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Seetharam K, Thyagaturu H, Ferreira GL, Patel A, Patel C, Elahi A, Pachulski R, Shah J, Mir P, Thodimela A, Pala M, Thet Z, Hamirani Y. Broadening Perspectives of Artificial Intelligence in Echocardiography. Cardiol Ther 2024; 13:267-279. [PMID: 38703292 PMCID: PMC11093957 DOI: 10.1007/s40119-024-00368-3] [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: 11/13/2023] [Accepted: 04/11/2024] [Indexed: 05/06/2024] Open
Abstract
Echocardiography frequently serves as the first-line treatment of diagnostic imaging for several pathological entities in cardiology. Artificial intelligence (AI) has been growing substantially in information technology and various commercial industries. Machine learning (ML), a branch of AI, has been shown to expand the capabilities and potential of echocardiography. ML algorithms expand the field of echocardiography by automated assessment of the ejection fraction and left ventricular function, integrating novel approaches such as speckle tracking or tissue Doppler echocardiography or vector flow mapping, improved phenotyping, distinguishing between cardiac conditions, and incorporating information from mobile health and genomics. In this review article, we assess the impact of AI and ML in echocardiography.
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Affiliation(s)
- Karthik Seetharam
- Division of Cardiovascular Disease, West Virgina University, Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA.
- Wyckoff Heights Medical Center, Brooklyn, NY, USA.
| | - Harshith Thyagaturu
- Division of Cardiovascular Disease, West Virgina University, Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | | | - Aditya Patel
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Chinmay Patel
- University of Pittsburg Medical Center, Harrisburg, PA, USA
| | - Asim Elahi
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Roman Pachulski
- St. John's Episcopal Hospital - South Shore, New York, NY, USA
| | - Jilan Shah
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Parvez Mir
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | | | - Manya Pala
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Zeyar Thet
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Yasmin Hamirani
- Robert Woods Johnson University Hospital/Rutgers University, New Brusnwick, NJ, USA
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von Haehling S, Assmus B, Bekfani T, Dworatzek E, Edelmann F, Hashemi D, Hellenkamp K, Kempf T, Raake P, Schütt KA, Wachter R, Schulze PC, Hasenfuss G, Böhm M, Bauersachs J. Heart failure with preserved ejection fraction: diagnosis, risk assessment, and treatment. Clin Res Cardiol 2024:10.1007/s00392-024-02396-4. [PMID: 38602566 DOI: 10.1007/s00392-024-02396-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 02/02/2024] [Indexed: 04/12/2024]
Abstract
The aetiology of heart failure with preserved ejection fraction (HFpEF) is heterogenous and overlaps with that of several comorbidities like atrial fibrillation, diabetes mellitus, chronic kidney disease, valvular heart disease, iron deficiency, or sarcopenia. The diagnosis of HFpEF involves evaluating cardiac dysfunction through imaging techniques and assessing increased left ventricular filling pressure, which can be measured directly or estimated through various proxies including natriuretic peptides. To better narrow down the differential diagnosis of HFpEF, European and American heart failure guidelines advocate the use of different algorithms including comorbidities that require diagnosis and rigorous treatment during the evaluation process. Therapeutic recommendations differ between guidelines. Whilst sodium glucose transporter 2 inhibitors have a solid evidence base, the recommendations differ with regard to the use of inhibitors of the renin-angiotensin-aldosterone axis. Unless indicated for specific comorbidities, the use of beta-blockers should be discouraged in HFpEF. The aim of this article is to provide an overview of the current state of the art in HFpEF diagnosis, clinical evaluation, and treatment.
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Affiliation(s)
- Stephan von Haehling
- Department of Cardiology and Pneumology, University of Göttingen Medical Center, Robert-Koch-Strasse 40, 37075, Göttingen, Germany.
- German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany.
| | - Birgit Assmus
- Department of Cardiology and Angiology, Universitätsklinikum Gießen und Marburg, Giessen, Germany
| | - Tarek Bekfani
- Department of Cardiology and Angiology, Universitätsklinikum Magdeburg, Magdeburg, Germany
| | - Elke Dworatzek
- Institute of Gender in Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Frank Edelmann
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité - Medical Heart Center of Charité and German Heart Institute Berlin, Campus Virchow-Klinikum, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
| | - Djawid Hashemi
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité - Medical Heart Center of Charité and German Heart Institute Berlin, Campus Virchow-Klinikum, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Digital Clinician Scientist Program, Berlin, Germany
| | - Kristian Hellenkamp
- Department of Cardiology and Pneumology, University of Göttingen Medical Center, Robert-Koch-Strasse 40, 37075, Göttingen, Germany
| | - Tibor Kempf
- Department of Cardiology and Angiology, Hannover Medical School, Hannover, Germany
| | - Philipp Raake
- I. Medical Department, Cardiology, Pneumology, Endocrinology and Intensive Care Medicine, University Hospital Augsburg, University of Augsburg, Augsburg, Germany
| | - Katharina A Schütt
- Department of Internal Medicine I, University Hospital RWTH Aachen, Aachen, Germany
| | - Rolf Wachter
- Department of Cardiology and Pneumology, University of Göttingen Medical Center, Robert-Koch-Strasse 40, 37075, Göttingen, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany
- Klinik und Poliklinik für Kardiologie, Universitätsklinikum Leipzig, Leipzig, Germany
| | - Paul Christian Schulze
- Department of Internal Medicine I, Division of Cardiology, University Hospital Jena, FSU, Jena, Germany
| | - Gerd Hasenfuss
- Department of Cardiology and Pneumology, University of Göttingen Medical Center, Robert-Koch-Strasse 40, 37075, Göttingen, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany
| | - Michael Böhm
- Kardiologie, Angiologie und Internistische Intensivmedizin, Klinik für Innere Medizin III, Universitätsklinikum des Saarlandes, Saarland University, Homburg, Germany
| | - Johann Bauersachs
- Department of Cardiology and Angiology, Hannover Medical School, Hannover, Germany
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Aziz D, Maganti K, Yanamala N, Sengupta P. The Role of Artificial Intelligence in Echocardiography: A Clinical Update. Curr Cardiol Rep 2023; 25:1897-1907. [PMID: 38091196 DOI: 10.1007/s11886-023-02005-2] [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: 11/21/2023] [Indexed: 01/26/2024]
Abstract
PURPOSE OF REVIEW In echocardiography, there has been robust development of artificial intelligence (AI) tools for image recognition, automated measurements, image segmentation, and patient prognostication that has created a monumental shift in the study of AI and machine learning models. However, integrating these measurements into complex disease recognition and therapeutic interventions remains challenging. While the tools have been developed, there is a lack of evidence regarding implementing heterogeneous systems for guiding clinical decision-making and therapeutic action. RECENT FINDINGS Newer AI modalities have shown concrete positive data in terms of user-guided image acquisition and processing, precise determination of both basic and advanced quantitative echocardiographic features, and the potential to construct predictive models, all with the possibility of seamless integration into clinical decision support systems. AI in echocardiography is a powerful and ever-growing tool with the potential for revolutionary effects on the practice of cardiology. In this review article, we explore the growth of AI and its applications in echocardiography, along with clinical implications and the associated regulatory, legal, and ethical considerations.
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Affiliation(s)
- Daniel Aziz
- Department of Internal Medicine, Rutgers - Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Kameswari Maganti
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA
| | - Naveena Yanamala
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA
| | - Partho Sengupta
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA.
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O'Donnell C, Sanchez PA, Celestin B, McConnell MV, Haddad F. The Echocardiographic Evaluation of the Right Heart: Current and Future Advances. Curr Cardiol Rep 2023; 25:1883-1896. [PMID: 38041726 DOI: 10.1007/s11886-023-02001-6] [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] [Accepted: 11/17/2023] [Indexed: 12/03/2023]
Abstract
PURPOSE OF REVIEW To discuss physiologic and methodologic advances in the echocardiographic assessment of right heart (RH) function, including the emergence of artificial intelligence (AI) and point-of-care ultrasound. RECENT FINDINGS Recent studies have highlighted the prognostic value of right ventricular (RV) longitudinal strain, RV end-systolic dimensions, and right atrial (RA) size and function in pulmonary hypertension and heart failure. While RA pressure is a central marker of right heart diastolic function, the recent emphasis on venous excess imaging (VExUS) has provided granularity to the systemic consequences of RH failure. Several methodological advances are also changing the landscape of RH imaging including post-processing 3D software to delineate the non-longitudinal (radial, anteroposterior, and circumferential) components of RV function, as well as AI segmentation- and non-segmentation-based quantification. Together with recent guidelines and advances in AI technology, the field is shifting from specific RV functional metrics to integrated RH disease-specific phenotypes. A modern echocardiographic evaluation of RH function should focus on the entire cardiopulmonary venous unit-from the venous to the pulmonary arterial system. Together, a multi-parametric approach, guided by physiology and AI algorithms, will help define novel integrated RH profiles for improved disease detection and monitoring.
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Affiliation(s)
- Christian O'Donnell
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
| | - Pablo Amador Sanchez
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Bettia Celestin
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
- Vera Moulton Wall Center for Pulmonary Vascular Disease, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael V McConnell
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Francois Haddad
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
- Vera Moulton Wall Center for Pulmonary Vascular Disease, Stanford University School of Medicine, Stanford, CA, USA
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Sun D, Hu Y, Li Y, Yu X, Chen X, Shen P, Tang X, Wang Y, Lai C, Kang B, Bai Z, Ni Z, Wang N, Wang R, Guan L, Zhou W, Gao Y. Chamber Attention Network (CAN): Towards interpretable diagnosis of pulmonary artery hypertension using echocardiography. J Adv Res 2023:S2090-1232(23)00317-X. [PMID: 37926144 DOI: 10.1016/j.jare.2023.10.013] [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: 05/21/2023] [Revised: 08/20/2023] [Accepted: 10/24/2023] [Indexed: 11/07/2023] Open
Abstract
INTRODUCTION Accurate identification of pulmonary arterial hypertension (PAH) in primary care and rural areas can be a challenging task. However, recent advancements in computer vision offer the potential for automated systems to detect PAH from echocardiography. OBJECTIVES Our aim was to develop a precise and efficient diagnostic model for PAH tailored to the unique requirements of intelligent diagnosis, especially in challenging locales like high-altitude regions. METHODS We proposed the Chamber Attention Network (CAN) for PAH identification from echocardiographic images, trained on a dataset comprising 13,912 individual subjects. A convolutional neural network (CNN) for view classification was used to select the clinically relevant apical four chamber (A4C) and parasternal long axis (PLAX) views for PAH diagnosis. To assess the importance of different heart chambers in PAH diagnosis, we developed a novel Chamber Attention Module. RESULTS The experimental results demonstrated that: 1) The substantial correspondence between our obtained chamber attention vector and clinical expertise suggested that our model was highly interpretable, potentially uncovering diagnostic insights overlooked by the clinical community. 2) The proposed CAN model exhibited superior image-level accuracy and faster convergence on the internal validation dataset compared to the other four models. Furthermore, our CAN model outperformed the others on the external test dataset, with image-level accuracies of 82.53% and 83.32% for A4C and PLAX, respectively. 3) Implementation of the voting strategy notably enhanced the positive predictive value (PPV) and negative predictive value (NPV) of individual-level classification results, enhancing the reliability of our classification outcomes. CONCLUSIONS These findings indicate that CAN is a feasible technique for AI-assisted PAH diagnosis, providing new insights into cardiac structural changes observed in echocardiography.
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Affiliation(s)
- Dezhi Sun
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Yangyi Hu
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Yunming Li
- Department of Information, Medical Support Center, The General Hospital of Western Theater Command, Chengdu 610083, Sichuan, China
| | - Xianbiao Yu
- Department of Ultrasonic Diagnosis, Army 954 Hospital, Shannan 856000, Tibet, China
| | - Xi Chen
- Department of Respiratory Medicine, Army 954 Hospital, Shannan 856000, Tibet, China
| | - Pan Shen
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xianglin Tang
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Yihao Wang
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Chengcai Lai
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Bo Kang
- Department of Academic Affairs, Army 954 Hospital, Shannan 856000, Tibet, China
| | - Zhijie Bai
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Zhexin Ni
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Ningning Wang
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Rui Wang
- General Hospital of Xinjiang Military Region of the Chinese People's Liberation Army, Urumqi 830000, Xinjiang, China
| | - Lina Guan
- General Hospital of Xinjiang Military Region of the Chinese People's Liberation Army, Urumqi 830000, Xinjiang, China
| | - Wei Zhou
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China.
| | - Yue Gao
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China.
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Alkhodari M, Xiong Z, Khandoker AH, Hadjileontiadis LJ, Leeson P, Lapidaire W. The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare. Expert Rev Cardiovasc Ther 2023; 21:531-543. [PMID: 37300317 DOI: 10.1080/14779072.2023.2223978] [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] [Received: 01/04/2023] [Accepted: 06/06/2023] [Indexed: 06/12/2023]
Abstract
INTRODUCTION Guidelines advise ongoing follow-up of patients after hypertensive disorders of pregnancy (HDP) to assess cardiovascular risk and manage future patient-specific pregnancy conditions. However, there are limited tools available to monitor patients, with those available tending to be simple risk assessments that lack personalization. A promising approach could be the emerging artificial intelligence (AI)-based techniques, developed from big patient datasets to provide personalized recommendations for preventive advice. AREAS COVERED In this narrative review, we discuss the impact of integrating AI and big data analysis for personalized cardiovascular care, focusing on the management of HDP. EXPERT OPINION The pathophysiological response of women to pregnancy varies, and deeper insight into each response can be gained through a deeper analysis of the medical history of pregnant women based on clinical records and imaging data. Further research is required to be able to implement AI for clinical cases using multi-modality and multi-organ assessment, and this could expand both knowledge on pregnancy-related disorders and personalized treatment planning.
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Affiliation(s)
- Mohanad Alkhodari
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University of Science and Tehcnology, Abu Dhabi, UAE
| | - Zhaohan Xiong
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Ahsan H Khandoker
- Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University of Science and Tehcnology, Abu Dhabi, UAE
| | - Leontios J Hadjileontiadis
- Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University of Science and Tehcnology, Abu Dhabi, UAE
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Paul Leeson
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Winok Lapidaire
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
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Salte IM, Østvik A, Olaisen SH, Karlsen S, Dahlslett T, Smistad E, Eriksen-Volnes TK, Brunvand H, Haugaa KH, Edvardsen T, Dalen H, Lovstakken L, Grenne B. Deep Learning for Improved Precision and Reproducibility of Left Ventricular Strain in Echocardiography: A Test-Retest Study. J Am Soc Echocardiogr 2023; 36:788-799. [PMID: 36933849 DOI: 10.1016/j.echo.2023.02.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 02/22/2023] [Accepted: 02/22/2023] [Indexed: 03/20/2023]
Abstract
AIMS Assessment of left ventricular (LV) function by echocardiography is hampered by modest test-retest reproducibility. A novel artificial intelligence (AI) method based on deep learning provides fully automated measurements of LV global longitudinal strain (GLS) and may improve the clinical utility of echocardiography by reducing user-related variability. The aim of this study was to assess within-patient test-retest reproducibility of LV GLS measured by the novel AI method in repeated echocardiograms recorded by different echocardiographers and to compare the results to manual measurements. METHODS Two test-retest data sets (n = 40 and n = 32) were obtained at separate centers. Repeated recordings were acquired in immediate succession by 2 different echocardiographers at each center. For each data set, 4 readers measured GLS in both recordings using a semiautomatic method to construct test-retest interreader and intrareader scenarios. Agreement, mean absolute difference, and minimal detectable change (MDC) were compared to analyses by AI. In a subset of 10 patients, beat-to-beat variability in 3 cardiac cycles was assessed by 2 readers and AI. RESULTS Test-retest variability was lower with AI compared with interreader scenarios (data set I: MDC = 3.7 vs 5.5, mean absolute difference = 1.4 vs 2.1, respectively; data set II: MDC = 3.9 vs 5.2, mean absolute difference = 1.6 vs 1.9, respectively; all P < .05). There was bias in GLS measurements in 13 of 24 test-retest interreader scenarios (largest bias, 3.2 strain units). In contrast, there was no bias in measurements by AI. Beat-to-beat MDCs were 1,5, 2.1, and 2.3 for AI and the 2 readers, respectively. Processing time for analyses of GLS by the AI method was 7.9 ± 2.8 seconds. CONCLUSION A fast AI method for automated measurements of LV GLS reduced test-retest variability and removed bias between readers in both test-retest data sets. By improving the precision and reproducibility, AI may increase the clinical utility of echocardiography.
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Affiliation(s)
- Ivar M Salte
- Department of Medicine, Hospital of Southern Norway, Kristiansand, Norway; Faculty of Medicine, University of Oslo, Oslo, Norway; ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Andreas Østvik
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Medical Image Analysis, Health Research, SINTEF Digital, Trondheim, Norway
| | - Sindre H Olaisen
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Sigve Karlsen
- Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Medicine, Hospital of Southern Norway, Arendal, Norway
| | - Thomas Dahlslett
- Department of Medicine, Hospital of Southern Norway, Arendal, Norway
| | - Erik Smistad
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Medical Image Analysis, Health Research, SINTEF Digital, Trondheim, Norway
| | - Torfinn K Eriksen-Volnes
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway
| | - Harald Brunvand
- Department of Medicine, Hospital of Southern Norway, Arendal, Norway
| | - Kristina H Haugaa
- Faculty of Medicine, University of Oslo, Oslo, Norway; ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway; Faculty of Medicine, Karolinska Institutet and Cardiovascular Division, Karolinska University Hospital, Stockholm, Sweden
| | - Thor Edvardsen
- Faculty of Medicine, University of Oslo, Oslo, Norway; ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Håvard Dalen
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Lasse Lovstakken
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Bjørnar Grenne
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway.
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Vidal-Perez R, Grapsa J, Bouzas-Mosquera A, Fontes-Carvalho R, Vazquez-Rodriguez JM. Current role and future perspectives of artificial intelligence in echocardiography. World J Cardiol 2023; 15:284-292. [PMID: 37397831 PMCID: PMC10308270 DOI: 10.4330/wjc.v15.i6.284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/02/2023] [Accepted: 06/21/2023] [Indexed: 06/26/2023] Open
Abstract
Echocardiography is an essential tool in diagnostic cardiology and is fundamental to clinical care. Artificial intelligence (AI) can help health care providers serving as a valuable diagnostic tool for physicians in the field of echocardiography specially on the automation of measurements and interpretation of results. In addition, it can help expand the capabilities of research and discover alternative pathways in medical management specially on prognostication. In this review article, we describe the current role and future perspectives of AI in echocardiography.
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Affiliation(s)
- Rafael Vidal-Perez
- Servicio de Cardiología, Unidad de Imagen y Función Cardíaca, Complexo Hospitalario Universitario A Coruña Centro de Investigación Biomédica en Red-Instituto de Salud Carlos III, A Coruña 15006, Spain
| | - Julia Grapsa
- Department of Cardiology, Guys and St Thomas NHS Trust, London SE1 7EH, United Kingdom
| | - Alberto Bouzas-Mosquera
- Servicio de Cardiología, Unidad de Imagen y Función Cardíaca, Complexo Hospitalario Universitario A Coruña Centro de Investigación Biomédica en Red-Instituto de Salud Carlos III, A Coruña 15006, Spain
| | - Ricardo Fontes-Carvalho
- Cardiology Department, Centro Hospitalar de Vila Nova de Gaia/Espinho, Vilanova de Gaia 4434-502, Portugal
- Cardiovascular R&D Centre - UnIC@RISE, Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, Porto 4200-319, Portugal
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Al-Mallah MH. Artificial intelligence in nuclear cardiology: your crucial role in transforming potential into reality. J Nucl Cardiol 2023; 30:1293-1296. [PMID: 37156962 DOI: 10.1007/s12350-023-03276-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Affiliation(s)
- Mouaz H Al-Mallah
- Houston Methodist DeBakey Heart and Vascular Center, 6550 Fannin Street, Smith Tower - Suite 1801, Houston, TX, 77030, USA.
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Durr AJ, Korol AS, Hathaway QA, Kunovac A, Taylor AD, Rizwan S, Pinti MV, Hollander JM. Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus. PLoS One 2023; 18:e0285512. [PMID: 37155623 PMCID: PMC10166525 DOI: 10.1371/journal.pone.0285512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 04/25/2023] [Indexed: 05/10/2023] Open
Abstract
Speckle tracking echocardiography (STE) has been utilized to evaluate independent spatial alterations in the diabetic heart, but the progressive manifestation of regional and segmental cardiac dysfunction in the type 2 diabetic (T2DM) heart remains understudied. Therefore, the objective of this study was to elucidate if machine learning could be utilized to reliably describe patterns of the progressive regional and segmental dysfunction that are associated with the development of cardiac contractile dysfunction in the T2DM heart. Non-invasive conventional echocardiography and STE datasets were utilized to segregate mice into two pre-determined groups, wild-type and Db/Db, at 5, 12, 20, and 25 weeks. A support vector machine model, which classifies data using a single line, or hyperplane, that best separates each class, and a ReliefF algorithm, which ranks features by how well each feature lends to the classification of data, were used to identify and rank cardiac regions, segments, and features by their ability to identify cardiac dysfunction. STE features more accurately segregated animals as diabetic or non-diabetic when compared with conventional echocardiography, and the ReliefF algorithm efficiently ranked STE features by their ability to identify cardiac dysfunction. The Septal region, and the AntSeptum segment, best identified cardiac dysfunction at 5, 20, and 25 weeks, with the AntSeptum also containing the greatest number of features which differed between diabetic and non-diabetic mice. Cardiac dysfunction manifests in a spatial and temporal fashion, and is defined by patterns of regional and segmental dysfunction in the T2DM heart which are identifiable using machine learning methodologies. Further, machine learning identified the Septal region and AntSeptum segment as locales of interest for therapeutic interventions aimed at ameliorating cardiac dysfunction in T2DM, suggesting that machine learning may provide a more thorough approach to managing contractile data with the intention of identifying experimental and therapeutic targets.
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Affiliation(s)
- Andrya J Durr
- Division of Exercise Physiology, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
| | - Anna S Korol
- Department of Neuroscience, Rockefeller Neuroscience Institute, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
| | - Quincy A Hathaway
- Division of Exercise Physiology, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- Center for Inhalation Toxicology (iTOX), West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
| | - Amina Kunovac
- Division of Exercise Physiology, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- Center for Inhalation Toxicology (iTOX), West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
| | - Andrew D Taylor
- Division of Exercise Physiology, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
| | - Saira Rizwan
- Division of Exercise Physiology, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
| | - Mark V Pinti
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- West Virginia University School of Pharmacy, Morgantown, West Virginia, United States of America
- Department of Physiology and Pharmacology, West Virginia University School of Pharmacy, Morgantown, West Virginia, United States of America
| | - John M Hollander
- Division of Exercise Physiology, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
- Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
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11
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Nittas V, Daniore P, Landers C, Gille F, Amann J, Hubbs S, Puhan MA, Vayena E, Blasimme A. Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. PLOS DIGITAL HEALTH 2023; 2:e0000189. [PMID: 36812620 PMCID: PMC9931290 DOI: 10.1371/journal.pdig.0000189] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 01/02/2023] [Indexed: 02/04/2023]
Abstract
Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field's potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.
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Affiliation(s)
- Vasileios Nittas
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Paola Daniore
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Constantin Landers
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Felix Gille
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Julia Amann
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Shannon Hubbs
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Milo Alan Puhan
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Alessandro Blasimme
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
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12
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Angelaki E, Barmparis GD, Kochiadakis G, Maragkoudakis S, Savva E, Kampanieris E, Kassotakis S, Kalomoirakis P, Vardas P, Tsironis GP, Marketou ME. Artificial intelligence-based opportunistic screening for the detection of arterial hypertension through ECG signals. J Hypertens 2022; 40:2494-2501. [PMID: 36189460 DOI: 10.1097/hjh.0000000000003286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
OBJECTIVES Hypertension is a major risk factor for cardiovascular disease (CVD), which often escapes the diagnosis or should be confirmed by several office visits. The ECG is one of the most widely used diagnostic tools and could be of paramount importance in patients' initial evaluation. METHODS We used machine learning techniques based on clinical parameters and features derived from the ECG, to detect hypertension in a population without CVD. We enrolled 1091 individuals who were classified as hypertensive or normotensive, and trained a Random Forest model, to detect the existence of hypertension. We then calculated the values for the Shapley additive explanations (SHAP), a sophisticated feature importance analysis, to interpret each feature's role in the Random Forest's results. RESULTS Our Random Forest model was able to distinguish hypertensive from normotensive patients with accuracy 84.2%, specificity 78.0%, sensitivity 84.0% and area under the receiver-operating curve 0.89, using a decision threshold of 0.6. Age, BMI, BMI-adjusted Cornell criteria (BMI multiplied by RaVL+SV 3 ), R wave amplitude in aVL and BMI-modified Sokolow-Lyon voltage (BMI divided by SV 1 +RV 5 ), were the most important anthropometric and ECG-derived features in terms of the success of our model. CONCLUSION Our machine learning algorithm is effective in the detection of hypertension in patients using ECG-derived and basic anthropometric criteria. Our findings open new horizon in the detection of many undiagnosed hypertensive individuals who have an increased CVD risk.
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Affiliation(s)
- Eleni Angelaki
- Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, Crete, Greece
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Georgios D Barmparis
- Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, Crete, Greece
| | - George Kochiadakis
- Department of Cardiology, Heraklion University Hospital, Heraklion, Greece
| | | | - Eirini Savva
- Department of Cardiology, Heraklion University Hospital, Heraklion, Greece
| | | | - Spyros Kassotakis
- Department of Cardiology, Heraklion University Hospital, Heraklion, Greece
| | | | - Panos Vardas
- Department of Cardiology, Heraklion University Hospital, Heraklion, Greece
- Heart Sector, Hygeia Hospitals Group, Athens, Greece
| | - Giorgos P Tsironis
- Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, Crete, Greece
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Maria E Marketou
- Department of Cardiology, Heraklion University Hospital, Heraklion, Greece
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13
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Nguyen MB, Villemain O, Friedberg MK, Lovstakken L, Rusin CG, Mertens L. Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes. FRONTIERS IN RADIOLOGY 2022; 2:881777. [PMID: 37492680 PMCID: PMC10365116 DOI: 10.3389/fradi.2022.881777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 08/01/2022] [Indexed: 07/27/2023]
Abstract
Artificial intelligence (AI) is frequently used in non-medical fields to assist with automation and decision-making. The potential for AI in pediatric cardiology, especially in the echocardiography laboratory, is very high. There are multiple tasks AI is designed to do that could improve the quality, interpretation, and clinical application of echocardiographic data at the level of the sonographer, echocardiographer, and clinician. In this state-of-the-art review, we highlight the pertinent literature on machine learning in echocardiography and discuss its applications in the pediatric echocardiography lab with a focus on automation of the pediatric echocardiogram and the use of echo data to better understand physiology and outcomes in pediatric cardiology. We also discuss next steps in utilizing AI in pediatric echocardiography.
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Affiliation(s)
- Minh B. Nguyen
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
- Department of Pediatric Cardiology, Baylor College of Medicine, Houston, TX, United States
| | - Olivier Villemain
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Mark K. Friedberg
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Lasse Lovstakken
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Craig G. Rusin
- Department of Pediatric Cardiology, Baylor College of Medicine, Houston, TX, United States
| | - Luc Mertens
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
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14
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Abstract
In this digital era, artificial intelligence (AI) is establishing a strong foothold in commercial industry and the field of technology. These effects are trickling into the healthcare industry, especially in the clinical arena of cardiology. Machine learning (ML) algorithms are making substantial progress in various subspecialties of cardiology. This will have a positive impact on patient care and move the field towards precision medicine. In this review article, we explore the progress of ML in cardiovascular imaging, electrophysiology, heart failure, and interventional cardiology.
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15
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Dell'Angela L, Nicolosi GL. Artificial intelligence applied to cardiovascular imaging, a critical focus on echocardiography: The point-of-view from "the other side of the coin". JOURNAL OF CLINICAL ULTRASOUND : JCU 2022; 50:772-780. [PMID: 35466409 DOI: 10.1002/jcu.23215] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/16/2022] [Accepted: 04/19/2022] [Indexed: 06/14/2023]
Abstract
Cardiovascular imaging has achieved a crucial role in the management of cardiovascular diseases. In this field, echocardiography advantages include wide availability, portability, and affordability, at a relatively low cost. However, echocardiographic assessment requires highly trained operators, and implies high observer variability, as compared with the other cardiac imaging modalities. Hence, artificial intelligence might be extremely helpful. From the point-of-view of the peripheral "Spoke" Hospital potential user ("the other side of the coin"), artificial intelligence development appears very slow in the clinical arena. Many limitations are still present, and require full involvement, cooperation, and coordination of professional operators into Hub-and-Spoke network.
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Affiliation(s)
- Luca Dell'Angela
- Emergency Department, Cardiology Division, Gorizia & Monfalcone Hospital, ASUGI, Gorizia, Italy
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16
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Azarine A, Scalbert F, Garçon P. Cardiac functional imaging. Presse Med 2022; 51:104119. [PMID: 35321846 DOI: 10.1016/j.lpm.2022.104119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 01/27/2022] [Accepted: 03/11/2022] [Indexed: 01/01/2023] Open
Abstract
During the last 20 years, cardiac imaging has drastically evolved. Positron emission tomography (PET), fast three-dimensional (3D) imaging with the latest generations of echocardiography & multi-detector computed tomography (CT), stress perfusion assessed by magnetic resonance imaging (MRI), blood flow analysis using four-dimensional (4D) flow MRI, all these techniques offer new trends for optimal noninvasive functional cardiac imaging. Dynamic functional imaging is obtained by acquiring images of the heart at different phases of the cardiac cycle, allowing assessment of cardiac motion, function, and perfusion. Between CT and Cardiac MRI (CMR), CMR has the best temporal resolution, which is suitable for functional imaging while cardiac CT provides higher spatial resolution with isotropic data that have an identical resolution in the three dimensions of the space. The latest generations of CT scanners enable whole heart assessment in one beat, offering also an acceptable temporal resolution with the possibility to display the images in a dynamic mode. Another rapidly growing technique using functional and molecular imaging for the assessment of biological and metabolic pathways is the PET using radio-labeled tracers. Meanwhile, the oldest cardiac imaging tool with doppler ultrasound technology has never stopped evolving. Echocardiography today performs 3D imaging, stress perfusion, and myocardial strain assessment, with high temporal resolution. It still is the first line and more accessible exam for the patient. These different modalities are complementary and may be even combined into PET-CT or PET-MRI. The ability to combine the functional/molecular data with anatomical images may implement a new dimension to our diagnostic tools.
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Affiliation(s)
- Arshid Azarine
- Radiology Department, Groupe Hospitalier Paris Saint-Joseph, 185, Rue Raymond Losserand, 75014, Paris, France.
| | - François Scalbert
- Nuclear Medecine Department, Hôpital Bichat-Claude Bernard, 46 rue Henri Huchard, 75877, Paris, France
| | - Philippe Garçon
- Cardiology Department, Groupe Hospitalier Paris Saint-Joseph, 185, Rue Raymond Losserand, 75014, Paris, France
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17
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Artificial Intelligence Advances in the World of Cardiovascular Imaging. Healthcare (Basel) 2022; 10:healthcare10010154. [PMID: 35052317 PMCID: PMC8776229 DOI: 10.3390/healthcare10010154] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/11/2022] [Accepted: 01/11/2022] [Indexed: 02/04/2023] Open
Abstract
The tremendous advances in digital information and communication technology have entered everything from our daily lives to the most intricate aspects of medical and surgical care. These advances are seen in electronic and mobile health and allow many new applications to further improve and make the diagnoses of patient diseases and conditions more precise. In the area of digital radiology with respect to diagnostics, the use of advanced imaging tools and techniques is now at the center of evaluation and treatment. Digital acquisition and analysis are central to diagnostic capabilities, especially in the field of cardiovascular imaging. Furthermore, the introduction of artificial intelligence (AI) into the world of digital cardiovascular imaging greatly broadens the capabilities of the field both with respect to advancement as well as with respect to complete and accurate diagnosis of cardiovascular conditions. The application of AI in recognition, diagnostics, protocol automation, and quality control for the analysis of cardiovascular imaging modalities such as echocardiography, nuclear cardiac imaging, cardiovascular computed tomography, cardiovascular magnetic resonance imaging, and other imaging, is a major advance that is improving rapidly and continuously. We document the innovations in the field of cardiovascular imaging that have been brought about by the acceptance and implementation of AI in relation to healthcare professionals and patients in the cardiovascular field.
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18
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Seetharam K, Shrestha S, Sengupta PP. Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence. Interv Cardiol 2021; 16:e31. [PMID: 34754333 PMCID: PMC8559149 DOI: 10.15420/icr.2020.04] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 06/18/2021] [Indexed: 12/13/2022] Open
Abstract
Artificial Intelligence (AI) is the simulation of human intelligence in machines so they can perform various actions and execute decision-making. Machine learning (ML), a branch of AI, can analyse information from data and discover novel patterns. AI and ML are rapidly gaining prominence in healthcare as data become increasingly complex. These algorithms can enhance the role of cardiovascular imaging by automating many tasks or calculations, find new patterns or phenotypes in data and provide alternative diagnoses. In interventional cardiology, AI can assist in intraprocedural guidance, intravascular imaging and provide additional information to the operator. AI is slowly expanding its boundaries into interventional cardiology and can fundamentally alter the field. In this review, the authors discuss how AI can enhance the role of cardiovascular imaging and imaging in interventional cardiology.
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Affiliation(s)
- Karthik Seetharam
- West Virginia University Medicine Heart and Vascular Institute Morgantown, WV, US
| | - Sirish Shrestha
- West Virginia University Medicine Heart and Vascular Institute Morgantown, WV, US
| | - Partho P Sengupta
- West Virginia University Medicine Heart and Vascular Institute Morgantown, WV, US
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19
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Seetharam K, Bhat P, Orris M, Prabhu H, Shah J, Asti D, Chawla P, Mir T. Artificial intelligence and machine learning in cardiovascular computed tomography. World J Cardiol 2021; 13:546-555. [PMID: 34754399 PMCID: PMC8554359 DOI: 10.4330/wjc.v13.i10.546] [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: 05/08/2021] [Revised: 07/10/2021] [Accepted: 08/13/2021] [Indexed: 02/06/2023] Open
Abstract
Computed tomography (CT) is emerging as a prominent diagnostic modality in the field of cardiovascular imaging. Artificial intelligence (AI) is making significant strides in the field of information technology, the commercial industry, and health care. Machine learning (ML), a branch of AI, can optimize the performance of CT and augment the assessment of coronary artery disease. These ML platforms can automate multiple tasks, perform calculations, and integrate information from a variety of data sources. In this review article, we explore the ML in CT imaging.
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Affiliation(s)
- Karthik Seetharam
- Department of Cardiology, West Virgina University, Morgan Town, NY 26501, United States
| | - Premila Bhat
- Department of Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Maxine Orris
- Department of Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Hejmadi Prabhu
- Department of Cardiology, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Jilan Shah
- Department of Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Deepak Asti
- Department of Cardiology, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Preety Chawla
- Department of Cardiology, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
| | - Tanveer Mir
- Department of Internal Medicine, Wyckoff Heights Medical Center, Brooklyn, NY 11237, United States
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20
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Abstract
Endotyping is an emerging concept in which diseases are classified into distinct subtypes based on underlying molecular mechanisms. Heart failure (HF) is a complex clinical syndrome that encompasses multiple endotypes with differential risks of adverse events, and varying responses to treatment. Identifying these distinct endotypes requires molecular-level investigation involving multi-"omics" approaches, including genomics, transcriptomics, proteomics, and metabolomics. The derivation of these HF endotypes has important implications in promoting individualized treatment and facilitating more targeted selection of patients for clinical trials, as well as in potentially revealing new pathways of disease that may serve as therapeutic targets. One challenge in the integrated analysis of high-throughput omics and detailed clinical data is that it requires the ability to handle "big data", a task for which machine learning is well suited. In particular, unsupervised machine learning has the ability to uncover novel endotypes of disease in an unbiased approach. In this review, we will discuss recent efforts to identify HF endotypes and cover approaches involving proteomics, transcriptomics, and genomics, with a focus on machine-learning methods.
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Affiliation(s)
- Lusha W Liang
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center
| | - Yuichi J Shimada
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center
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21
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Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors. BMC Med Inform Decis Mak 2021; 21:187. [PMID: 34116660 PMCID: PMC8196502 DOI: 10.1186/s12911-021-01535-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 05/23/2021] [Indexed: 11/20/2022] Open
Abstract
Background Extensive clinical evidence suggests that a preventive screening of coronary heart disease (CHD) at an earlier stage can greatly reduce the mortality rate. We use 64 two-dimensional speckle tracking echocardiography (2D-STE) features and seven clinical features to predict whether one has CHD. Methods We develop a machine learning approach that integrates a number of popular classification methods together by model stacking, and generalize the traditional stacking method to a two-step stacking method to improve the diagnostic performance. Results By borrowing strengths from multiple classification models through the proposed method, we improve the CHD classification accuracy from around 70–87.7% on the testing set. The sensitivity of the proposed method is 0.903 and the specificity is 0.843, with an AUC of 0.904, which is significantly higher than those of the individual classification models. Conclusion Our work lays a foundation for the deployment of speckle tracking echocardiography-based screening tools for coronary heart disease. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-021-01535-5.
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22
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Schuuring MJ, Išgum I, Cosyns B, Chamuleau SAJ, Bouma BJ. Routine Echocardiography and Artificial Intelligence Solutions. Front Cardiovasc Med 2021; 8:648877. [PMID: 33708808 PMCID: PMC7940184 DOI: 10.3389/fcvm.2021.648877] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 02/05/2021] [Indexed: 12/28/2022] Open
Abstract
Introduction: Echocardiography is widely used because of its portability, high temporal resolution, absence of radiation, and due to the low-costs. Over the past years, echocardiography has been recommended by the European Society of Cardiology in most cardiac diseases for both diagnostic and prognostic purposes. These recommendations have led to an increase in number of performed studies each requiring diligent processing and reviewing. The standard work pattern of image analysis including quantification and reporting has become highly resource intensive and time consuming. Existence of a large number of datasets with digital echocardiography images and recent advent of AI technology have created an environment in which artificial intelligence (AI) solutions can be developed successfully to automate current manual workflow. Methods and Results: We report on published AI solutions for echocardiography analysis on methods' performance, characteristics of the used data and imaged population. Contemporary AI applications are available for automation and advent in the image acquisition, analysis, reporting and education. AI solutions have been developed for both diagnostic and predictive tasks in echocardiography. Left ventricular function assessment and quantification have been most often performed. Performance of automated image view classification, image quality enhancement, cardiac function assessment, disease classification, and cardiac event prediction was overall good but most studies lack external evaluation. Conclusion: Contemporary AI solutions for image acquisition, analysis, reporting and education are developed for relevant tasks with promising performance. In the future major benefit of AI in echocardiography is expected from improvements in automated analysis and interpretation to reduce workload and improve clinical outcome. Some of the challenges have yet to be overcome, however, none of them are insurmountable.
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Affiliation(s)
- Mark J. Schuuring
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, Netherlands
| | - Ivana Išgum
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Biomedical Engineering and Physics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Radiology and Nuclear Medicine, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers -Location Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Bernard Cosyns
- Department of Cardiology, University Hospital Brussel, Brussels, Belgium
| | - Steven A. J. Chamuleau
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers -Location Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Berto J. Bouma
- Amsterdam University Medical Centers -Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, Netherlands
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23
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Seetharam K, Min JK. Artificial Intelligence and Machine Learning in Cardiovascular Imaging. Methodist Debakey Cardiovasc J 2021; 16:263-271. [PMID: 33500754 DOI: 10.14797/mdcj-16-4-263] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Cardiovascular disease is the leading cause of mortality in Western countries and leads to a spectrum of complications that can complicate patient management. The emergence of artificial intelligence (AI) has garnered significant interest in many industries, and the field of cardiovascular imaging is no exception. Machine learning (ML) especially is showing significant promise in various diagnostic imaging modalities. As conventional statistics are reaching their apex in computational capabilities, ML can explore new possibilities and unravel hidden relationships. This will have a positive impact on diagnosis and prognosis for cardiovascular imaging. In this in-depth review, we highlight the role of AI and ML for various cardiovascular imaging modalities.
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24
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Angelaki E, Marketou ME, Barmparis GD, Patrianakos A, Vardas PE, Parthenakis F, Tsironis GP. Detection of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG-based approach. J Clin Hypertens (Greenwich) 2021; 23:935-945. [PMID: 33507615 PMCID: PMC8678829 DOI: 10.1111/jch.14200] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/07/2021] [Accepted: 01/10/2021] [Indexed: 01/19/2023]
Abstract
Cardiac remodeling is recognized as an important aspect of cardiovascular disease (CVD) progression. Machine learning (ML) techniques were applied to basic clinical parameters and electrocardiographic features, in order to detect abnormal left ventricular geometry (LVG) even before the onset of left ventricular hypertrophy (LVH), in a population without established CVD. The authors enrolled 528 patients with and without essential hypertension, but no other indications of CVD. All patients underwent a full echocardiographic evaluation and were classified into 3 groups; normal geometry (NG), concentric remodeling without LVH (CR), and LVH. Abnormal LVG was identified as increased relative wall thickness (RWT) and/or left ventricular mass index (LVMi). The authors trained supervised ML models to classify patients with abnormal LVG and calculated SHAP values to perform feature importance and interaction analysis. Hypertension, age, body mass index over the Sokolow‐Lyon voltage, QRS‐T angle, and QTc duration were some of the most important features. Our model was able to distinguish NG from CR+LVH combined, with 87% accuracy on an unseen test set, 75% specificity, 97% sensitivity, and area under the receiver operating curve (AUC/ROC) equal to 0.91. The authors also trained our model to classify NG and CR (NG + CR) against those with LVH, with 89% test set accuracy, 93% specificity, 67% sensitivity, and an AUC/ROC value of 0.89, for a 0.4 decision threshold. Our ML algorithm effectively detects abnormal LVG even at early stages. Innovative solutions are needed to improve risk stratification of patients without established CVD, and ML may enable progress in this direction.
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Affiliation(s)
- Eleni Angelaki
- Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, Heraklion, Greece.,Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Maria E Marketou
- Department of Cardiology, Heraklion University Hospital, Heraklion, Greece
| | - Georgios D Barmparis
- Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, Heraklion, Greece
| | | | - Panos E Vardas
- Department of Cardiology, Heraklion University Hospital, Heraklion, Greece.,Heart Sector, Hygeia Hospitals Group, Athens, Greece
| | | | - Giorgos P Tsironis
- Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, Heraklion, Greece.,Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
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Donal E, Muraru D, Badano L. Artificial intelligence and the promise of uplifting echocardiography. Heart 2021; 107:heartjnl-2020-318718. [PMID: 33436492 DOI: 10.1136/heartjnl-2020-318718] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Affiliation(s)
- Erwan Donal
- Cardiology, CHU Rennes, Rennes, Bretagne, France
| | - Denisa Muraru
- Department of Cardiac, Thoracic and Vascular Sciences, University of Milan-Bicocca, Milano, Lombardia, Italy
| | - Luigi Badano
- School of Medicine and Surgery, University of Milan-Bicocca, Milano, Italy
- Department of Cardiological, Neurological and Metabolic Sciences, Istituto Auxologico Italiano Istituto di Ricovero e Cura a Carattere Scientifico, Milano, Italy
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Seetharam K, Brito D, Farjo PD, Sengupta PP. The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review. Front Cardiovasc Med 2020; 7:618849. [PMID: 33426010 PMCID: PMC7786371 DOI: 10.3389/fcvm.2020.618849] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 12/08/2020] [Indexed: 12/15/2022] Open
Abstract
In this current digital landscape, artificial intelligence (AI) has established itself as a powerful tool in the commercial industry and is an evolving technology in healthcare. Cutting-edge imaging modalities outputting multi-dimensional data are becoming increasingly complex. In this era of data explosion, the field of cardiovascular imaging is undergoing a paradigm shift toward machine learning (ML) driven platforms. These diverse algorithms can seamlessly analyze information and automate a range of tasks. In this review article, we explore the role of ML in the field of cardiovascular imaging.
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Affiliation(s)
- Karthik Seetharam
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
| | - Daniel Brito
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
| | - Peter D Farjo
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
| | - Partho P Sengupta
- Department of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, United States
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