, enabling the discovery of hidden relationships in data and handling more complex tasks than traditional methods. This review aims to present a comprehensive overview of the main concepts related to AI and its subset. Additionally, we review the existing literature on AI-based models in the differential diagnosis of cardiomyopathy phenotypes, and we finally examine the advantages and limitations of these AI approaches.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
| | - Francesco Pisu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoin™, Roseville, CA 95661, USA;
| | - Roberta Montisci
- Department of Cardiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy;
| | - Marco Gatti
- Department of Radiology, Università degli Studi di Torino, 10129 Turin, Italy;
| | | | - Xiangyang Gong
- Radiology Department, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou 310014, China;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
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Cau R, Pisu F, Suri JS, Mannelli L, Scaglione M, Masala S, Saba L. Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media? Diagnostics (Basel) 2023; 13:2061. [PMID: 37370956 PMCID: PMC10297403 DOI: 10.3390/diagnostics13122061] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/10/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
In recent years, cardiovascular imaging examinations have experienced exponential growth due to technological innovation, and this trend is consistent with the most recent chest pain guidelines. Contrast media have a crucial role in cardiovascular magnetic resonance (CMR) imaging, allowing for more precise characterization of different cardiovascular diseases. However, contrast media have contraindications and side effects that limit their clinical application in determinant patients. The application of artificial intelligence (AI)-based techniques to CMR imaging has led to the development of non-contrast models. These AI models utilize non-contrast imaging data, either independently or in combination with clinical and demographic data, as input to generate diagnostic or prognostic algorithms. In this review, we provide an overview of the main concepts pertaining to AI, review the existing literature on non-contrast AI models in CMR, and finally, discuss the strengths and limitations of these AI models and their possible future development.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, University Hospital of Cagliari, 09042 Monserrato, Italy; (R.C.); (F.P.)
| | - Francesco Pisu
- Department of Radiology, University Hospital of Cagliari, 09042 Monserrato, Italy; (R.C.); (F.P.)
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA;
| | | | - Mariano Scaglione
- Department of Radiology, University Hospital of Sassari, 07100 Sassari, Italy; (M.S.); (S.M.)
| | - Salvatore Masala
- Department of Radiology, University Hospital of Sassari, 07100 Sassari, Italy; (M.S.); (S.M.)
| | - Luca Saba
- Department of Radiology, University Hospital of Cagliari, 09042 Monserrato, Italy; (R.C.); (F.P.)
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Vidal-Perez R, Vazquez-Rodriguez JM. Role of artificial intelligence in cardiology. World J Cardiol 2023; 15:116-118. [PMID: 37124979 PMCID: PMC10130891 DOI: 10.4330/wjc.v15.i4.116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/19/2023] [Accepted: 04/10/2023] [Indexed: 04/20/2023] Open
Abstract
Artificial intelligence (AI) is the process of having a computational program that can perform tasks of human intelligence by mimicking human thought processes. AI is a rapidly evolving transdisciplinary field which integrates many elements to develop algorithms that aim to simulate human intuition, decision-making, and object recognition. The overarching aims of AI in cardiovascular medicine are threefold: To optimize patient care, improve efficiency, and improve clinical outcomes. In cardiology, there has been a growth in the potential sources of new patient data, as well as advances in investigations and therapies, which position the field well to uniquely benefit from AI. In this editorial, we highlight some of the main research priorities currently and where the next steps are heading us.
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Affiliation(s)
- Rafael Vidal-Perez
- Servicio de Cardiología, Unidad de Imagen y Función Cardíaca, Complexo Hospitalario Universitario A Coruña Centro de Investigación Biomédica en Red-Instituto de Salud Carlos III, A Coruña 15006, A Coruña, Spain
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Diao K, Liang HQ, Yin HK, Yuan MJ, Gu M, Yu PX, He S, Sun J, Song B, Li K, He Y. Multi-channel deep learning model-based myocardial spatial-temporal morphology feature on cardiac MRI cine images diagnoses the cause of LVH. Insights Imaging 2023; 14:70. [PMID: 37093501 PMCID: PMC10126185 DOI: 10.1186/s13244-023-01401-0] [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: 11/23/2022] [Accepted: 03/08/2023] [Indexed: 04/25/2023] Open
Abstract
BACKGROUND To develop a fully automatic framework for the diagnosis of cause for left ventricular hypertrophy (LVH) via cardiac cine images. METHODS A total of 302 LVH patients with cine MRI images were recruited as the primary cohort. Another 53 LVH patients prospectively collected or from multi-centers were used as the external test dataset. Different models based on the cardiac regions (Model 1), segmented ventricle (Model 2) and ventricle mask (Model 3) were constructed. The diagnostic performance was accessed by the confusion matrix with respect to overall accuracy. The capability of the predictive models for binary classification of cardiac amyloidosis (CA), hypertrophic cardiomyopathy (HCM) or hypertensive heart disease (HHD) were also evaluated. Additionally, the diagnostic performance of best Model was compared with that of 7 radiologists/cardiologists. RESULTS Model 3 showed the best performance with an overall classification accuracy up to 77.4% in the external test datasets. On the subtasks for identifying CA, HCM or HHD only, Model 3 also achieved the best performance with AUCs yielding 0.895-0.980, 0.879-0.984 and 0.848-0.983 in the validation, internal test and external test datasets, respectively. The deep learning model showed non-inferior diagnostic capability to the cardiovascular imaging expert and outperformed other radiologists/cardiologists. CONCLUSION The combined model based on the mask of left ventricular segmented from multi-sequences cine MR images shows favorable and robust performance in diagnosing the cause of left ventricular hypertrophy, which could be served as a noninvasive tool and help clinical decision.
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Affiliation(s)
- Kaiyue Diao
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Hong-Qing Liang
- Department of Radiology, First Affiliated Hospital to Army Medical University (Third Military Medical University Southwest Hospital), Chongqing, China
| | - Hong-Kun Yin
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Ming-Jing Yuan
- Department of Radiology, Yongchuan Hospital, Chongqing Medical University, Chongqing, China
| | - Min Gu
- Department of Radiology, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing, China
| | - Peng-Xin Yu
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Sen He
- Department of Cardiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - Jiayu Sun
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Department of Radiology, Sanya Municipal People's Hospital, Sanya, Hainan, China
| | - Kang Li
- West China Biomedical Big Data Center, Med-X Center for Informatics, West China Hospital, Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, China.
| | - Yong He
- Department of Cardiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
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Auto-MyIn: Automatic diagnosis of myocardial infarction via multiple GLCMs, CNNs, and SVMs. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Counseller Q, Aboelkassem Y. Recent technologies in cardiac imaging. FRONTIERS IN MEDICAL TECHNOLOGY 2023; 4:984492. [PMID: 36704232 PMCID: PMC9872125 DOI: 10.3389/fmedt.2022.984492] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 11/30/2022] [Indexed: 01/11/2023] Open
Abstract
Cardiac imaging allows physicians to view the structure and function of the heart to detect various heart abnormalities, ranging from inefficiencies in contraction, regulation of volumetric input and output of blood, deficits in valve function and structure, accumulation of plaque in arteries, and more. Commonly used cardiovascular imaging techniques include x-ray, computed tomography (CT), magnetic resonance imaging (MRI), echocardiogram, and positron emission tomography (PET)/single-photon emission computed tomography (SPECT). More recently, even more tools are at our disposal for investigating the heart's physiology, performance, structure, and function due to technological advancements. This review study summarizes cardiac imaging techniques with a particular interest in MRI and CT, noting each tool's origin, benefits, downfalls, clinical application, and advancement of cardiac imaging in the near future.
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Affiliation(s)
- Quinn Counseller
- College of Health Sciences, University of Michigan, Flint, MI, United States
| | - Yasser Aboelkassem
- College of Innovation and Technology, University of Michigan, Flint, MI, United States,Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, United States,Correspondence: Yasser Aboelkassem
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Nittas V, Daniore P, Landers C, Gille F, Amann J, Hubbs S, Puhan MA, Vayena E, Blasimme A. Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. PLOS DIGITAL HEALTH 2023; 2:e0000189. [PMID: 36812620 PMCID: PMC9931290 DOI: 10.1371/journal.pdig.0000189] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 01/02/2023] [Indexed: 02/04/2023]
Abstract
Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field's potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.
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Affiliation(s)
- Vasileios Nittas
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Paola Daniore
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Constantin Landers
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Felix Gille
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Julia Amann
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Shannon Hubbs
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Milo Alan Puhan
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Alessandro Blasimme
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
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Wellnhofer E. Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging. Front Cardiovasc Med 2022; 9:890809. [PMID: 35935648 PMCID: PMC9354141 DOI: 10.3389/fcvm.2022.890809] [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: 03/06/2022] [Accepted: 06/13/2022] [Indexed: 12/02/2022] Open
Abstract
Recent progress in digital health data recording, advances in computing power, and methodological approaches that extract information from data as artificial intelligence are expected to have a disruptive impact on technology in medicine. One of the potential benefits is the ability to extract new and essential insights from the vast amount of data generated during health care delivery every day. Cardiovascular imaging is boosted by new intelligent automatic methods to manage, process, segment, and analyze petabytes of image data exceeding historical manual capacities. Algorithms that learn from data raise new challenges for regulatory bodies. Partially autonomous behavior and adaptive modifications and a lack of transparency in deriving evidence from complex data pose considerable problems. Controlling new technologies requires new controlling techniques and ongoing regulatory research. All stakeholders must participate in the quest to find a fair balance between innovation and regulation. The regulatory approach to artificial intelligence must be risk-based and resilient. A focus on unknown emerging risks demands continuous surveillance and clinical evaluation during the total product life cycle. Since learning algorithms are data-driven, high-quality data is fundamental for good machine learning practice. Mining, processing, validation, governance, and data control must account for bias, error, inappropriate use, drifts, and shifts, particularly in real-world data. Regulators worldwide are tackling twenty-first century challenges raised by “learning” medical devices. Ethical concerns and regulatory approaches are presented. The paper concludes with a discussion on the future of responsible artificial intelligence.
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Haq IU, Chhatwal K, Sanaka K, Xu B. Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects. Vasc Health Risk Manag 2022; 18:517-528. [PMID: 35855754 PMCID: PMC9288176 DOI: 10.2147/vhrm.s279337] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 07/07/2022] [Indexed: 11/23/2022] Open
Abstract
Cardiovascular disease (CVD) represents a significant and increasing burden on healthcare systems. Artificial intelligence (AI) is a rapidly evolving transdisciplinary field employing machine learning (ML) techniques, which aim to simulate human intuition to offer cost-effective and scalable solutions to better manage CVD. ML algorithms are increasingly being developed and applied in various facets of cardiovascular medicine, including and not limited to heart failure, electrophysiology, valvular heart disease and coronary artery disease. Within heart failure, AI algorithms can augment diagnostic capabilities and clinical decision-making through automated cardiac measurements. Occult cardiac disease is increasingly being identified using ML from diagnostic data. Improved diagnostic and prognostic capabilities using ML algorithms are enhancing clinical care of patients with valvular heart disease and coronary artery disease. The growth of AI techniques is not without inherent challenges, most important of which is the need for greater external validation through multicenter, prospective clinical trials.
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Affiliation(s)
- Ikram U Haq
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | | | | | - Bo Xu
- Section of Cardiovascular Imaging, Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Miller Family Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
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Jiang R, Yeung DF, Behnami D, Luong C, Tsang MYC, Jue J, Gin K, Nair P, Abolmaesumi P, Tsang TSM. A Novel Continuous Left Ventricular Diastolic Function Score Using Machine Learning. J Am Soc Echocardiogr 2022; 35:1247-1255. [PMID: 35753590 DOI: 10.1016/j.echo.2022.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 06/02/2022] [Accepted: 06/05/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Unlike left ventricular (LV) ejection fraction, which provides a precise, reliable, and prognostically valuable measure of systolic function, there is no single analogous measure of LV diastolic function. OBJECTIVES We aimed to develop a continuous score to grade LV diastolic function using machine learning modeling of echocardiographic data. METHODS Consecutive echo studies performed at a tertiary care centre between February 1, 2010 and March 31, 2016 were assessed, excluding studies containing features that would interfere with diastolic function assessment as well as studies in which one or more parameters within the contemporary diastolic function assessment algorithm were not reported. Diastolic function was graded based on 2016 American Society of Echocardiography (ASE) / European Association of Cardiovascular Imaging (EACVI) guidelines, excluding indeterminate studies. Machine learning models were trained (SVM [support vector machine], DT [decision tree], XGB [XGBoost], and DNN [dense neural network]) to classify studies within the training set by diastolic dysfunction severity, blinded to the ASE/EACVI classification. The DNN model was retrained to generate a regression model (R-DNN) to predict a continuous LV diastolic function score. RESULTS A total of 28,986 studies were included; 23,188 studies were used to train the models and 5798 studies were used for validation. The models were able to reclassify studies with high agreement to the ASE/EACVI algorithm (SVM 83%, DT 100%, XGB 100%, DNN 98%). The continuous diastolic function score corresponded well with ASE/EACVI guidelines, with scores of 1.00 ± 0.01 for studies with normal function; and 0.74 ± 0.05, 0.51 ± 0.06, and 0.27 ± 0.11 for mild, moderate, and severe diastolic dysfunction respectively (mean ± 1 standard deviation). A score of <0.91 predicted abnormal diastolic function (AUC 0.99) while a score of <0.65 predicted elevated filling pressure (AUC 0.99). CONCLUSIONS Machine learning can assimilate echocardiographic data and generate an automated continuous diastolic function score that corresponds well with current diastolic function grading recommendations.
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Affiliation(s)
- River Jiang
- Division of Cardiology, University of British Columbia, Gordon & Leslie Diamond Health Care Centre, 2775 Laurel St, 9th Floor, Vancouver, BC, Canada V5Z 1M9
| | - Darwin F Yeung
- Division of Cardiology, University of British Columbia, Gordon & Leslie Diamond Health Care Centre, 2775 Laurel St, 9th Floor, Vancouver, BC, Canada V5Z 1M9
| | - Delaram Behnami
- Department of Electrical and Computer Engineering, University of British Columbia, 5500-2332 Main Mall, Vancouver, BC, Canada V6T 1Z4
| | - Christina Luong
- Division of Cardiology, University of British Columbia, Gordon & Leslie Diamond Health Care Centre, 2775 Laurel St, 9th Floor, Vancouver, BC, Canada V5Z 1M9
| | - Michael Y C Tsang
- Division of Cardiology, University of British Columbia, Gordon & Leslie Diamond Health Care Centre, 2775 Laurel St, 9th Floor, Vancouver, BC, Canada V5Z 1M9
| | - John Jue
- Division of Cardiology, University of British Columbia, Gordon & Leslie Diamond Health Care Centre, 2775 Laurel St, 9th Floor, Vancouver, BC, Canada V5Z 1M9
| | - Ken Gin
- Division of Cardiology, University of British Columbia, Gordon & Leslie Diamond Health Care Centre, 2775 Laurel St, 9th Floor, Vancouver, BC, Canada V5Z 1M9
| | - Parvathy Nair
- Division of Cardiology, University of British Columbia, Gordon & Leslie Diamond Health Care Centre, 2775 Laurel St, 9th Floor, Vancouver, BC, Canada V5Z 1M9
| | - Purang Abolmaesumi
- Department of Electrical and Computer Engineering, University of British Columbia, 5500-2332 Main Mall, Vancouver, BC, Canada V6T 1Z4
| | - Teresa S M Tsang
- Division of Cardiology, University of British Columbia, Gordon & Leslie Diamond Health Care Centre, 2775 Laurel St, 9th Floor, Vancouver, BC, Canada V5Z 1M9.
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Martinez DSL, Noseworthy PA, Akbilgic O, Herrmann J, Ruddy KJ, Hamid A, Maddula R, Singh A, Davis R, Gunturkun F, Jefferies JL, Brown SA. Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2022; 15:100129. [PMID: 35721662 PMCID: PMC9202996 DOI: 10.1016/j.ahjo.2022.100129] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/20/2022] [Accepted: 03/21/2022] [Indexed: 01/21/2023]
Abstract
Cardiovascular disease is a leading cause of death among cancer survivors, second only to cancer recurrence or development of new tumors. Cardio-oncology has therefore emerged as a relatively new specialty focused on prevention and management of cardiovascular consequences of cancer therapies. Yet challenges remain regarding precision and accuracy with predicting individuals at highest risk for cardiotoxicity. Barriers such as access to care also limit screening and early diagnosis to improve prognosis. Thus, developing innovative approaches for prediction and early detection of cardiovascular illness in this population is critical. In this review, we provide an overview of the present state of machine learning applications in cardio-oncology. We begin by outlining some factors that should be considered while utilizing machine learning algorithms. We then examine research in which machine learning has been applied to improve prediction of cardiac dysfunction in cancer survivors. We also highlight the use of artificial intelligence (AI) in conjunction with electrocardiogram (ECG) to predict cardiac malfunction and also atrial fibrillation (AF), and we discuss the potential role of wearables. Additionally, the article summarizes future prospects and critical takeaways for the application of machine learning in cardio-oncology. This study is the first in a series on artificial intelligence in cardio-oncology, and complements our manuscript on echocardiography and other forms of imaging relevant to cancer survivors cared for in cardiology clinical practice.
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Affiliation(s)
- Daniel Sierra-Lara Martinez
- Coronary Care Unit, National Institute of Cardiology/Instituto Nacional de Cardiologia, Ciudad de Mexico, Mexico
| | | | - Oguz Akbilgic
- Department of Health Informatics and Data Science, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, IL, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Wake Forest, NC, USA
| | - Joerg Herrmann
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Ashima Singh
- Institute of Health and Equity, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Robert Davis
- Center for Biomedical Informatics, University of Tennessee Health Sciences Center, USA
| | - Fatma Gunturkun
- Center for Biomedical Informatics, University of Tennessee Health Sciences Center, USA
| | - John L. Jefferies
- Division of Cardiovascular Diseases, University of Tennessee Health Sciences Center, USA
- Department of Epidemiology, St. Jude Children's Research Hospital, USA
| | - Sherry-Ann Brown
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
- Cardio-Oncology Program, Division of Cardiovascular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
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Krajcer Z. Artificial Intelligence in Cardiovascular Medicine: Historical Overview, Current Status, and Future Directions. Tex Heart Inst J 2022; 49:480956. [PMID: 35481866 DOI: 10.14503/thij-20-7527] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Artificial intelligence and machine learning are rapidly gaining popularity in every aspect of our daily lives, and cardiovascular medicine is no exception. Here, we provide physicians with an overview of the past, present, and future of artificial intelligence applications in cardiovascular medicine. We describe essential and powerful examples of machine-learning applications in industry and elsewhere. Finally, we discuss the latest technologic advances, as well as the benefits and limitations of artificial intelligence and machine learning in cardiovascular medicine.
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Affiliation(s)
- Zvonimir Krajcer
- Department of Cardiology, Texas Heart Institute, Houston, Texas.,Division of Cardiology, Department of Internal Medicine, Baylor College of Medicine, Houston, Texas
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14
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Wang S, Patel H, Miller T, Ameyaw K, Narang A, Chauhan D, Anand S, Anyanwu E, Besser SA, Kawaji K, Liu XP, Lang RM, Mor-Avi V, Patel AR. AI Based CMR Assessment of Biventricular Function: Clinical Significance of Intervendor Variability and Measurement Errors. JACC Cardiovasc Imaging 2022; 15:413-427. [PMID: 34656471 PMCID: PMC8917993 DOI: 10.1016/j.jcmg.2021.08.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 08/09/2021] [Accepted: 08/17/2021] [Indexed: 12/30/2022]
Abstract
OBJECTIVES The aim of this study was to determine whether left ventricular ejection fraction (LVEF) and right ventricular ejection fraction (RVEF) and left ventricular mass (LVM) measurements made using 3 fully automated deep learning (DL) algorithms are accurate and interchangeable and can be used to classify ventricular function and risk-stratify patients as accurately as an expert. BACKGROUND Artificial intelligence is increasingly used to assess cardiac function and LVM from cardiac magnetic resonance images. METHODS Two hundred patients were identified from a registry of individuals who underwent vasodilator stress cardiac magnetic resonance. LVEF, LVM, and RVEF were determined using 3 fully automated commercial DL algorithms and by a clinical expert (CLIN) using conventional methodology. Additionally, LVEF values were classified according to clinically important ranges: <35%, 35% to 50%, and ≥50%. Both ejection fraction values and classifications made by the DL ejection fraction approaches were compared against CLIN ejection fraction reference. Receiver-operating characteristic curve analysis was performed to evaluate the ability of CLIN and each of the DL classifications to predict major adverse cardiovascular events. RESULTS Excellent correlations were seen for each DL-LVEF compared with CLIN-LVEF (r = 0.83-0.93). Good correlations were present between DL-LVM and CLIN-LVM (r = 0.75-0.85). Modest correlations were observed between DL-RVEF and CLIN-RVEF (r = 0.59-0.68). A >10% error between CLIN and DL ejection fraction was present in 5% to 18% of cases for the left ventricle and 23% to 43% for the right ventricle. LVEF classification agreed with CLIN-LVEF classification in 86%, 80%, and 85% cases for the 3 DL-LVEF approaches. There were no differences among the 4 approaches in associations with major adverse cardiovascular events for LVEF, LVM, and RVEF. CONCLUSIONS This study revealed good agreement between automated and expert-derived LVEF and similarly strong associations with outcomes, compared with an expert. However, the ability of these automated measurements to accurately classify left ventricular function for treatment decision remains limited. DL-LVM showed good agreement with CLIN-LVM. DL-RVEF approaches need further refinements.
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Affiliation(s)
- Shuo Wang
- University of Chicago, Chicago, Illinois,Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Hena Patel
- University of Chicago, Chicago, Illinois
| | | | | | | | | | | | | | | | - Keigo Kawaji
- University of Chicago, Chicago, Illinois,Illinois Institute of Technology, Chicago, Illinois
| | - Xing-Peng Liu
- Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
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15
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Thomas LB, Mastorides SM, Viswanadhan NA, Jakey CE, Borkowski AA. Artificial Intelligence: Review of Current and Future Applications in Medicine. Fed Pract 2022; 38:527-538. [PMID: 35136337 DOI: 10.12788/fp.0174] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Background The role of artificial intelligence (AI) in health care is expanding rapidly. Currently, there are at least 29 US Food and Drug Administration-approved AI health care devices that apply to numerous medical specialties and many more are in development. Observations With increasing expectations for all health care sectors to deliver timely, fiscally-responsible, high-quality health care, AI has potential utility in numerous areas, such as image analysis, improved workflow and efficiency, public health, and epidemiology, to aid in processing large volumes of patient and medical data. In this review, we describe basic terminology, principles, and general AI applications relating to health care. We then discuss current and future applications for a variety of medical specialties. Finally, we discuss the future potential of AI along with the potential risks and limitations of current AI technology. Conclusions AI can improve diagnostic accuracy, increase patient safety, assist with patient triage, monitor disease progression, and assist with treatment decisions.
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Affiliation(s)
- L Brannon Thomas
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
| | - Stephen M Mastorides
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
| | | | - Colleen E Jakey
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
| | - Andrew A Borkowski
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
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16
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Taylor AM. The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging. Pediatr Radiol 2022; 52:2131-2138. [PMID: 34936019 PMCID: PMC9537201 DOI: 10.1007/s00247-021-05218-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/13/2021] [Accepted: 10/05/2021] [Indexed: 11/24/2022]
Abstract
Artificial intelligence (AI) offers the potential to change many aspects of paediatric cardiac imaging. At present, there are only a few clinically validated examples of AI applications in this field. This review focuses on the use of AI in paediatric cardiovascular MRI, using examples from paediatric cardiovascular MRI, adult cardiovascular MRI and other radiologic experience.
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Affiliation(s)
- Andrew M. Taylor
- Great Ormond Street Hospital for Children, Zayed Centre for Research, 20 Guildford St., Room 3.7, London, WC1N 1DZ UK ,Cardiovascular Imaging, UCL Institute of Cardiovascular Science, London, UK
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17
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de Siqueira VS, Borges MM, Furtado RG, Dourado CN, da Costa RM. Artificial intelligence applied to support medical decisions for the automatic analysis of echocardiogram images: A systematic review. Artif Intell Med 2021; 120:102165. [PMID: 34629153 DOI: 10.1016/j.artmed.2021.102165] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 08/07/2021] [Accepted: 08/31/2021] [Indexed: 12/16/2022]
Abstract
The echocardiogram is a test that is widely used in Heart Disease Diagnoses. However, its analysis is largely dependent on the physician's experience. In this regard, artificial intelligence has become an essential technology to assist physicians. This study is a Systematic Literature Review (SLR) of primary state-of-the-art studies that used Artificial Intelligence (AI) techniques to automate echocardiogram analyses. Searches on the leading scientific article indexing platforms using a search string returned approximately 1400 articles. After applying the inclusion and exclusion criteria, 118 articles were selected to compose the detailed SLR. This SLR presents a thorough investigation of AI applied to support medical decisions for the main types of echocardiogram (Transthoracic, Transesophageal, Doppler, Stress, and Fetal). The article's data extraction indicated that the primary research interest of the studies comprised four groups: 1) Improvement of image quality; 2) identification of the cardiac window vision plane; 3) quantification and analysis of cardiac functions, and; 4) detection and classification of cardiac diseases. The articles were categorized and grouped to show the main contributions of the literature to each type of ECHO. The results indicate that the Deep Learning (DL) methods presented the best results for the detection and segmentation of the heart walls, right and left atrium and ventricles, and classification of heart diseases using images/videos obtained by echocardiography. The models that used Convolutional Neural Network (CNN) and its variations showed the best results for all groups. The evidence produced by the results presented in the tabulation of the studies indicates that the DL contributed significantly to advances in echocardiogram automated analysis processes. Although several solutions were presented regarding the automated analysis of ECHO, this area of research still has great potential for further studies to improve the accuracy of results already known in the literature.
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Affiliation(s)
- Vilson Soares de Siqueira
- Federal Institute of Tocantins, Av. Bernado Sayão, S/N, Santa Maria, Colinas do Tocantins, TO, Brazil; Federal University of Goias, Alameda Palmeiras, Quadra D, Câmpus Samambaia, Goiânia, GO, Brazil.
| | - Moisés Marcos Borges
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil
| | - Rogério Gomes Furtado
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil
| | - Colandy Nunes Dourado
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil. http://www.cdigoias.com.br
| | - Ronaldo Martins da Costa
- Federal University of Goias, Alameda Palmeiras, Quadra D, Câmpus Samambaia, Goiânia, GO, Brazil.
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18
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Morita SX, Kusunose K, Haga A, Sata M, Hasegawa K, Raita Y, Reilly MP, Fifer MA, Maurer MS, Shimada YJ. Deep Learning Analysis of Echocardiographic Images to Predict Positive Genotype in Patients With Hypertrophic Cardiomyopathy. Front Cardiovasc Med 2021; 8:669860. [PMID: 34513940 PMCID: PMC8429777 DOI: 10.3389/fcvm.2021.669860] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 08/09/2021] [Indexed: 11/26/2022] Open
Abstract
Genetic testing provides valuable insights into family screening strategies, diagnosis, and prognosis in patients with hypertrophic cardiomyopathy (HCM). On the other hand, genetic testing carries socio-economical and psychological burdens. It is therefore important to identify patients with HCM who are more likely to have positive genotype. However, conventional prediction models based on clinical and echocardiographic parameters offer only modest accuracy and are subject to intra- and inter-observer variability. We therefore hypothesized that deep convolutional neural network (DCNN, a type of deep learning) analysis of echocardiographic images improves the predictive accuracy of positive genotype in patients with HCM. In each case, we obtained parasternal short- and long-axis as well as apical 2-, 3-, 4-, and 5-chamber views. We employed DCNN algorithm to predict positive genotype based on the input echocardiographic images. We performed 5-fold cross-validations. We used 2 reference models—the Mayo HCM Genotype Predictor score (Mayo score) and the Toronto HCM Genotype score (Toronto score). We compared the area under the receiver-operating-characteristic curve (AUC) between a combined model using the reference model plus DCNN-derived probability and the reference model. We calculated the p-value by performing 1,000 bootstrapping. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, we examined the net reclassification improvement. We included 99 adults with HCM who underwent genetic testing. Overall, 45 patients (45%) had positive genotype. The new model combining Mayo score and DCNN-derived probability significantly outperformed Mayo score (AUC 0.86 [95% CI 0.79–0.93] vs. 0.72 [0.61–0.82]; p < 0.001). Similarly, the new model combining Toronto score and DCNN-derived probability exhibited a higher AUC compared to Toronto score alone (AUC 0.84 [0.76–0.92] vs. 0.75 [0.65–0.85]; p = 0.03). An improvement in the sensitivity, specificity, PPV, and NPV was also achieved, along with significant net reclassification improvement. In conclusion, compared to the conventional models, our new model combining the conventional and DCNN-derived models demonstrated superior accuracy to predict positive genotype in patients with HCM.
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Affiliation(s)
- Sae X Morita
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States
| | - Kenya Kusunose
- Department of Cardiovascular Medicine, Tokushima University, Tokushima, Japan
| | - Akihiro Haga
- Department of Medical Image Informatics, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Masataka Sata
- Department of Cardiovascular Medicine, Tokushima University, Tokushima, Japan
| | - Kohei Hasegawa
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Yoshihiko Raita
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Muredach P Reilly
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States.,Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, NY, United States
| | - Michael A Fifer
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Mathew S Maurer
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States
| | - Yuichi J Shimada
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States
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19
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Lee S, Lam SH, Hernandes Rocha TA, Fleischman RJ, Staton CA, Taylor R, Limkakeng AT. Machine Learning and Precision Medicine in Emergency Medicine: The Basics. Cureus 2021; 13:e17636. [PMID: 34646684 PMCID: PMC8485701 DOI: 10.7759/cureus.17636] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/01/2021] [Indexed: 12/28/2022] Open
Abstract
As machine learning (ML) and precision medicine become more readily available and used in practice, emergency physicians must understand the potential advantages and limitations of the technology. This narrative review focuses on the key components of machine learning, artificial intelligence, and precision medicine in emergency medicine (EM). Based on the content expertise, we identified articles from EM literature. The authors provided a narrative summary of each piece of literature. Next, the authors provided an introduction of the concepts of ML, artificial intelligence as an extension of ML, and precision medicine. This was followed by concrete examples of their applications in practice and research. Subsequently, we shared our thoughts on how to consume the existing research in these subjects and conduct high-quality research for academic emergency medicine. We foresee that the EM community will continue to adapt machine learning, artificial intelligence, and precision medicine in research and practice. We described several key components using our expertise.
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Affiliation(s)
- Sangil Lee
- Emergency Medicine, University of Iowa Carver College of Medicine, Iowa City, USA
| | - Samuel H Lam
- Emergency Medicine, Sutter Medical Center, Sacramento, USA
| | | | | | - Catherine A Staton
- Division of Emergency Medicine, Department of Surgery, Duke University School of Medicine, Durham, USA
| | - Richard Taylor
- Department of Emergency Medicine, Yale University, New Haven, USA
| | - Alexander T Limkakeng
- Division of Emergency Medicine, Department of Surgery, Duke University School of Medicine, Durham, USA
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20
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Cardiac risk stratification in cancer patients: A longitudinal patient-patient network analysis. PLoS Med 2021; 18:e1003736. [PMID: 34339408 PMCID: PMC8366997 DOI: 10.1371/journal.pmed.1003736] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 08/16/2021] [Accepted: 07/15/2021] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Cardiovascular disease is a leading cause of death in general population and the second leading cause of mortality and morbidity in cancer survivors after recurrent malignancy in the United States. The growing awareness of cancer therapy-related cardiac dysfunction (CTRCD) has led to an emerging field of cardio-oncology; yet, there is limited knowledge on how to predict which patients will experience adverse cardiac outcomes. We aimed to perform unbiased cardiac risk stratification for cancer patients using our large-scale, institutional electronic medical records. METHODS AND FINDINGS We built a large longitudinal (up to 22 years' follow-up from March 1997 to January 2019) cardio-oncology cohort having 4,632 cancer patients in Cleveland Clinic with 5 diagnosed cardiac outcomes: atrial fibrillation, coronary artery disease, heart failure, myocardial infarction, and stroke. The entire population includes 84% white Americans and 11% black Americans, and 59% females versus 41% males, with median age of 63 (interquartile range [IQR]: 54 to 71) years old. We utilized a topology-based K-means clustering approach for unbiased patient-patient network analyses of data from general demographics, echocardiogram (over 25,000), lab testing, and cardiac factors (cardiac). We performed hazard ratio (HR) and Kaplan-Meier analyses to identify clinically actionable variables. All confounding factors were adjusted by Cox regression models. We performed random-split and time-split training-test validation for our model. We identified 4 clinically relevant subgroups that are significantly correlated with incidence of cardiac outcomes and mortality. Among the 4 subgroups, subgroup I (n = 625) has the highest risk of de novo CTRCD (28%) with an HR of 3.05 (95% confidence interval (CI) 2.51 to 3.72). Patients in subgroup IV (n = 1,250) had the worst survival probability (HR 4.32, 95% CI 3.82 to 4.88). From longitudinal patient-patient network analyses, the patients in subgroup I had a higher percentage of de novo CTRCD and a worse mortality within 5 years after the initiation of cancer therapies compared to long-time exposure (6 to 20 years). Using clinical variable network analyses, we identified that serum levels of NT-proB-type Natriuretic Peptide (NT-proBNP) and Troponin T are significantly correlated with patient's mortality (NT-proBNP > 900 pg/mL versus NT-proBNP = 0 to 125 pg/mL, HR = 2.95, 95% CI 2.28 to 3.82, p < 0.001; Troponin T > 0.05 μg/L versus Troponin T ≤ 0.01 μg/L, HR = 2.08, 95% CI 1.83 to 2.34, p < 0.001). Study limitations include lack of independent cardio-oncology cohorts from different healthcare systems to evaluate the generalizability of the models. Meanwhile, the confounding factors, such as multiple medication usages, may influence the findings. CONCLUSIONS In this study, we demonstrated that the patient-patient network clustering methodology is clinically intuitive, and it allows more rapid identification of cancer survivors that are at greater risk of cardiac dysfunction. We believed that this study holds great promise for identifying novel cardiac risk subgroups and clinically actionable variables for the development of precision cardio-oncology.
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21
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Guo Y, Zhang Y, Lyu T, Prosperi M, Wang F, Xu H, Bian J. The application of artificial intelligence and data integration in COVID-19 studies: a scoping review. J Am Med Inform Assoc 2021; 28:2050-2067. [PMID: 34151987 PMCID: PMC8344463 DOI: 10.1093/jamia/ocab098] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 05/03/2021] [Accepted: 05/06/2021] [Indexed: 12/23/2022] Open
Abstract
Objective To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling. Materials and Methods We searched 2 major COVID-19 literature databases, the National Institutes of Health’s LitCovid and the World Health Organization’s COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening. Results In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications. Discussion Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias. Conclusion There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.
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Affiliation(s)
- Yi Guo
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.,Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, Florida, USA
| | - Yahan Zhang
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Tianchen Lyu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.,Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, Florida, USA
| | - Mattia Prosperi
- Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.,Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, Florida, USA
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22
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Haq IU, Haq I, Xu B. Artificial intelligence in personalized cardiovascular medicine and cardiovascular imaging. Cardiovasc Diagn Ther 2021; 11:911-923. [PMID: 34295713 DOI: 10.21037/cdt.2020.03.09] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 03/05/2020] [Indexed: 12/27/2022]
Abstract
The collection of large, heterogeneous electronic datasets and imaging from patients with cardiovascular disease (CVD) has lent itself to the use of sophisticated analysis using artificial intelligence (AI). AI techniques such as machine learning (ML) are able to identify relationships between data points by linking input to output variables using a combination of different functions, such as neural networks. In cardiovascular medicine, this is especially pertinent for classification, diagnosis, risk prediction and treatment guidance. Common cardiovascular data sources from patients include genomic data, cardiovascular imaging, wearable sensors and electronic health records (EHR). Leveraging AI in analysing such data points: (I) for clinicians: more accurate and streamlined image interpretation and diagnosis; (II) for health systems: improved workflow and reductions in medical errors; (III) for patients: promoting health with further education and promoting primary and secondary cardiovascular health prevention. This review addresses the need for AI in cardiovascular medicine by reviewing recent literature in different cardiovascular imaging modalities: electrocardiography, echocardiography, cardiac computed tomography, cardiac nuclear imaging, and cardiac magnetic resonance (CMR) imaging as well as the role of EHR. This review aims to conceptulise these studies in relation to their clinical applications, potential limitations and future opportunities and directions.
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Affiliation(s)
- Ikram-Ul Haq
- Imperial College London Faculty of Medicine, London, UK
| | - Iqraa Haq
- Imperial College London Faculty of Medicine, London, UK
| | - Bo Xu
- Section of Cardiovascular Imaging, Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Family Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
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23
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A retrospective analysis of cardiovascular adverse events associated with immune checkpoint inhibitors. CARDIO-ONCOLOGY 2021; 7:19. [PMID: 34049595 PMCID: PMC8161966 DOI: 10.1186/s40959-021-00106-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 05/04/2021] [Indexed: 12/11/2022]
Abstract
Background Modern therapies in oncology have increased cancer survivorship, as well as the incidence of cardiovascular adverse events. While immune checkpoint inhibitors have shown significant clinical impact in several cancer types, the incidence of immune-related cardiovascular (CV) adverse events poses an additional health concern and has been reported. Methods We performed a retrospective analysis of the FDA Adverse Event Reporting System data of suspect product reports for immunotherapy and classical chemotherapy from January 2010–March 2020. We identified 90,740 total adverse event reports related to immune checkpoint inhibitors and classical chemotherapy. Results We found that myocarditis was significantly associated with patients receiving anti-program cell death protein 1 (PD-1) or anti-program death ligand 1 (PD-L1), odds ratio (OR) = 23.86 (95% confidence interval [CI] 11.76–48.42, (adjusted p-value) q < 0.001), and combination immunotherapy, OR = 7.29 (95% CI 1.03–51.89, q = 0.047). Heart failure was significantly associated in chemotherapy compared to PD-(L)1, OR = 0.50 (95% CI 0.37–0.69, q < 0.001), CTLA4, OR = 0.08 (95% CI 0.03–0.20, q < 0.001), and combination immunotherapy, OR = 0.25 (95% CI 0.13–0.48, q < 0.001). Additionally, we observe a sex-specificity towards males in cardiac adverse reports for arrhythmias, OR = 0.81 (95% CI 0.75–0.87, q < 0.001), coronary artery disease, 0.63 (95% CI 0.53–0.76, q < 0.001), myocardial infarction, OR = 0.60 (95% CI 0.53–0.67, q < 0.001), myocarditis, OR = 0.59 (95% CI 0.47–0.75, q < 0.001) and pericarditis, OR = 0.5 (95% CI 0.35–0.73, q < 0.001). Conclusion Our study provides the current risk estimates of cardiac adverse events in patients treated with immunotherapy compared to conventional chemotherapy. Understanding the clinical risk factors that predispose immunotherapy-treated cancer patients to often fatal CV adverse events will be crucial in Cardio-Oncology management. Supplementary Information The online version contains supplementary material available at 10.1186/s40959-021-00106-x.
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24
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Nabi W, Bansal A, Xu B. Applications of artificial intelligence and machine learning approaches in echocardiography. Echocardiography 2021; 38:982-992. [PMID: 33982820 DOI: 10.1111/echo.15048] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 03/18/2021] [Accepted: 03/29/2021] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence and machine learning approaches have become increasingly applied in the field of echocardiography to streamline diagnostic and prognostic assessments, and to support treatment decisions. Artificial intelligence and machine learning have been applied to aid image acquisition and automation. They have also been applied to the integration of clinical and imaging data. Applications of artificial intelligence and machine learning approaches in echocardiography in conjunction with health information databases may be promising in improving the classification and treatment of many cardiac conditions. This review article provides an overview of the applications of artificial intelligence and machine learning approaches in echocardiography.
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Affiliation(s)
- Wafa Nabi
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Agam Bansal
- Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Bo Xu
- Section of Cardiovascular Imaging, Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Miller Family Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
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25
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Aortic Annular Sizing Using Novel Software in Three-Dimensional Transesophageal Echocardiography for Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2021; 11:diagnostics11050751. [PMID: 33922239 PMCID: PMC8145366 DOI: 10.3390/diagnostics11050751] [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: 03/08/2021] [Revised: 04/15/2021] [Accepted: 04/21/2021] [Indexed: 02/06/2023] Open
Abstract
(1) Background: We performed this study to evaluate the agreement between novel automated software of three-dimensional transesophageal echocardiography (3D-TEE) and multidetector computed tomography (MDCT) for aortic annular measurements of preprocedural transcatheter aortic valve replacement (TAVR); (2) Methods: PubMed, EMBASE, Web of Science, and Cochrane Library (Wiley) databases were systematically searched for studies that compared 3D-TEE and MDCT as the reference standard for aortic annular measurement of the following parameters: annular area, annular perimeter, area derived-diameter, perimeter derived-diameter, maximum and minimum diameter. Meta-analytic methods were utilized to determine the pooled correlations and mean differences between 3D-TEE and MDCT. Heterogeneity and publication bias were also assessed. Meta-regression analyses were performed based on the potential factors affecting the correlation of aortic annular area; (3) Results: A total of 889 patients from 10 studies were included in the meta-analysis. Pooled correlation coefficients between 3D-TEE and MDCT of annulus area, perimeter, area derived-diameter, perimeter derived-diameter, maximum and minimum diameter measurements were strong 0.89 (95% CI: 0.84–0.92), 0.88 (95% CI: 0.83–0.92), 0.87 (95% CI: 0.77–0.93), 0.87 (95% CI: 0.77–0.93), 0.79 (95% CI: 0.64–0.87), and 0.75 (95% CI: 0.61–0.84) (Overall p < 0.0001), respectively. Pooled mean differences between 3D-TEE and MDCT of annulus area, perimeter, area derived-diameter, perimeter derived-diameter, maximum and minimum diameter measurements were −20.01 mm2 ((95% CI: −35.37 to −0.64), p = 0.011), −2.31 mm ((95% CI: −3.31 to −1.31), p < 0.0001), −0.22 mm ((95% CI: −0.73 to 0.29), p = 0.40), −0.47 mm ((95% CI: −1.06 to 0.12), p = 0.12), −1.36 mm ((95% CI: −2.43 to −0.30), p = 0.012), and 0.31 mm ((95% CI: −0.15 to 0.77), p = 0.18), respectively. There were no statistically significant associations with the baseline patient characteristics of sex, age, left ventricular ejection fraction, mean transaortic gradient, and aortic valve area to the correlation between 3D-TEE and MDCT for aortic annular area sizing; (4) Conclusions: The present study implies that 3D-TEE using novel software tools, automatically analysis, is feasible to MDCT for annulus sizing in clinical practice.
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Xu B, Lo Presti Vega S, Reyaldeen R. Artificial intelligence in structural heart disease interventions - The future is near. Trends Cardiovasc Med 2021; 32:160-162. [PMID: 33667645 DOI: 10.1016/j.tcm.2021.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Accepted: 02/20/2021] [Indexed: 11/25/2022]
Affiliation(s)
- Bo Xu
- Section of Cardiovascular Imaging, Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Miller Family Heart, Vascular and Thoracic Institute, Cleveland Clinic, 9500 Euclid Avenue, Desk J1-5, Cleveland, OH 44195, USA.
| | - Saberio Lo Presti Vega
- Section of Cardiovascular Imaging, Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Miller Family Heart, Vascular and Thoracic Institute, Cleveland Clinic, 9500 Euclid Avenue, Desk J1-5, Cleveland, OH 44195, USA
| | - Reza Reyaldeen
- Section of Cardiovascular Imaging, Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Miller Family Heart, Vascular and Thoracic Institute, Cleveland Clinic, 9500 Euclid Avenue, Desk J1-5, Cleveland, OH 44195, USA
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Zhou Y, Hou Y, Hussain M, Brown SA, Budd T, Tang WHW, Abraham J, Xu B, Shah C, Moudgil R, Popovic Z, Cho L, Kanj M, Watson C, Griffin B, Chung MK, Kapadia S, Svensson L, Collier P, Cheng F. Machine Learning-Based Risk Assessment for Cancer Therapy-Related Cardiac Dysfunction in 4300 Longitudinal Oncology Patients. J Am Heart Assoc 2020; 9:e019628. [PMID: 33241727 PMCID: PMC7763760 DOI: 10.1161/jaha.120.019628] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Background The growing awareness of cardiovascular toxicity from cancer therapies has led to the emerging field of cardio-oncology, which centers on preventing, detecting, and treating patients with cardiac dysfunction before, during, or after cancer treatment. Early detection and prevention of cancer therapy-related cardiac dysfunction (CTRCD) play important roles in precision cardio-oncology. Methods and Results This retrospective study included 4309 cancer patients between 1997 and 2018 whose laboratory tests and cardiovascular echocardiographic variables were collected from the Cleveland Clinic institutional electronic medical record database (Epic Systems). Among these patients, 1560 (36%) were diagnosed with at least 1 type of CTRCD, and 838 (19%) developed CTRCD after cancer therapy (de novo). We posited that machine learning algorithms can be implemented to predict CTRCDs in cancer patients according to clinically relevant variables. Classification models were trained and evaluated for 6 types of cardiovascular outcomes, including coronary artery disease (area under the receiver operating characteristic curve [AUROC], 0.821; 95% CI, 0.815-0.826), atrial fibrillation (AUROC, 0.787; 95% CI, 0.782-0.792), heart failure (AUROC, 0.882; 95% CI, 0.878-0.887), stroke (AUROC, 0.660; 95% CI, 0.650-0.670), myocardial infarction (AUROC, 0.807; 95% CI, 0.799-0.816), and de novo CTRCD (AUROC, 0.802; 95% CI, 0.797-0.807). Model generalizability was further confirmed using time-split data. Model inspection revealed several clinically relevant variables significantly associated with CTRCDs, including age, hypertension, glucose levels, left ventricular ejection fraction, creatinine, and aspartate aminotransferase levels. Conclusions This study suggests that machine learning approaches offer powerful tools for cardiac risk stratification in oncology patients by utilizing large-scale, longitudinal patient data from healthcare systems.
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Affiliation(s)
- Yadi Zhou
- Genomic Medicine Institute Lerner Research InstituteCleveland Clinic Cleveland OH
| | - Yuan Hou
- Genomic Medicine Institute Lerner Research InstituteCleveland Clinic Cleveland OH
| | - Muzna Hussain
- Robert and Suzanne Tomsich Department of Cardiovascular Medicine Sydell and Arnold Miller Family Heart and Vascular Institute Cleveland Clinic Cleveland OH.,School of Medicine Dentistry and Biomedical Sciences Wellcome-Wolfson Institute of Experimental MedicineQueen's University Belfast United Kingdom
| | - Sherry-Ann Brown
- Cardio-Oncology Program Division of Cardiovascular Medicine Medical College of Wisconsin Milwaukee WI
| | - Thomas Budd
- Department of Hematology/Medical Oncology Taussig Cancer InstituteCleveland Clinic Cleveland OH
| | - W H Wilson Tang
- Robert and Suzanne Tomsich Department of Cardiovascular Medicine Sydell and Arnold Miller Family Heart and Vascular Institute Cleveland Clinic Cleveland OH.,Department of Molecular Medicine Cleveland Clinic Lerner College of MedicineCase Western Reserve University Cleveland OH
| | - Jame Abraham
- Department of Hematology/Medical Oncology Taussig Cancer InstituteCleveland Clinic Cleveland OH
| | - Bo Xu
- Robert and Suzanne Tomsich Department of Cardiovascular Medicine Sydell and Arnold Miller Family Heart and Vascular Institute Cleveland Clinic Cleveland OH
| | - Chirag Shah
- Department of Radiation Oncology Taussig Cancer InstituteCleveland Clinic Cleveland OH
| | - Rohit Moudgil
- Robert and Suzanne Tomsich Department of Cardiovascular Medicine Sydell and Arnold Miller Family Heart and Vascular Institute Cleveland Clinic Cleveland OH
| | - Zoran Popovic
- Robert and Suzanne Tomsich Department of Cardiovascular Medicine Sydell and Arnold Miller Family Heart and Vascular Institute Cleveland Clinic Cleveland OH
| | - Leslie Cho
- Robert and Suzanne Tomsich Department of Cardiovascular Medicine Sydell and Arnold Miller Family Heart and Vascular Institute Cleveland Clinic Cleveland OH
| | - Mohamed Kanj
- Robert and Suzanne Tomsich Department of Cardiovascular Medicine Sydell and Arnold Miller Family Heart and Vascular Institute Cleveland Clinic Cleveland OH
| | - Chris Watson
- School of Medicine Dentistry and Biomedical Sciences Wellcome-Wolfson Institute of Experimental MedicineQueen's University Belfast United Kingdom
| | - Brian Griffin
- Robert and Suzanne Tomsich Department of Cardiovascular Medicine Sydell and Arnold Miller Family Heart and Vascular Institute Cleveland Clinic Cleveland OH
| | - Mina K Chung
- Robert and Suzanne Tomsich Department of Cardiovascular Medicine Sydell and Arnold Miller Family Heart and Vascular Institute Cleveland Clinic Cleveland OH.,Department of Molecular Medicine Cleveland Clinic Lerner College of MedicineCase Western Reserve University Cleveland OH
| | - Samir Kapadia
- Robert and Suzanne Tomsich Department of Cardiovascular Medicine Sydell and Arnold Miller Family Heart and Vascular Institute Cleveland Clinic Cleveland OH
| | - Lars Svensson
- Department of Cardiovascular Surgery Cleveland Clinic Cleveland OH
| | - Patrick Collier
- Robert and Suzanne Tomsich Department of Cardiovascular Medicine Sydell and Arnold Miller Family Heart and Vascular Institute Cleveland Clinic Cleveland OH.,Department of Molecular Medicine Cleveland Clinic Lerner College of MedicineCase Western Reserve University Cleveland OH
| | - Feixiong Cheng
- Genomic Medicine Institute Lerner Research InstituteCleveland Clinic Cleveland OH.,Department of Hematology/Medical Oncology Taussig Cancer InstituteCleveland Clinic Cleveland OH.,Case Comprehensive Cancer Center Case Western Reserve University School of Medicine Cleveland OH
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Zéboulon P, Debellemanière G, Bouvet M, Gatinel D. Corneal Topography Raw Data Classification Using a Convolutional Neural Network. Am J Ophthalmol 2020; 219:33-39. [PMID: 32533948 DOI: 10.1016/j.ajo.2020.06.005] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 05/18/2020] [Accepted: 06/03/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE We investigated the efficiency of a convolutional neural network applied to corneal topography raw data to classify examinations of 3 categories: normal, keratoconus (KC), and history of refractive surgery (RS). DESIGN Retrospective machine-learning experimental study. METHODS A total of 3,000 Orbscan examinations (1,000 of each class) of different patients of our institution were selected for model training and validation. One hundred examinations of each class were randomly assigned to the test set. For each examination, the raw numerical data from "elevation against the anterior best fit sphere (BFS)," "elevation against the posterior BFS" "axial anterior curvature," and "pachymetry" maps were used. Each map was a square matrix of 2,500 values. The 4 maps were stacked and used as if they were 4 channels of a single image.A convolutional neural network was built and trained on the training set. Classification accuracy and class wise sensitivity and specificity were calculated for the validation set. RESULTS Overall classification accuracy of the validation set (n = 300) was 99.3% (98.3%-100%). Sensitivity and specificity were, respectively, 100% and 100% for KC, 100% and 99% (94.9%-100%) for normal examinations, and 98% (97.4%-100%) and 100% for RS examinations. CONCLUSION Using combined corneal topography raw data with a convolutional neural network is an effective way to classify examinations and probably the most thorough way to automatically analyze corneal topography. It should be considered for other routine tasks performed on corneal topography, such as refractive surgery screening.
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Cardiac magnetic resonance imaging and computed tomography for the pediatric cardiologist. PROGRESS IN PEDIATRIC CARDIOLOGY 2020. [DOI: 10.1016/j.ppedcard.2020.101273] [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: 11/23/2022]
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