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Kusk MW, Hess S, Gerke O, Kristensen LD, Oxlund CS, Ormstrup TE, Christiansen JM, Foley SJ. Minimal dose CT for left ventricular ejection fraction and combination with chest-abdomen-pelvis CT. Eur J Radiol Open 2024; 13:100583. [PMID: 39026598 PMCID: PMC11255516 DOI: 10.1016/j.ejro.2024.100583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 06/05/2024] [Accepted: 06/17/2024] [Indexed: 07/20/2024] Open
Abstract
Objectives This prospective study tested the diagnostic accuracy, and absolute agreement with MRI of a low-dose CT protocol for left ventricular ejection fraction (LVEF) measurement. Furthermore we assessed its potential for combining it with Chest-Abdomen-Pelvis CT (CAP-CT) for a one-stop examination. Materials & methods Eighty-two patients underwent helical low-dose CT. Cardiac magnetic resonance imaging (MRI) was the reference standard. In fifty patients, CAP-CT was performed concurrently, using a modified injection protocol. In these, LVEF was measured with radioisotope cardiography (MUGA). Patients >18 years, without contrast media or MRI contraindications, were included. Bias was measured with Bland-Altman analysis, classification accuracy with Receiver Operating Characteristics, and inter-reader agreement with Intra-Class Correlation Coefficient (ICC). Correlation was examined using Pearson's correlation coefficients. CAP image quality was compared to previous scans with visual grading characteristics. Results The mean CT dose-length-product (DLP) was 51.8 mGycm, for an estimated effective dose of 1.4 mSv, compared to 5.7 mSv for MUGA. CT LVEF bias was between 2 % and 10 %, overestimating end-diastolic volume. When corrected for bias, sensitivity and specificity of 100 and 98.5 % for classifying reduced LVEF (50 % MRI value) was achieved. ICC for MUGA was significantly lower than MRI and CT. Distinction of renal medulla and cortex was reduced in the CAP scan, but proportion of diagnostic scans was not significantly different from standard protocol. Conclusion When corrected for inter-modality bias, CT classifies patients with reduced LVEF with high accuracy at a quarter of MUGA dose and can be combined with CAP-CT without loss of diagnostic quality.
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Affiliation(s)
- Martin Weber Kusk
- Radiography & Diagnostic Imaging, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
- Department of Radiology & Nuclear Medicine, University Hospital of Southern Denmark, Esbjerg 6700, Denmark
- Department of Regional Health Research, Faculty of Health Sciences, University of Southern Denmark, Odense M 5230, Denmark
| | - Søren Hess
- Department of Regional Health Research, Faculty of Health Sciences, University of Southern Denmark, Odense M 5230, Denmark
- Department of Nuclear Medicine, Odense University Hospital, Odense 5000, Denmark
| | - Oke Gerke
- Department of Nuclear Medicine, Odense University Hospital, Odense 5000, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense 5000, Denmark
| | | | | | - Tina Elisabeth Ormstrup
- Department of Radiology & Nuclear Medicine, University Hospital of Southern Denmark, Esbjerg 6700, Denmark
| | | | - Shane J. Foley
- Radiography & Diagnostic Imaging, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
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2
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Sengupta PP, Dey D, Davies RH, Duchateau N, Yanamala N. Challenges for augmenting intelligence in cardiac imaging. Lancet Digit Health 2024:S2589-7500(24)00142-0. [PMID: 39214759 DOI: 10.1016/s2589-7500(24)00142-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 05/15/2024] [Accepted: 06/17/2024] [Indexed: 09/04/2024]
Abstract
Artificial Intelligence (AI), through deep learning, has brought automation and predictive capabilities to cardiac imaging. However, despite considerable investment, tangible health-care cost reductions remain unproven. Although AI holds promise, there has been insufficient time for both methodological development and prospective clinical trials to establish its advantage over human interpretations in terms of its effect on patient outcomes. Challenges such as data scarcity, privacy issues, and ethical concerns impede optimal AI training. Furthermore, the absence of a unified model for the complex structure and function of the heart and evolving domain knowledge can introduce heuristic biases and influence underlying assumptions in model development. Integrating AI into diverse institutional picture archiving and communication systems and devices also presents a clinical hurdle. This hurdle is further compounded by an absence of high-quality labelled data, difficulty sharing data between institutions, and non-uniform and inadequate gold standards for external validations and comparisons of model performance in real-world settings. Nevertheless, there is a strong push in industry and academia for AI solutions in medical imaging. This Series paper reviews key studies and identifies challenges that require a pragmatic change in the approach for using AI for cardiac imaging, whereby AI is viewed as augmented intelligence to complement, not replace, human judgement. The focus should shift from isolated measurements to integrating non-linear and complex data towards identifying disease phenotypes-emphasising pattern recognition where AI excels. Algorithms should enhance imaging reports, enriching patients' understanding, communication between patients and clinicians, and shared decision making. The emergence of professional standards and guidelines is essential to address these developments and ensure the safe and effective integration of AI in cardiac imaging.
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Affiliation(s)
- Partho P Sengupta
- Division of Cardiovascular Disease and Hypertension, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Rhodri H Davies
- Institute of Cardiovascular Science, University College London, London, UK
| | - Nicolas Duchateau
- CREATIS, INSA, CNRS UMR 5220, INSERM U1294, Université Lyon 1, UJM Saint-Etienne, Lyon, France; Institut Universitaire de France, Paris, France
| | - Naveena Yanamala
- Division of Cardiovascular Disease and Hypertension, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
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Kirschfink A, Frick M, Al Ateah G, Kneizeh K, Alnaimi A, Dettori R, Schuett K, Marx N, Altiok E. Evaluation of the Truncated Cone-Rhomboid Pyramid Formula for Simplified Right Ventricular Quantification: A Cardiac Magnetic Resonance Study. J Clin Med 2024; 13:2850. [PMID: 38792392 PMCID: PMC11121834 DOI: 10.3390/jcm13102850] [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: 04/12/2024] [Revised: 05/08/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024] Open
Abstract
Background/Objective: Cardiac magnetic resonance (CMR) is the reference method for right ventricular (RV) volume and function analysis, but time-consuming manual segmentation and corrections of imperfect automatic segmentations are needed. This study sought to evaluate the applicability of an echocardiographically established truncated cone-rhomboid pyramid formula (CPF) for simplified RV quantification using CMR. Methods: A total of 70 consecutive patients assigned to RV analysis using CMR were included. As standard method, the manual contouring of RV-short axis planes was performed for the measurement of end-diastolic volume (EDV) and end-systolic volume (ESV). Additionally, two linear measurements in four-chamber views were obtained in systole and diastole: basal diameters at the level of tricuspid valve (Dd and Ds) and baso-apical lengths from the center of tricuspid valve to the RV apex (Ld and Ls) were measured for the calculation of RV-EDV = 1.21 × Dd2 × Ld and RV-ESV = 1.21 × Ds 2 × Ls using CPF. Results: RV volumes using CPF were slightly higher than those using standard CMR analysis (RV-EDV index: 86.2 ± 29.4 mL/m2 and RV-ESV index: 51.5 ± 22.5 mL/m2 vs. RV-EDV index: 81.7 ± 24.1 mL/m2 and RV-ESV index: 44.5 ± 23.2 mL/m2) and RV-EF was lower (RV-EF: 41.1 ± 13.5% vs. 48.4 ± 13.7%). Both methods had a strong correlation of RV volumes (ΔRV-EDV index = -4.5 ± 19.0 mL/m2; r = 0.765, p < 0.0001; ΔRV-ESV index = -7.0 ± 14.4 mL/m2; r = 0.801, p < 0.0001). Conclusions: Calculations of RV volumes and function using CPF assuming the geometrical model of a truncated cone-rhomboid pyramid anatomy of RV is feasible, with a strong correlation to measurements using standard CMR analysis, and only two systolic and diastolic linear measurements in four-chamber views are needed.
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Affiliation(s)
- Annemarie Kirschfink
- Department of Cardiology, Angiology and Intensive Care, University Hospital, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Michael Frick
- Department of Cardiology, Angiology and Intensive Care, University Hospital, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Ghazi Al Ateah
- Department of Cardiology, Nephrology and Internal Intensive Care Medicine, Rhein-Maas Klinikum, Mauerfeldchen 25, 52146 Wuerselen, Germany
| | - Kinan Kneizeh
- Department of Cardiology, Angiology and Intensive Care, University Hospital, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Anas Alnaimi
- Department of Cardiology, Angiology and Intensive Care, University Hospital, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Rosalia Dettori
- Department of Cardiology, Angiology and Intensive Care, University Hospital, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Katharina Schuett
- Department of Cardiology, Angiology and Intensive Care, University Hospital, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Nikolaus Marx
- Department of Cardiology, Angiology and Intensive Care, University Hospital, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Ertunc Altiok
- Department of Cardiology, Angiology and Intensive Care, University Hospital, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
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4
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Parker J, Coey J, Alambrouk T, Lakey SM, Green T, Brown A, Maxwell I, Ripley DP. Evaluating a Novel AI Tool for Automated Measurement of the Aortic Root and Valve in Cardiac Magnetic Resonance Imaging. Cureus 2024; 16:e59647. [PMID: 38832163 PMCID: PMC11146459 DOI: 10.7759/cureus.59647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/03/2024] [Indexed: 06/05/2024] Open
Abstract
Objective Evaluating an artificial intelligence (AI) tool (AIATELLA, version 1.0; AIATELLA Oy, Helsinki, Finland) in interpreting cardiac magnetic resonance (CMR) imaging to produce measurements of the aortic root and valve by comparison of accuracy and efficiency with that of three National Health Service (NHS) cardiologists. Methods AI-derived aortic root and valve measurements were recorded alongside manual measurements from three experienced NHS consultant cardiologists (CCs) over three separate sites in the northeast part of the United Kingdom. The study utilised a comprehensive dataset of CMR images, with the intraclass correlation coefficient (ICC) being the primary measure of concordance between the AI and the cardiologist assessments. Patient imaging was anonymised and blinded at the point of transfer to a secure data server. Results The study demonstrates a high level of concordance between AI assessment of the aortic root and valve with NHS cardiologists (ICC of 0.98). Notably, the AI delivered results in 2.6 seconds (+/- 0.532) compared to a mean of 334.5 seconds (+/- 61.9) by the cardiologists, a statistically significant improvement in efficiency without compromising accuracy. Conclusion AI's accuracy and speed of analysis suggest that it could be a valuable tool in cardiac diagnostics, addressing the challenges of time-consuming and variable clinician-based assessments. This research reinforces AI's role in optimising the patient journey and improving the efficiency of the diagnostic pathway.
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Affiliation(s)
- Jack Parker
- Health and Life Sciences, Northumbria University, Newcastle upon Tyne, GBR
- Imaging, AIATELLA Oy, Helsinki, FIN
- Imaging, AIATELLA Ltd., Newcastle upon Tyne, GBR
| | - James Coey
- School of Medicine, St. George's University, Newcastle upon Tyne, GBR
- Health and Life Sciences, Northumbria University, Newcastle upon Tyne, GBR
- Imaging, AIATELLA Oy, Helsinki, FIN
| | - Tarek Alambrouk
- School of Medicine, St. George's University, Newcastle upon Tyne, GBR
| | - Samuel M Lakey
- Cardiology, Northumbria Healthcare NHS Foundation Trust, Newcastle upon Tyne, GBR
| | - Thomas Green
- Cardiology, Northumbria Healthcare NHS Foundation Trust, Newcastle upon Tyne, GBR
| | - Alexander Brown
- Cardiology, Northumbria Healthcare NHS Foundation Trust, Newcastle upon Tyne, GBR
| | - Ian Maxwell
- Faculty of Health Sciences and Wellbeing, University of Sunderland, Sunderland, GBR
| | - David P Ripley
- Cardiology, Northumbria Healthcare NHS Foundation Trust, Newcastle upon Tyne, GBR
- Faculty of Health Sciences and Wellbeing, University of Sunderland, Sunderland, GBR
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5
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Pieroni M, Namdar M, Olivotto I, Desnick RJ. Anderson-Fabry disease management: role of the cardiologist. Eur Heart J 2024; 45:1395-1409. [PMID: 38486361 DOI: 10.1093/eurheartj/ehae148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 02/04/2024] [Accepted: 02/27/2024] [Indexed: 04/22/2024] Open
Abstract
Anderson-Fabry disease (AFD) is a lysosomal storage disorder characterized by glycolipid accumulation in cardiac cells, associated with a peculiar form of hypertrophic cardiomyopathy (HCM). Up to 1% of patients with a diagnosis of HCM indeed have AFD. With the availability of targeted therapies for sarcomeric HCM and its genocopies, a timely differential diagnosis is essential. Specifically, the therapeutic landscape for AFD is rapidly evolving and offers increasingly effective, disease-modifying treatment options. However, diagnosing AFD may be difficult, particularly in the non-classic phenotype with prominent or isolated cardiac involvement and no systemic red flags. For many AFD patients, the clinical journey from initial clinical manifestations to diagnosis and appropriate treatment remains challenging, due to late recognition or utter neglect. Consequently, late initiation of treatment results in an exacerbation of cardiac involvement, representing the main cause of morbidity and mortality, irrespective of gender. Optimal management of AFD patients requires a dedicated multidisciplinary team, in which the cardiologist plays a decisive role, ranging from the differential diagnosis to the prevention of complications and the evaluation of timing for disease-specific therapies. The present review aims to redefine the role of cardiologists across the main decision nodes in contemporary AFD clinical care and drug discovery.
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Affiliation(s)
- Maurizio Pieroni
- Cardiovascular Department, San Donato Hospital, Via Pietro Nenni 22, 52100 Arezzo, Italy
| | - Mehdi Namdar
- Cardiology Division, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Iacopo Olivotto
- Cardiomyopathy Unit, Careggi Hospital and Meyer Children's Hospital IRCCS, Florence, Italy
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Robert J Desnick
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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6
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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: 7] [Impact Index Per Article: 7.0] [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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7
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Morales MA, Manning WJ, Nezafat R. Present and Future Innovations in AI and Cardiac MRI. Radiology 2024; 310:e231269. [PMID: 38193835 PMCID: PMC10831479 DOI: 10.1148/radiol.231269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 10/21/2023] [Accepted: 10/26/2023] [Indexed: 01/10/2024]
Abstract
Cardiac MRI is used to diagnose and treat patients with a multitude of cardiovascular diseases. Despite the growth of clinical cardiac MRI, complicated image prescriptions and long acquisition protocols limit the specialty and restrain its impact on the practice of medicine. Artificial intelligence (AI)-the ability to mimic human intelligence in learning and performing tasks-will impact nearly all aspects of MRI. Deep learning (DL) primarily uses an artificial neural network to learn a specific task from example data sets. Self-driving scanners are increasingly available, where AI automatically controls cardiac image prescriptions. These scanners offer faster image collection with higher spatial and temporal resolution, eliminating the need for cardiac triggering or breath holding. In the future, fully automated inline image analysis will most likely provide all contour drawings and initial measurements to the reader. Advanced analysis using radiomic or DL features may provide new insights and information not typically extracted in the current analysis workflow. AI may further help integrate these features with clinical, genetic, wearable-device, and "omics" data to improve patient outcomes. This article presents an overview of AI and its application in cardiac MRI, including in image acquisition, reconstruction, and processing, and opportunities for more personalized cardiovascular care through extraction of novel imaging markers.
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Affiliation(s)
- Manuel A. Morales
- From the Department of Medicine, Cardiovascular Division (M.A.M.,
W.J.M., R.N.), and Department of Radiology (W.J.M.), Beth Israel Deaconess
Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA
02215
| | - Warren J. Manning
- From the Department of Medicine, Cardiovascular Division (M.A.M.,
W.J.M., R.N.), and Department of Radiology (W.J.M.), Beth Israel Deaconess
Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA
02215
| | - Reza Nezafat
- From the Department of Medicine, Cardiovascular Division (M.A.M.,
W.J.M., R.N.), and Department of Radiology (W.J.M.), Beth Israel Deaconess
Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA
02215
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8
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Wang J, Zhang N, Wang S, Liang W, Zhao H, Xia W, Zhu J, Zhang Y, Zhang W, Chai S. AI approach to biventricular function assessment in cine-MRI: an ultra-small training dataset and multivendor study. Phys Med Biol 2023; 68:245025. [PMID: 37918023 DOI: 10.1088/1361-6560/ad0903] [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] [Received: 07/03/2023] [Accepted: 11/01/2023] [Indexed: 11/04/2023]
Abstract
Objective. It was a great challenge to train an excellent and generalized model on an ultra-small data set composed of multi-orientation cardiac cine magnetic resonance imaging (MRI) images. We try to develop a 3D deep learning method based on an ultra-small training data set from muti-orientation cine MRI images and assess its performance of automated biventricular structure segmentation and function assessment in multivendor.Approach. We completed the training and testing of our deep learning networks using only heart datasets of 150 cases (90 cases for training and 60 cases for testing). This datasets were obtained from three different MRI vendors and each subject included two phases of the cardiac cycle and three cine sequences. A 3D deep learning algorithm combining Transformers and U-Net was trained. The performance of the segmentation was evaluated using the Dice metric and Hausdorff distance (HD). Based on this, the manual and automatic results of cardiac function parameters were compared with Pearson correlation, intraclass correlation coefficient (ICC) and Bland-Altman analysis in multivendor.Main results. The results show that the average Dice of 0.92, 0.92, 0.94 and HD95 of 2.50, 1.36, 1.37 for three sequences. The automatic and manual results of seven parameters were excellently correlated with the lowestr2 value of 0.824 and the highest of 0.983. The ICC (0.908-0.989,P< 0.001) showed that the results were highly consistent. Bland-Altman with a 95% limit of agreement showed there was no significant difference except for the difference in RVESV (P= 0.005) and LVM (P< 0.001).Significance. The model had high accuracy in segmentation and excellent correlation and consistency in function assessment. It provides a fast and effective method for studying cardiac MRI and heart disease.
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Affiliation(s)
- Jing Wang
- School of Information Science and Engineering, University of Jinan, People's Republic of China
| | - Nan Zhang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, People's Republic of China
| | - Shuyu Wang
- Department of Electric Information Engineering, Shandong Youth University of Political Science, People's Republic of China
| | - Wei Liang
- Department of Ecological Environment Statistics, Department of ecological environment of Shandong, People's Republic of China
| | - Haiyue Zhao
- Department of Electric Information Engineering, Shandong Youth University of Political Science, People's Republic of China
| | - Weili Xia
- Department of Science and Education, Shandong Mental Health Center, People's Republic of China
| | - Jianlei Zhu
- Department of Neuromodulation Center, Shandong Mental Health Center, People's Republic of China
| | - Yan Zhang
- Department of Radiology, Shandong Mental Health Center, People's Republic of China
| | - Wei Zhang
- Department of Medical Ultrasound, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, People's Republic of China
| | - Senchun Chai
- Department of Automation, School of automation, Beijing Institute of Technology, People's Republic of China
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9
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Teis A, Delgado V. Artificial Intelligence, Left Atrial Ventricular Coupling Index, and Stress Cardiac Magnetic Resonance: An Interesting Combination. JACC Cardiovasc Imaging 2023; 16:1303-1305. [PMID: 37204385 DOI: 10.1016/j.jcmg.2023.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 03/21/2023] [Indexed: 05/20/2023]
Affiliation(s)
- Albert Teis
- Department of Cardiology, Heart Institute, University Hospital Germans Trias i Pujol, Badalona, Spain
| | - Victoria Delgado
- Department of Cardiology, Heart Institute, University Hospital Germans Trias i Pujol, Badalona, Spain.
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10
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Mariscal-Harana J, Asher C, Vergani V, Rizvi M, Keehn L, Kim RJ, Judd RM, Petersen SE, Razavi R, King AP, Ruijsink B, Puyol-Antón E. An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2023; 4:370-383. [PMID: 37794871 PMCID: PMC10545512 DOI: 10.1093/ehjdh/ztad044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/05/2023] [Accepted: 07/12/2023] [Indexed: 10/06/2023]
Abstract
Aims Artificial intelligence (AI) techniques have been proposed for automating analysis of short-axis (SAX) cine cardiac magnetic resonance (CMR), but no CMR analysis tool exists to automatically analyse large (unstructured) clinical CMR datasets. We develop and validate a robust AI tool for start-to-end automatic quantification of cardiac function from SAX cine CMR in large clinical databases. Methods and results Our pipeline for processing and analysing CMR databases includes automated steps to identify the correct data, robust image pre-processing, an AI algorithm for biventricular segmentation of SAX CMR and estimation of functional biomarkers, and automated post-analysis quality control to detect and correct errors. The segmentation algorithm was trained on 2793 CMR scans from two NHS hospitals and validated on additional cases from this dataset (n = 414) and five external datasets (n = 6888), including scans of patients with a range of diseases acquired at 12 different centres using CMR scanners from all major vendors. Median absolute errors in cardiac biomarkers were within the range of inter-observer variability: <8.4 mL (left ventricle volume), <9.2 mL (right ventricle volume), <13.3 g (left ventricular mass), and <5.9% (ejection fraction) across all datasets. Stratification of cases according to phenotypes of cardiac disease and scanner vendors showed good performance across all groups. Conclusion We show that our proposed tool, which combines image pre-processing steps, a domain-generalizable AI algorithm trained on a large-scale multi-domain CMR dataset and quality control steps, allows robust analysis of (clinical or research) databases from multiple centres, vendors, and cardiac diseases. This enables translation of our tool for use in fully automated processing of large multi-centre databases.
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Affiliation(s)
- Jorge Mariscal-Harana
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
| | - Clint Asher
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
- Department of Adult and Paediatric Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, Westminster Bridge Road, London SE1 7EH, London, UK
| | - Vittoria Vergani
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
| | - Maleeha Rizvi
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
- Department of Adult and Paediatric Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, Westminster Bridge Road, London SE1 7EH, London, UK
| | - Louise Keehn
- Department of Clinical Pharmacology, King’s College London British Heart Foundation Centre, St Thomas’ Hospital, London, Westminster Bridge Road, London SE1 7EH, UK
| | - Raymond J Kim
- Division of Cardiology, Department of Medicine, Duke University, 40 Duke Medicine Circle, Durham, NC 27710, USA
| | - Robert M Judd
- Division of Cardiology, Department of Medicine, Duke University, 40 Duke Medicine Circle, Durham, NC 27710, USA
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, W Smithfield, London EC1A 7BE, UK
- Health Data Research UK, Gibbs Building, 215 Euston Rd., London NW1 2BE, UK
- Alan Turing Institute, 96 Euston Rd., London NW1 2DB, UK
| | - Reza Razavi
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
- Department of Adult and Paediatric Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, Westminster Bridge Road, London SE1 7EH, London, UK
| | - Andrew P King
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
| | - Bram Ruijsink
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
- Department of Adult and Paediatric Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, Westminster Bridge Road, London SE1 7EH, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Esther Puyol-Antón
- School of Biomedical Engineering & Imaging Sciences Rayne Institute, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road London SE1 7EH
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11
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Bradley AJ, Ghawanmeh M, Govi AM, Covas P, Panjrath G, Choi AD. Emerging Roles for Artificial Intelligence in Heart Failure Imaging. Heart Fail Clin 2023; 19:531-543. [PMID: 37714592 DOI: 10.1016/j.hfc.2023.03.005] [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: 09/17/2023]
Abstract
Artificial intelligence (AI) applications are expanding in cardiac imaging. AI research has shown promise in workflow optimization, disease diagnosis, and integration of clinical and imaging data to predict patient outcomes. The diagnostic and prognostic paradigm of heart failure is heavily reliant on cardiac imaging. As AI becomes increasingly validated and integrated into clinical practice, AI influence on heart failure management will grow. This review discusses areas of current research and potential clinical applications in AI as applied to heart failure cardiac imaging.
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Affiliation(s)
- Andrew J Bradley
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
| | - Malik Ghawanmeh
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Ashley M Govi
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Pedro Covas
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Gurusher Panjrath
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA. https://twitter.com/PanjrathG
| | - Andrew D Choi
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA. https://twitter.com/AChoiHeart
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12
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Sehly A, He A, Agris J, Konstantopoulos J, Joyner J, Flack J, Kwok S, Chow BJW, Ko B, Ridner M, Ihdayhid AR, Dwivedi G. Deep learning-based computed tomography quantification of left ventricular mass. EUROPEAN HEART JOURNAL. IMAGING METHODS AND PRACTICE 2023; 1:qyad043. [PMID: 39045069 PMCID: PMC11195721 DOI: 10.1093/ehjimp/qyad043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/25/2024]
Affiliation(s)
- Amro Sehly
- Cardiology Department, Fiona Stanley Hospital, 11 Robin Warren Drive, Murdoch, WA 6150, Australia
| | - Albert He
- Cardiology Department, Fiona Stanley Hospital, 11 Robin Warren Drive, Murdoch, WA 6150, Australia
| | - Jacob Agris
- Artrya Ltd, 1257 Hay St, West Perth, WA 6005, Australia
| | | | - Jack Joyner
- Artrya Ltd, 1257 Hay St, West Perth, WA 6005, Australia
| | - Julien Flack
- Artrya Ltd, 1257 Hay St, West Perth, WA 6005, Australia
| | - Simon Kwok
- Artrya Ltd, 1257 Hay St, West Perth, WA 6005, Australia
| | - Benjamin J W Chow
- Cardiology Department, University of Ottawa Heart Institute, Ottawa, ON, Canada
| | - Brian Ko
- Monash Heart, Monash Cardiovascular Research Centre, Melbourne, Australia
| | | | - Abdul Rahman Ihdayhid
- Cardiology Department, Fiona Stanley Hospital, 11 Robin Warren Drive, Murdoch, WA 6150, Australia
- Harry Perkins Institute of Medical Research, 5 Robin Warren Drive, Murdoch, WA 6150, Australia
- Curtin University, Kent Street, Bentley, WA 6102, Australia
| | - Girish Dwivedi
- Cardiology Department, Fiona Stanley Hospital, 11 Robin Warren Drive, Murdoch, WA 6150, Australia
- Harry Perkins Institute of Medical Research, 5 Robin Warren Drive, Murdoch, WA 6150, Australia
- University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
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13
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Jaltotage B, Ihdayhid AR, Lan NSR, Pathan F, Patel S, Arnott C, Figtree G, Kritharides L, Shamsul Islam SM, Chow CK, Rankin JM, Nicholls SJ, Dwivedi G. Artificial Intelligence in Cardiology: An Australian Perspective. Heart Lung Circ 2023; 32:894-904. [PMID: 37507275 DOI: 10.1016/j.hlc.2023.06.703] [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] [Received: 04/28/2023] [Revised: 06/22/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023]
Abstract
Significant advances have been made in artificial intelligence technology in recent years. Many health care applications have been investigated to assist clinicians and the technology is close to being integrated into routine clinical practice. The high prevalence of cardiac disease in Australia places overwhelming demands on the existing health care system, challenging its capacity to provide quality patient care. Artificial intelligence has emerged as a promising solution. This discussion paper provides an Australian perspective on the current state of artificial intelligence in cardiology, including the benefits and challenges of implementation. This paper highlights some current artificial intelligence applications in cardiology, while also detailing challenges such as data privacy, ethical considerations, and integration within existing health infrastructures. Overall, this paper aims to provide insights into the potential benefits of artificial intelligence in cardiology, while also acknowledging the barriers that need to be addressed to ensure safe and effective implementation into an Australian health system.
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Affiliation(s)
- Biyanka Jaltotage
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia. https://twitter.com/cardiacimager
| | - Abdul Rahman Ihdayhid
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia; School of Medicine, Curtin University, Perth, Australia; Harry Perkins Institute of Medical Research, School of Medicine, University of Western Australia, Perth, Australia
| | - Nick S R Lan
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia; Harry Perkins Institute of Medical Research, School of Medicine, University of Western Australia, Perth, Australia
| | - Faraz Pathan
- Department of Cardiology, Nepean Hospital and Charles Perkins Centre, Nepean Clinical School, Faculty of Medicine and Health, Sydney University, Sydney, NSW, Australia
| | - Sanjay Patel
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney, NSW, Australia and The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia
| | - Clare Arnott
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney, NSW, Australia and The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia
| | - Gemma Figtree
- Kolling Institute, Royal North Shore Hospital and Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Leonard Kritharides
- Department of Cardiology, Concord Repatriation General Hospital and ANZAC Research Institute, University of Sydney, Sydney, NSW, Australia
| | | | - Clara K Chow
- Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - James M Rankin
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia
| | | | - Girish Dwivedi
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia; Harry Perkins Institute of Medical Research, School of Medicine, University of Western Australia, Perth, Australia.
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Reich C, Meder B. The Heart and Artificial Intelligence-How Can We Improve Medicine Without Causing Harm. Curr Heart Fail Rep 2023; 20:271-279. [PMID: 37291432 PMCID: PMC10250175 DOI: 10.1007/s11897-023-00606-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/08/2023] [Indexed: 06/10/2023]
Abstract
PURPOSE OF REVIEW The introduction of Artificial Intelligence into the healthcare system offers enormous opportunities for biomedical research, the improvement of patient care, and cost reduction in high-end medicine. Digital concepts and workflows are already playing an increasingly important role in cardiology. The fusion of computer science and medicine offers great transformative potential and enables enormous acceleration processes in cardiovascular medicine. RECENT FINDINGS As medical data becomes smart, it is also becoming more valuable and vulnerable to malicious actors. In addition, the gap between what is technically possible and what is allowed by privacy legislation is growing. Principles of the General Data Protection Regulation that have been in force since May 2018, such as transparency, purpose limitation, and data minimization, seem to hinder the development and use of Artificial Intelligence. Concepts to secure data integrity and incorporate legal and ethical principles can help to avoid the potential risks of digitization and may result in an European leadership in regard to privacy protection and AI. The following review provides an overview of relevant aspects of Artificial Intelligence and Machine Learning, highlights selected applications in cardiology, and discusses central ethical and legal considerations.
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Affiliation(s)
- Christoph Reich
- Department of Internal Medicine III, Precision Digital Health, University of Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany
- Informatics for Life, Heidelberg, Germany
- German Center for Cardiovascular Research (DZHK), Heidelberg, Germany
| | - Benjamin Meder
- Department of Internal Medicine III, Precision Digital Health, University of Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany.
- Informatics for Life, Heidelberg, Germany.
- German Center for Cardiovascular Research (DZHK), Heidelberg, Germany.
- Department of Genetics, Genome Technology Center, Stanford University, Stanford, CA, USA.
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Difficult and Thin-Walled: The Challenges of Imaging the Right Ventricle for Clinical Decision Making. JACC. CARDIOVASCULAR IMAGING 2023; 16:42-45. [PMID: 36599568 DOI: 10.1016/j.jcmg.2022.10.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 10/20/2022] [Indexed: 01/07/2023]
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Gupta MD, Kunal S, Girish M, Gupta A, Yadav R. Artificial intelligence in Cardiology: the past, present and future. Indian Heart J 2022; 74:265-269. [PMID: 35917970 PMCID: PMC9453051 DOI: 10.1016/j.ihj.2022.07.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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Ambale-Venkatesh B, Lima JAC. Human-in-the-Loop Artificial Intelligence in Cardiac MRI. Radiology 2022; 305:80-81. [PMID: 35699584 DOI: 10.1148/radiol.221132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Bharath Ambale-Venkatesh
- From the Department of Radiology (B.A.V.) and School of Medicine (J.A.C.L.), Johns Hopkins University, 600 N Wolfe St, MR 110, Baltimore, MD 21287
| | - João A C Lima
- From the Department of Radiology (B.A.V.) and School of Medicine (J.A.C.L.), Johns Hopkins University, 600 N Wolfe St, MR 110, Baltimore, MD 21287
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Wang S, Chauhan D, Patel H, Amir-Khalili A, da Silva IF, Sojoudi A, Friedrich S, Singh A, Landeras L, Miller T, Ameyaw K, Narang A, Kawaji K, Tang Q, Mor-Avi V, Patel AR. Assessment of right ventricular size and function from cardiovascular magnetic resonance images using artificial intelligence. J Cardiovasc Magn Reson 2022; 24:27. [PMID: 35410226 PMCID: PMC8996592 DOI: 10.1186/s12968-022-00861-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 03/29/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Theoretically, artificial intelligence can provide an accurate automatic solution to measure right ventricular (RV) ejection fraction (RVEF) from cardiovascular magnetic resonance (CMR) images, despite the complex RV geometry. However, in our recent study, commercially available deep learning (DL) algorithms for RVEF quantification performed poorly in some patients. The current study was designed to test the hypothesis that quantification of RV function could be improved in these patients by using more diverse CMR datasets in addition to domain-specific quantitative performance evaluation metrics during the cross-validation phase of DL algorithm development. METHODS We identified 100 patients from our prior study who had the largest differences between manually measured and automated RVEF values. Automated RVEF measurements were performed using the original version of the algorithm (DL1), an updated version (DL2) developed from a dataset that included a wider range of RV pathology and validated using multiple domain-specific quantitative performance evaluation metrics, and conventional methodology performed by a core laboratory (CORE). Each of the DL-RVEF approaches was compared against CORE-RVEF reference values using linear regression and Bland-Altman analyses. Additionally, RVEF values were classified into 3 categories: ≤ 35%, 35-50%, and ≥ 50%. Agreement between RVEF classifications made by the DL approaches and the CORE measurements was tested. RESULTS CORE-RVEF and DL-RVEFs were obtained in all patients (feasibility of 100%). DL2-RVEF correlated with CORE-RVEF better than DL1-RVEF (r = 0.87 vs. r = 0.42), with narrower limits of agreement. As a result, DL2 algorithm also showed increasing accuracy from 0.53 to 0.80 for categorizing RV function. CONCLUSIONS The use of a new DL algorithm cross-validated on a dataset with a wide range of RV pathology using multiple domain-specific metrics resulted in a considerable improvement in the accuracy of automated RVEF measurements. This improvement was demonstrated in patients whose images were the most challenging and resulted in the largest RVEF errors. These findings underscore the critical importance of this strategy in the development of DL approaches for automated CMR measurements.
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Affiliation(s)
- Shuo Wang
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
- Peking University Shougang Hospital, Beijing, China
| | - Daksh Chauhan
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | - Hena Patel
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | | | | | | | | | - Amita Singh
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | - Luis Landeras
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | - Tamari Miller
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | - Keith Ameyaw
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | | | - Keigo Kawaji
- Illinois Institute of Technology, Chicago, IL, USA
| | - Qiang Tang
- Peking University Shougang Hospital, Beijing, China
| | - Victor Mor-Avi
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | - Amit R Patel
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA.
- Department of Radiology, University of Chicago, Chicago, IL, USA.
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Sengupta PP, Chandrashekhar Y. Imaging With Deep Learning: Sharpening the Cutting Edge. JACC Cardiovasc Imaging 2022; 15:547-549. [PMID: 35272811 DOI: 10.1016/j.jcmg.2022.02.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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20
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Gomberg-Maitland M, Patel AR. TAVR: We need the RIGHT focus. J Cardiovasc Comput Tomogr 2021; 16:166-167. [PMID: 34972662 DOI: 10.1016/j.jcct.2021.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 12/20/2021] [Accepted: 12/21/2021] [Indexed: 10/19/2022]
Affiliation(s)
- Mardi Gomberg-Maitland
- Department of Medicine, George Washington School of Medicine and Health Science, Washington, DC, USA.
| | - Amit R Patel
- Department of Medicine and Radiology, University of Chicago, Chicago, IL, USA
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21
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Building Confidence in AI-Interpreted CMR. JACC Cardiovasc Imaging 2021; 15:428-430. [PMID: 34922861 DOI: 10.1016/j.jcmg.2021.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 10/14/2021] [Indexed: 11/23/2022]
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