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Chan LKM, Mao BP, Zhu R. A bibliometric analysis of perioperative medicine and artificial intelligence. J Perioper Pract 2025:17504589251320811. [PMID: 40035147 DOI: 10.1177/17504589251320811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2025]
Abstract
BACKGROUND Artificial intelligence holds the potential to transform perioperative medicine by leveraging complex datasets to predict risks and optimise patient management in response to rising surgical volumes and patient complexity. AIM This bibliometric analysis aims to analyse trends, contributions, collaborations and research hotspots in artificial intelligence and perioperative medicine. METHODS A Scopus search on 11 October 2024 identified articles on artificial intelligence in perioperative medicine. Relevant peer-reviewed studies were screened by two reviewers, with a third resolving discrepancies. Data were analysed using VOSviewer, Biblioshiny and Microsoft Excel. RESULTS A total of 240 articles were included; 84% of articles were published after 2018, indicating rapid recent growth. The United States, China and Italy led contributions. Single-country publications comprised 76.6% of the dataset, reflecting limited international collaboration. Key research areas included perioperative risk prediction, intraoperative monitoring, blood management and echocardiography. CONCLUSION Artificial intelligence in perioperative medicine is rapidly advancing but requires increased international collaboration to fully realise its potential.
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Affiliation(s)
- Luke Kar Man Chan
- Department of Anaesthesia, Concord Repatriation General Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- School of Medicine and Dentistry, Griffith University, Southport, QLD, Australia
| | - Brooke Perrin Mao
- Department of Anaesthesia, Concord Repatriation General Hospital, Sydney, NSW, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Rebecca Zhu
- School of Medicine, The University of Notre Dame, Sydney, NSW, Australia
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Anisuzzaman D, Malins JG, Jackson JI, Lee E, Naser JA, Rostami B, Greason G, Bird JG, Friedman PA, Oh JK, Pellikka PA, Thaden JJ, Lopez-Jimenez F, Attia ZI, Pislaru SV, Kane GC. Leveraging Comprehensive Echo Data to Power Artificial Intelligence Models for Handheld Cardiac Ultrasound. MAYO CLINIC PROCEEDINGS. DIGITAL HEALTH 2025; 3:100194. [PMID: 40207004 PMCID: PMC11975991 DOI: 10.1016/j.mcpdig.2025.100194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/11/2025]
Abstract
Objective To develop a fully end-to-end deep learning framework capable of estimating left ventricular ejection fraction (LVEF), estimating patient age, and classifying patient sex from echocardiographic videos, including videos collected using handheld cardiac ultrasound (HCU). Patients and Methods Deep learning models were trained using retrospective transthoracic echocardiography (TTE) data collected in Mayo Clinic Rochester and surrounding Mayo Clinic Health System sites (training: 6432 studies and internal validation: 1369 studies). Models were then evaluated using retrospective TTE data from the 3 Mayo Clinic sites (Rochester, n=1970; Arizona, n=1367; Florida, n=1562) before being applied to a prospective dataset of handheld ultrasound and TTE videos collected from 625 patients. Study data were collected between January 1, 2018 and February 29, 2024. Results Models showed strong performance on the retrospective TTE datasets (LVEF regression: root mean squared error (RMSE)=6.83%, 6.53%, and 6.95% for Rochester, Arizona, and Florida cohorts, respectively; classification of LVEF ≤40% versus LVEF > 40%: area under curve (AUC)=0.962, 0.967, and 0.980 for Rochester, Arizona, and Florida, respectively; age: RMSE=9.44% for Rochester; sex: AUC=0.882 for Rochester), and performed comparably for prospective HCU versus TTE data (LVEF regression: RMSE=6.37% for HCU vs 5.57% for TTE; LVEF classification: AUC=0.974 vs 0.981; age: RMSE=10.35% vs 9.32%; sex: AUC=0.896 vs 0.933). Conclusion Robust TTE datasets can be used to effectively power HCU deep learning models, which in turn demonstrates focused diagnostic images can be obtained with handheld devices.
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Affiliation(s)
- D.M. Anisuzzaman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | - John I. Jackson
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Eunjung Lee
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Jwan A. Naser
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Behrouz Rostami
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Grace Greason
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Jared G. Bird
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Paul A. Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Jae K. Oh
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | - Jeremy J. Thaden
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | - Zachi I. Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Sorin V. Pislaru
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Garvan C. Kane
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
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Tolu‐Akinnawo OZ, Ezekwueme F, Omolayo O, Batheja S, Awoyemi T. Advancements in Artificial Intelligence in Noninvasive Cardiac Imaging: A Comprehensive Review. Clin Cardiol 2025; 48:e70087. [PMID: 39871619 PMCID: PMC11772728 DOI: 10.1002/clc.70087] [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: 11/13/2024] [Accepted: 01/06/2025] [Indexed: 01/29/2025] Open
Abstract
BACKGROUND Technological advancements in artificial intelligence (AI) are redefining cardiac imaging by providing advanced tools for analyzing complex health data. AI is increasingly applied across various imaging modalities, including echocardiography, magnetic resonance imaging (MRI), computed tomography (CT), and nuclear imaging, to enhance diagnostic workflows and improve patient outcomes. HYPOTHESIS Integrating AI into cardiac imaging enhances image quality, accelerates processing times, and improves diagnostic accuracy, enabling timely and personalized interventions that lead to better health outcomes. METHODS A comprehensive literature review was conducted to examine the impact of machine learning and deep learning algorithms on diagnostic accuracy, the detection of subtle patterns and anomalies, and key challenges such as data quality, patient safety, and regulatory barriers. RESULTS Findings indicate that AI integration in cardiac imaging enhances image quality, reduces processing times, and improves diagnostic precision, contributing to better clinical decision-making. Emerging machine learning techniques demonstrate the ability to identify subtle cardiac abnormalities that traditional methods may overlook. However, significant challenges persist, including data standardization, regulatory compliance, and patient safety concerns. CONCLUSIONS AI holds transformative potential in cardiac imaging, significantly advancing diagnosis and patient outcomes. Overcoming barriers to implementation will require ongoing collaboration among clinicians, researchers, and regulatory bodies. Further research is essential to ensure the safe, ethical, and effective integration of AI in cardiology, supporting its broader application to improve cardiovascular health.
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Affiliation(s)
| | - Francis Ezekwueme
- Department of Internal MedicineUniversity of Pittsburgh Medical CenterMcKeesportPennsylvaniaUSA
| | - Olukunle Omolayo
- Department of Internal MedicineLugansk State Medical UniversityLuganskUkraine
| | - Sasha Batheja
- Department of Internal MedicineGovernment Medical CollegePatialaPunjabIndia
| | - Toluwalase Awoyemi
- Department of Internal MedicineFeinberg School of Medicine, Northwestern UniversityChicagoIllinoisUSA
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Brasier N, Wang J, Gao W, Sempionatto JR, Dincer C, Ates HC, Güder F, Olenik S, Schauwecker I, Schaffarczyk D, Vayena E, Ritz N, Weisser M, Mtenga S, Ghaffari R, Rogers JA, Goldhahn J. Applied body-fluid analysis by wearable devices. Nature 2024; 636:57-68. [PMID: 39633192 PMCID: PMC12007731 DOI: 10.1038/s41586-024-08249-4] [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: 08/07/2023] [Accepted: 10/18/2024] [Indexed: 12/07/2024]
Abstract
Wearable sensors are a recent paradigm in healthcare, enabling continuous, decentralized, and non- or minimally invasive monitoring of health and disease. Continuous measurements yield information-rich time series of physiological data that are holistic and clinically meaningful. Although most wearable sensors were initially restricted to biophysical measurements, the next generation of wearable devices is now emerging that enable biochemical monitoring of both small and large molecules in a variety of body fluids, such as sweat, breath, saliva, tears and interstitial fluid. Rapidly evolving data analysis and decision-making technologies through artificial intelligence has accelerated the application of wearables around the world. Although recent pilot trials have demonstrated the clinical applicability of these wearable devices, their widespread adoption will require large-scale validation across various conditions, ethical consideration and sociocultural acceptance. Successful translation of wearable devices from laboratory prototypes into clinical tools will further require a comprehensive transitional environment involving all stakeholders. The wearable device platforms must gain acceptance among different user groups, add clinical value for various medical indications, be eligible for reimbursements and contribute to public health initiatives. In this Perspective, we review state-of-the-art wearable devices for body-fluid analysis and their translation into clinical applications, and provide insight into their clinical purpose.
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Affiliation(s)
- Noé Brasier
- Collegium Helveticum, Zurich, Switzerland.
- Institute of Translational Medicine, ETH Zurich, Zurich, Switzerland.
| | - Joseph Wang
- Department of Chemical and Nano Engineering, University of California San Diego, La Jolla, CA, USA
| | - Wei Gao
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA
| | - Juliane R Sempionatto
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA
| | - Can Dincer
- FIT Freiburg Center for Interactive Materials and Bioinspired Technologies, University of Freiburg, Freiburg, Germany
- Department of Microsystems Engineering (IMTEK), University of Freiburg, Freiburg, Germany
- Munich Institute of Biomedical Engineering - MIBE, Department of Electrical Engineering, TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - H Ceren Ates
- FIT Freiburg Center for Interactive Materials and Bioinspired Technologies, University of Freiburg, Freiburg, Germany
- Department of Microsystems Engineering (IMTEK), University of Freiburg, Freiburg, Germany
| | - Firat Güder
- Department of Bioengineering, Imperial College London, London, UK
| | - Selin Olenik
- Department of Bioengineering, Imperial College London, London, UK
| | - Ivo Schauwecker
- European Patients Academy on Therapeutic Innovation (EUPATI CH), Zurich, Switzerland
- Digital Trial Innovation Platform (dtip), ETH Zurich, Zurich, Switzerland
| | | | - Effy Vayena
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Nicole Ritz
- University Children's Hospital Basel UKBB, Basel, Switzerland
- Paediatric Infectious Diseases and Vaccinology, University Children's Hospital Basel, Basel, Switzerland
- Department of Paediatrics and Paediatric Infectious Diseases, Children's Hospital, Lucerne Cantonal Hospital, Lucerne, Switzerland
| | - Maja Weisser
- Department of Health Systems, Impact Evaluation and Policy, Ifakara Health Institute, Ifakara, Tanzania
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, Basel, Switzerland
| | - Sally Mtenga
- Department of Health Systems, Impact Evaluation and Policy, Ifakara Health Institute, Ifakara, Tanzania
| | - Roozbeh Ghaffari
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
- Epicore Biosystems Inc, Cambridge, MA, USA
| | - John A Rogers
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
- Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA
- Department of Chemistry, Northwestern University, Evanston, IL, USA
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Jörg Goldhahn
- Institute of Translational Medicine, ETH Zurich, Zurich, Switzerland
- Digital Trial Innovation Platform (dtip), ETH Zurich, Zurich, Switzerland
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Bae S. AI-Based Automated Echocardiographic Analysis is Expected to Revolutionize Clinical Practice. Korean Circ J 2024; 54:757-759. [PMID: 39542453 PMCID: PMC11569946 DOI: 10.4070/kcj.2024.0303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2024] [Accepted: 09/18/2024] [Indexed: 11/17/2024] Open
Affiliation(s)
- SungA Bae
- Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Yongin Severance Hospital, Yongin, Korea
- Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin, Korea.
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Skalidis I, Tzimas G, Antiochos P, Suc G, Lu H, Salihu A, Fournier S, Muller O, Maurizi N, Arangalage D. Artificial Intelligence, Virtual Reality, and the Metaverse in Cardiovascular Imaging: Tools for Transformation or Technological Overreach? Echocardiography 2024; 41:e70015. [PMID: 39440894 DOI: 10.1111/echo.70015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2024] [Revised: 10/10/2024] [Accepted: 10/13/2024] [Indexed: 10/25/2024] Open
Affiliation(s)
- Ioannis Skalidis
- Department of Cardiology, Lausanne University Hospital, CHUV, Lausanne, Switzerland
- School of Medicine, University of Crete, Rethimno, Greece
| | - Georgios Tzimas
- Department of Cardiology, Lausanne University Hospital, CHUV, Lausanne, Switzerland
| | - Panagiotis Antiochos
- Department of Cardiology, Lausanne University Hospital, CHUV, Lausanne, Switzerland
| | - Gaspard Suc
- Department of Cardiology, Bichat-Claude Bernard Hospital and Université Paris Cité, Paris, France
| | - Henri Lu
- Department of Cardiology, Lausanne University Hospital, CHUV, Lausanne, Switzerland
| | - Adil Salihu
- Department of Cardiology, Lausanne University Hospital, CHUV, Lausanne, Switzerland
| | - Stephane Fournier
- Department of Cardiology, Lausanne University Hospital, CHUV, Lausanne, Switzerland
| | - Olivier Muller
- Department of Cardiology, Lausanne University Hospital, CHUV, Lausanne, Switzerland
| | - Niccolo' Maurizi
- Department of Cardiology, Lausanne University Hospital, CHUV, Lausanne, Switzerland
| | - Dimitri Arangalage
- Department of Cardiology, Lausanne University Hospital, CHUV, Lausanne, Switzerland
- Department of Cardiology, Bichat-Claude Bernard Hospital and Université Paris Cité, Paris, France
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7
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Jang Y, Choi H, Yoon YE, Jeon J, Kim H, Kim J, Jeong D, Ha S, Hong Y, Lee SA, Park J, Choi W, Choi HM, Hwang IC, Cho GY, Chang HJ. An Artificial Intelligence-Based Automated Echocardiographic Analysis: Enhancing Efficiency and Prognostic Evaluation in Patients With Revascularized STEMI. Korean Circ J 2024; 54:743-756. [PMID: 39434367 PMCID: PMC11569939 DOI: 10.4070/kcj.2024.0060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 08/07/2024] [Accepted: 08/14/2024] [Indexed: 10/23/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). METHODS The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. RESULTS The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81-0.92 and intraclass correlation coefficients ranging 0.74-0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. CONCLUSIONS Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.
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Affiliation(s)
- Yeonggul Jang
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Korea
- Ontact Health Inc., Seoul, Korea
| | - Hyejung Choi
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Yeonyee E Yoon
- Ontact Health Inc., Seoul, Korea
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
| | - Jaeik Jeon
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Korea
- Ontact Health Inc., Seoul, Korea
| | | | - Jiyeon Kim
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Korea
- Department of Internal Medicine, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Korea
| | - Dawun Jeong
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Korea
- Department of Internal Medicine, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Korea
| | - Seongmin Ha
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Korea
- Ontact Health Inc., Seoul, Korea
- Graduate School of Biomedical Engineering, Yonsei University College of Medicine, Seoul, Korea
| | - Youngtaek Hong
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Korea
- Ontact Health Inc., Seoul, Korea
| | - Seung-Ah Lee
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Korea
- Ontact Health Inc., Seoul, Korea
| | - Jiesuck Park
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Wonsuk Choi
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Cardiovascular Center, Sheikh Khalifa Specialty Hospital, Ras Al Khaimah, United Arab Emirates
| | - Hong-Mi Choi
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - In-Chang Hwang
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Goo-Yeong Cho
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Hyuk-Jae Chang
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Korea
- Ontact Health Inc., Seoul, Korea
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea
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Baek HS, Kim J, Jeong C, Lee J, Ha J, Jo K, Kim MH, Sohn TS, Lee IS, Lee JM, Lim DJ. Deep Learning Analysis With Gray Scale and Doppler Ultrasonography Images to Differentiate Graves' Disease. J Clin Endocrinol Metab 2024; 109:2872-2881. [PMID: 38609169 DOI: 10.1210/clinem/dgae254] [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: 09/04/2023] [Revised: 03/20/2024] [Accepted: 04/10/2024] [Indexed: 04/14/2024]
Abstract
CONTEXT Thyrotoxicosis requires accurate and expeditious differentiation between Graves' disease (GD) and thyroiditis to ensure effective treatment decisions. OBJECTIVE This study aimed to develop a machine learning algorithm using ultrasonography and Doppler images to differentiate thyrotoxicosis subtypes, with a focus on GD. METHODS This study included patients who initially presented with thyrotoxicosis and underwent thyroid ultrasonography at a single tertiary hospital. A total of 7719 ultrasonography images from 351 patients with GD and 2980 images from 136 patients with thyroiditis were used. Data augmentation techniques were applied to enhance the algorithm's performance. Two deep learning models, Xception and EfficientNetB0_2, were employed. Performance metrics such as accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score were calculated for both models. Image preprocessing, neural network model generation, and neural network training results verification were performed using DEEP:PHI® platform. RESULTS The Xception model achieved 84.94% accuracy, 89.26% sensitivity, 73.17% specificity, 90.06% PPV, 71.43% NPV, and an F1 score of 89.66 for the diagnosis of GD. The EfficientNetB0_2 model exhibited 85.31% accuracy, 90.28% sensitivity, 71.78% specificity, 89.71% PPV, 73.05% NPV, and an F1 score of 89.99. CONCLUSION Machine learning models based on ultrasound and Doppler images showed promising results with high accuracy and sensitivity in differentiating GD from thyroiditis.
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Affiliation(s)
- Han-Sang Baek
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu 11765, Republic of Korea
| | - Jinyoung Kim
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 07345, Republic of Korea
| | - Chaiho Jeong
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu 11765, Republic of Korea
| | - Jeongmin Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 03312, Republic of Korea
| | - Jeonghoon Ha
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Kwanhoon Jo
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Incheon 21431, Republic of Korea
| | - Min-Hee Kim
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 03312, Republic of Korea
| | - Tae Seo Sohn
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu 11765, Republic of Korea
| | - Ihn Suk Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Daejeon 34943, Republic of Korea
| | - Jong Min Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Daejeon 34943, Republic of Korea
| | - Dong-Jun Lim
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
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Cerdas MG, Pandeti S, Reddy L, Grewal I, Rawoot A, Anis S, Todras J, Chouihna S, Salma S, Lysak Y, Khan SA. The Role of Artificial Intelligence and Machine Learning in Cardiovascular Imaging and Diagnosis: Current Insights and Future Directions. Cureus 2024; 16:e72311. [PMID: 39583537 PMCID: PMC11585328 DOI: 10.7759/cureus.72311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/20/2024] [Indexed: 11/26/2024] Open
Abstract
Cardiovascular diseases (CVDs) are the major cause of mortality worldwide, emphasizing the critical need for timely and accurate diagnosis. Artificial intelligence (AI) and machine learning (ML) have become revolutionary tools in the healthcare system with significant potential for cardiovascular diagnosis and imaging. AI and ML techniques, including supervised and unsupervised learning, logistic regression, deep learning models, neural networks, and convolutional neural networks (CNNs), have significantly advanced cardiovascular imaging. Applications in echocardiography include left and right ventricular segmentation, ejection fraction measurement, and wall motion analysis. AI and ML hold substantial promise for revolutionizing cardiovascular imaging, demonstrating improvements in diagnostic accuracy and efficiency. This narrative review aims to explore the current applications, advantages, challenges, and future pathways of AI and ML in cardiovascular imaging, highlighting their impact on different imaging modalities and their integration into clinical practice.
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Affiliation(s)
| | | | | | - Inayat Grewal
- Radiology, Government Medical College and Hospital, Chandigarh, IND
| | - Asiya Rawoot
- Internal Medicine, Maharashtra University of Health Sciences, Nashik, IND
| | - Samia Anis
- Internal Medicine, Dow University of Health Sciences, Karachi, PAK
| | - Jade Todras
- Biology, Suffolk County Community College, New York, USA
| | - Sami Chouihna
- Internal Medicine, University of Toronto, Toronto, CAN
| | - Saba Salma
- Internal Medicine, Wayne State University Detroit Medical Center, Detroit, USA
| | - Yuliya Lysak
- Internal Medicine, St. George's University, True Blue, GRD
| | - Saad Ahmed Khan
- Internal Medicine, Wayne State University Detroit Medical Center, Detroit, USA
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Wu S, Liu B, Fan H, Zhong Y, Yang Y, Yao A. Using ultrasound radiomics to forecast adverse cardiovascular events in patients with acute coronary syndrome after percutaneous coronary intervention. Echocardiography 2024; 41:e15907. [PMID: 39158954 DOI: 10.1111/echo.15907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 08/03/2024] [Accepted: 08/06/2024] [Indexed: 08/20/2024] Open
Abstract
OBJECTIVE Exploring the performance of ultrasound-based radiomics in forecasting major adverse cardiovascular events (MACE) within 1 year following percutaneous coronary intervention (PCI) of acute coronary syndrome (ACS) patients. METHODS In this research, 161 ACS patients who underwent PCI were included (114 patients were randomly assigned to the training set and 47 patients to the validation set). Every patient received echocardiography 3-7 days after PCI and followed up for 1 year. The radiomics features related to MACE occurrence were extracted and selected to formulate the RAD score. Building ultrasound personalized model by incorporating RAD score, LVEF, LVGLS, and NT-ProBNP. The model's capacity to predict was tested using ROC curves. RESULTS Multifactorial logistic regression analysis of RAD score with clinical data and echocardiographic parameters indicated RAD score and LVGLS as independent risk factors for the occurrence of MACE. The RAD score predicted MACE, with AUC values of 0.85 and 0.86 in the training and validation sets. The ultrasound personalized model had a superior ability to predict the occurrence of MACE, with AUC values of 0.88 and 0.92, which were higher than those of the clinical model (with AUC of 0.72 and 0.80) without RAD score (Z = 3.711, 2.043, P < .001, P = .041). Furthermore, DCA indicated that the ultrasound personalization model presented a more favorable net clinical benefit. CONCLUSIONS Ultrasound radiomics can be a reliable tool to predict the incidence of MACE after PCI in patients with ACS and provides quantifiable data for personalized clinical treatment.
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Affiliation(s)
- Shutian Wu
- Department of Ultrasound Medicine, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Biaohu Liu
- Department of Ultrasound Medicine, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Haiyun Fan
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yuxin Zhong
- Department of Ultrasound Medicine, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - You Yang
- Department of Ultrasound Medicine, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Aling Yao
- Department of Quality Control, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
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11
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Huang H, Perone F, Leung KSK, Ullah I, Lee Q, Chew N, Liu T, Tse G. The Utility of Artificial Intelligence and Machine Learning in the Diagnosis of Takotsubo Cardiomyopathy: A Systematic Review. HEART AND MIND 2024; 8:165-176. [DOI: 10.4103/hm.hm-d-23-00061] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 03/01/2024] [Indexed: 10/15/2024] Open
Abstract
Abstract
Introduction:
Takotsubo cardiomyopathy (TTC) is a cardiovascular disease caused by physical/psychological stressors with significant morbidity if left untreated. Because TTC often mimics acute myocardial infarction in the absence of obstructive coronary disease, the condition is often underdiagnosed in the population. Our aim was to discuss the role of artificial intelligence (AI) and machine learning (ML) in diagnosing TTC.
Methods:
We systematically searched electronic databases from inception until April 8, 2023, for studies on the utility of AI- or ML-based algorithms in diagnosing TTC compared with other cardiovascular diseases or healthy controls. We summarized major findings in a narrative fashion and tabulated relevant numerical parameters.
Results:
Five studies with a total of 920 patients were included. Four hundred and forty-seven were diagnosed with TTC via International Classification of Diseases codes or the Mayo Clinic diagnostic criteria, while there were 473 patients in the comparator group (29 of healthy controls, 429 of myocardial infarction, and 14 of acute myocarditis). Hypertension and smoking were the most common comorbidities in both cohorts, but there were no statistical differences between TTC and comparators. Two studies utilized deep-learning algorithms on transthoracic echocardiographic images, while the rest incorporated supervised ML on cardiac magnetic resonance imaging, 12-lead electrocardiographs, and brain magnetic resonance imaging. All studies found that AI-based algorithms can increase the diagnostic rate of TTC when compared to healthy controls or myocardial infarction patients. In three of these studies, AI-based algorithms had higher sensitivity and specificity compared to human readers.
Conclusion:
AI and ML algorithms can improve the diagnostic capacity of TTC and additionally reduce erroneous human error in differentiating from MI and healthy individuals.
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Affiliation(s)
- Helen Huang
- Faculty of Medicine and Health Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, PowerHealth Institute, Hong Kong, China
| | - Francesco Perone
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, PowerHealth Institute, Hong Kong, China
- Cardiac Rehabilitation Unit, Rehabilitation Clinic “Villa delle Magnolie”, Caserta, Italy
| | - Keith Sai Kit Leung
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, PowerHealth Institute, Hong Kong, China
- Faculty of Health and Life Sciences, Aston University Medical School, Aston University, Birmingham, UK
- Hull University Teaching Hospitals, National Health Service Trust, Yorkshire, UK
| | - Irfan Ullah
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, PowerHealth Institute, Hong Kong, China
- Kabir Medical College, Gandhara University, Peshawar, Pakistan
- Department of Internal Medicine, Khyber Teaching Hospital, Peshawar, Pakistan
| | - Quinncy Lee
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, PowerHealth Institute, Hong Kong, China
| | - Nicholas Chew
- Department of Cardiology, National University Heart Centre, National University Health System, Singapore
| | - Tong Liu
- Department of Cardiology, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Gary Tse
- Department of Cardiology, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
- Kent and Medway Medical School, Canterbury, UK
- School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, China
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12
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Jeong D, Jung S, Yoon YE, Jeon J, Jang Y, Ha S, Hong Y, Cho J, Lee SA, Choi HM, Chang HJ. Artificial intelligence-enhanced automation for M-mode echocardiographic analysis: ensuring fully automated, reliable, and reproducible measurements. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2024; 40:1245-1256. [PMID: 38652399 DOI: 10.1007/s10554-024-03095-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 03/25/2024] [Indexed: 04/25/2024]
Abstract
To enhance M-mode echocardiography's utility for measuring cardiac structures, we developed and evaluated an artificial intelligence (AI)-based automated analysis system for M-mode images through the aorta and left atrium [M-mode (Ao-LA)], and through the left ventricle [M-mode (LV)]. Our system, integrating two deep neural networks (DNN) for view classification and image segmentation, alongside an auto-measurement algorithm, was developed using 5,958 M-mode images [3,258 M-mode (LA-Ao), and 2,700 M-mode (LV)] drawn from a nationwide echocardiographic dataset collated from five tertiary hospitals. The performance of view classification and segmentation DNNs were evaluated on 594 M-mode images, while automatic measurement accuracy was tested on separate internal test set with 100 M-mode images as well as external test set with 280 images (140 sinus rhythm and 140 atrial fibrillation). Performance evaluation showed the view classification DNN's overall accuracy of 99.8% and segmentation DNN's Dice similarity coefficient of 94.3%. Within the internal test set, all automated measurements, including LA, Ao, and LV wall and cavity, resonated strongly with expert evaluations, exhibiting Pearson's correlation coefficients (PCCs) of 0.81-0.99. This performance persisted in the external test set for both sinus rhythm (PCC, 0.84-0.98) and atrial fibrillation (PCC, 0.70-0.97). Notably, automatic measurements, consistently offering multi-cardiac cycle readings, showcased a stronger correlation with the averaged multi-cycle manual measurements than with those of a single representative cycle. Our AI-based system for automatic M-mode echocardiographic analysis demonstrated excellent accuracy, reproducibility, and speed. This automated approach has the potential to improve efficiency and reduce variability in clinical practice.
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Affiliation(s)
- Dawun Jeong
- Department of Internal Medicine, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Sunghee Jung
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Ontact Health Inc, Seoul, South Korea
| | - Yeonyee E Yoon
- Ontact Health Inc, Seoul, South Korea.
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Gumi-Ro 173, Bundang-Gu, Seongnam, Gyeonggi-Do, 13620, South Korea.
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea.
| | | | | | - Seongmin Ha
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Ontact Health Inc, Seoul, South Korea
- Graduate School of Biomedical Engineering, Yonsei University College of Medicine, Seoul, South Korea
| | - Youngtaek Hong
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Ontact Health Inc, Seoul, South Korea
| | | | | | - Hong-Mi Choi
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Gumi-Ro 173, Bundang-Gu, Seongnam, Gyeonggi-Do, 13620, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Hyuk-Jae Chang
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Ontact Health Inc, Seoul, South Korea
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea
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13
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Jain A, Singam A, Mudiganti VNKS. Echocardiography as a Vital Tool in Assessing Shock: A Comprehensive Review. Cureus 2024; 16:e57310. [PMID: 38690492 PMCID: PMC11059330 DOI: 10.7759/cureus.57310] [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: 03/20/2024] [Accepted: 03/29/2024] [Indexed: 05/02/2024] Open
Abstract
Shock is a critical condition characterized by inadequate tissue perfusion, leading to cellular hypoxia and organ dysfunction. Early and accurate assessment is crucial for timely intervention and improved patient outcomes. Echocardiography has emerged as a vital tool in the assessment of shock, offering real-time visualization of cardiac anatomy, function, and hemodynamics. This comprehensive review aims to elucidate the role of echocardiography in shock assessment by providing an overview of its principles, techniques, and clinical applications. We discuss the importance of early diagnosis, identification of underlying pathology, monitoring response to therapy, and prognostic value offered by echocardiography in managing shock. Furthermore, we explore its utility in different types of shock, including hypovolemic, cardiogenic, distributive, and obstructive shock. Challenges and limitations of echocardiography, as well as future directions and innovations, are also discussed. Through a synthesis of current evidence and clinical insights, this review underscores the significance of echocardiography in optimizing shock management and highlights areas for further research and development.
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Affiliation(s)
- Abhishek Jain
- Critical Care Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Amol Singam
- Critical Care Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - V N K Srinivas Mudiganti
- Critical Care Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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14
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Qin T, Li S. Artificial intelligence in stress echocardiography. Asian J Surg 2024; 47:786-787. [PMID: 37903690 DOI: 10.1016/j.asjsur.2023.10.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 10/06/2023] [Indexed: 11/01/2023] Open
Affiliation(s)
- Tingting Qin
- Department of Ultrasound, Affiliated Hospital of Jining Medical University, Jining, China
| | - Sha Li
- Department of Ultrasound, Affiliated Hospital of Jining Medical University, Jining, China.
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15
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Vasile CM, Iriart X. Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve. Diagnostics (Basel) 2023; 13:3137. [PMID: 37835880 PMCID: PMC10572870 DOI: 10.3390/diagnostics13193137] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 09/26/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023] Open
Abstract
Advancements in artificial intelligence (AI) have rapidly transformed various sectors, and the field of echocardiography is no exception. AI-driven technologies hold immense potential to revolutionize echo labs' diagnostic capabilities and improve patient care. This paper explores the importance for echo labs to embrace AI and stay ahead of the curve in harnessing its power. Our manuscript provides an overview of the growing impact of AI on medical imaging, specifically echocardiography. It highlights how AI-driven algorithms can enhance image quality, automate measurements, and accurately diagnose cardiovascular diseases. Additionally, we emphasize the importance of training echo lab professionals in AI implementation to optimize its integration into routine clinical practice. By embracing AI, echo labs can overcome challenges such as workload burden and diagnostic accuracy variability, improving efficiency and patient outcomes. This paper highlights the need for collaboration between echocardiography laboratory experts, AI researchers, and industry stakeholders to drive innovation and establish standardized protocols for implementing AI in echocardiography. In conclusion, this article emphasizes the importance of AI adoption in echocardiography labs, urging practitioners to proactively integrate AI technologies into their workflow and take advantage of their present opportunities. Embracing AI is not just a choice but an imperative for echo labs to maintain their leadership and excel in delivering state-of-the-art cardiac care in the era of advanced medical technologies.
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Affiliation(s)
- Corina Maria Vasile
- Department of Pediatric and Adult Congenital Cardiology, Bordeaux University Hospital, 33600 Pessac, France
| | - Xavier Iriart
- Department of Pediatric and Adult Congenital Cardiology, Bordeaux University Hospital, 33600 Pessac, France
- IHU Liryc—Electrophysiology and Heart Modelling Institute, Bordeaux University Foundation, 33600 Pessac, France
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16
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Barry T, Farina JM, Chao CJ, Ayoub C, Jeong J, Patel BN, Banerjee I, Arsanjani R. The Role of Artificial Intelligence in Echocardiography. J Imaging 2023; 9:50. [PMID: 36826969 PMCID: PMC9962859 DOI: 10.3390/jimaging9020050] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/03/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.
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Affiliation(s)
- Timothy Barry
- Department of Cardiovascular Diseases, Mayo Clinic Arizona, Scottsdale, AZ 85054, USA
| | - Juan Maria Farina
- Department of Cardiovascular Diseases, Mayo Clinic Arizona, Scottsdale, AZ 85054, USA
| | - Chieh-Ju Chao
- Department of Cardiovascular Diseases, Mayo Clinic Rochester, Rochester, MN 55902, USA
| | - Chadi Ayoub
- Department of Cardiovascular Diseases, Mayo Clinic Arizona, Scottsdale, AZ 85054, USA
| | - Jiwoong Jeong
- School of Computing and Augmented Intelligence, Arizona State University, Phoenix, AZ 85004, USA
| | - Bhavik N. Patel
- School of Computing and Augmented Intelligence, Arizona State University, Phoenix, AZ 85004, USA
- Department of Radiology, Mayo Clinic Arizona, Scottsdale, AZ 85054, USA
| | - Imon Banerjee
- School of Computing and Augmented Intelligence, Arizona State University, Phoenix, AZ 85004, USA
- Department of Radiology, Mayo Clinic Arizona, Scottsdale, AZ 85054, USA
| | - Reza Arsanjani
- Department of Cardiovascular Diseases, Mayo Clinic Arizona, Scottsdale, AZ 85054, USA
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17
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Jone PN, Gearhart A, Lei H, Xing F, Nahar J, Lopez-Jimenez F, Diller GP, Marelli A, Wilson L, Saidi A, Cho D, Chang AC. Artificial Intelligence in Congenital Heart Disease: Current State and Prospects. JACC. ADVANCES 2022; 1:100153. [PMID: 38939457 PMCID: PMC11198540 DOI: 10.1016/j.jacadv.2022.100153] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 10/04/2022] [Accepted: 10/07/2022] [Indexed: 06/29/2024]
Abstract
The current era of big data offers a wealth of new opportunities for clinicians to leverage artificial intelligence to optimize care for pediatric and adult patients with a congenital heart disease. At present, there is a significant underutilization of artificial intelligence in the clinical setting for the diagnosis, prognosis, and management of congenital heart disease patients. This document is a call to action and will describe the current state of artificial intelligence in congenital heart disease, review challenges, discuss opportunities, and focus on the top priorities of artificial intelligence-based deployment in congenital heart disease.
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Affiliation(s)
- Pei-Ni Jone
- Section of Pediatric Cardiology, Department of Pediatrics, Lurie Children’s Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Addison Gearhart
- Department of Cardiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Howard Lei
- Division of Pediatric Cardiology, Children’s Hospital of Orange County, Orange, California, USA
| | - Fuyong Xing
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Jai Nahar
- Department of Cardiology, Children's National Hospital, Washington, DC, USA
| | | | - Gerhard-Paul Diller
- Department of Cardiology III-Adult Congenital and Valvular Heart Disease, University Hospital Muenster, Muenster, Germany
- Adult Congenital Heart Centre and National Centre for Pulmonary Hypertension, Royal Brompton and Harefield National Health Service Foundation Trust, Imperial College London, London, UK
- National Register for Congenital Heart Defects, Berlin, Germany
| | - Ariane Marelli
- McGill Adult Unit for Congenital Heart Disease Excellence, Department of Medicine, McGill University, Montréal, Québec, Canada
| | - Laura Wilson
- Department of Pediatrics, University of Florida-Congenital Heart Center, Gainesville, Florida, USA
| | - Arwa Saidi
- Department of Pediatrics, University of Florida-Congenital Heart Center, Gainesville, Florida, USA
| | - David Cho
- Department of Cardiology, University of California at Los Angeles, Los Angeles, California, USA
| | - Anthony C. Chang
- Division of Pediatric Cardiology, Children’s Hospital of Orange County, Orange, California, USA
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18
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Kwan A, Demosthenes E, Salto G, Ouyang D, Nguyen T, Nwabuo CC, Luong E, Hoang A, Osypiuk E, Stantchev P, Kim EH, Hiremath P, Li D, Vasan R, Xanthakis V, Cheng S. Cardiac microstructural alterations measured by echocardiography identify sex-specific risk for heart failure. Heart 2022; 108:1800-1806. [PMID: 35680379 PMCID: PMC9626911 DOI: 10.1136/heartjnl-2022-320876] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 05/16/2022] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVE Established preclinical imaging assessments of heart failure (HF) risk are based on macrostructural cardiac remodelling. Given that microstructural alterations may also influence HF risk, particularly in women, we examined associations between microstructural alterations and incident HF. METHODS We studied N=2511 adult participants (mean age 65.7±8.8 years, 56% women) of the Framingham Offspring Study who were free of cardiovascular disease at baseline. We employed texture analysis of echocardiography to quantify microstructural alteration, based on the high spectrum signal intensity coefficient (HS-SIC). We examined its relations to incident HF in sex-pooled and sex-specific Cox models accounting for traditional HF risk factors and macrostructural alterations. RESULTS We observed 94 new HF events over 7.4±1.7 years. Individuals with higher HS-SIC had increased risk for incident HF (HR 1.67 per 1-SD in HS-SIC, 95% CI 1.31 to 2.13; p<0.0001). Adjusting for age and antihypertensive medication use, this association was significant in women (p=0.02) but not men (p=0.78). Adjusting for traditional risk factors (including body mass index, total/high-density lipoprotein cholesterol, blood pressure traits, diabetes and smoking) attenuated the association in women (HR 1.30, p=0.07), with mediation of HF risk by the HS-SIC seen for a majority of these risk factors. However, the HS-SIC association with HF in women remained significant after adjusting for relative wall thickness (representing macrostructure alteration) in addition to these risk factors (HR 1.47, p=0.02). CONCLUSIONS Cardiac microstructural alterations are associated with elevated risk for HF, particularly in women. Microstructural alteration may identify sex-specific pathways by which individuals progress from risk factors to clinical HF.
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Affiliation(s)
- Alan Kwan
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | | | - Gerran Salto
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Framingham Heart Study, Framingham, Massachusetts, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Trevor Nguyen
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Chike C Nwabuo
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Ronin Institute, Montclair, New Jersey, USA
| | - Eric Luong
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Amy Hoang
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Ewa Osypiuk
- Framingham Heart Study, Framingham, Massachusetts, USA
| | | | - Elizabeth H Kim
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Pranoti Hiremath
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Debiao Li
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Ramachandran Vasan
- Framingham Heart Study, Framingham, Massachusetts, USA
- Departments of Medicine, Biostatistics, and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts, USA
| | - Vanessa Xanthakis
- Framingham Heart Study, Framingham, Massachusetts, USA
- Departments of Medicine, Biostatistics, and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts, USA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Framingham Heart Study, Framingham, Massachusetts, USA
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19
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Nedadur R, Wang B, Tsang W. Artificial intelligence for the echocardiographic assessment of valvular heart disease. Heart 2022; 108:1592-1599. [PMID: 35144983 PMCID: PMC9554049 DOI: 10.1136/heartjnl-2021-319725] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 12/29/2021] [Indexed: 11/18/2022] Open
Abstract
Developments in artificial intelligence (AI) have led to an explosion of studies exploring its application to cardiovascular medicine. Due to the need for training and expertise, one area where AI could be impactful would be in the diagnosis and management of valvular heart disease. This is because AI can be applied to the multitude of data generated from clinical assessments, imaging and biochemical testing during the care of the patient. In the area of valvular heart disease, the focus of AI has been on the echocardiographic assessment and phenotyping of patient populations to identify high-risk groups. AI can assist image acquisition, view identification for review, and segmentation of valve and cardiac structures for automated analysis. Using image recognition algorithms, aortic and mitral valve disease states have been directly detected from the images themselves. Measurements obtained during echocardiographic valvular assessment have been integrated with other clinical data to identify novel aortic valve disease subgroups and describe new predictors of aortic valve disease progression. In the future, AI could integrate echocardiographic parameters with other clinical data for precision medical management of patients with valvular heart disease.
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Affiliation(s)
- Rashmi Nedadur
- Division of Cardiac Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Bo Wang
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute of Artificial Intelligence, University of Toronto, Toronto, Ontario, Canada
- Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada
| | - Wendy Tsang
- Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada
- Division of Cardiology, University of Toronto, Toronto, Ontario, Canada
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20
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Kim S, Park HB, Jeon J, Arsanjani R, Heo R, Lee SE, Moon I, Yoo SK, Chang HJ. Fully automated quantification of cardiac chamber and function assessment in 2-D echocardiography: clinical feasibility of deep learning-based algorithms. Int J Cardiovasc Imaging 2022; 38:1047-1059. [PMID: 35152371 PMCID: PMC11143010 DOI: 10.1007/s10554-021-02482-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/24/2021] [Indexed: 12/20/2022]
Abstract
We aimed to compare the segmentation performance of the current prominent deep learning (DL) algorithms with ground-truth segmentations and to validate the reproducibility of the manually created 2D echocardiographic four cardiac chamber ground-truth annotation. Recently emerged DL based fully-automated chamber segmentation and function assessment methods have shown great potential for future application in aiding image acquisition, quantification, and suggestion for diagnosis. However, the performance of current DL algorithms have not previously been compared with each other. In addition, the reproducibility of ground-truth annotations which are the basis of these algorithms have not yet been fully validated. We retrospectively enrolled 500 consecutive patients who underwent transthoracic echocardiogram (TTE) from December 2019 to December 2020. Simple U-net, Res-U-net, and Dense-U-net algorithms were compared for the segmentation performances and clinical indices such as left atrial volume (LAV), left ventricular end diastolic volume (LVEDV), left ventricular end systolic volume (LVESV), LV mass, and ejection fraction (EF) were evaluated. The inter- and intra-observer variability analysis was performed by two expert sonographers for a randomly selected echocardiographic view in 100 patients (apical 2-chamber, apical 4-chamber, and parasternal short axis views). The overall performance of all DL methods was excellent [average dice similarity coefficient (DSC) 0.91 to 0.95 and average Intersection over union (IOU) 0.83 to 0.90], with the exception of LV wall area on PSAX view (average DSC of 0.83, IOU 0.72). In addition, there were no significant difference in clinical indices between ground truth and automated DL measurements. For inter- and intra-observer variability analysis, the overall intra observer reproducibility was excellent: LAV (ICC = 0.995), LVEDV (ICC = 0.996), LVESV (ICC = 0.997), LV mass (ICC = 0.991) and EF (ICC = 0.984). The inter-observer reproducibility was slightly lower as compared to intraobserver agreement: LAV (ICC = 0.976), LVEDV (ICC = 0.982), LVESV (ICC = 0.970), LV mass (ICC = 0.971), and EF (ICC = 0.899). The three current prominent DL-based fully automated methods are able to reliably perform four-chamber segmentation and quantification of clinical indices. Furthermore, we were able to validate the four cardiac chamber ground-truth annotation and demonstrate an overall excellent reproducibility, but still with some degree of inter-observer variability.
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Affiliation(s)
- Sekeun Kim
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Graduate Program of Biomedical Engineering, Yonsei University College of Medicine, Seoul, South Korea
| | - Hyung-Bok Park
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Department of Cardiology, Catholic Kwandong University International St. Mary's Hospital, Incheon, South Korea
| | - Jaeik Jeon
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Reza Arsanjani
- Department of Cardiovascular Diseases, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Ran Heo
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Department of Cardiology, Hanyang University Seoul Hospital, Hanyang University College of Medicine, Seoul, South Korea
| | - Sang-Eun Lee
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Department of Cardiology, Ewha Womans University Seoul Hospital, Seoul, South Korea
| | - Inki Moon
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea
- Division of Cardiology, Department of Internal Medicine, Soonchunghyang University Bucheon Hospital, Bucheon, South Korea
| | - Sun Kook Yoo
- Department of Medical Engineering, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
| | - Hyuk-Jae Chang
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, South Korea.
- Division of Cardiology, Department of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
- Ontact Health Co., Ltd., Seoul, South Korea.
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Blaivas M, Blaivas L. Machine learning algorithm using publicly available echo database for simplified “visual estimation” of left ventricular ejection fraction. World J Exp Med 2022; 12:16-25. [PMID: 35433318 PMCID: PMC8968469 DOI: 10.5493/wjem.v12.i2.16] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 12/14/2021] [Accepted: 03/07/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Left ventricular ejection fraction calculation automation typically requires complex algorithms and is dependent of optimal visualization and tracing of endocardial borders. This significantly limits usability in bedside clinical applications, where ultrasound automation is needed most.
AIM To create a simple deep learning (DL) regression-type algorithm to visually estimate left ventricular (LV) ejection fraction (EF) from a public database of actual patient echo examinations and compare results to echocardiography laboratory EF calculations.
METHODS A simple DL architecture previously proven to perform well on ultrasound image analysis, VGG16, was utilized as a base architecture running within a long short term memory algorithm for sequential image (video) analysis. After obtaining permission to use the Stanford EchoNet-Dynamic database, researchers randomly removed approximately 15% of the approximately 10036 echo apical 4-chamber videos for later performance testing. All database echo examinations were read as part of comprehensive echocardiography study performance and were coupled with EF, end systolic and diastolic volumes, key frames and coordinates for LV endocardial tracing in csv file. To better reflect point-of-care ultrasound (POCUS) clinical settings and time pressure, the algorithm was trained on echo video correlated with calculated ejection fraction without incorporating additional volume, measurement and coordinate data. Seventy percent of the original data was used for algorithm training and 15% for validation during training. The previously randomly separated 15% (1263 echo videos) was used for algorithm performance testing after training completion. Given the inherent variability of echo EF measurement and field standards for evaluating algorithm accuracy, mean absolute error (MAE) and root mean square error (RMSE) calculations were made on algorithm EF results compared to Echo Lab calculated EF. Bland-Atlman calculation was also performed. MAE for skilled echocardiographers has been established to range from 4% to 5%.
RESULTS The DL algorithm visually estimated EF had a MAE of 8.08% (95%CI 7.60 to 8.55) suggesting good performance compared to highly skill humans. The RMSE was 11.98 and correlation of 0.348.
CONCLUSION This experimental simplified DL algorithm showed promise and proved reasonably accurate at visually estimating LV EF from short real time echo video clips. Less burdensome than complex DL approaches used for EF calculation, such an approach may be more optimal for POCUS settings once improved upon by future research and development.
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Affiliation(s)
- Michael Blaivas
- Department of Medicine, University of South Carolina School of Medicine, Roswell, GA 30076, United States
| | - Laura Blaivas
- Department of Environmental Science, Michigan State University, Roswell, Georgia 30076, United States
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22
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Echocardiographic Advances in Dilated Cardiomyopathy. J Clin Med 2021; 10:jcm10235518. [PMID: 34884220 PMCID: PMC8658091 DOI: 10.3390/jcm10235518] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/05/2021] [Accepted: 11/23/2021] [Indexed: 12/29/2022] Open
Abstract
Although the overall survival of patients with dilated cardiomyopathy (DCM) has improved significantly in the last decades, a non-negligible proportion of DCM patients still shows an unfavorable prognosis. DCM patients not only need imaging techniques that are effective in diagnosis, but also suitable for long-term follow-up with frequent re-evaluations. The exponential growth of echocardiography’s technology and performance in recent years has resulted in improved diagnostic accuracy, stratification, management and follow-up of patients with DCM. This review summarizes some new developments in echocardiography and their promising applications in DCM. Although nowadays cardiac magnetic resonance (CMR) remains the gold standard technique in DCM, the echocardiographic advances and novelties proposed in the manuscript, if properly integrated into clinical practice, could bring echocardiography closer to CMR in terms of accuracy and may certify ultrasound as the technique of choice in the follow-up of DCM patients. The application in DCM patients of novel echocardiographic techniques represents an interesting emergent research area for scholars in the near future.
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