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Onnis C, van Assen M, Muscogiuri E, Muscogiuri G, Gershon G, Saba L, De Cecco CN. The Role of Artificial Intelligence in Cardiac Imaging. Radiol Clin North Am 2024; 62:473-488. [PMID: 38553181 DOI: 10.1016/j.rcl.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Artificial intelligence (AI) is having a significant impact in medical imaging, advancing almost every aspect of the field, from image acquisition and postprocessing to automated image analysis with outreach toward supporting decision making. Noninvasive cardiac imaging is one of the main and most exciting fields for AI development. The aim of this review is to describe the main applications of AI in cardiac imaging, including CT and MR imaging, and provide an overview of recent advancements and available clinical applications that can improve clinical workflow, disease detection, and prognostication in cardiac disease.
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Affiliation(s)
- Carlotta Onnis
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA; Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, SS 554 km 4,500 Monserrato, Cagliari 09042, Italy. https://twitter.com/CarlottaOnnis
| | - Marly van Assen
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA. https://twitter.com/marly_van_assen
| | - Emanuele Muscogiuri
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA; Division of Thoracic Imaging, Department of Radiology, University Hospitals Leuven, Herestraat 49, Leuven 3000, Belgium
| | - Giuseppe Muscogiuri
- Department of Diagnostic and Interventional Radiology, Papa Giovanni XXIII Hospital, Piazza OMS, 1, Bergamo BG 24127, Italy. https://twitter.com/GiuseppeMuscog
| | - Gabrielle Gershon
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA. https://twitter.com/gabbygershon
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, SS 554 km 4,500 Monserrato, Cagliari 09042, Italy. https://twitter.com/lucasabaITA
| | - Carlo N De Cecco
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA; Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University, Emory University Hospital, 1365 Clifton Road Northeast, Suite AT503, Atlanta, GA 30322, USA.
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Hanneman K, Playford D, Dey D, van Assen M, Mastrodicasa D, Cook TS, Gichoya JW, Williamson EE, Rubin GD. Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e296-e311. [PMID: 38193315 DOI: 10.1161/cir.0000000000001202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.
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3
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Ahmad A, Imran M, Ahsan H. Biomarkers as Biomedical Bioindicators: Approaches and Techniques for the Detection, Analysis, and Validation of Novel Biomarkers of Diseases. Pharmaceutics 2023; 15:1630. [PMID: 37376078 DOI: 10.3390/pharmaceutics15061630] [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: 03/24/2023] [Revised: 05/24/2023] [Accepted: 05/29/2023] [Indexed: 06/29/2023] Open
Abstract
A biomarker is any measurable biological moiety that can be assessed and measured as a potential index of either normal or abnormal pathophysiology or pharmacological responses to some treatment regimen. Every tissue in the body has a distinct biomolecular make-up, which is known as its biomarkers, which possess particular features, viz., the levels or activities (the ability of a gene or protein to carry out a particular body function) of a gene, protein, or other biomolecules. A biomarker refers to some feature that can be objectively quantified by various biochemical samples and evaluates the exposure of an organism to normal or pathological procedures or their response to some drug interventions. An in-depth and comprehensive realization of the significance of these biomarkers becomes quite important for the efficient diagnosis of diseases and for providing the appropriate directions in case of multiple drug choices being presently available, which can benefit any patient. Presently, advancements in omics technologies have opened up new possibilities to obtain novel biomarkers of different types, employing genomic strategies, epigenetics, metabolomics, transcriptomics, lipid-based analysis, protein studies, etc. Particular biomarkers for specific diseases, their prognostic capabilities, and responses to therapeutic paradigms have been applied for screening of various normal healthy, as well as diseased, tissue or serum samples, and act as appreciable tools in pharmacology and therapeutics, etc. In this review, we have summarized various biomarker types, their classification, and monitoring and detection methods and strategies. Various analytical techniques and approaches of biomarkers have also been described along with various clinically applicable biomarker sensing techniques which have been developed in the recent past. A section has also been dedicated to the latest trends in the formulation and designing of nanotechnology-based biomarker sensing and detection developments in this field.
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Affiliation(s)
- Anas Ahmad
- Julia McFarlane Diabetes Research Centre (JMDRC), Department of Microbiology, Immunology and Infectious Diseases, Snyder Institute for Chronic Diseases, Hotchkiss Brain Institute, Cumming School of Medicine, Foothills Medical Centre, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Mohammad Imran
- Therapeutics Research Group, Frazer Institute, Faculty of Medicine, University of Queensland, Brisbane 4102, Australia
| | - Haseeb Ahsan
- Department of Biochemistry, Faculty of Dentistry, Jamia Millia Islamia, New Delhi 110025, India
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Åkesson J, Ostenfeld E, Carlsson M, Arheden H, Heiberg E. Deep learning can yield clinically useful right ventricular segmentations faster than fully manual analysis. Sci Rep 2023; 13:1216. [PMID: 36681759 PMCID: PMC9867728 DOI: 10.1038/s41598-023-28348-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 01/17/2023] [Indexed: 01/22/2023] Open
Abstract
Right ventricular (RV) volumes are commonly obtained through time-consuming manual delineations of cardiac magnetic resonance (CMR) images. Deep learning-based methods can generate RV delineations, but few studies have assessed their ability to accelerate clinical practice. Therefore, we aimed to develop a clinical pipeline for deep learning-based RV delineations and validate its ability to reduce the manual delineation time. Quality-controlled delineations in short-axis CMR scans from 1114 subjects were used for development. Time reduction was assessed by two observers using 50 additional clinical scans. Automated delineations were subjectively rated as (A) sufficient for clinical use, or as needing (B) minor or (C) major corrections. Times were measured for manual corrections of delineations rated as B or C, and for fully manual delineations on all 50 scans. Fifty-eight % of automated delineations were rated as A, 42% as B, and none as C. The average time was 6 min for a fully manual delineation, 2 s for an automated delineation, and 2 min for a minor correction, yielding a time reduction of 87%. The deep learning-based pipeline could substantially reduce the time needed to manually obtain clinically applicable delineations, indicating ability to yield right ventricular assessments faster than fully manual analysis in clinical practice. However, these results may not generalize to clinics using other RV delineation guidelines.
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Affiliation(s)
- Julius Åkesson
- Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden.
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden.
| | - Ellen Ostenfeld
- Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Marcus Carlsson
- Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Håkan Arheden
- Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Einar Heiberg
- Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
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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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Staszak M, Staszak K, Wieszczycka K, Bajek A, Roszkowski K, Tylkowski B. Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1568] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Maciej Staszak
- Institute of Technology and Chemical Engineering Poznan University of Technology Poznan Poland
| | - Katarzyna Staszak
- Institute of Technology and Chemical Engineering Poznan University of Technology Poznan Poland
| | - Karolina Wieszczycka
- Institute of Technology and Chemical Engineering Poznan University of Technology Poznan Poland
| | - Anna Bajek
- Department of Tissue Engineering Collegium Medicum, Nicolaus Copernicus University Bydgoszcz Poland
| | - Krzysztof Roszkowski
- Department of Oncology Collegium Medicum Nicolaus Copernicus University Bydgoszcz Poland
| | - Bartosz Tylkowski
- Department of Chemical Engineering University Rovira i Virgili Tarragona Spain
- Eurecat, Centre Tecnològic de Catalunya Chemical Technologies Unit Tarragona Spain
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Naseri Jahfari A, Tax D, Reinders M, van der Bilt I. Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View. JMIR Med Inform 2022; 10:e29434. [PMID: 35044316 PMCID: PMC8811688 DOI: 10.2196/29434] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 11/22/2021] [Accepted: 12/04/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Wearable technology has the potential to improve cardiovascular health monitoring by using machine learning. Such technology enables remote health monitoring and allows for the diagnosis and prevention of cardiovascular diseases. In addition to the detection of cardiovascular disease, it can exclude this diagnosis in symptomatic patients, thereby preventing unnecessary hospital visits. In addition, early warning systems can aid cardiologists in timely treatment and prevention. OBJECTIVE This study aims to systematically assess the literature on detecting and predicting outcomes of patients with cardiovascular diseases by using machine learning with data obtained from wearables to gain insights into the current state, challenges, and limitations of this technology. METHODS We searched PubMed, Scopus, and IEEE Xplore on September 26, 2020, with no restrictions on the publication date and by using keywords such as "wearables," "machine learning," and "cardiovascular disease." Methodologies were categorized and analyzed according to machine learning-based technology readiness levels (TRLs), which score studies on their potential to be deployed in an operational setting from 1 to 9 (most ready). RESULTS After the removal of duplicates, application of exclusion criteria, and full-text screening, 55 eligible studies were included in the analysis, covering a variety of cardiovascular diseases. We assessed the quality of the included studies and found that none of the studies were integrated into a health care system (TRL<6), prospective phase 2 and phase 3 trials were absent (TRL<7 and 8), and group cross-validation was rarely used. These issues limited these studies' ability to demonstrate the effectiveness of their methodologies. Furthermore, there seemed to be no agreement on the sample size needed to train these studies' models, the size of the observation window used to make predictions, how long participants should be observed, and the type of machine learning model that is suitable for predicting cardiovascular outcomes. CONCLUSIONS Although current studies show the potential of wearables to monitor cardiovascular events, their deployment as a diagnostic or prognostic cardiovascular clinical tool is hampered by the lack of a realistic data set and proper systematic and prospective evaluation.
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Affiliation(s)
- Arman Naseri Jahfari
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, Netherlands.,Department of Cardiology, Haga Teaching Hospital, The Hague, Netherlands
| | - David Tax
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, Netherlands
| | - Marcel Reinders
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, Netherlands
| | - Ivo van der Bilt
- Department of Cardiology, Haga Teaching Hospital, The Hague, Netherlands
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Paixão GMDM, Santos BC, Araujo RMD, Ribeiro MH, Moraes JLD, Ribeiro AL. Machine Learning na Medicina: Revisão e Aplicabilidade. Arq Bras Cardiol 2022; 118:95-102. [PMID: 35195215 PMCID: PMC8959062 DOI: 10.36660/abc.20200596] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 12/02/2020] [Indexed: 01/04/2023] Open
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Nakamura T, Sasano T. Artificial intelligence and cardiology: Current status and perspective: Artificial Intelligence and Cardiology. J Cardiol 2021; 79:326-333. [PMID: 34895982 DOI: 10.1016/j.jjcc.2021.11.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 10/27/2021] [Indexed: 12/19/2022]
Abstract
The development of artificial intelligence (AI) began in the mid-20th century but has been rapidly accelerating in the past decade. Reflecting the development of digital health over the past few years, this trend is also seen in medicine. The field of cardiovascular medicine uses a wide variety and a large amount of biosignals, so there are many situations where AI can contribute. The development of AI is in progress for all aspects of the healthcare system, including the prevention, screening, and treatment of diseases and the prediction of the prognosis. AI is expected to be used to provide specialist-level medical care, even in a situation where medical resources are scarce. However, like other medical devices, the concept and mechanism of AI must be fully understood when used; otherwise, it may be used inappropriately, resulting in detriment to the patient. Therefore, it is important to understand what we need to know as a cardiologist handling AI. This review introduces the basics and principles of AI, then shows how far the current development of AI has come, and finally gives a brief introduction of how to start the AI development for those who want to develop their own AI.
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Affiliation(s)
- Tomofumi Nakamura
- Department of Cardiovascular Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Tetsuo Sasano
- Department of Cardiovascular Medicine, Tokyo Medical and Dental University, Tokyo, Japan.
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Convolutional Neural Networks for Fully Automated Diagnosis of Cardiac Amyloidosis by Cardiac Magnetic Resonance Imaging. J Pers Med 2021; 11:jpm11121268. [PMID: 34945740 PMCID: PMC8705947 DOI: 10.3390/jpm11121268] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/24/2021] [Accepted: 11/24/2021] [Indexed: 12/13/2022] Open
Abstract
Aims: We tested the hypothesis that artificial intelligence (AI)-powered algorithms applied to cardiac magnetic resonance (CMR) images could be able to detect the potential patterns of cardiac amyloidosis (CA). Readers in CMR centers with a low volume of referrals for the detection of myocardial storage diseases or a low volume of CMRs, in general, may overlook CA. In light of the growing prevalence of the disease and emerging therapeutic options, there is an urgent need to avoid misdiagnoses. Methods and Results: Using CMR data from 502 patients (CA: n = 82), we trained convolutional neural networks (CNNs) to automatically diagnose patients with CA. We compared the diagnostic accuracy of different state-of-the-art deep learning techniques on common CMR imaging protocols in detecting imaging patterns associated with CA. As a result of a 10-fold cross-validated evaluation, the best-performing fine-tuned CNN achieved an average ROC AUC score of 0.96, resulting in a diagnostic accuracy of 94% sensitivity and 90% specificity. Conclusions: Applying AI to CMR to diagnose CA may set a remarkable milestone in an attempt to establish a fully computational diagnostic path for the diagnosis of CA, in order to support the complex diagnostic work-up requiring a profound knowledge of experts from different disciplines.
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Birkhoff DC, van Dalen ASH, Schijven MP. A Review on the Current Applications of Artificial Intelligence in the Operating Room. Surg Innov 2021; 28:611-619. [PMID: 33625307 PMCID: PMC8450995 DOI: 10.1177/1553350621996961] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background. Artificial intelligence (AI) is an era upcoming in medicine and, more recently, in the operating room (OR). Existing literature elaborates mainly on the future possibilities and expectations for AI in surgery. The aim of this study is to systematically provide an overview of the current actual AI applications used to support processes inside the OR. Methods. PubMed, Embase, Cochrane Library, and IEEE Xplore were searched using inclusion criteria for relevant articles up to August 25th, 2020. No study types were excluded beforehand. Articles describing current AI applications for surgical purposes inside the OR were reviewed. Results. Nine studies were included. An overview of the researched and described applications of AI in the OR is provided, including procedure duration prediction, gesture recognition, intraoperative cancer detection, intraoperative video analysis, workflow recognition, an endoscopic guidance system, knot-tying, and automatic registration and tracking of the bone in orthopedic surgery. These technologies are compared to their, often non-AI, baseline alternatives. Conclusions. Currently described applications of AI in the OR are limited to date. They may, however, have a promising future in improving surgical precision, reduce manpower, support intraoperative decision-making, and increase surgical safety. Nonetheless, the application and implementation of AI inside the OR still has several challenges to overcome. Clear regulatory, organizational, and clinical conditions are imperative for AI to redeem its promise. Future research on use of AI in the OR should therefore focus on clinical validation of AI applications, the legal and ethical considerations, and on evaluation of implementation trajectory.
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Affiliation(s)
- David C. Birkhoff
- Department of Surgery, Amsterdam UMC, University of Amsterdam, The Netherlands
| | | | - Marlies P. Schijven
- Department of Surgery, Amsterdam Gastroenterology and Metabolism, University of Amsterdam, The Netherlands
- institution-id-type="Ringgold" />Li Ka Shing Knowledge Institute, institution-id-type="Ringgold" />St Michaels Hospital, Toronto, Canada
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12
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Abstract
Rapid development of artificial intelligence (AI) is gaining grounds in medicine. Its huge impact and inevitable necessity are also reflected in cardiovascular imaging. Although AI would probably never replace doctors, it can significantly support and improve their productivity and diagnostic performance. Many algorithms have already proven useful at all stages of the cardiac imaging chain. Their crucial practical applications include classification, automatic quantification, notification, diagnosis, and risk prediction. Consequently, more reproducible and repeatable studies are obtained, and personalized reports may be available to any patient. Utilization of AI also increases patient safety and decreases healthcare costs. Furthermore, AI is particularly useful for beginners in the field of cardiac imaging as it provides anatomic guidance and interpretation of complex imaging results. In contrast, lack of interpretability and explainability in AI carries a risk of harmful recommendations. This review was aimed at summarizing AI principles, essential execution requirements, and challenges as well as its recent applications in cardiovascular imaging.
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13
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Deepa D, Singh Y, Wang MC, Hu W. An automated method for detecting atrial fat using convolutional neural network. Proc Inst Mech Eng H 2021; 235:1329-1334. [PMID: 34227422 DOI: 10.1177/09544119211029745] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Atrial Fibrillation (A-fib) is a common cardiac rhythm problem in the population these days in which irregular heartbeat leads to blood clots, heart failure, stroke, and other significant clinical complications. Researchers have found that the atrial fat can lead to AF in most patients. To develop an automated method for detecting the epicardial fat present in the atrium using a Convolutional Neural Network. Cardiac Computed Tomography (CT) images of ten patients were pre-processed to remove the unwanted structure around the heart. An automated pixel value masking was done to locate the epicardial fat in the atrium and a 3D view of the heart was constructed for correct visualization of the location of the fat. A fast and fully automated Convolutional Neural Network (CNN) was applied to detect the atrial epicardial fat through feature selection from the CT images. We achieved 89.22% accuracy, 90.18% sensitivity, and 88.52% specificity in the detection of atrial epicardial fat using our CNN architecture. Our results showed that this CNN-based method can be helpful in atrial epicardial fat detection. Since Deep learning techniques add robustness, rapidness, and reliability, this study provides an unutilized way to detect the atrial fat tissue.
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Affiliation(s)
- Deepa Deepa
- Biomedical Engineering, Chung Yuan Christian University, Zhongli, Taoyuan city
| | - Yashbir Singh
- Biomedical Engineering, Chung Yuan Christian University, Zhongli, Taoyuan city
| | - Ming Chen Wang
- Biomedical Engineering, Chung Yuan Christian University, Zhongli, Taoyuan city
| | - Weichih Hu
- Biomedical Engineering, Chung Yuan Christian University, Zhongli, Taoyuan city
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Field M, Hardcastle N, Jameson M, Aherne N, Holloway L. Machine learning applications in radiation oncology. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2021; 19:13-24. [PMID: 34307915 PMCID: PMC8295850 DOI: 10.1016/j.phro.2021.05.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 05/19/2021] [Accepted: 05/22/2021] [Indexed: 12/23/2022]
Abstract
Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for cancer patients. Harnessing this potential requires standardization of tools and data, and focused collaboration between fields of expertise. The rapid advancement of radiation oncology treatment technologies presents opportunities for machine learning integration with investments targeted towards data quality, data extraction, software, and engagement with clinical expertise. In this review, we provide an overview of machine learning concepts before reviewing advances in applying machine learning to radiation oncology and integrating these techniques into the radiation oncology workflows. Several key areas are outlined in the radiation oncology workflow where machine learning has been applied and where it can have a significant impact in terms of efficiency, consistency in treatment and overall treatment outcomes. This review highlights that machine learning has key early applications in radiation oncology due to the repetitive nature of many tasks that also currently have human review. Standardized data management of routinely collected imaging and radiation dose data are also highlighted as enabling engagement in research utilizing machine learning and the ability integrate these technologies into clinical workflow to benefit patients. Physicists need to be part of the conversation to facilitate this technical integration.
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Affiliation(s)
- Matthew Field
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.,Ingham Institute for Applied Medical Research, Sydney, NSW, Australia
| | - Nicholas Hardcastle
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
| | - Michael Jameson
- GenesisCare, Alexandria, NSW, Australia.,St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Australia
| | - Noel Aherne
- Mid North Coast Cancer Institute, NSW, Australia.,Rural Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Lois Holloway
- South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.,Ingham Institute for Applied Medical Research, Sydney, NSW, Australia.,Cancer Therapy Centre, Liverpool Hospital, Sydney, NSW, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
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15
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Slart RHJA, Williams MC, Juarez-Orozco LE, Rischpler C, Dweck MR, Glaudemans AWJM, Gimelli A, Georgoulias P, Gheysens O, Gaemperli O, Habib G, Hustinx R, Cosyns B, Verberne HJ, Hyafil F, Erba PA, Lubberink M, Slomka P, Išgum I, Visvikis D, Kolossváry M, Saraste A. Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT. Eur J Nucl Med Mol Imaging 2021; 48:1399-1413. [PMID: 33864509 PMCID: PMC8113178 DOI: 10.1007/s00259-021-05341-z] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 03/25/2021] [Indexed: 12/18/2022]
Abstract
In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.
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Affiliation(s)
- Riemer H J A Slart
- Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands.
- Faculty of Science and Technology Biomedical, Photonic Imaging, University of Twente, Enschede, The Netherlands.
| | - Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging facility QMRI, Edinburgh, UK
| | - Luis Eduardo Juarez-Orozco
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Christoph Rischpler
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Marc R Dweck
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging facility QMRI, Edinburgh, UK
| | - Andor W J M Glaudemans
- Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
| | | | - Panagiotis Georgoulias
- Department of Nuclear Medicine, Faculty of Medicine, University of Thessaly, University Hospital of Larissa, Larissa, Greece
| | - Olivier Gheysens
- Department of Nuclear Medicine, Cliniques Universitaires Saint-Luc and Institute of Clinical and Experimental Research (IREC), Université catholique de Louvain (UCLouvain), Brussels, Belgium
| | | | - Gilbert Habib
- APHM, Cardiology Department, La Timone Hospital, Marseille, France
- IRD, APHM, MEPHI, IHU-Méditerranée Infection, Aix Marseille Université, Marseille, France
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, ULiège, Liège, Belgium
| | - Bernard Cosyns
- Department of Cardiology, Centrum voor Hart en Vaatziekten, Universitair Ziekenhuis Brussel, 101 Laarbeeklaan, 1090, Brussels, Belgium
| | - Hein J Verberne
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Fabien Hyafil
- Department of Nuclear Medicine, DMU IMAGINA, Georges-Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, F-75015, Paris, France
- University of Paris, PARCC, INSERM, F-75006, Paris, France
| | - Paola A Erba
- Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
- Department of Nuclear Medicine (P.A.E.), University of Pisa, Pisa, Italy
- Department of Translational Research and New Technology in Medicine (P.A.E.), University of Pisa, Pisa, Italy
| | - Mark Lubberink
- Department of Surgical Sciences/Radiology, Uppsala University, Uppsala, Sweden
- Medical Physics, Uppsala University Hospital, Uppsala, Sweden
| | - Piotr Slomka
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ivana Išgum
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC - location AMC, University of Amsterdam, 1105, Amsterdam, AZ, Netherlands
| | | | - Márton Kolossváry
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68 Városmajor Street, Budapest, Hungary
| | - Antti Saraste
- Turku PET Centre, Turku University Hospital, University of Turku, Turku, Finland
- Heart Center, Turku University Hospital, Turku, Finland
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16
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Schuuring MJ, Išgum I, Cosyns B, Chamuleau SAJ, Bouma BJ. Routine Echocardiography and Artificial Intelligence Solutions. Front Cardiovasc Med 2021; 8:648877. [PMID: 33708808 PMCID: PMC7940184 DOI: 10.3389/fcvm.2021.648877] [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: 01/02/2021] [Accepted: 02/05/2021] [Indexed: 12/28/2022] Open
Abstract
Introduction: Echocardiography is widely used because of its portability, high temporal resolution, absence of radiation, and due to the low-costs. Over the past years, echocardiography has been recommended by the European Society of Cardiology in most cardiac diseases for both diagnostic and prognostic purposes. These recommendations have led to an increase in number of performed studies each requiring diligent processing and reviewing. The standard work pattern of image analysis including quantification and reporting has become highly resource intensive and time consuming. Existence of a large number of datasets with digital echocardiography images and recent advent of AI technology have created an environment in which artificial intelligence (AI) solutions can be developed successfully to automate current manual workflow. Methods and Results: We report on published AI solutions for echocardiography analysis on methods' performance, characteristics of the used data and imaged population. Contemporary AI applications are available for automation and advent in the image acquisition, analysis, reporting and education. AI solutions have been developed for both diagnostic and predictive tasks in echocardiography. Left ventricular function assessment and quantification have been most often performed. Performance of automated image view classification, image quality enhancement, cardiac function assessment, disease classification, and cardiac event prediction was overall good but most studies lack external evaluation. Conclusion: Contemporary AI solutions for image acquisition, analysis, reporting and education are developed for relevant tasks with promising performance. In the future major benefit of AI in echocardiography is expected from improvements in automated analysis and interpretation to reduce workload and improve clinical outcome. Some of the challenges have yet to be overcome, however, none of them are insurmountable.
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Affiliation(s)
- Mark J. Schuuring
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, Netherlands
| | - Ivana Išgum
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Biomedical Engineering and Physics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Radiology and Nuclear Medicine, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers -Location Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Bernard Cosyns
- Department of Cardiology, University Hospital Brussel, Brussels, Belgium
| | - Steven A. J. Chamuleau
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers -Location Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Berto J. Bouma
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, Netherlands
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17
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Gordo C, Núñez-Córdoba JM, Mateo R. Root causes of adverse drug events in hospitals and artificial intelligence capabilities for prevention. J Adv Nurs 2021; 77:3168-3175. [PMID: 33624324 DOI: 10.1111/jan.14779] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 01/08/2021] [Accepted: 01/16/2021] [Indexed: 11/29/2022]
Abstract
AIMS To identify and prioritize the root causes of adverse drug events (ADEs) in hospitals and to assess the ability of artificial intelligence (AI) capabilities to prevent ADEs. DESIGN A mixed method design was used. METHODS A cross-sectional study for hospitals in Spain was carried out between February and April 2019 to identify and prioritize the root causes of ADEs. A nominal group technique was also used to assess the ability of AI capabilities to prevent ADEs. RESULTS The main root cause of ADEs was a lack of adherence to safety protocols (64.8%), followed by identification errors (57.4%), and fragile and polymedicated patients (44.4%). An analysis of the AI capabilities to prevent the root causes of ADEs showed that identification and reading are two potentially useful capabilities. CONCLUSION Identification error is one of the main root causes of drug adverse events and AI capabilities could potentially prevent drug adverse events. IMPACT This study highlights the role of AI capabilities in safely identifying both patients and drugs, which is a crucial part of the medication administration process, and how this can prevent ADEs in hospitals.
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Affiliation(s)
- Cristina Gordo
- Healthcare Quality Service, Clínica Universidad de Navarra, Pamplona, Spain
| | - Jorge M Núñez-Córdoba
- Research Support Service, Central Clinical Trials Unit, Clínica Universidad de Navarra, Pamplona, Spain.,Department of Preventive Medicine and Public Health, School of Medicine, University of Navarra, Pamplona, Spain
| | - Ricardo Mateo
- Department of Business, School of Economics and Business, University of Navarra, Pamplona, Spain
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Abstract
PURPOSE OF REVIEW Artificial intelligence is a broad set of sophisticated computer-based statistical tools that have become widely available. Cardiovascular medicine with its large data repositories, need for operational efficiency and growing focus on precision care is set to be transformed by artificial intelligence. Applications range from new pathophysiologic discoveries to decision support for individual patient care to optimization of system-wide logistical processes. RECENT FINDINGS Machine learning is the dominant form of artificial intelligence wherein complex statistical algorithms 'learn' by deducing patterns in datasets. Supervised machine learning uses classified large data to train an algorithm to accurately predict the outcome, whereas in unsupervised machine learning, the algorithm uncovers mathematical relationships within unclassified data. Artificial multilayered neural networks or deep learning is one of the most successful tools. Artificial intelligence has demonstrated superior efficacy in disease phenomapping, early warning systems, risk prediction, automated processing and interpretation of imaging, and increasing operational efficiency. SUMMARY Artificial intelligence demonstrates the ability to learn through assimilation of large datasets to unravel complex relationships, discover prior unfound pathophysiological states and develop predictive models. Artificial intelligence needs widespread exploration and adoption for large-scale implementation in cardiovascular practice.
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Affiliation(s)
- Sagar Ranka
- Department of Cardiovascular Medicine, The University of Kansas, Health System, Kansas City, Kansas, USA
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19
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Khomtchouk BB, Tran DT, Vand KA, Might M, Gozani O, Assimes TL. Cardioinformatics: the nexus of bioinformatics and precision cardiology. Brief Bioinform 2020; 21:2031-2051. [PMID: 31802103 PMCID: PMC7947182 DOI: 10.1093/bib/bbz119] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 08/08/2019] [Accepted: 08/13/2019] [Indexed: 12/12/2022] Open
Abstract
Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17 million deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, most bioinformatics and computational biology research and funding to date has been concentrated predominantly on cancer research, with a relatively modest footprint in CVD. In this paper, we review the existing literary landscape and critically assess the unmet need to further develop an emerging field at the multidisciplinary interface of bioinformatics and precision cardiovascular medicine, which we refer to as 'cardioinformatics'.
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Affiliation(s)
- Bohdan B Khomtchouk
- Department of Biology, Stanford University, Stanford, CA, USA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Medicine, Section of Computational Biomedicine and Biomedical Data Science, University of Chicago, Chicago, IL, USA
| | - Diem-Trang Tran
- School of Computing, University of Utah, Salt Lake City, UT, USA
| | | | - Matthew Might
- Hugh Kaul Personalized Medicine Institute, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Or Gozani
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Themistocles L Assimes
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
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20
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Abidov A, Chehab O. Cardiovascular risk assessment models: Have we found the perfect solution yet? J Nucl Cardiol 2020; 27:2375-2385. [PMID: 30793251 DOI: 10.1007/s12350-019-01642-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Accepted: 01/28/2019] [Indexed: 12/27/2022]
Affiliation(s)
- Aiden Abidov
- Division of Cardiology/Department of Internal Medicine, Wayne State University, Detroit, MI, USA.
- Cardiology Section/Department of Internal Medicine, John D. Dingell VA Medical Center, 4646 John R., Detroit, MI, 48201, USA.
| | - Omar Chehab
- Division of Cardiology/Department of Internal Medicine, Wayne State University, Detroit, MI, USA
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21
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Ma C, Wang X, Wu J, Cheng X, Xia L, Xue F, Qiu L. Real-world big-data studies in laboratory medicine: Current status, application, and future considerations. Clin Biochem 2020; 84:21-30. [DOI: 10.1016/j.clinbiochem.2020.06.014] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 06/25/2020] [Accepted: 06/30/2020] [Indexed: 12/20/2022]
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Impact of Early Revascularization on Major Adverse Cardiovascular Events in Relation to Automatically Quantified Ischemia. JACC Cardiovasc Imaging 2020; 14:644-653. [PMID: 32828784 DOI: 10.1016/j.jcmg.2020.05.039] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 05/26/2020] [Accepted: 05/27/2020] [Indexed: 12/30/2022]
Abstract
OBJECTIVES Using a contemporary, multicenter international single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) registry, this study characterized the potential major adverse cardiovascular event(s) (MACE) benefit of early revascularization based on automatic quantification of ischemia. BACKGROUND Prior single-center data reported an association between moderate to severe ischemia SPECT-MPI and reduced cardiac death with early revascularization. METHODS Consecutive patients from a multicenter, international registry who underwent 99mTc SPECT-MPI between 2009 and 2014 with solid-state scanners were included. Ischemia was quantified automatically as ischemic total perfusion deficit (TPD). Early revascularization was defined as within 90 days. The primary outcome was MACE (death, myocardial infarction, and unstable angina). A propensity score was developed to adjust for nonrandomization of revascularization; then, multivariable Cox modeling adjusted for propensity score and demographics was used to predict MACE. RESULTS In total, 19,088 patients were included, with a mean follow-up of 4.7 ± 1.6 years, during which MACE occurred in 1,836 (9.6%) patients. There was a significant interaction between ischemic TPD modeled as a continuous variable and early revascularization (interaction p value: 0.012). In this model, there was a trend toward reduced MACE in patients with >5.4% ischemic TPD and a significant association with reduced MACE in patients with >10.2% ischemic TPD. CONCLUSIONS In this large, international, multicenter study reflecting contemporary cardiology practice, early revascularization of patients with >10.2% ischemia on SPECT-MPI, quantified automatically, was associated with reduced MACE.
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23
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Li S, Wang Z, Visser LC, Wisner ER, Cheng H. Pilot study: Application of artificial intelligence for detecting left atrial enlargement on canine thoracic radiographs. Vet Radiol Ultrasound 2020; 61:611-618. [PMID: 32783354 PMCID: PMC7689842 DOI: 10.1111/vru.12901] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 05/11/2020] [Accepted: 05/17/2020] [Indexed: 12/15/2022] Open
Abstract
Although deep learning has been explored extensively for computer‐aided medical imaging diagnosis in human medicine, very little has been done in veterinary medicine. The goal of this retrospective, pilot project was to apply the deep learning artificial intelligence technique using thoracic radiographs for detection of canine left atrial enlargement and compare results with those of veterinary radiologist interpretations. Seven hundred ninety‐two right lateral radiographs from canine patients with thoracic radiographs and contemporaneous echocardiograms were used to train, validate, and test a convolutional neural network algorithm. The accuracy, sensitivity, and specificity for determination of left atrial enlargement were then compared with those of board‐certified veterinary radiologists as recorded on radiology reports. The accuracy, sensitivity, and specificity were 82.71%, 68.42%, and 87.09%, respectively, using an accuracy driven variant of the convolutional neural network algorithm and 79.01%, 73.68%, and 80.64%, respectively, using a sensitivity driven variant. By comparison, accuracy, sensitivity, and specificity achieved by board‐certified veterinary radiologists was 82.71%, 68.42%, and 87.09%, respectively. Although overall accuracy of the accuracy driven convolutional neural network algorithm and veterinary radiologists was identical, concordance between the two approaches was 85.19%. This study documents proof‐of‐concept for application of deep learning techniques for computer‐aided diagnosis in veterinary medicine.
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Affiliation(s)
- Shen Li
- William R. Pritchard Veterinary Medical Teaching Hospital, School of Veterinary MedicineUniversity of CaliforniaDavisCaliforniaUSA
| | - Zigui Wang
- Department of Animal SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | - Lance C. Visser
- Department of Medicine and Epidemiology, School of Veterinary MedicineUniversity of CaliforniaDavisCaliforniaUSA
| | - Erik R. Wisner
- Department of Surgical and Radiological Sciences, School of Veterinary MedicineUniversity of CaliforniaDavisCaliforniaUSA
| | - Hao Cheng
- Department of Animal SciencesUniversity of CaliforniaDavisCaliforniaUSA
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24
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Retson TA, Masutani EM, Golden D, Hsiao A. Clinical Performance and Role of Expert Supervision of Deep Learning for Cardiac Ventricular Volumetry: A Validation Study. Radiol Artif Intell 2020; 2:e190064. [PMID: 32797119 DOI: 10.1148/ryai.2020190064] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 02/21/2020] [Accepted: 03/27/2020] [Indexed: 11/11/2022]
Abstract
Purpose To evaluate the performance of a deep learning (DL) algorithm for clinical measurement of right and left ventricular volume and function across cardiac MR images obtained for a range of clinical indications and pathologies. Materials and Methods A retrospective, Health Insurance Portability and Accountability Act-compliant study was conducted using the first 200 noncongenital clinical cardiac MRI examinations from June 2015 to June 2017 for which volumetry was available. Images were analyzed using commercially available software for automated DL-based and manual contouring of biventricular volumes. Fully automated measurements were compared using Pearson correlations, relative volume errors, and Bland-Altman analyses. Manual, automated, and expert revised contours for 50 MR images were examined by comparing regional Dice coefficients at the base, midventricle, and apex to further analyze the contour quality. Results Fully automated and manual left ventricular volumes were strongly correlated for end-systolic volume (ESV: Pearson r = 0.99, P < .001), end-diastolic volume (EDV: r = 0.97, P < .001), and ejection fraction (EF: r = 0.94, P < .001). Right ventricular measurements were also correlated for ESV (r = 0.93, P < .001), EDV (r = 0.92, P < .001), and EF (r = 0.73, P < .001). Visual inspection of segmentation quality showed most errors (73%) occurred at the cardiac base. Mean Dice coefficients between manual, automated, and expert revised contours ranged from 0.92 to 0.95, with greatest variance at the base and apex. Conclusion Fully automated ventricular segmentation by the tested algorithm provides contours and ventricular volumes that could be used to aid expert segmentation, but can benefit from expert supervision, particularly to resolve errors at the basal and apical slices. Supplemental material is available for this article. © RSNA, 2020.
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Affiliation(s)
- Tara A Retson
- Department of Radiology, Altman Clinical and Translational Research Institute, University of California, San Diego, 9452 Medical Center Dr, 4th Floor, La Jolla, CA 92037 (T.A.R., A.H.); Department of Bioengineering, University of California San Diego School of Medicine, La Jolla, Calif (E.M.M.); and Arterys, San Francisco, Calif (D.G.)
| | - Evan M Masutani
- Department of Radiology, Altman Clinical and Translational Research Institute, University of California, San Diego, 9452 Medical Center Dr, 4th Floor, La Jolla, CA 92037 (T.A.R., A.H.); Department of Bioengineering, University of California San Diego School of Medicine, La Jolla, Calif (E.M.M.); and Arterys, San Francisco, Calif (D.G.)
| | - Daniel Golden
- Department of Radiology, Altman Clinical and Translational Research Institute, University of California, San Diego, 9452 Medical Center Dr, 4th Floor, La Jolla, CA 92037 (T.A.R., A.H.); Department of Bioengineering, University of California San Diego School of Medicine, La Jolla, Calif (E.M.M.); and Arterys, San Francisco, Calif (D.G.)
| | - Albert Hsiao
- Department of Radiology, Altman Clinical and Translational Research Institute, University of California, San Diego, 9452 Medical Center Dr, 4th Floor, La Jolla, CA 92037 (T.A.R., A.H.); Department of Bioengineering, University of California San Diego School of Medicine, La Jolla, Calif (E.M.M.); and Arterys, San Francisco, Calif (D.G.)
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25
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Tang F, Bai C, Zhao XX, Yuan WF. Artificial Intelligence and Myocardial Contrast Enhancement Pattern. Curr Cardiol Rep 2020; 22:77. [PMID: 32632670 DOI: 10.1007/s11886-020-01306-0] [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] [Indexed: 10/23/2022]
Abstract
PURPOSE OF REVIEW Machine learning (ML) and deep learning (DL) are two important categories of AI algorithms. Nowadays, AI technology has been gradually applied to cardiac magnetic resonance imaging (CMRI), covering the fields of myocardial contrast enhancement (MCE) pattern and automatic ventricular segmentation. This paper mainly discusses the relationship between machine learning and deep learning based on AI and pattern of MCE in CMRI. RECENT FINDINGS It found that some histogram and GLCM parameters in ML algorithm had significant statistical differences in diagnosis of cardiomyopathy and differentiation of fibrosis and normal myocardial tissue. In the DL algorithm, there was no significant difference between CNN and observers in measuring myocardial fibrosis. The rapid development of texture parameter analysis methods would promote the medical imaging based on AI into a new era. Histogram and GLCM parameters are the research hotspot of unsupervised learning of MCE images. CNN has a great advantage in automatically identifying and quantifying myocardial fibrosis reflected by LGE images.
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Affiliation(s)
- Fang Tang
- Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, The 278th Baoguang Avenue, Xindu District, Chengdu, Sichuan, 610500, People's Republic of China
| | - Chen Bai
- Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, The 278th Baoguang Avenue, Xindu District, Chengdu, Sichuan, 610500, People's Republic of China
| | - Xin-Xiang Zhao
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, The 374th Dianmian Road, Wuhua District, Kunming, Yunnan, 650101, People's Republic of China
| | - Wei-Feng Yuan
- Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, The 278th Baoguang Avenue, Xindu District, Chengdu, Sichuan, 610500, People's Republic of China.
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26
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Wolterink JM. Left ventricle segmentation in the era of deep learning. J Nucl Cardiol 2020; 27:988-991. [PMID: 30834498 DOI: 10.1007/s12350-019-01674-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Accepted: 02/20/2019] [Indexed: 10/27/2022]
Affiliation(s)
- Jelmer M Wolterink
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
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27
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Bachtiger P, Plymen CM, Pabari PA, Howard JP, Whinnett ZI, Opoku F, Janering S, Faisal AA, Francis DP, Peters NS. Artificial Intelligence, Data Sensors and Interconnectivity: Future Opportunities for Heart Failure. Card Fail Rev 2020; 6:e11. [PMID: 32514380 PMCID: PMC7265101 DOI: 10.15420/cfr.2019.14] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 01/23/2020] [Indexed: 11/08/2022] Open
Abstract
A higher proportion of patients with heart failure have benefitted from a wide and expanding variety of sensor-enabled implantable devices than any other patient group. These patients can now also take advantage of the ever-increasing availability and affordability of consumer electronics. Wearable, on- and near-body sensor technologies, much like implantable devices, generate massive amounts of data. The connectivity of all these devices has created opportunities for pooling data from multiple sensors – so-called interconnectivity – and for artificial intelligence to provide new diagnostic, triage, risk-stratification and disease management insights for the delivery of better, more personalised and cost-effective healthcare. Artificial intelligence is also bringing important and previously inaccessible insights from our conventional cardiac investigations. The aim of this article is to review the convergence of artificial intelligence, sensor technologies and interconnectivity and the way in which this combination is set to change the care of patients with heart failure.
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Affiliation(s)
- Patrik Bachtiger
- Imperial Centre for Cardiac Engineering, National Heart and Lung Institute, Imperial College London, UK
| | - Carla M Plymen
- Department of Cardiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital London, UK
| | - Punam A Pabari
- Department of Cardiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital London, UK
| | - James P Howard
- Imperial Centre for Cardiac Engineering, National Heart and Lung Institute, Imperial College London, UK.,Department of Cardiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital London, UK
| | - Zachary I Whinnett
- Department of Cardiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital London, UK
| | - Felicia Opoku
- IT Department, Imperial College Healthcare NHS London, UK
| | | | - Aldo A Faisal
- Departments of Bioengineering and Computing, Data Science Institute, Imperial College London, UK
| | - Darrel P Francis
- Imperial Centre for Cardiac Engineering, National Heart and Lung Institute, Imperial College London, UK.,Department of Cardiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital London, UK
| | - Nicholas S Peters
- Imperial Centre for Cardiac Engineering, National Heart and Lung Institute, Imperial College London, UK.,Department of Cardiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital London, UK
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28
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Lopez-Jimenez F, Attia Z, Arruda-Olson AM, Carter R, Chareonthaitawee P, Jouni H, Kapa S, Lerman A, Luong C, Medina-Inojosa JR, Noseworthy PA, Pellikka PA, Redfield MM, Roger VL, Sandhu GS, Senecal C, Friedman PA. Artificial Intelligence in Cardiology: Present and Future. Mayo Clin Proc 2020; 95:1015-1039. [PMID: 32370835 DOI: 10.1016/j.mayocp.2020.01.038] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 01/30/2020] [Accepted: 01/31/2020] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) is a nontechnical, popular term that refers to machine learning of various types but most often to deep neural networks. Cardiology is at the forefront of AI in medicine. For this review, we searched PubMed and MEDLINE databases with no date restriction using search terms related to AI and cardiology. Articles were selected for inclusion on the basis of relevance. We highlight the major achievements in recent years in nearly all areas of cardiology and underscore the mounting evidence suggesting how AI will take center stage in the field. Artificial intelligence requires a close collaboration among computer scientists, clinical investigators, clinicians, and other users in order to identify the most relevant problems to be solved. Best practices in the generation and implementation of AI include the selection of ideal data sources, taking into account common challenges during the interpretation, validation, and generalizability of findings, and addressing safety and ethical concerns before final implementation. The future of AI in cardiology and in medicine in general is bright as the collaboration between investigators and clinicians continues to excel.
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Affiliation(s)
| | - Zachi Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | - Rickey Carter
- Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL
| | | | - Hayan Jouni
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Amir Lerman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Christina Luong
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN
| | | | | | - Veronique L Roger
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN
| | | | - Conor Senecal
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
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Abstract
In two experiments, we trained pigeons (Columba livia) to sort visual images (obtained by clinical myocardial perfusion imaging techniques) depicting different degrees of human cardiac disfunction (myocardial hypoperfusion of the left ventricle) into normal and abnormal categories by providing food reward only after correct choice responses. Pigeons proved to be highly proficient at categorizing pseudo-colorized images as well as highly sensitive to the degree of the perfusion deficit depicted in the abnormal images. In later testing, the pigeons completely transferred discriminative responding to novel stimuli, demonstrating that they had fully learned the normal and abnormal categories. Yet, these pigeons failed to transfer discriminative responding to grayscale images containing no color information. We therefore trained a second cohort of pigeons to categorize grayscale image sets from the outset. These birds required substantially more training to achieve similar levels of performance. Yet, they too completely transferred discriminative responding to novel stimuli by relying on both global and local disparities in brightness between the normal and abnormal images. These results confirm that pseudo-colorization can enhance pigeons' categorization of human cardiac images, a result also found with human observers. Overall, our findings further document the potential of the pigeon as a useful aide in studies of medical image perception.
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Affiliation(s)
- Victor M Navarro
- Department of Psychological and Brain Sciences, The University of Iowa, Iowa City, IA, 52242, USA
| | - Edward A Wasserman
- Department of Psychological and Brain Sciences, The University of Iowa, Iowa City, IA, 52242, USA.
| | - Piotr Slomka
- Cedars-Sinai Medical Center, University of California, Los Angeles, CA, USA
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Seetharam K, Shrestha S, Sengupta PP. Artificial Intelligence in Cardiac Imaging. US CARDIOLOGY REVIEW 2020. [DOI: 10.15420/usc.2019.19.2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Machine learning (ML), a subset of artificial intelligence, is showing promising results in cardiology, especially in cardiac imaging. ML algorithms are allowing cardiologists to explore new opportunities and make discoveries not seen with conventional approaches. This offers new opportunities to enhance patient care and open new gateways in medical decision-making. This review highlights the role of ML in cardiac imaging for precision phenotyping and prognostication of cardiac disorders.
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Affiliation(s)
- Karthik Seetharam
- Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, WV
| | - Sirish Shrestha
- Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, WV
| | - Partho P Sengupta
- Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, WV
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31
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Juarez-Orozco LE, Knol RJJ, Sanchez-Catasus CA, Martinez-Manzanera O, van der Zant FM, Knuuti J. Machine learning in the integration of simple variables for identifying patients with myocardial ischemia. J Nucl Cardiol 2020; 27:147-155. [PMID: 29790017 DOI: 10.1007/s12350-018-1304-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 05/07/2018] [Indexed: 12/14/2022]
Abstract
BACKGROUND A significant number of variables are obtained when characterizing patients suspected with myocardial ischemia or at risk of MACE. Guidelines typically use a handful of them to support further workup or therapeutic decisions. However, it is likely that the numerous available predictors maintain intrinsic complex interrelations. Machine learning (ML) offers the possibility to elucidate complex patterns within data to optimize individual patient classification. We evaluated the feasibility and performance of ML in utilizing simple accessible clinical and functional variables for the identification of patients with ischemia or an elevated risk of MACE as determined through quantitative PET myocardial perfusion reserve (MPR). METHODS 1,234 patients referred to Nitrogen-13 ammonia PET were analyzed. Demographic (4), clinical (8), and functional variables (9) were retrieved and input into a cross-validated ML workflow consisting of feature selection and modeling. Two PET-defined outcome variables were operationalized: (1) any myocardial ischemia (regional MPR < 2.0) and (2) an elevated risk of MACE (global MPR < 2.0). ROC curves were used to evaluate ML performance. RESULTS 16 features were included for boosted ensemble ML. ML achieved an AUC of 0.72 and 0.71 in identifying patients with myocardial ischemia and with an elevated risk of MACE, respectively. ML performance was superior to logistic regression when the latter used the ESC guidelines risk models variables for both PET-defined labels (P < .001 and P = .01, respectively). CONCLUSIONS ML is feasible and applicable in the evaluation and utilization of simple and accessible predictors for the identification of patients who will present myocardial ischemia and an elevated risk of MACE in quantitative PET imaging.
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Affiliation(s)
- Luis Eduardo Juarez-Orozco
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland.
| | - Remco J J Knol
- Cardiac Imaging Division Alkmaar, Department of Nuclear Medicine, Northwest Clinics, Alkmaar, The Netherlands
| | - Carlos A Sanchez-Catasus
- Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Octavio Martinez-Manzanera
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Friso M van der Zant
- Cardiac Imaging Division Alkmaar, Department of Nuclear Medicine, Northwest Clinics, Alkmaar, The Netherlands
| | - Juhani Knuuti
- Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland
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Garcia EV, Slomka P, Moody JB, Germano G, Ficaro EP. Quantitative Clinical Nuclear Cardiology, Part 1: Established Applications. J Nucl Cardiol 2020; 27:189-201. [PMID: 31654215 DOI: 10.1007/s12350-019-01906-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 07/11/2019] [Indexed: 11/30/2022]
Abstract
Single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has attained widespread clinical acceptance as a standard of care for patients with known or suspected coronary artery disease (CAD). A significant contribution to this success has been the use of computer techniques to provide objective quantitative assessment in the standardization of the interpretation of these studies. Software platforms have been developed as a pipeline to provide the quantitative algorithms researched, developed and validated to be clinically useful so diagnosticians everywhere can benefit from these tools. The goal of this CME article (PART 1) is to describe the many quantitative tools that are clinically established and more importantly how clinicians should use them routinely in the interpretation, clinical management and therapy guidance of patients with CAD.
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Affiliation(s)
- Ernest V Garcia
- Department of Radiology and Imaging Sciences, Emory University, 101 Woodruff Circle, Room 1203, Atlanta, GA, 30322, USA.
| | - Piotr Slomka
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Guido Germano
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Edward P Ficaro
- INVIA Medical Imaging Solutions, Ann Arbor, MI, USA
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
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33
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Rogers MA, Aikawa E. Cardiovascular calcification: artificial intelligence and big data accelerate mechanistic discovery. Nat Rev Cardiol 2020; 16:261-274. [PMID: 30531869 DOI: 10.1038/s41569-018-0123-8] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Cardiovascular calcification is a health disorder with increasing prevalence and high morbidity and mortality. The only available therapeutic options for calcific vascular and valvular heart disease are invasive transcatheter procedures or surgeries that do not fully address the wide spectrum of these conditions; therefore, an urgent need exists for medical options. Cardiovascular calcification is an active process, which provides a potential opportunity for effective therapeutic targeting. Numerous biological processes are involved in calcific disease, including matrix remodelling, transcriptional regulation, mitochondrial dysfunction, oxidative stress, calcium and phosphate signalling, endoplasmic reticulum stress, lipid and mineral metabolism, autophagy, inflammation, apoptosis, loss of mineralization inhibition, impaired mineral resorption, cellular senescence and extracellular vesicles that act as precursors of microcalcification. Advances in molecular imaging and big data technology, including in multiomics and network medicine, and the integration of these approaches are helping to provide a more comprehensive map of human disease. In this Review, we discuss ectopic calcification processes in the cardiovascular system, with an emphasis on emerging mechanistic knowledge obtained through patient data and advances in imaging methods, experimental models and multiomics-generated big data. We also highlight the potential and challenges of artificial intelligence, machine learning and deep learning to integrate imaging and mechanistic data for drug discovery.
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Affiliation(s)
- Maximillian A Rogers
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. .,Center for Excellence in Vascular Biology, Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Abstract
Three-dimensional (3D) printing of human tissues and organs has been an exciting area of research for almost three decades [Bonassar and Vacanti. J Cell Biochem. 72(Suppl 30-31):297-303 (1998)]. The primary goal of bioprinting, presently, is achieving printed constructs with the overarching aim toward fully functional tissues and organs. Technology, in hand with the development of bioinks, has been identified as the key to this success. As a result, the place of computer-aided systems (design and manufacturing-CAD/CAM) cannot be underestimated and plays a significant role in this area. Unlike many reviews in this field, this chapter focuses on the technology required for 3D bioprinting from an initial background followed by the exciting area of medical imaging and how it plays a role in bioprinting. Extraction and classification of tissue types from 3D scans is discussed in addition to modeling and simulation capabilities of scanned systems. After that, the necessary area of transferring the 3D model to the printer is explored. The chapter closes with a discussion of the current state-of-the-art and inherent challenges facing the research domain to achieve 3D tissue and organ printing.
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Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford) 2020; 2020:baaa010. [PMID: 32185396 PMCID: PMC7078068 DOI: 10.1093/database/baaa010] [Citation(s) in RCA: 138] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 01/05/2020] [Accepted: 01/21/2020] [Indexed: 02/06/2023]
Abstract
Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.
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Affiliation(s)
- Zeeshan Ahmed
- Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, 112 Paterson Street, New Brunswick, NJ, USA
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson Street, New Brunswick, NJ, USA
- Department of Genetics and Genome Sciences, School of Medicine, University of Connecticut Health Center, 263 Farmington Ave., Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, 67 North Eagleville Road, Storrs, CT, USA
| | - Khalid Mohamed
- Department of Genetics and Genome Sciences, School of Medicine, University of Connecticut Health Center, 263 Farmington Ave., Farmington, CT, USA
| | - Saman Zeeshan
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - XinQi Dong
- Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, 112 Paterson Street, New Brunswick, NJ, USA
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson Street, New Brunswick, NJ, USA
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Lenchik L, Heacock L, Weaver AA, Boutin RD, Cook TS, Itri J, Filippi CG, Gullapalli RP, Lee J, Zagurovskaya M, Retson T, Godwin K, Nicholson J, Narayana PA. Automated Segmentation of Tissues Using CT and MRI: A Systematic Review. Acad Radiol 2019; 26:1695-1706. [PMID: 31405724 PMCID: PMC6878163 DOI: 10.1016/j.acra.2019.07.006] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 07/17/2019] [Accepted: 07/17/2019] [Indexed: 01/10/2023]
Abstract
RATIONALE AND OBJECTIVES The automated segmentation of organs and tissues throughout the body using computed tomography and magnetic resonance imaging has been rapidly increasing. Research into many medical conditions has benefited greatly from these approaches by allowing the development of more rapid and reproducible quantitative imaging markers. These markers have been used to help diagnose disease, determine prognosis, select patients for therapy, and follow responses to therapy. Because some of these tools are now transitioning from research environments to clinical practice, it is important for radiologists to become familiar with various methods used for automated segmentation. MATERIALS AND METHODS The Radiology Research Alliance of the Association of University Radiologists convened an Automated Segmentation Task Force to conduct a systematic review of the peer-reviewed literature on this topic. RESULTS The systematic review presented here includes 408 studies and discusses various approaches to automated segmentation using computed tomography and magnetic resonance imaging for neurologic, thoracic, abdominal, musculoskeletal, and breast imaging applications. CONCLUSION These insights should help prepare radiologists to better evaluate automated segmentation tools and apply them not only to research, but eventually to clinical practice.
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Affiliation(s)
- Leon Lenchik
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157.
| | - Laura Heacock
- Department of Radiology, NYU Langone, New York, New York
| | - Ashley A Weaver
- Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Robert D Boutin
- Department of Radiology, University of California Davis School of Medicine, Sacramento, California
| | - Tessa S Cook
- Department of Radiology, University of Pennsylvania, Philadelphia Pennsylvania
| | - Jason Itri
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157
| | - Christopher G Filippi
- Department of Radiology, Donald and Barbara School of Medicine at Hofstra/Northwell, Lenox Hill Hospital, NY, New York
| | - Rao P Gullapalli
- Department of Radiology, University of Maryland School of Medicine, Baltimore, Maryland
| | - James Lee
- Department of Radiology, University of Kentucky, Lexington, Kentucky
| | | | - Tara Retson
- Department of Radiology, University of California San Diego, San Diego, California
| | - Kendra Godwin
- Medical Library, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Joey Nicholson
- NYU Health Sciences Library, NYU School of Medicine, NYU Langone Health, New York, New York
| | - Ponnada A Narayana
- Department of Diagnostic and Interventional Imaging, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas
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37
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Dorado-Díaz PI, Sampedro-Gómez J, Vicente-Palacios V, Sánchez PL. Aplicaciones de la inteligencia artificial en cardiología: el futuro ya está aquí. Rev Esp Cardiol 2019. [DOI: 10.1016/j.recesp.2019.05.016] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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Abstract
Unstable coronary plaques that are prone to erosion and rupture are the major cause of acute coronary syndromes. Our expanding understanding of the biological mechanisms of coronary atherosclerosis and rapid technological advances in the field of medical imaging has established cardiac computed tomography as a first-line diagnostic test in the assessment of suspected coronary artery disease, and as a powerful method of detecting the vulnerable plaque and patient. Cardiac computed tomography can provide a noninvasive, yet comprehensive, qualitative and quantitative assessment of coronary plaque burden, detect distinct high-risk morphological plaque features, assess the hemodynamic significance of coronary lesions and quantify the coronary inflammatory burden by tracking the effects of arterial inflammation on the composition of the adjacent perivascular fat. Furthermore, advances in machine learning, computational fluid dynamic modeling, and the development of targeted contrast agents continue to expand the capabilities of cardiac computed tomography imaging. In our Review, we discuss the current role of cardiac computed tomography in the assessment of coronary atherosclerosis, highlighting its dual function as a clinical and research tool that provides a wealth of structural and functional information, with far-reaching diagnostic and prognostic implications.
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Affiliation(s)
- Evangelos K. Oikonomou
- From the Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom
| | - Henry W. West
- From the Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom
| | - Charalambos Antoniades
- From the Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, United Kingdom
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Dorado-Díaz PI, Sampedro-Gómez J, Vicente-Palacios V, Sánchez PL. Applications of Artificial Intelligence in Cardiology. The Future is Already Here. ACTA ACUST UNITED AC 2019; 72:1065-1075. [PMID: 31611150 DOI: 10.1016/j.rec.2019.05.014] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 05/20/2019] [Indexed: 02/01/2023]
Abstract
There is currently no other hot topic like the ability of current technology to develop capabilities similar to those of human beings, even in medicine. This ability to simulate the processes of human intelligence with computer systems is known as artificial intelligence (AI). This article aims to clarify the various terms that still sound foreign to us, such as AI, machine learning (ML), deep learning (DL), and big data. It also provides an in-depth description of the concept of AI and its types; the learning techniques and technology used by ML; cardiac imaging analysis with DL; and the contribution of this technological revolution to classical statistics, as well as its current limitations, legal aspects, and initial applications in cardiology. To do this, we conducted a detailed PubMed search on the evolution of original contributions on AI to the various areas of application in cardiology in the last 5 years and identified 673 research articles. We provide 19 detailed examples from distinct areas of cardiology that, by using AI, have shown diagnostic and therapeutic improvements, and which will aid understanding of ML and DL methodology.
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Affiliation(s)
- P Ignacio Dorado-Díaz
- Servicio de Cardiología, Hospital Universitario de Salamanca-Instituto de Investigación Biomédica de Salamanca (IBSAL), Universidad de Salamanca, Salamanca, Spain; Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, Madrid, Spain
| | - Jesús Sampedro-Gómez
- Servicio de Cardiología, Hospital Universitario de Salamanca-Instituto de Investigación Biomédica de Salamanca (IBSAL), Universidad de Salamanca, Salamanca, Spain; Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, Madrid, Spain
| | - Víctor Vicente-Palacios
- Servicio de Cardiología, Hospital Universitario de Salamanca-Instituto de Investigación Biomédica de Salamanca (IBSAL), Universidad de Salamanca, Salamanca, Spain; Philips Healthcare, Madrid, Spain
| | - Pedro L Sánchez
- Servicio de Cardiología, Hospital Universitario de Salamanca-Instituto de Investigación Biomédica de Salamanca (IBSAL), Universidad de Salamanca, Salamanca, Spain; Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, Madrid, Spain.
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40
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A fully automated pipeline for mining abdominal aortic aneurysm using image segmentation. Sci Rep 2019; 9:13750. [PMID: 31551507 PMCID: PMC6760111 DOI: 10.1038/s41598-019-50251-8] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 08/19/2019] [Indexed: 11/24/2022] Open
Abstract
Imaging software have become critical tools in the diagnosis and the treatment of abdominal aortic aneurysms (AAA). The aim of this study was to develop a fully automated software system to enable a fast and robust detection of the vascular system and the AAA. The software was designed from a dataset of injected CT-scans images obtained from 40 patients with AAA. Pre-processing steps were performed to reduce the noise of the images using image filters. The border propagation based method was used to localize the aortic lumen. An online error detection was implemented to correct errors due to the propagation in anatomic structures with similar pixel value located close to the aorta. A morphological snake was used to segment 2D or 3D regions. The software allowed an automatic detection of the aortic lumen and the AAA characteristics including the presence of thrombus and calcifications. 2D and 3D reconstructions visualization were available to ease evaluation of both algorithm precision and AAA properties. By enabling a fast and automated detailed analysis of the anatomic characteristics of the AAA, this software could be useful in clinical practice and research and be applied in a large dataset of patients.
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Rahman M, Watabe H, Shidahara M, Watanuki S, Tashiro M, Mori T, Ito S, Ohsaki Y. Renal statistical map for positron emission tomography with [O-15] water. AMERICAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING 2019; 9:193-202. [PMID: 31516765 PMCID: PMC6737348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 07/08/2019] [Indexed: 06/10/2023]
Abstract
Image statistics are frequently used for functional and molecular imaging research in which images from a patient group with a specific diagnosis are compared with images from a healthy control group who have been matched for demographic variables. The success of image statistics for brain imaging has encouraged us to develop a method for obtaining volumetrically normalized kidney to perform image statistics so that we can locally visualize the statistical significant difference comparing voxel by voxel between certain groups in terms kidney blood flow kinetic parameters. For the development of this evolutionary process, we first volumetrically normalized all subjects, which include healthy control (HC) and chronic renal failure (CRF) patients, 15O water PET image with respect to one HC subject's MRI image using affine transformation. Then 15O kinetic parametric images of normalized kidneys were obtained through the basis function method. Finally, the statistical map of these parametric images was produced using the threshold-free cluster enhancement based permutation method. Kinetic parameters of kidney namely, uptake rate constant (K1), clearance rate constant (k2) and blood volume (Va), were found to be notably lower in CRF than those of in HC and k2 parameter was found to be more stable compared to K1 and Va. The statistical map of these parametric images allowed us to visualize local significant differences statistically (P<0.05) between HC and CRF groups. Though PET and MRI techniques have enormous potentiality for functional and molecular imaging of kidney, these are, at best, in experimental level. It is speculated that statistical mapping of kidney could play a significant role in the successful implementation of functional and molecular kidney imaging. However, more research involving a larger sample size and improved normalization technique will be needed for the robustness of the process.
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Affiliation(s)
- Mahabubur Rahman
- Division of Radiation Protection and Safety Control, Cyclotron and Radioisotope Center (CYRIC), Graduate School of Biomedical Engineering, Tohoku UniversityAoba-6-3, Aramaki, Aoba-ku, Sendai-shi, Miyagi-ken 980-0845, Japan
- Bangladesh Atomic Energy CommissionE-12/A, Shahid Shahabuddin Shorok, Dhaka 1207, Bangladesh
- Bangladesh Atomic Energy Regulatory AuthorityE-12/A, Shahid Shahabuddin Shorok, Dhaka 1207, Bangladesh
| | - Hiroshi Watabe
- Division of Radiation Protection and Safety Control, Cyclotron and Radioisotope Center (CYRIC), Graduate School of Biomedical Engineering, Tohoku UniversityAoba-6-3, Aramaki, Aoba-ku, Sendai-shi, Miyagi-ken 980-0845, Japan
| | - Miho Shidahara
- Division of Applied Quantum Medical Engineering, Department of Quantum Science and Energy Engineering, Graduate School of Engineering, Tohoku UniversityAoba-6-3, Aramaki, Aoba-ku, Sendai-shi, Miyagi-ken 980-0845, Japan
| | - Shoichi Watanuki
- Division of Cyclotron Nuclear Medicine, CYRIC, Tohoku UniversityAoba-6-3, Aramaki, Aoba-ku, Sendai-shi, Miyagi-ken 980-0845, Japan
| | - Manabu Tashiro
- Division of Cyclotron Nuclear Medicine, CYRIC, Tohoku UniversityAoba-6-3, Aramaki, Aoba-ku, Sendai-shi, Miyagi-ken 980-0845, Japan
| | - Takefumi Mori
- Division of Nephrology and Endocrinology, Tohoku Medical and Pharmaceutical University1-15-1 Fukumuro, Miyagion-ku, Sendai 983-8536, Japan
| | - Sadayoshi Ito
- Division of Nephrology, Endocrinology and Vascular Medicine, Tohoku University Hospital, Tohoku University Graduate School of Medicine1-1 Seiryo-cho, Aoba-ku, Sendai 980-8574, Japan
| | - Yusuke Ohsaki
- Division of Integrated Renal Replacement Therapy, Tohoku University Graduate School of Medicine1-1 Seiryo-cho, Aoba-ku, Sendai 980-8574, Japan
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Garcia EV, Slomka P, Moody JB, Germano G, Ficaro EP. Quantitative Clinical Nuclear Cardiology, Part 1: Established Applications. J Nucl Med 2019; 60:1507-1516. [PMID: 31375569 DOI: 10.2967/jnumed.119.229799] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 07/11/2019] [Indexed: 01/10/2023] Open
Abstract
SPECT myocardial perfusion imaging has attained widespread clinical acceptance as a standard of care for patients with known or suspected coronary artery disease. A significant contribution to this success has been the use of computer techniques to provide objective quantitative assessment in the standardization of the interpretation of these studies. Software platforms have been developed as a pipeline to provide the quantitative algorithms researched, developed and validated to be clinically useful so diagnosticians everywhere can benefit from these tools. The goal of this continuing medical education article (part 1) is to describe the many quantitative tools that are clinically established and, more importantly, how clinicians should use them routinely in interpretation, clinical management, and therapy guidance for patients with coronary artery disease.
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Affiliation(s)
- Ernest V Garcia
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia
| | - Piotr Slomka
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | | | - Guido Germano
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Edward P Ficaro
- INVIA Medical Imaging Solutions, Ann Arbor, Michigan; and.,Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
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43
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Litjens G, Ciompi F, Wolterink JM, de Vos BD, Leiner T, Teuwen J, Išgum I. State-of-the-Art Deep Learning in Cardiovascular Image Analysis. JACC Cardiovasc Imaging 2019; 12:1549-1565. [DOI: 10.1016/j.jcmg.2019.06.009] [Citation(s) in RCA: 141] [Impact Index Per Article: 28.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 05/13/2019] [Accepted: 06/13/2019] [Indexed: 02/07/2023]
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Di Sopra L, Piccini D, Coppo S, Stuber M, Yerly J. An automated approach to fully self‐gated free‐running cardiac and respiratory motion‐resolved 5D whole‐heart MRI. Magn Reson Med 2019; 82:2118-2132. [DOI: 10.1002/mrm.27898] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 06/13/2019] [Accepted: 06/14/2019] [Indexed: 11/10/2022]
Affiliation(s)
- Lorenzo Di Sopra
- Department of Diagnostic and Interventional Radiology Lausanne University Hospital Lausanne Switzerland
| | - Davide Piccini
- Department of Diagnostic and Interventional Radiology Lausanne University Hospital Lausanne Switzerland
- Advanced Clinical Imaging Technology Siemens Healthcare Lausanne Switzerland
| | - Simone Coppo
- Department of Diagnostic and Interventional Radiology Lausanne University Hospital Lausanne Switzerland
| | - Matthias Stuber
- Department of Diagnostic and Interventional Radiology Lausanne University Hospital Lausanne Switzerland
- Center for Biomedical Imaging Lausanne Switzerland
| | - Jérôme Yerly
- Department of Diagnostic and Interventional Radiology Lausanne University Hospital Lausanne Switzerland
- Center for Biomedical Imaging Lausanne Switzerland
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Seetharam K, Shrestha S, Sengupta PP. Artificial Intelligence in Cardiovascular Medicine. CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2019; 21:25. [PMID: 31089906 PMCID: PMC7561035 DOI: 10.1007/s11936-019-0728-1] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
PURPOSE OF REVIEW The ripples of artificial intelligence are being felt in various sectors of human life. Machine learning, a subset of artificial intelligence, extracts information from large databases of information and is gaining traction in various fields of cardiology. In this review, we highlight noteworthy examples of machine learning utilization in echocardiography, nuclear cardiology, computed tomography, and magnetic resonance imaging over the past year. RECENT FINDINGS In the past year, machine learning (ML) has expanded its boundaries in cardiology with several positive results. Some studies have integrated clinical and imaging information to further augment the accuracy of these ML algorithms. All the studies mentioned in this review have clearly demonstrated superior results of ML in relation to conventional approaches for identifying obstructions or predicting major adverse events in reference to conventional approaches. As the influx of data arriving from gradually evolving technologies in health care and wearable devices continues to be more complex, ML may serve as the bridge to transcend the gap between health care and patients in the future. In order to facilitate a seamless transition between both, a few issues must be resolved for a successful implementation of ML in health care.
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Affiliation(s)
- Karthik Seetharam
- WVU Heart & Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | - Sirish Shrestha
- WVU Heart & Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | - Partho P Sengupta
- WVU Heart & Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA.
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Al-Haddad R, Ismailani US, Rotstein BH. Current and Future Cardiovascular PET Radiopharmaceuticals. PET Clin 2019; 14:293-305. [DOI: 10.1016/j.cpet.2018.12.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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Robinson AA, Bourque JM. Emerging Techniques for Cardiovascular PET. CARDIOVASCULAR INNOVATIONS AND APPLICATIONS 2019; 4:13-24. [PMID: 34552704 PMCID: PMC8455121 DOI: 10.15212/cvia.2019.0004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The application of positron emission tomography (PET) to cardiac disease has yielded tremendous developments in the evaluation of coronary artery, myocardial, and valvular heart disease over the past several decades. These advances have included development of new radiotracers, incremental technological improvements, and coupling of PET with other non-invasive cardiac imaging modalities. The current era has seen rapid, successive and wide-ranging advances in PET myocardial perfusion and metabolic imaging. This review will address emerging techniques in cardiovascular PET imaging, including the measurement of absolute myocardial blood flow (MBF), use of novel tracers, and other advances in heart failure, infection imaging, and valvular disease.
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Affiliation(s)
- Austin A. Robinson
- Cardiovascular Division and the Cardiovascular Imaging Center, Department of Medicine, University of Virginia Health System, Charlottesville, VA
| | - Jamieson M. Bourque
- Cardiovascular Division and the Cardiovascular Imaging Center, Department of Medicine, University of Virginia Health System, Charlottesville, VA
- Department of Radiology, University of Virginia Health System, Charlottesville, VA
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Dey D, Slomka PJ, Leeson P, Comaniciu D, Shrestha S, Sengupta PP, Marwick TH. Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review. J Am Coll Cardiol 2019; 73:1317-1335. [PMID: 30898208 PMCID: PMC6474254 DOI: 10.1016/j.jacc.2018.12.054] [Citation(s) in RCA: 303] [Impact Index Per Article: 60.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 12/13/2018] [Indexed: 12/11/2022]
Abstract
Data science is likely to lead to major changes in cardiovascular imaging. Problems with timing, efficiency, and missed diagnoses occur at all stages of the imaging chain. The application of artificial intelligence (AI) is dependent on robust data; the application of appropriate computational approaches and tools; and validation of its clinical application to image segmentation, automated measurements, and eventually, automated diagnosis. AI may reduce cost and improve value at the stages of image acquisition, interpretation, and decision-making. Moreover, the precision now possible with cardiovascular imaging, combined with "big data" from the electronic health record and pathology, is likely to better characterize disease and personalize therapy. This review summarizes recent promising applications of AI in cardiology and cardiac imaging, which potentially add value to patient care.
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Affiliation(s)
- Damini Dey
- Departments of Biomedical Sciences and Medicine, Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, Los Angeles, California
| | - Piotr J Slomka
- Departments of Biomedical Sciences and Medicine, Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, Los Angeles, California
| | - Paul Leeson
- Oxford Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | | | - Sirish Shrestha
- Section of Cardiology, West Virginia University, Morgantown, West Virginia
| | - Partho P Sengupta
- Section of Cardiology, West Virginia University, Morgantown, West Virginia
| | - Thomas H Marwick
- Baker Heart and Diabetes Research Institute, Melbourne, Australia.
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Artificial Intelligence in Nuclear Cardiology: Adding Value to Prognostication. CURRENT CARDIOVASCULAR IMAGING REPORTS 2019. [DOI: 10.1007/s12410-019-9490-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Affiliation(s)
- Yoav Mintz
- Department of General Surgery, Hadassah Hebrew-University Medical Center, Jerusalem, Israel
| | - Ronit Brodie
- Department of General Surgery, Hadassah Hebrew-University Medical Center, Jerusalem, Israel
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