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Yanagi Y, Amano M, Tamai Y, Mizumoto A, Nakagawa S, Moriuchi K, Asano R, Kurashima S, Irie Y, Sakamoto T, Amaki M, Kanzaki H, Ohta Y, Morita Y, Ogo T, Kitai T, Izumi C. Accuracy of Shunt Volume Measured by Three-Dimensional Echocardiography and Cardiac Magnetic Resonance in Patients With an Atrial Septal Defect and a Dilated Right Ventricle. J Am Soc Echocardiogr 2024; 37:797-805. [PMID: 38754748 DOI: 10.1016/j.echo.2024.04.016] [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: 03/07/2024] [Revised: 04/27/2024] [Accepted: 04/30/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND The accuracy of right ventricular (RV) quantification by three-dimensional echocardiography (3DE) has been reported mainly in patients with a normal right ventricle (RV). However, there are no data regarding the accuracy of 3DE in patients with a dilated RV, as in shunt diseases. In this study, we evaluated the accuracy of 3DE and that of volumetric (Vol) cardiac magnetic resonance (CMR) for assessment of RV and left ventricular (LV) stroke volume (SV) and the pulmonary (Qp)/systemic (Qs) blood flow ratio in patients with an atrial septal defect (ASD) using the two-dimensional phase contrast (2DPC) method as the gold standard. METHODS We retrospectively investigated 83 patients with ASD who underwent transcatheter closure and clinically indicated CMR and 3DE examinations. The ratio Qp/Qs was calculated using RV and LV SV measured by full-volume volumetric 3DE (Vol-3DE) and CMR (Vol-CMR) and by two-dimensional pulsed Doppler quantification (2D-Dop); the parameters were compared using 2DPC-CMR as the gold standard. RESULTS There was no significant difference in the Qp/Qs value between 2DPC-CMR and Vol-3DE (2.29 ± 0.70 vs 2.21 ± 0.63, P = .79) and 2D-Dop (vs 2.21 ± 0.65, P = 1.00); however, a significant difference was found between 2DPC-CMR and Vol-CMR (P < .001). The Qp/Qs value obtained using Vol-3DE showed the best correlation with 2DPC-CMR (r = 0.93, P < .001). The RV and LV SV values obtained by Vol-3DE showed the best correlation with 2DPC-CMR (RV SV, r = 0.82, P < .001; LV SV, r = 0.73, P < .001), although the absolute values were underestimated. CONCLUSION Qp/Qs was more accurately evaluated by Vol-3DE than by Vol-CMR or 2D-Dop. Three-dimensional echocardiography assessment was feasible and reproducible even in a dilated RV.
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Affiliation(s)
- Yoshiki Yanagi
- Department of Heart Failure and Transplantation, National Cerebral and Cardiovascular Center, Suita, Japan; Department of Clinical Laboratory, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Masashi Amano
- Department of Heart Failure and Transplantation, National Cerebral and Cardiovascular Center, Suita, Japan.
| | - Yurie Tamai
- Department of Clinical Laboratory, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Ayaka Mizumoto
- Department of Clinical Laboratory, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Shoko Nakagawa
- Department of Heart Failure and Transplantation, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Kenji Moriuchi
- Department of Heart Failure and Transplantation, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Ryotaro Asano
- Division of Pulmonary Circulation, Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Shinichi Kurashima
- Department of Heart Failure and Transplantation, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Yuki Irie
- Department of Heart Failure and Transplantation, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Takahiro Sakamoto
- Department of Heart Failure and Transplantation, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Makoto Amaki
- Department of Heart Failure and Transplantation, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Hideaki Kanzaki
- Department of Heart Failure and Transplantation, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Yasutoshi Ohta
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Yoshiaki Morita
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Takeshi Ogo
- Division of Pulmonary Circulation, Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Takeshi Kitai
- Department of Heart Failure and Transplantation, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Chisato Izumi
- Department of Heart Failure and Transplantation, National Cerebral and Cardiovascular Center, Suita, Japan
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Freed BH, Mukherjee M. Echoing Disagreements: Navigating the Divide Between 2D and 3D Right Ventricular Assessment. J Am Soc Echocardiogr 2024; 37:687-689. [PMID: 38754747 DOI: 10.1016/j.echo.2024.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 05/08/2024] [Indexed: 05/18/2024]
Affiliation(s)
- Benjamin H Freed
- Northwestern University Feinberg School of Medicine, Division of Cardiology, Chicago, Illinois.
| | - Monica Mukherjee
- Johns Hopkins University School of Medicine, Baltimore, Maryland
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Qin Y, Qin X, Zhang J, Guo X. Artificial intelligence: The future for multimodality imaging of right ventricle. Int J Cardiol 2024; 404:131970. [PMID: 38490268 DOI: 10.1016/j.ijcard.2024.131970] [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: 11/26/2023] [Revised: 03/05/2024] [Accepted: 03/12/2024] [Indexed: 03/17/2024]
Abstract
The crucial pathophysiological and prognostic roles of the right ventricle in various diseases have been well-established. Nonetheless, conventional cardiovascular imaging modalities are frequently associated with intrinsic limitations when evaluating right ventricular (RV) morphology and function. The integration of artificial intelligence (AI) in multimodality imaging presents a promising avenue to circumvent these obstacles, paving the way for future fully automated imaging paradigms. This review aimed to address the current challenges faced by clinicians and researchers in integrating RV imaging and AI technology, to provide a comprehensive overview of the current applications of AI in RV imaging, and to offer insights into future directions, opportunities, and potential challenges in this rapidly advancing field.
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Affiliation(s)
- Yuhan Qin
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xiaohan Qin
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Jing Zhang
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xiaoxiao Guo
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
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Adams LC, Bressem KK, Ziegeler K, Vahldiek JL, Poddubnyy D. Artificial intelligence to analyze magnetic resonance imaging in rheumatology. Joint Bone Spine 2024; 91:105651. [PMID: 37797827 DOI: 10.1016/j.jbspin.2023.105651] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 08/29/2023] [Accepted: 09/26/2023] [Indexed: 10/07/2023]
Abstract
Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance imaging (MRI) has become indispensable in rheumatology, but interpretation remains laborious and variable. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers a means to improve and advance MRI analysis. This review examines current AI applications in rheumatology MRI analysis, addressing diagnostic support, disease classification, activity assessment, and progression monitoring. AI demonstrates promise, with high sensitivity, specificity, and accuracy, achieving or surpassing expert performance. The review also discusses clinical implementation challenges and future research directions to enhance rheumatic disease diagnosis and management.
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Affiliation(s)
- Lisa C Adams
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Keno K Bressem
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Katharina Ziegeler
- Department of Hematology, Oncology , and Cancer Immunology, Campus Charité Mitte, Charité Universitätsmedizin Berlin, Germany; Evidia Radiologie am Rheumazentrum Ruhrgebiet, Germany
| | - Janis L Vahldiek
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Denis Poddubnyy
- Department of Gastroenterology, Infectious Diseases and Rheumatology (including Nutrition Medicine), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
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Corbett L, O'Driscoll P, Paton M, Oxborough D, Surkova E. Role and application of three-dimensional transthoracic echocardiography in the assessment of left and right ventricular volumes and ejection fraction: a UK nationwide survey. Echo Res Pract 2024; 11:8. [PMID: 38566154 PMCID: PMC10988951 DOI: 10.1186/s44156-024-00044-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 03/06/2024] [Indexed: 04/04/2024] Open
Abstract
Three-dimensional echocardiography (3DE) imaging has permitted advancements in the quantification of left ventricular (LV) and right ventricular (RV) volumes and ejection fraction. We evaluated the availability of 3DE equipment / analysis software, the integration of 3DE assessment of the LV and RV in routine clinical practice, current training provisions in 3DE, and aimed to ascertain barriers preventing the routine use of 3DE for volumetric analysis. Through the British Society of Echocardiography (BSE) regional representatives' network, echocardiographers were invited to participate in an open online survey. A total of 181 participants from echocardiography departments in the United Kingdom (UK), the majority from tertiary centres (61%), completed the 28-question survey. For 3DE quantification, 3DE-LV was adopted more frequently than 3DE-RV (48% vs 11%, respectively). Imaging feasibility was a recognised factor in 3DE RV and LV adoption. Many respondents had access to 3D probes (93%). The largest observed barriers to 3DE routine use were training deficiencies, with 83% reporting they would benefit from additional training opportunities and the duration of time permitted for the scan, with 68% of responders reporting allowances of less than the BSE standard of 45-60 min per patient (8% < 30-min). Furthermore, of those respondents who had undertaken professional accreditation, competence in 3DE was not formally assessed in 89%. This UK survey also reported good accessibility to magnetic resonance imaging (72%), which was related to overall 3DE adoption. In summary, although 3DE is now readily available, it remains underutilised. Further training opportunities, integrated formal assessment, improved adoption of BSE minimum recommended scanning times, alongside industry and societal support, may increase 3DE utilisation in routine practice.
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Affiliation(s)
- Liam Corbett
- Liverpool Heart and Chest Hospital NHS Foundation Trust, Liverpool, UK.
| | | | | | - David Oxborough
- Research Institute of Sports and Exercise Science and Liverpool Centre for Cardiovascular Science, Liverpool, UK
| | - Elena Surkova
- Royal Brompton and Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
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Randazzo M, Maffessanti F, Kotta A, Grapsa J, Lang RM, Addetia K. Added value of 3D echocardiography in the diagnosis and prognostication of patients with right ventricular dysfunction. Front Cardiovasc Med 2023; 10:1263864. [PMID: 38179507 PMCID: PMC10764503 DOI: 10.3389/fcvm.2023.1263864] [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: 07/20/2023] [Accepted: 11/22/2023] [Indexed: 01/06/2024] Open
Abstract
Recent inroads into percutaneous-based options for the treatment of tricuspid valve disease has brought to light how little we know about the behavior of the right ventricle in both health and disease and how incomplete our assessment of right ventricular (RV) physiology and function is using current non-invasive technology, in particular echocardiography. The purpose of this review is to provide an overview of what three-dimensional echocardiography (3DE) can offer currently to enhance RV evaluation and what the future may hold if we continue to improve the 3D evaluation of the right heart.
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Affiliation(s)
- Michael Randazzo
- Department of Medicine, Section of Cardiology, University of Chicago Heart and Vascular Center, Chicago, IL, United States
| | | | - Alekhya Kotta
- Department of Internal Medicine, Baylor College of Medicine, Houston, TX, United States
| | - Julia Grapsa
- Department of Cardiology, Guys and St Thomas NHS Trust, London, United Kingdom
| | - Roberto M. Lang
- Department of Medicine, Section of Cardiology, University of Chicago Heart and Vascular Center, Chicago, IL, United States
| | - Karima Addetia
- Department of Medicine, Section of Cardiology, University of Chicago Heart and Vascular Center, Chicago, IL, United States
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Colak A, Erdemir AG, Hazirolan T, Pirat B, Eroglu S, Aydinalp A, Muderrisoglu H, Sade LE. Multiparametric assessment of right ventricular function in heart transplant recipients by echocardiography and relations with pulmonary hemodynamics. Echocardiography 2023; 40:1350-1355. [PMID: 37955614 DOI: 10.1111/echo.15713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/19/2023] [Accepted: 10/29/2023] [Indexed: 11/14/2023] Open
Abstract
OBJECTIVE Right ventricular (RV) dilatation and dysfunction are usually present in heart transplant (HTx) patients and worsened with residual pulmonary hypertension (PH). We aimed to determine the ability of different echocardiographic modalities to evaluate RV function in comparison with cardiac magnetic resonance (CMR) and their relations with pulmonary hemodynamics in HTx patients. METHODS A total of 62 data sets [echocardiographic, hemodynamic, and CMR] were acquired from 35 HTx patients. Comprehensive echocardiography, including two-dimensional (2D) transthoracic echocardiography, speckle tracking echocardiography, and three-dimensional (3D) echocardiography, was performed. Mean pulmonary artery pressure (mPAP) was obtained invasively from right heart catheterization. The correlations between all echocardiographic parameters and CMR imaging data and the differences between patients with and without residual PH were evaluated. RESULTS Diastolic and systolic RV volumes and RV ejection fraction (RVEF) by 3D echocardiography correlated strongly with CMR-derived volumes and RVEF (r = .91, r = .79, r = .64; p < .0001 for each, respectively). Among other parameters, RV fractional area change (r = .439; p < .001) and RV free wall longitudinal strain (RVFW-LS) (r = -.34; p < .05) correlated moderately with CMR-RVEF, whereas tricuspid annulus S' velocity (r = .29; p < .05) and tricuspid annular systolic plane excursion (r = .27; p < .05) correlated weakly with CMR-RVEF. Additionally, 3D-RVEF and RVFW-LS were significantly decreased in studies with mPAP ≥ 20 mm Hg in comparison to those with mPAP < 20 mm Hg (47.7 ± 3.7 vs. 50.9 ± 5.3, p = .04 and -15.5 ± 3.1 vs. -17.5 ± 3, p = .03, respectively). CONCLUSION The best method for the evaluation of RV function in HTx recipients is 3D echocardiography. Besides, the subclinical impact of residual PH on RV function can be best determined by RVFW-LS and 3D-RVEF in these patients.
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Affiliation(s)
- Ayse Colak
- Department of Cardiology, Dokuz Eylul University Faculty of Medicine, İzmir, Turkey
- Department of Cardiology, Baskent University Faculty of Medicine, Ankara, Turkey
| | - Ahmet Gurkan Erdemir
- Department of Radiology, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - Tuncay Hazirolan
- Department of Radiology, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - Bahar Pirat
- Department of Cardiology, Baskent University Faculty of Medicine, Ankara, Turkey
| | - Serpil Eroglu
- Department of Cardiology, Baskent University Faculty of Medicine, Ankara, Turkey
| | - Alp Aydinalp
- Department of Cardiology, Baskent University Faculty of Medicine, Ankara, Turkey
| | - Haldun Muderrisoglu
- Department of Cardiology, Baskent University Faculty of Medicine, Ankara, Turkey
| | - Leyla Elif Sade
- UPMC-Heart and Vascular Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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Bai W, Chen Y, Zhong Y, Deng L, Li D, Zhu W, Rao L. Assessment of mitral valve geometry in nonvalvular atrial fibrillation patients with or without ventricular dysfunction: insights from high volume rate three-dimensional transesophageal echocardiography. Int J Cardiovasc Imaging 2023; 39:2427-2436. [PMID: 37665486 PMCID: PMC10691988 DOI: 10.1007/s10554-023-02940-9] [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: 04/20/2023] [Accepted: 08/18/2023] [Indexed: 09/05/2023]
Abstract
Meticulous understanding of the mechanisms underpinning mitral regurgitation in atrial fibrillation (AF) patients is crucial to optimize therapeutic strategies. The morphologic characteristics of mitral valves in atrial functional mitral regurgitation (FMR) patients with and without left ventricular (LV) dysfunction were evaluated by high volume rate (HVR) three-dimensional transesophageal echocardiography (3D-TEE). In our study, 68 of 265 AF patients who underwent 3D-TEE were selected, including 36 patients with AF, FMR, and preserved LV function (AFMR group) and 32 patients with AF, FMR, and LV dysfunction (VFMR group). In addition, 36 fever patients without heart disease were included in the control group. Group comparisons were performed by one-way analysis of variance for continuous variables. The left atrium (LA) was enlarged in the AFMR and VFMR groups compared with the control group. The mitral annulus (MA) in the AFMR group was enlarged and flattened compared with the control group and was smaller than in the VFMR group. The annulus area fraction was significantly diminished in the AFMR and VFMR groups, indicative of reduced MA contractility. The posterior mitral leaflet (PML) angle was smallest in the AFMR group and largest in the control group, whereas the distal anterior mitral leaflet angle did not significantly differ among the three groups. LA remodeling causes expansion of the MA and reduced MA contractility, disruption of the annular saddle shape, and atriogenic PML tethering. Comparison of atrial FMR patients with and without LV dysfunction indicates that atriogenic PML tethering is an important factor that aggravates FMR. HVR 3D-TEE improves the 3D temporal resolution greatly.
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Affiliation(s)
- Wenjuan Bai
- Department of Cardiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan, 610041, China
| | - Ying Chen
- Department of Cardiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan, 610041, China
| | - Yue Zhong
- Department of Cardiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan, 610041, China
| | - Ling Deng
- Department of Cardiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan, 610041, China
| | - Dayan Li
- Department of Cardiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan, 610041, China
| | - Wei Zhu
- Department of Cardiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan, 610041, China
| | - Li Rao
- Department of Cardiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan, 610041, China.
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Kundu P, Schäfer M, Le L, Thomas T, Jone PN, Hunter KS. Three-Dimensional, Right Ventricular Surface Strain Computation From Three-Dimensional Echocardiographic Images From Patients With Pediatric Pulmonary Hypertension. J Biomech Eng 2023; 145:111011. [PMID: 37542708 DOI: 10.1115/1.4063121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 07/17/2023] [Indexed: 08/07/2023]
Abstract
Right Ventricular (RV) dysfunction is routinely assessed with echocardiographic-derived global longitudinal strain (GLS). GLS is measured from a two-dimensional echo image and is increasingly accepted as a means for assessing RV function. However, any two-dimensional (2D) analysis cannot visualize the asymmetrical deformation of the RV nor visualize strain over the entire RV surface. We believe three-dimensional surface (3DS) strain, obtained from 3D echo will better evaluate myocardial mechanics. Components of 3DS strain (longitudinal, LS; circumferential, CS; longitudinal-circumferential shear, ɣCL; principal strains PSMax and PSMin; max shear, ɣMax; and principal angle θMax) were computed from RV surface meshes obtained with 3D echo from 50 children with associated pulmonary arterial hypertension (PAH), 43 children with idiopathic PAH, and 50 healthy children by computing strains from a discretized displacement field. All 3DS freewall (FW) normal strain (LS, CS, PSMax, and PSMin) showed significant decline at end-systole in PH groups (p < 0.0001 for all), as did FW-ɣMax (p = 0.0012). FW-θMax also changed in disease (p < 0.0001). Limits of agreement analysis suggest that 3DS LS, PSMax, and PSMin are related to GLS. 3DS strains showed significant heterogeneity over the 3D surface of the RV. Components of 3DS strain agree with existing clinical strain measures, well classify normal -versus- PAH subjects, and suggest that strains change direction on the myocardial surface due to disease. This last finding is similar to that of myocardial fiber realignment in disease, but further work is needed to establish true associations.
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Affiliation(s)
- Priyamvada Kundu
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, 12705 E. Montview Ave., Suite 100, Aurora, CO 80045-7109
| | - Michal Schäfer
- Heart Institute, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045
| | - Lisa Le
- Heart Institute, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045
| | - Thomas Thomas
- Heart Institute, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045
| | - Pei-Ni Jone
- Ann & Robert H. Lurie Children's Hospital of Chicago, 225 East Chicago Avenue, Chicago, IL 60611-2605
| | - Kendall S Hunter
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, 12705 E. Montview Ave., Suite 100, Aurora, CO 80045-7109
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Bradley AJ, Ghawanmeh M, Govi AM, Covas P, Panjrath G, Choi AD. Emerging Roles for Artificial Intelligence in Heart Failure Imaging. Heart Fail Clin 2023; 19:531-543. [PMID: 37714592 DOI: 10.1016/j.hfc.2023.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/17/2023]
Abstract
Artificial intelligence (AI) applications are expanding in cardiac imaging. AI research has shown promise in workflow optimization, disease diagnosis, and integration of clinical and imaging data to predict patient outcomes. The diagnostic and prognostic paradigm of heart failure is heavily reliant on cardiac imaging. As AI becomes increasingly validated and integrated into clinical practice, AI influence on heart failure management will grow. This review discusses areas of current research and potential clinical applications in AI as applied to heart failure cardiac imaging.
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Affiliation(s)
- Andrew J Bradley
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
| | - Malik Ghawanmeh
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Ashley M Govi
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Pedro Covas
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Gurusher Panjrath
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA. https://twitter.com/PanjrathG
| | - Andrew D Choi
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA. https://twitter.com/AChoiHeart
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Defoe M, Lam W, Becher H, Lydell C, Hong Y, Sidhu S. Right ventricular ejection fraction derived from intraoperative three-dimensional transesophageal echocardiography versus cardiac magnetic resonance imaging. Can J Anaesth 2023; 70:1576-1586. [PMID: 37752378 DOI: 10.1007/s12630-023-02569-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 04/14/2023] [Accepted: 05/09/2023] [Indexed: 09/28/2023] Open
Abstract
PURPOSE Right ventricle (RV) assessment is critical during cardiac surgery. Traditional assessment consists of visual estimation and measurement of validated parameters. Cardiac magnetic resonance imaging (cMRI) is the gold standard for RV analysis, and transthoracic three-dimensional (3D) echocardiography is validated against this. We aimed to show that intraoperative 3D transesophageal echocardiography (TEE) RV assessment is feasible and can produce results that correlate with cMRI. METHODS We recruited cardiac surgery patients who underwent cMRI within the preceding twelve preoperative months. An anesthetic protocol was followed pre-sternotomy and a 3D RV data set was acquired. We used TOMTEC 4D RV-Function to derive RV end-diastolic volume (EDV), end-systolic volume (ESV), and ejection fraction (EF). We compared these data with the corresponding MRI values. RESULTS Twenty-five patients were included. Transesophageal echocardiography EDV and ESV differed from MRI measurements with a mean bias of -53 mL (95% confidence interval [CI], -80 to 26) and -21 mL (95% CI, -34 to -9). Transesophageal echocardiography EF did not differ significantly, with a mean bias of -4% (95% CI, -8 to 1). Results were unchanged after excluding MRIs older than 180 days. Correlation coefficients for EDV, ESV, and EF were r = 0.85, 0.91, and 0.80, respectively. Interclass correlation coefficients for EDV, ESV, and EF were 0.86, 0.89, and 0.96, respectively. CONCLUSIONS Intraoperative TEE RV, EDV, and ESV are underestimated relative to cMRI because of analysis, anesthetic, and ventilation factors. The EF showed a low mean difference, and all values showed strong correlation with MRI. Reproducibility and feasibility were excellent and increased use in clinical practice should be considered.
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Affiliation(s)
- Marc Defoe
- Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, AB, Canada
| | - Wing Lam
- Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, AB, Canada
| | - Harald Becher
- Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, AB, Canada
| | - Carmen Lydell
- Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Yongzhe Hong
- Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, AB, Canada
- Division of Cardiac Surgery, Department of Surgery, University of Alberta, Edmonton, AB, Canada
| | - Surita Sidhu
- Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, AB, Canada.
- Department of Anesthesiology and Pain Medicine, University of Alberta, 2-150 Clinical Sciences Building, 11350 83rd Avenue, Edmonton, AB, T6G 2G3, Canada.
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Hsia BC, Lai A, Singh S, Samtani R, Bienstock S, Liao S, Stern E, LaRocca G, Sanz J, Lerakis S, Croft L, Carrasso S, Rosenmann D, DeMaria A, Stone GW, Goldman ME. Validation of American Society of Echocardiography Guideline-Recommended Parameters of Right Ventricular Dysfunction Using Artificial Intelligence Compared With Cardiac Magnetic Resonance Imaging. J Am Soc Echocardiogr 2023; 36:967-977. [PMID: 37331608 DOI: 10.1016/j.echo.2023.05.015] [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: 03/16/2023] [Revised: 05/25/2023] [Accepted: 05/28/2023] [Indexed: 06/20/2023]
Abstract
BACKGROUND Right ventricular (RV) function is important in the evaluation of cardiac function, but its assessment using standard transthoracic echocardiography (TTE) remains challenging. Cardiac magnetic resonance imaging (CMR) is considered the gold standard. The American Society of Echocardiography recommends surrogate measures of RV function and RV ejection fraction (RVEF) by TTE, including fractional area change (FAC), free wall strain (FWS), and tricuspid annular planar systolic excursion (TAPSE), but they require technical expertise in acquisition and quantification. METHODS The aim of this study was to evaluate the sensitivity, specificity, and positive and negative predictive values of FAC, FWS, and TAPSE derived using a rapid, novel artificial intelligence (AI) software (LVivoRV) from a single-plane transthoracic echocardiographic apical four-chamber, RV-focused view without ultrasound-enhancing agents for detecting abnormal RV function compared with CMR-derived RVEF. RV dysfunction was defined as RVEF < 50% and RVEF < 40% on CMR. RESULTS TTE and CMR were performed within a median of 10 days (interquartile range, 2-32 days) of each other in 225 consecutive patients without interval procedural or pharmacologic intervention. The sensitivity and negative predictive value to detect CMR-defined RV dysfunction when all three AI-derived parameters (FAC, FWS, and TAPSE) were abnormal were 91% and 96%, while those of expert physician reads were 91% and 97%. Specificity and positive predictive value were lower (50% and 32%) compared with expert physician-read echocardiograms (82% and 56%). CONCLUSIONS AI-derived measurements of FAC, FWS, and TAPSE had excellent sensitivity and negative predictive value for ruling out significant RV dysfunction (CMR RVEF < 40%), comparable with that of expert physician readers, but lower specificity. Thus AI, using American Society of Echocardiography guidelines, may serve as a useful screening tool for rapid bedside assessment to exclude significant RV dysfunction.
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Affiliation(s)
- Brian C Hsia
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ashton Lai
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Supreet Singh
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Rajeev Samtani
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Solomon Bienstock
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Steve Liao
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Eric Stern
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Gina LaRocca
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Javier Sanz
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Stamatios Lerakis
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Lori Croft
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | | | | | - Anthony DeMaria
- Sulpizio Cardiovascular Center, University of California, San Diego, San Diego, California
| | - Gregg W Stone
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Martin E Goldman
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
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13
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Barbieri A, Mantovani F, Ciampi Q, Barchitta A, Faganello G, Miceli S, Parato VM, Tota A, Trocino G, Antonini-Canterin F, Carerj S, Pepi M. Current national availability of advanced echocardiography imaging: real world data from an Italian Society of Echocardiography and Cardiovascular Imaging survey. EUROPEAN HEART JOURNAL. IMAGING METHODS AND PRACTICE 2023; 1:qyad046. [PMID: 39045082 PMCID: PMC11195755 DOI: 10.1093/ehjimp/qyad046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 12/16/2023] [Indexed: 07/25/2024]
Abstract
Aims Advanced echocardiographic imaging (AEI) techniques, such as three-dimensional (3D) and multi-chamber speckle-tracking deformation imaging (strain) analysis, have been shown to be more accurate in assessing heart chamber geometry and function when compared with conventional echocardiography providing additional prognostic value. However, incorporating AEI alongside standard examinations may be heterogeneous between echo laboratories (echo labs). Thus, our goal was to gain a better understanding of the many AEI modalities that are available and employed in Italy. Methods and results The Italian Society of Echocardiography and Cardiovascular Imaging (SIECVI) conducted a national survey over a month (November 2022) to describe the use of AEI in Italy. Data were retrieved via an electronic survey based on a structured questionnaire uploaded on the SIECVI website. Data obtained from 173 echo labs were divided into 3 groups, according to the numbers of echocardiograms performed: <250 exams (low-volume activity, 53 centres), between 251 and 550 exams (moderate-volume activity, 62 centres), and ≥550 exams (high-volume activity, 58 centres). Transthoracic echocardiography (TTE) 3D was in use in 75% of centres with a consistent difference between low (55%), medium (71%), and high activity volume (85%) (P = 0.002), while 3D transoesophageal echocardiography (TEE) was in use in 84% of centres, reaching the 95% in high activity volume echo labs (P = 0.006). In centres with available 3D TTE, it was used for the left ventricle (LV) analysis in 67%, for the right ventricle (RV) in 45%, and for the left atrium (LA) in 40%, showing greater use in high-volume centres compared with low- and medium-volume centres (all P < 0.04). Strain analysis was utilized in most echo labs (80%), with a trend towards greater use in high-volume centres than low- and medium-volume centres (77%, 74%, and 90%, respectively; P = 0.08). In centres with available strain analysis, it was mainly employed for the LV (80%) and much less frequently for the RV and LA (49% and 48%, respectively). Conclusion In Italy, the AEI modalities are more frequently available in centres with high-volume activity but employed only in a few applications, being more frequent in analysing the LV compared with the RV and LA. Therefore, the echocardiography community and SIECVI should promote uniformity and effective training across the Italian centres. Meanwhile, collaborations across centres with various resources and expertise should be encouraged to use the benefits of the AEI.
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Affiliation(s)
- Andrea Barbieri
- Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Via del Pozzo, 71, Modena 41124, Italy
| | - Francesca Mantovani
- Cardiology Division, Azienda USL—IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Quirino Ciampi
- Cardiology Division, Fatebenefratelli Hospital, Benevento, Italy
| | - Agata Barchitta
- Semi-Intensive Care Unit, Padova University Hospital, Padova, Italy
| | | | - Sofia Miceli
- Geriatric Division, University Hospital Mater Domini, Catanzaro, Italy
| | - Vito Maurizio Parato
- Cardiology Division, Madonna del Soccorso Hospital, San Benedetto del Tronto (AP), Italy
| | - Antonio Tota
- Cardiology Division, Polyclinic Hospital, Bari, Italy
| | - Giuseppe Trocino
- Non Invasive Cardiac Imaging Department, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
| | | | - Scipione Carerj
- Cardiology Division, University Hospital Polyclinic G. Martino, University of Messina, Messina, Italy
| | - Mauro Pepi
- Cardiology Division, Centro Cardiologico Monzino, IRCCS, Milano, Italy
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14
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Molnár AÁ, Sánta A, Merkely B. Echocardiography Imaging of the Right Ventricle: Focus on Three-Dimensional Echocardiography. Diagnostics (Basel) 2023; 13:2470. [PMID: 37568832 PMCID: PMC10416971 DOI: 10.3390/diagnostics13152470] [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: 04/30/2023] [Revised: 06/28/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
Right ventricular function strongly predicts cardiac death and adverse cardiac events in patients with cardiac diseases. However, the accurate right ventricular assessment by two-dimensional echocardiography is limited due to its complex anatomy, shape, and load dependence. Advances in cardiac imaging and three-dimensional echocardiography provided more reliable information on right ventricular volumes and function without geometrical assumptions. Furthermore, the pathophysiology of right ventricular dysfunction and tricuspid regurgitation is frequently connected. Three-dimensional echocardiography allows a more in-depth structural and functional evaluation of the tricuspid valve. Understanding the anatomy and pathophysiology of the right side of the heart may help in diagnosing and managing the disease by using reliable imaging tools. The present review describes the challenging echocardiographic assessment of the right ventricle and tricuspid valve apparatus in clinical practice with a focus on three-dimensional echocardiography.
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Affiliation(s)
- Andrea Ágnes Molnár
- Heart and Vascular Center, Semmelweis University, 1085 Budapest, Hungary; (A.S.); (B.M.)
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15
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Appadurai V, Safdur T, Narang A. Assessment of Right Ventricle Function and Tricuspid Regurgitation in Heart Failure: Current Advances in Diagnosis and Imaging. Heart Fail Clin 2023; 19:317-328. [PMID: 37230647 DOI: 10.1016/j.hfc.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Right ventricular (RV) systolic dysfunction increases mortality among heart failure patients, and therefore, accurate diagnosis and monitoring is paramount. RV anatomy and function are complex, usually requiring a combination of imaging modalities to completely quantitate volumes and function. Tricuspid regurgitation usually occurs with RV dysfunction, and quantifying this valvular lesion also may require multiple imaging modalities. Echocardiography is the first-line imaging tool for identifying RV dysfunction, with cardiac MRI and cardiac computed tomography adding valuable additional information.
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Affiliation(s)
- Vinesh Appadurai
- Bluhm Cardiovascular Institute, Northwestern University, 676 North St Clair Street Suite 19-100 Galter Pavilion, Chicago, IL 60611, USA; School of Medicine, The University of Queensland, St Lucia, QLD, 4067 Australia
| | - Taimur Safdur
- Bluhm Cardiovascular Institute, Northwestern University, 676 North St Clair Street Suite 19-100 Galter Pavilion, Chicago, IL 60611, USA
| | - Akhil Narang
- Bluhm Cardiovascular Institute, Northwestern University, 676 North St Clair Street Suite 19-100 Galter Pavilion, Chicago, IL 60611, USA.
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16
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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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17
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Brown TN, Brogan TV. Right ventricular dysfunction in patients with acute respiratory distress syndrome receiving venovenous extracorporeal membrane oxygenation. Front Cardiovasc Med 2023; 10:1027300. [PMID: 37265572 PMCID: PMC10229794 DOI: 10.3389/fcvm.2023.1027300] [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: 08/24/2022] [Accepted: 04/10/2023] [Indexed: 06/03/2023] Open
Abstract
Acute respiratory distress syndrome is characterized by non-cardiogenic pulmonary edema, decreased pulmonary compliance, and abnormalities in gas exchange, especially hypoxemia. Patients with acute respiratory distress syndrome (ARDS) who receive support with venovenous (V-V) extracorporeal membrane oxygenation (ECMO) usually have severe lung disease. Many patients with ARDS have associated pulmonary vascular injury which can result in elevated pulmonary vascular resistance and right heart dysfunction. Since V-V ECMO relies upon preserved cardiac function, right heart failure has important implications for patient evaluation, management, and outcomes. Worsening right heart function complicates ARDS and disease processes. Given the increasing use of ECMO to support patients with ARDS, an understanding of right ventricular-ECMO and cardiopulmonary interactions is essential for the clinician. A narrative review of the manifestations of right heart dysfunction, as well as diagnosis and management strategies for the patient with ARDS on ECMO, is provided.
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Affiliation(s)
- Tyler N. Brown
- Pediatric Critical Care Medicine, University of Washington School of Medicine, Seattle Children’s Hospital, Seattle, Washington, United States
| | - Thomas V. Brogan
- Department of Pediatrics, University of Washington School of Medicine, Seattle Children’s Hospital, Seattle, Washington, United States
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18
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Lin A, Pieszko K, Park C, Ignor K, Williams MC, Slomka P, Dey D. Artificial intelligence in cardiovascular imaging: enhancing image analysis and risk stratification. BJR Open 2023; 5:20220021. [PMID: 37396483 PMCID: PMC10311632 DOI: 10.1259/bjro.20220021] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 03/14/2023] [Accepted: 04/03/2023] [Indexed: 07/04/2023] Open
Abstract
In this review, we summarize state-of-the-art artificial intelligence applications for non-invasive cardiovascular imaging modalities including CT, MRI, echocardiography, and nuclear myocardial perfusion imaging.
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Affiliation(s)
| | | | - Caroline Park
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Katarzyna Ignor
- Department of Interventional Cardiology, Collegium Medicum, University of Zielona Góra, Zielona Góra, Poland
| | - Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Piotr Slomka
- Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
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19
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Hahn RT, Lerakis S, Delgado V, Addetia K, Burkhoff D, Muraru D, Pinney S, Friedberg MK. Multimodality Imaging of Right Heart Function: JACC Scientific Statement. J Am Coll Cardiol 2023; 81:1954-1973. [PMID: 37164529 DOI: 10.1016/j.jacc.2023.03.392] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/01/2023] [Accepted: 03/02/2023] [Indexed: 05/12/2023]
Abstract
Right ventricular (RV) size and function assessed by multimodality imaging are associated with outcomes in a variety of cardiovascular diseases. Understanding RV anatomy and physiology is essential in appreciating the strengths and weaknesses of current imaging methods and gives these measurements greater context. The adaptation of the right ventricle to different types and severity of stress, particularly over time, is specific to the cardiovascular disease process. Multimodality imaging parameters, which determine outcomes, reflect the ability to image the initial and longitudinal RV response to stress. This paper will review the standard and novel imaging methods for assessing RV function and the impact of these parameters on outcomes in specific disease states.
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Affiliation(s)
- Rebecca T Hahn
- Department of Medicine, Columbia University Medical Center/NewYork-Presbyterian Hospital, New York, New York, USA.
| | | | - Victoria Delgado
- Hospital University Germans Trias i Pujol Hospital, Badalona, Barcelona, Spain
| | - Karima Addetia
- Department of Medicine, University of Chicago, Chicago, Illinois, USA
| | | | - Denisa Muraru
- Department of Cardiology, Istituto Auxologico Italiano, IRCCS, Milan, Italy; Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Sean Pinney
- Department of Medicine, University of Chicago, Chicago, Illinois, USA
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20
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Krittanawong C, Omar AMS, Narula S, Sengupta PP, Glicksberg BS, Narula J, Argulian E. Deep Learning for Echocardiography: Introduction for Clinicians and Future Vision: State-of-the-Art Review. Life (Basel) 2023; 13:life13041029. [PMID: 37109558 PMCID: PMC10145844 DOI: 10.3390/life13041029] [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: 02/17/2023] [Revised: 03/30/2023] [Accepted: 04/03/2023] [Indexed: 04/29/2023] Open
Abstract
Exponential growth in data storage and computational power is rapidly narrowing the gap between translating findings from advanced clinical informatics into cardiovascular clinical practice. Specifically, cardiovascular imaging has the distinct advantage in providing a great quantity of data for potentially rich insights, but nuanced interpretation requires a high-level skillset that few individuals possess. A subset of machine learning, deep learning (DL), is a modality that has shown promise, particularly in the areas of image recognition, computer vision, and video classification. Due to a low signal-to-noise ratio, echocardiographic data tend to be challenging to classify; however, utilization of robust DL architectures may help clinicians and researchers automate conventional human tasks and catalyze the extraction of clinically useful data from the petabytes of collected imaging data. The promise is extending far and beyond towards a contactless echocardiographic exam-a dream that is much needed in this time of uncertainty and social distancing brought on by a stunning pandemic culture. In the current review, we discuss state-of-the-art DL techniques and architectures that can be used for image and video classification, and future directions in echocardiographic research in the current era.
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Affiliation(s)
- Chayakrit Krittanawong
- Cardiology Division, NYU Langone Health, NYU School of Medicine, New York, NY 10016, USA
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
| | - Alaa Mabrouk Salem Omar
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
| | - Sukrit Narula
- Department of Medicine, Yale School of Medicine, New Haven, CT 06512, USA
| | - Partho P Sengupta
- Robert Wood Johnson University Hospital, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jagat Narula
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
| | - Edgar Argulian
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
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21
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Oliveira ALA, de Oliveira MEP, Guimarães LV, Trindade GM, Chaves GM, Gonçalves ACP, de Souza TJF, Moraes LS, Lujan VSC, Faria LSDP, Manuel V. Evaluation of right ventricle systolic function after tetralogy of Fallot repair: A systematic review comparing cardiac magnetic resonance and global longitudinal strain. Echocardiography 2023; 40:4-14. [PMID: 36478414 DOI: 10.1111/echo.15486] [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: 08/01/2021] [Revised: 09/23/2022] [Accepted: 10/24/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Most patients who undergo tetralogy of Fallot (TOF) repair experience late right ventricle (RV) dysfunction due to pulmonary valve regurgitation (PVR). Cardiac magnetic resonance (CMR) is the gold standard method for evaluating RV during follow-up. Global longitudinal strain (GLS) has been introduced as a novel method for the assessment of RV dysfunction. We aimed to compare the feasibility of GLS and CMR for assessing RV function after TOF repair. METHODS We systematically reviewed the English literature using PubMed, SciELO and Google Scholar for articles published between January 1, 2015, and December 31, 2020. Articles evaluating RV function comparing by GLS and CMR after TOF repair were included. RESULTS Nine studies including 465 patients were analyzed. Most patients were men (280; 60%), the male:female ratio was 1.5:1, and the age range was .8 to 57.7 years. The mean follow-up time was 6 to 32 months. The correlation between RV GLS and RV ejection fraction (EF) by CMR was negative for the articles and varied from moderate to strong (r = -.45, r = -.60, r = -.76). CONCLUSION Right ventricle GLS can be considered for routine follow-up of TOF repair patients, even though CMR remains the noninvasive gold standard method. Using a single parameter may not allow comparison of the accuracy of 3D RV EF by using CMR and GLS. Further studies with a larger number of patients undergoing TOF repair are required to evaluate the correlation between these examinations.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Valdano Manuel
- Cardiothoracic Center, Clinica Girassol, Luanda, Angola.,Division of Cardiovascular Surgery, Heart Institute (InCor), Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
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22
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Muscogiuri G, Volpato V, Cau R, Chiesa M, Saba L, Guglielmo M, Senatieri A, Chierchia G, Pontone G, Dell’Aversana S, Schoepf UJ, Andrews MG, Basile P, Guaricci AI, Marra P, Muraru D, Badano LP, Sironi S. Application of AI in cardiovascular multimodality imaging. Heliyon 2022; 8:e10872. [PMID: 36267381 PMCID: PMC9576885 DOI: 10.1016/j.heliyon.2022.e10872] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/23/2022] [Accepted: 09/27/2022] [Indexed: 12/16/2022] Open
Abstract
Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging.
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Affiliation(s)
- Giuseppe Muscogiuri
- Department of Radiology, Istituto Auxologico Italiano IRCCS, San Luca Hospital, Italy,School of Medicine, University of Milano-Bicocca, Milan, Italy,Corresponding author.
| | - Valentina Volpato
- Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy,IRCCS Ospedale Galeazzi - Sant'Ambrogio, University Cardiology Department, Milan, Italy
| | - Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari, Polo di Monserrato, Cagliari, Italy
| | | | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari, Polo di Monserrato, Cagliari, Italy
| | - Marco Guglielmo
- Department of Cardiology, Division of Heart and Lungs, Utrecht University, Utrecht University Medical Center, Utrecht, the Netherlands
| | | | | | | | - Serena Dell’Aversana
- Department of Radiology, Ospedale S. Maria Delle Grazie - ASL Napoli 2 Nord, Pozzuoli, Italy
| | - U. Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr., Charleston, SC, USA
| | - Mason G. Andrews
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr., Charleston, SC, USA
| | - Paolo Basile
- University Cardiology Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Andrea Igoren Guaricci
- University Cardiology Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Paolo Marra
- Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
| | - Denisa Muraru
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Luigi P. Badano
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Cardiac, Neurological and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Sandro Sironi
- School of Medicine, University of Milano-Bicocca, Milan, Italy,Department of Radiology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
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Feldhütter EK, Domenech O, Vezzosi T, Tognetti R, Eberhard J, Friederich J, Wess G. Right ventricular size and function evaluated by various echocardiographic indices in dogs with pulmonary hypertension. Vet Med (Auckl) 2022; 36:1882-1891. [PMID: 36168939 DOI: 10.1111/jvim.16496] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 07/07/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND Three-dimensional (3D) echocardiography and 2-dimensional (2D) strain measurements of the right ventricle (RV) are important indices in humans with pulmonary hypertension (PH) and need further evaluation in dogs with PH. OBJECTIVES To evaluate various RV size and function indices in dogs with PH and to examine differences between pre- and postcapillary PH. ANIMALS A total of 311 client-owned dogs: 100 dogs with PH, 31 with postcapillary and 69 with precapillary PH, and 211 healthy control dogs. METHODS Retro- and prospective, multicenter study. Size and function of the RV was determined using several indices, derived using dedicated RV software, including 3D RV end-diastolic volume (EDVn), end-systolic volume (ESVn), ejection fraction, 2D global and free wall RV longitudinal strain (RVLS), end-diastolic area, end-systolic area, fractional area change, tricuspid annular plane systolic excursion, and tissue Doppler imaging-derived systolic myocardial velocity of the lateral tricuspid annulus (S'n). RESULTS The EDVn (1.8 vs 2.5 mL/kg0.942 , P < .01) and ESVn (0.8 vs 1.2 mL/kg0.962 , P < .001) were significantly larger in the PH group compared to healthy controls. Free wall RVLS was decreased in dogs with severe PH compared to controls (-24% vs -29.6%, P < .001). Dogs with precapillary PH had worse RV systolic function than dogs with postcapillary PH. CONCLUSION Three-dimensional echocardiography of the RV is a promising tool to detect RV changes in dogs with PH. Also, 2D strain measurements are able to detect decreased RV function and offer several advantages compared to conventional indices.
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Affiliation(s)
| | | | - Tommaso Vezzosi
- Anicura Istituto Veterinario Novara, Novara, Italy.,Department of Veterinary Sciences, University of Pisa, Pisa, Italy
| | - Rosalba Tognetti
- Department of Veterinary Sciences, University of Pisa, Pisa, Italy
| | - Jenny Eberhard
- Clinic of Small Animal Medicine, LMU University, Munich, Germany
| | - Jana Friederich
- Clinic of Small Animal Medicine, LMU University, Munich, Germany
| | - Gerhard Wess
- Clinic of Small Animal Medicine, LMU University, Munich, Germany
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Nair N. Use of machine learning techniques to identify risk factors for RV failure in LVAD patients. Front Cardiovasc Med 2022; 9:848789. [PMID: 36186964 PMCID: PMC9515379 DOI: 10.3389/fcvm.2022.848789] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 07/25/2022] [Indexed: 11/25/2022] Open
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Stock JD, Rothstein ES, Friedman SE, Gemignani AS, Costa SP, Milbridge AJ, Zhang R, Taub CC, O'Rourke DJ, Palac RT. Lateral annular systolic excursion ratio: A novel measurement of right ventricular systolic function by two-dimensional echocardiography. Front Cardiovasc Med 2022; 9:971302. [PMID: 36119732 PMCID: PMC9479059 DOI: 10.3389/fcvm.2022.971302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 08/12/2022] [Indexed: 11/13/2022] Open
Abstract
IntroductionAccurate assessment of right ventricular (RV) systolic function has prognostic and therapeutic implications in many disease states. Echocardiography remains the most frequently deployed imaging modality for this purpose, but estimation of RV systolic function remains challenging. The purpose of this study was to evaluate the diagnostic performance of a novel measurement of RV systolic function called lateral annular systolic excursion ratio (LASER), which is the fractional shortening of the lateral tricuspid annulus to apex distance, compared to right ventricular ejection fraction (RVEF) derived by cardiac magnetic resonance imaging (CMR).MethodsA retrospective cohort of 78 consecutive patients who underwent clinically indicated CMR and transthoracic echocardiography within 30 days were identified from a database. Parameters of RV function measured included: tricuspid annular plane systolic excursion (TAPSE) by M-mode, tissue Doppler S', fractional area change (FAC) and LASER. These measurements were compared to RVEF derived by CMR using Pearson's correlation coefficients and receiver operating characteristic curves.ResultsLASER was measurable in 75 (96%) of patients within the cohort. Right ventricular systolic dysfunction, by CMR measurement, was present in 37% (n = 29) of the population. LASER has moderate positive correlation with RVEF (r = 0.54) which was similar to FAC (r = 0.56), S' (r = 0.49) and TAPSE (r = 0.37). Receiver operating characteristic curves demonstrated that LASER (AUC = 0.865) outperformed fractional area change (AUC = 0.767), tissue Doppler S' (AUC = 0.744) and TAPSE (AUC = 0.645). A cohort derived dichotomous cutoff of 0.2 for LASER was shown to provide optimal diagnostic characteristics (sensitivity of 75%, specificity of 87% and accuracy of 83%) for identifying abnormal RV function. LASER had the highest sensitivity, accuracy, positive and negative predictive values among the parameters studied in the cohort.ConclusionsWithin the study cohort, LASER was shown to have moderate positive correlation with RVEF derived by CMR and more favorable diagnostic performance for detecting RV systolic dysfunction compared to conventional echocardiographic parameters while being simple to obtain and less dependent on image quality than FAC and emerging techniques.
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Affiliation(s)
- Jonathan D. Stock
- Heart and Vascular Center, Dartmouth-Hitchcock Medical Center, Lebanon, NH, United States
- Division of Cardiology, White River Junction VA Medical Center, White River Junction, VT, United States
| | - Eric S. Rothstein
- Heart and Vascular Center, Dartmouth-Hitchcock Medical Center, Lebanon, NH, United States
- *Correspondence: Eric S. Rothstein
| | - Scott E. Friedman
- Division of Cardiology, White River Junction VA Medical Center, White River Junction, VT, United States
| | - Anthony S. Gemignani
- Division of Cardiology, White River Junction VA Medical Center, White River Junction, VT, United States
| | - Salvatore P. Costa
- Heart and Vascular Center, Dartmouth-Hitchcock Medical Center, Lebanon, NH, United States
| | - Andrew J. Milbridge
- Heart and Vascular Center, Dartmouth-Hitchcock Medical Center, Lebanon, NH, United States
- Division of Cardiology, White River Junction VA Medical Center, White River Junction, VT, United States
| | - Rui Zhang
- Heart and Vascular Center, Dartmouth-Hitchcock Medical Center, Lebanon, NH, United States
- Division of Cardiology, White River Junction VA Medical Center, White River Junction, VT, United States
| | - Cynthia C. Taub
- Heart and Vascular Center, Dartmouth-Hitchcock Medical Center, Lebanon, NH, United States
- Division of Cardiology, White River Junction VA Medical Center, White River Junction, VT, United States
| | - Daniel J. O'Rourke
- Division of Cardiology, White River Junction VA Medical Center, White River Junction, VT, United States
| | - Robert T. Palac
- Division of Cardiology, White River Junction VA Medical Center, White River Junction, VT, United States
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Gautam N, Ghanta SN, Clausen A, Saluja P, Sivakumar K, Dhar G, Chang Q, DeMazumder D, Rabbat MG, Greene SJ, Fudim M, Al'Aref SJ. Contemporary Applications of Machine Learning for Device Therapy in Heart Failure. JACC. HEART FAILURE 2022; 10:603-622. [PMID: 36049812 DOI: 10.1016/j.jchf.2022.06.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 05/31/2022] [Accepted: 06/16/2022] [Indexed: 06/15/2023]
Abstract
Despite a better understanding of the underlying pathogenesis of heart failure (HF), pharmacotherapy, surgical, and percutaneous interventions do not prevent disease progression in all patients, and a significant proportion of patients end up requiring advanced therapies. Machine learning (ML) is gaining wider acceptance in cardiovascular medicine because of its ability to incorporate large, complex, and multidimensional data and to potentially facilitate the creation of predictive models not constrained by many of the limitations of traditional statistical approaches. With the coexistence of "big data" and novel advanced analytic techniques using ML, there is ever-increasing research into applying ML in the context of HF with the goal of improving patient outcomes. Through this review, the authors describe the basics of ML and summarize the existing published reports regarding contemporary applications of ML in device therapy for HF while highlighting the limitations to widespread implementation and its future promises.
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Affiliation(s)
- Nitesh Gautam
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Sai Nikhila Ghanta
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Alex Clausen
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Prachi Saluja
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Kalai Sivakumar
- Division of Cardiology, Department of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Gaurav Dhar
- Division of Cardiology, Department of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Qi Chang
- Department of Computer Science, Rutgers University, The State University of New Jersey, Newark, New Jersey, USA
| | | | - Mark G Rabbat
- Department of Cardiology, Loyola University Medical Center, Maywood, Illinois, USA
| | - Stephen J Greene
- Department of Cardiology, Duke University Medical Center, Durham, North Carolina, USA; Duke Clinical Research Institute, Durham, North Carolina, USA
| | - Marat Fudim
- Department of Cardiology, Duke University Medical Center, Durham, North Carolina, USA; Duke Clinical Research Institute, Durham, North Carolina, USA
| | - Subhi J Al'Aref
- Division of Cardiology, Department of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
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27
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Penso M, Ranalletta RA, Pepi M, Garlaschè A, Ali SG, Fusini L, Mantegazza V, Muratori M, Maragna R, Tamborini G. Comparison between Automatic and Semiautomatic System for the 3D Echocardiographic Multiparametric Evaluation of RV Function and Dimension. J Clin Med 2022; 11:jcm11154528. [PMID: 35956143 PMCID: PMC9369664 DOI: 10.3390/jcm11154528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 07/29/2022] [Accepted: 08/01/2022] [Indexed: 12/04/2022] Open
Abstract
Background: The right ventricle (RV) plays a pivotal role in cardiovascular diseases and 3-dimensional echocardiography (3DE) has gained acceptance for the evaluation of RV volumes and function. Recently, a new artificial intelligence (AI)–based automated 3DE software for RV evaluation has been proposed and validated against cardiac magnetic resonance. The aims of this study were three-fold: (i) feasibility of the AI-based 3DE RV quantification, (ii) comparison with the semi-automatic 3DE method and (iii) assessment of 2-dimensional echocardiography (2DE) and strain measurements obtained automatically. Methods: A total of 203 subject (122 normal and 81 patients) underwent a 2DE and both the semi-automatic and automatic 3DE methods for Doppler standard, RV volumes and ejection fraction (RVEF) measurements. Results: The automatic 3DE method was highly feasible, faster than 2DE and semi-automatic 3DE and data obtained were comparable with traditional measurements. Both in normal subjects and patients, the RVEF was similar to the two 3DE methods and 2DE and strain measurements obtained by the automated system correlated very well with the standard 2DE and strain ones. Conclusions: results showed that rapid analysis and excellent reproducibility of AI-based 3DE RV analysis supported the routine adoption of this automated method in the daily clinical workflow.
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Affiliation(s)
- Marco Penso
- Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (R.A.R.); (M.P.); (A.G.); (S.G.A.); (L.F.); (V.M.); (M.M.); (R.M.); (G.T.)
- Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, 20133 Milan, Italy
- Correspondence: ; Tel.: +39-3926930900
| | - Remo Antonio Ranalletta
- Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (R.A.R.); (M.P.); (A.G.); (S.G.A.); (L.F.); (V.M.); (M.M.); (R.M.); (G.T.)
| | - Mauro Pepi
- Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (R.A.R.); (M.P.); (A.G.); (S.G.A.); (L.F.); (V.M.); (M.M.); (R.M.); (G.T.)
| | - Anna Garlaschè
- Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (R.A.R.); (M.P.); (A.G.); (S.G.A.); (L.F.); (V.M.); (M.M.); (R.M.); (G.T.)
| | - Sarah Ghulam Ali
- Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (R.A.R.); (M.P.); (A.G.); (S.G.A.); (L.F.); (V.M.); (M.M.); (R.M.); (G.T.)
| | - Laura Fusini
- Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (R.A.R.); (M.P.); (A.G.); (S.G.A.); (L.F.); (V.M.); (M.M.); (R.M.); (G.T.)
- Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, 20133 Milan, Italy
| | - Valentina Mantegazza
- Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (R.A.R.); (M.P.); (A.G.); (S.G.A.); (L.F.); (V.M.); (M.M.); (R.M.); (G.T.)
| | - Manuela Muratori
- Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (R.A.R.); (M.P.); (A.G.); (S.G.A.); (L.F.); (V.M.); (M.M.); (R.M.); (G.T.)
| | - Riccardo Maragna
- Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (R.A.R.); (M.P.); (A.G.); (S.G.A.); (L.F.); (V.M.); (M.M.); (R.M.); (G.T.)
| | - Gloria Tamborini
- Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (R.A.R.); (M.P.); (A.G.); (S.G.A.); (L.F.); (V.M.); (M.M.); (R.M.); (G.T.)
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Feasibility and time-analysis of 3D and myocardial deformation versus conventional 2D echocardiography to assess cardiac chambers. J Am Soc Echocardiogr 2022; 35:1102-1105. [PMID: 35690298 DOI: 10.1016/j.echo.2022.05.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 05/26/2022] [Accepted: 05/28/2022] [Indexed: 11/20/2022]
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29
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Miao R, Gong J, Guo X, Guo D, Zhang X, Hu H, Zhong J, Yang Y, Li Y. Diagnostic value of miRNA expression and right ventricular echocardiographic functional parameters for chronic thromboembolic pulmonary hypertension with right ventricular dysfunction and injury. BMC Pulm Med 2022; 22:171. [PMID: 35488248 PMCID: PMC9052592 DOI: 10.1186/s12890-022-01962-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 04/22/2022] [Indexed: 11/16/2022] Open
Abstract
Background We aimed to establish the relationships between the expression of microRNAs (miRNAs) and echocardiographic right ventricular (RV) function parameters, and to explore the effectiveness and clinical value of miRNA expression in predicting RV injury and dysfunction in patients with chronic thromboembolic pulmonary hypertension (CTEPH). Methods In this retrospective study, clinical data were collected from eight CTEPH patients and eight healthy individuals. RV parameters on echocardiography were analyzed, and the expression levels of specific miRNAs were measured by quantitative real-time PCR. Correlation analysis was performed on structural and functional RV parameters and five candidate miRNAs (miR-20a-5p, miR-17-5p, miR-93-5p, miR-3202 and miR-665). The diagnostic value of RV functional parameters and miRNAs expression was assessed by receiver operating characteristic (ROC) curve analysis and C statistic. Results Among the tested miRNAs, miR-20a-5p expression showed the best correlation with echocardiographic RV functional parameters (P < 0.05), although the expression levels of miR-93-5p, miR-17-5p and miR-3202 showed positive associations with some RV parameters. ROC curve analysis demonstrated the ability of miR-20a-5p expression to predict RV dysfunction, with a maximum area under the curve of 0.952 (P = 0.003) when the predicted RV longitudinal strain was less than –20%. The C index for RV dysfunction prediction by the combination of miRNAs (miR-20a-5p, miR-93-5p and miR-17-5p) was 1.0, which was significantly larger than the values for miR-93-5p and miR-17-5p individually (P = 0.0337 and 0.0453, respectively). Conclusion Among the tested miRNAs, miR -20a-5p, miR -93-5p and miR -17-5p have potential value in the diagnosis of CTEPH based on the correlation between the abnormal expression of these miRNAs and echocardiographic parameters in CTEPH patients. miR-20a-5p showed the strongest correlation with echocardiographic RV functional parameters. Moreover, expression of a combination of miRNAs seemed to show excellent predictive power for RV dysfunction.
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Affiliation(s)
- Ran Miao
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China.,Medical Research Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China
| | - Juanni Gong
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China
| | - Xiaojuan Guo
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China
| | - Dichen Guo
- Department of Echocardiography, Heart Center, Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 100020, China
| | - Xinyuan Zhang
- Department of Echocardiography, Heart Center, Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 100020, China
| | - Huimin Hu
- Department of Echocardiography, Heart Center, Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 100020, China
| | - Jiuchang Zhong
- Heart Center and Beijing Key Laboratory of Hypertension, Beijing, 100020, China
| | - Yuanhua Yang
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China
| | - Yidan Li
- Department of Echocardiography, Heart Center, Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 100020, China.
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30
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Wang S, Chauhan D, Patel H, Amir-Khalili A, da Silva IF, Sojoudi A, Friedrich S, Singh A, Landeras L, Miller T, Ameyaw K, Narang A, Kawaji K, Tang Q, Mor-Avi V, Patel AR. Assessment of right ventricular size and function from cardiovascular magnetic resonance images using artificial intelligence. J Cardiovasc Magn Reson 2022; 24:27. [PMID: 35410226 PMCID: PMC8996592 DOI: 10.1186/s12968-022-00861-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 03/29/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Theoretically, artificial intelligence can provide an accurate automatic solution to measure right ventricular (RV) ejection fraction (RVEF) from cardiovascular magnetic resonance (CMR) images, despite the complex RV geometry. However, in our recent study, commercially available deep learning (DL) algorithms for RVEF quantification performed poorly in some patients. The current study was designed to test the hypothesis that quantification of RV function could be improved in these patients by using more diverse CMR datasets in addition to domain-specific quantitative performance evaluation metrics during the cross-validation phase of DL algorithm development. METHODS We identified 100 patients from our prior study who had the largest differences between manually measured and automated RVEF values. Automated RVEF measurements were performed using the original version of the algorithm (DL1), an updated version (DL2) developed from a dataset that included a wider range of RV pathology and validated using multiple domain-specific quantitative performance evaluation metrics, and conventional methodology performed by a core laboratory (CORE). Each of the DL-RVEF approaches was compared against CORE-RVEF reference values using linear regression and Bland-Altman analyses. Additionally, RVEF values were classified into 3 categories: ≤ 35%, 35-50%, and ≥ 50%. Agreement between RVEF classifications made by the DL approaches and the CORE measurements was tested. RESULTS CORE-RVEF and DL-RVEFs were obtained in all patients (feasibility of 100%). DL2-RVEF correlated with CORE-RVEF better than DL1-RVEF (r = 0.87 vs. r = 0.42), with narrower limits of agreement. As a result, DL2 algorithm also showed increasing accuracy from 0.53 to 0.80 for categorizing RV function. CONCLUSIONS The use of a new DL algorithm cross-validated on a dataset with a wide range of RV pathology using multiple domain-specific metrics resulted in a considerable improvement in the accuracy of automated RVEF measurements. This improvement was demonstrated in patients whose images were the most challenging and resulted in the largest RVEF errors. These findings underscore the critical importance of this strategy in the development of DL approaches for automated CMR measurements.
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Affiliation(s)
- Shuo Wang
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
- Peking University Shougang Hospital, Beijing, China
| | - Daksh Chauhan
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | - Hena Patel
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | | | | | | | | | - Amita Singh
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | - Luis Landeras
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | - Tamari Miller
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | - Keith Ameyaw
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | | | - Keigo Kawaji
- Illinois Institute of Technology, Chicago, IL, USA
| | - Qiang Tang
- Peking University Shougang Hospital, Beijing, China
| | - Victor Mor-Avi
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | - Amit R Patel
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA.
- Department of Radiology, University of Chicago, Chicago, IL, USA.
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31
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Hagberg E, Hagerman D, Johansson R, Hosseini N, Liu J, Björnsson E, Alvén J, Hjelmgren O. Semi-supervised learning with natural language processing for right ventricle classification in echocardiography-a scalable approach. Comput Biol Med 2022; 143:105282. [PMID: 35220074 DOI: 10.1016/j.compbiomed.2022.105282] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 01/30/2022] [Accepted: 01/30/2022] [Indexed: 11/20/2022]
Abstract
We created a deep learning model, trained on text classified by natural language processing (NLP), to assess right ventricular (RV) size and function from echocardiographic images. We included 12,684 examinations with corresponding written reports for text classification. After manual annotation of 1489 reports, we trained an NLP model to classify the remaining 10,651 reports. A view classifier was developed to select the 4-chamber or RV-focused view from an echocardiographic examination (n = 539). The final models were two image classification models trained on the predicted labels from the combined manual annotation and NLP models and the corresponding echocardiographic view to assess RV function (training set n = 11,008) and size (training set n = 9951. The text classifier identified impaired RV function with 99% sensitivity and 98% specificity and RV enlargement with 98% sensitivity and 98% specificity. The view classification model identified the 4-chamber view with 92% accuracy and the RV-focused view with 73% accuracy. The image classification models identified impaired RV function with 93% sensitivity and 72% specificity and an enlarged RV with 80% sensitivity and 85% specificity; agreement with the written reports was substantial (both κ = 0.65). Our findings show that models for automatic image assessment can be trained to classify RV size and function by using model-annotated data from written echocardiography reports. This pipeline for auto-annotation of the echocardiographic images, using a NLP model with medical reports as input, can be used to train an image-assessment model without manual annotation of images and enables fast and inexpensive expansion of the training dataset when needed.
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Affiliation(s)
- Eva Hagberg
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden.
| | - David Hagerman
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Richard Johansson
- Department of Computer Science and Engineering, University of Gothenburg, Gothenburg, Sweden
| | - Nasser Hosseini
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jan Liu
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Elin Björnsson
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Jennifer Alvén
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Ola Hjelmgren
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden
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Zhang B, Wang H, Meng Q, Tao J, Lu H, Wu W, Zhu Z, Wang H. Quantification of chronic aortic regurgitation using left and right ventricular stroke volumes obtained from two new automated three-dimensional transthoracic echocardiographic software: feasibility and accuracy. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2022; 38:789-799. [PMID: 34800186 DOI: 10.1007/s10554-021-02471-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 11/08/2021] [Indexed: 10/19/2022]
Abstract
The present study aimed to evaluate the feasibility and accuracy of chronic aortic regurgitation (CAR) quantification using left and right ventricular stroke volumes (LVSV and RVSV, respectively) obtained from two new automated three-dimensional transthoracic echocardiographic software-Dynamic HeartModel (DHM) and 3D Auto RV. Patients (n=116) with more than mild isolated CAR were included and divided into two groups: central (n=53) and eccentric CAR (n=63) groups. LVSV and RVSV were automatically measured by DHM and 3D Auto RV. Next, aortic regurgitant volume (ARVol) was calculated three ways: as the difference between LVSV and RVSV, by the two-dimensional proximal isovelocity surface area (PISA) method, and using effective regurgitant orifice area derived from real-time three-dimensional echocardiography (RT3DE) multiplied by CAR velocity time integral (the reference standard). DHM plus 3D Auto RV correlated well with RT3DE in ARVol measurement in both groups (central, r = 0.90; eccentric, r = 0.96), with no significant difference based on consistency analysis. In the eccentric group, PISA led to an obvious underestimation (mean difference= - 4.20 ml, P < 0.05). The kappa agreement between DHM plus 3D Auto RV and RT3DE in grading CAR severity in both groups was good (central, k = 0.89; eccentric, k = 0.86), but that between PISA and RT3DE in the eccentric CAR group was suboptimal (k = 0.74). This study indicates that ARVol quantification using DHM plus 3D Auto RV is feasible and reproducible in patients with more than mild isolated CAR. This new method has great correlation and agreement with RT3DE in ARVol measurement, with evident advantages over PISA in eccentric CAR.
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Affiliation(s)
- Bing Zhang
- Department of Echocardiography, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No.167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Han Wang
- Department of Echocardiography, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No.167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Qinglong Meng
- Department of Echocardiography, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No.167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Jia Tao
- Department of Echocardiography, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No.167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Hongquan Lu
- Department of Echocardiography, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No.167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Weichun Wu
- Department of Echocardiography, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No.167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Zhenhui Zhu
- Department of Echocardiography, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No.167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Hao Wang
- Department of Echocardiography, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No.167 Beilishi Road, Xicheng District, Beijing, 100037, China.
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Three-Dimensional Echocardiography Assessment of Right Ventricular Volumes and Function: Technological Perspective and Clinical Application. Diagnostics (Basel) 2022; 12:diagnostics12040806. [PMID: 35453854 PMCID: PMC9031180 DOI: 10.3390/diagnostics12040806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 03/12/2022] [Accepted: 03/15/2022] [Indexed: 12/10/2022] Open
Abstract
Right ventricular (RV) function has important prognostic value in a variety of cardiovascular diseases. Due to complex anatomy and mode of contractility, conventional two-dimensional echocardiography does not provide sufficient and accurate RV function assessment. Currently, three-dimensional echocardiography (3DE) allows for an excellent and reproducible assessment of RV function owing to overcoming these limitations of traditional echocardiography. This review focused on 3DE and discussed the following points: (i) acquisition of RV dataset for 3DE images, (ii) reliability, feasibility, and reproducibility of RV volumes and function measured by 3DE with different modalities, (iii) the clinical application of 3DE for RV function quantification.
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Li S, Hickey GW, Lander MM, Kanwar MK. Artificial Intelligence and Mechanical Circulatory Support. Heart Fail Clin 2022; 18:301-309. [DOI: 10.1016/j.hfc.2021.11.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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35
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Shen Y, Zhang H, Zhang Q, Zhang B, Ni Y, Zhao R, Hsi DH, Cheng L. Right Ventricular Ejection Fraction Assessed by Three-Dimensional Echocardiography Is Associated with Long-term Adverse Clinical Cardiac Events in Patients with Anthracycline Induced Cardiotoxicity. J Am Soc Echocardiogr 2022; 35:600-608.e3. [DOI: 10.1016/j.echo.2022.01.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 01/03/2022] [Accepted: 01/19/2022] [Indexed: 10/19/2022]
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36
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Feasibility, Reproducibility, and Prognostic Value of Fully Automated Measurement of Right Ventricular Longitudinal Strain. J Am Soc Echocardiogr 2022; 35:609-619. [DOI: 10.1016/j.echo.2022.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 01/25/2022] [Accepted: 01/26/2022] [Indexed: 11/20/2022]
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37
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Zhu Y, Bao Y, Zheng K, Zhou W, Zhang J, Sun R, Deng Y, Xia L, Liu Y. Quantitative assessment of right ventricular size and function with multiple parameters from artificial intelligence-based three-dimensional echocardiography: A comparative study with cardiac magnetic resonance. Echocardiography 2022; 39:223-232. [PMID: 35034377 DOI: 10.1111/echo.15292] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 12/11/2021] [Accepted: 12/26/2021] [Indexed: 01/25/2023] Open
Abstract
AIMS This study aimed to explore the validation and the diagnostic value of multiple right ventricle (RV) volumes and functional parameters derived from a novel artificial intelligence (AI)-based three-dimensional echocardiography (3DE) algorithm compared to cardiac magnetic resonance (CMR). METHODS AND RESULTS A total of 51 patients with a broad spectrum of clinical diagnoses were finally included in this study. AI-based RV 3DE was performed in a single-beat HeartModel mode within 24 hours after CMR. In the entire population, RV volumes and right ventricular ejection fraction (RVEF) measured by AI-based 3DE showed statistically significant correlations with the corresponding CMR analysis (p < 0.05 for all). However, the Bland-Altman plots indicated that these parameters were slightly underestimated by AI-based 3DE. Based on CMR derived RVEF < 45% as RV dysfunction, end-systolic volume (ESV), end-systolic volume index (ESVi), stroke volume (SV), and RVEF showed great diagnostic performance in identifying RV dysfunction, as well as some non-volumetric parameters, including tricuspid annular systolic excursion (TAPSE), fractional area change (FAC), and free-wall longitudinal strains (LS) (p < 0.05 for all). The cutoff value was 43% for RVEF with a sensitivity of 94% and specificity of 67%. CONCLUSION AI-based 3DE could provide rapid and accurate quantitation of the RV volumes and function with multiple parameters. Both volumetric and non-volumetric measurements derived from AI-based 3DE contributed to the identification of the RV dysfunction.
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Affiliation(s)
- Ying Zhu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuwei Bao
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kangchao Zheng
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Zhou
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jun Zhang
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ruiying Sun
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Youbin Deng
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liming Xia
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yani Liu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Wu G, Ji H. Short-term memory neural network-based cognitive computing in sports training complexity pattern recognition. Soft comput 2022:1-16. [PMID: 35035279 PMCID: PMC8747855 DOI: 10.1007/s00500-021-06568-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/11/2021] [Indexed: 11/30/2022]
Abstract
With the development of Chinese sports, many sports training researchers try to use artificial intelligence technology to study the training methods and training elements of athletes. However, in reality, these methods are often based on different basic training principles, resulting in the reduction in the generalization ability of artificial intelligence networks. This paper studies the complexity of sports training principles by using an artificial intelligence network model. Based on the improved model of dropout optimization algorithm, this paper proposes an artificial intelligence sports training node prediction method based on the combination of dropout optimization algorithm and short-term memory neural network (LSTM), which avoids the establishment of complex sports training models. Based on artificial intelligence operation and maintenance records and sports training core capacity experimental data, the maximum node static estimation of artificial intelligence sports training is realized. The research shows that the node prediction model is established by using the method described in this paper. Through experimental comparison and analysis, the model has high prediction accuracy. Due to the state memory function of LSTM, it has advantages in the prediction of 2000 data on a long time scale. The mean absolute error percentage of the prediction results is less than 3.4%, and the maximum absolute error percentage is less than 5.2%. The artificial intelligence network model in this paper has good generalization ability. Compared with other models, the model proposed in this paper can get more accurate prediction results in sports training of different groups and effectively alleviate the problem of overfitting. Therefore, traditional stadiums and gymnasiums should actively introduce artificial intelligence technology with a more positive attitude, to realize the development and innovation in technology application, service innovation, management efficiency, and function integration.
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Affiliation(s)
- Guang Wu
- College of Physical Education, Chongqing Technology and Business University, Chongqing, 400067 Nan’an China
| | - Hang Ji
- Shijiazhuang School of the Arts, Shijiazhuang, 050800 Hebei China
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39
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Feldhütter EK, Domenech O, Vezzosi T, Tognetti R, Sauter N, Bauer A, Eberhard J, Friederich J, Wess G. Echocardiographic reference intervals for right ventricular indices, including 3-dimensional volume and 2-dimensional strain measurements in healthy dogs. J Vet Intern Med 2021; 36:8-19. [PMID: 34874066 PMCID: PMC8783368 DOI: 10.1111/jvim.16331] [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: 06/22/2021] [Revised: 11/18/2021] [Accepted: 11/18/2021] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND There is currently a lack of reference intervals (RIs) for the novel measures like 3-dimensional (3D) echocardiography or speckle-tracking strain for assessment of right ventricular (RV) structure and function. OBJECTIVES To generate RIs and to determine the influence of age, heart rate, and body weight (BW) on various RV function indices using a dedicated RV software for 3D RV end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), global and free wall RV longitudinal strain (RVLS), end-diastolic area (RVEDA), end-systolic area (RVESA), fractional area change (FAC), tricuspid annular plane systolic excursion (TAPSE), and tissue Doppler imaging (TVI)-derived systolic myocardial velocity of the lateral tricuspid annulus (S'). ANIMALS Healthy adult client-owned dogs (n = 211) of various breeds and ages. METHODS Prospective study. Reference intervals were estimated as statistical prediction intervals using allometric scaling for BW-dependent variables. Right-sided (upper limit) or left-sided (lower limit) 95% RIs were calculated for every variable. Inter- and intraobserver variability was determined. RESULTS Most variables showed clinically acceptable repeatability with coefficient of variation less than 10. Upper or respectively lower RI after allometric scaling to normalize for different BWs were: EDVn ≤ 2.5 mL/kg0.942 , ESVn ≤ 1.2 mL/kg0.962 , TAPSEn ≥ 4.5 mm0.285 , RVEDAn ≤ 1.4 cm2 /kg0.665 , RVESAn ≤ 0.8 cm2 /kg0.695 , and TVI S'n ≥ 5.6 cm/s/kg0.186 . The calculated limits for indices without allometric normalization were: EF > 42.1%, FAC > 30.0%, free wall RVLS < -20.8%, and global RVLS < -18.3%. CONCLUSIONS Echocardiographic RIs for RV structure and function are provided.
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Affiliation(s)
| | | | - Tommaso Vezzosi
- Anicura Istituto Veterinario Novara, Novara, Italy.,Department of Veterinary Sciences, University of Pisa, Pisa, Italy
| | - Rosalba Tognetti
- Department of Veterinary Sciences, University of Pisa, Pisa, Italy
| | - Nadja Sauter
- Statistical Consulting Unit StaBLab, LMU University, Munich, Germany
| | - Alexander Bauer
- Statistical Consulting Unit StaBLab, LMU University, Munich, Germany
| | - Jenny Eberhard
- Clinic of Small Animal Medicine, LMU University, Munich, Germany
| | - Jana Friederich
- Clinic of Small Animal Medicine, LMU University, Munich, Germany
| | - Gerhard Wess
- Clinic of Small Animal Medicine, LMU University, Munich, Germany
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40
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Lo Muzio FP, Rozzi G, Rossi S, Luciani GB, Foresti R, Cabassi A, Fassina L, Miragoli M. Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects. J Clin Med 2021; 10:5330. [PMID: 34830612 PMCID: PMC8623430 DOI: 10.3390/jcm10225330] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 11/08/2021] [Accepted: 11/10/2021] [Indexed: 12/21/2022] Open
Abstract
The human right ventricle is barely monitored during open-chest surgery due to the absence of intraoperative imaging techniques capable of elaborating its complex function. Accordingly, artificial intelligence could not be adopted for this specific task. We recently proposed a video-based approach for the real-time evaluation of the epicardial kinematics to support medical decisions. Here, we employed two supervised machine learning algorithms based on our technique to predict the patients' outcomes before chest closure. Videos of the beating hearts were acquired before and after pulmonary valve replacement in twelve Tetralogy of Fallot patients and recordings were properly labeled as the "unhealthy" and "healthy" classes. We extracted frequency-domain-related features to train different supervised machine learning models and selected their best characteristics via 10-fold cross-validation and optimization processes. Decision surfaces were built to classify two additional patients having good and unfavorable clinical outcomes. The k-nearest neighbors and support vector machine showed the highest prediction accuracy; the patients' class was identified with a true positive rate ≥95% and the decision surfaces correctly classified the additional patients in the "healthy" (good outcome) or "unhealthy" (unfavorable outcome) classes. We demonstrated that classifiers employed with our video-based technique may aid cardiac surgeons in decision making before chest closure.
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Affiliation(s)
- Francesco Paolo Lo Muzio
- Department of Surgery, Dentistry, Pediatrics and Gynecology, University of Verona, 37134 Verona, Italy; (F.P.L.M.); (G.R.); (G.B.L.)
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (S.R.); (R.F.); (A.C.)
| | - Giacomo Rozzi
- Department of Surgery, Dentistry, Pediatrics and Gynecology, University of Verona, 37134 Verona, Italy; (F.P.L.M.); (G.R.); (G.B.L.)
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (S.R.); (R.F.); (A.C.)
- Humanitas Research Hospital—IRCCS, Via Manzoni 56, 20089 Rozzano, MI, Italy
| | - Stefano Rossi
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (S.R.); (R.F.); (A.C.)
| | - Giovanni Battista Luciani
- Department of Surgery, Dentistry, Pediatrics and Gynecology, University of Verona, 37134 Verona, Italy; (F.P.L.M.); (G.R.); (G.B.L.)
| | - Ruben Foresti
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (S.R.); (R.F.); (A.C.)
| | - Aderville Cabassi
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (S.R.); (R.F.); (A.C.)
| | - Lorenzo Fassina
- Department of Electrical, Computer and Biomedical Engineering (DIII), University of Pavia, 27100 Pavia, Italy
| | - Michele Miragoli
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (S.R.); (R.F.); (A.C.)
- Humanitas Research Hospital—IRCCS, Via Manzoni 56, 20089 Rozzano, MI, Italy
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Ahmad A, Li H, Wan X, Zhong Y, Zhang Y, Liu J, Gao Y, Qian M, Lin Y, Yi L, Zhang L, Li Y, Xie M. Feasibility and Accuracy of a Fully Automated Right Ventricular Quantification Software With Three-Dimensional Echocardiography: Comparison With Cardiac Magnetic Resonance. Front Cardiovasc Med 2021; 8:732893. [PMID: 34746251 PMCID: PMC8566539 DOI: 10.3389/fcvm.2021.732893] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/10/2021] [Indexed: 11/22/2022] Open
Abstract
Background: A novel, fully automated right ventricular (RV) software for three-dimensional quantification of RV volumes and function was developed. The direct comparison of the software performance with cardiac magnetic resonance (CMR) was limited. Therefore, the aim of this study was to test the feasibility, accuracy, and reproducibility of a fully automated RV quantification software against CMR imaging as a reference. Methods: A total of 170 patients who underwent both CMR and three-dimensional echocardiography were enrolled. RV end-diastolic volume (RVEDV), RV end-systolic volume (RVESV), and RV ejection fraction (RVEF) were obtained using fully automated three-dimensional RV quantification software and compared with a CMR reference. For inter-technical agreement, Spearman correlation and Bland–Altman analysis were used. Results: The fully automated RV quantification software was feasible in 149 patients. RVEDV and RVESV were underestimated, and RVEF was overestimated compared with CMR values. RV measurements obtained from the manual editing method correlated better with CMR values than that without manual editing (RVEDV, 0.924 vs. 0.794: RVESV, 0.955 vs. 0.854; RVEF, 0.941 vs. 0.781 respectively, all p < 0.0001) with less bias and narrower limit of agreement (LOA). The bias and LOA for RV volumes and EF using the automated software without and with manual editing were greater in patients with severely impaired RV function or low frame rate than those with normal and mild impaired RV function, or high frame rate. The fully automated RV three-dimensional measurements were highly reproducible. Conclusion: The novel fully automated RV software shows good feasibility and reproducibility, and the measurements had a high correlation with CMR values. These findings support the routine application of the novel 3D automated RV software in clinical practice.
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Affiliation(s)
- Ashfaq Ahmad
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - He Li
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xiaojing Wan
- Department of Ultrasound, The First Affiliated Hospital of SooChow University, Suzhou, China
| | - Yi Zhong
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yanting Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Juanjuan Liu
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Ying Gao
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Mingzhu Qian
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yixia Lin
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Luyang Yi
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Li Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.,Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen, China
| | - Yuman Li
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Mingxing Xie
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.,Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen, China.,Tongji Medical College and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
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de Siqueira VS, Borges MM, Furtado RG, Dourado CN, da Costa RM. Artificial intelligence applied to support medical decisions for the automatic analysis of echocardiogram images: A systematic review. Artif Intell Med 2021; 120:102165. [PMID: 34629153 DOI: 10.1016/j.artmed.2021.102165] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 08/07/2021] [Accepted: 08/31/2021] [Indexed: 12/16/2022]
Abstract
The echocardiogram is a test that is widely used in Heart Disease Diagnoses. However, its analysis is largely dependent on the physician's experience. In this regard, artificial intelligence has become an essential technology to assist physicians. This study is a Systematic Literature Review (SLR) of primary state-of-the-art studies that used Artificial Intelligence (AI) techniques to automate echocardiogram analyses. Searches on the leading scientific article indexing platforms using a search string returned approximately 1400 articles. After applying the inclusion and exclusion criteria, 118 articles were selected to compose the detailed SLR. This SLR presents a thorough investigation of AI applied to support medical decisions for the main types of echocardiogram (Transthoracic, Transesophageal, Doppler, Stress, and Fetal). The article's data extraction indicated that the primary research interest of the studies comprised four groups: 1) Improvement of image quality; 2) identification of the cardiac window vision plane; 3) quantification and analysis of cardiac functions, and; 4) detection and classification of cardiac diseases. The articles were categorized and grouped to show the main contributions of the literature to each type of ECHO. The results indicate that the Deep Learning (DL) methods presented the best results for the detection and segmentation of the heart walls, right and left atrium and ventricles, and classification of heart diseases using images/videos obtained by echocardiography. The models that used Convolutional Neural Network (CNN) and its variations showed the best results for all groups. The evidence produced by the results presented in the tabulation of the studies indicates that the DL contributed significantly to advances in echocardiogram automated analysis processes. Although several solutions were presented regarding the automated analysis of ECHO, this area of research still has great potential for further studies to improve the accuracy of results already known in the literature.
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Affiliation(s)
- Vilson Soares de Siqueira
- Federal Institute of Tocantins, Av. Bernado Sayão, S/N, Santa Maria, Colinas do Tocantins, TO, Brazil; Federal University of Goias, Alameda Palmeiras, Quadra D, Câmpus Samambaia, Goiânia, GO, Brazil.
| | - Moisés Marcos Borges
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil
| | - Rogério Gomes Furtado
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil
| | - Colandy Nunes Dourado
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil. http://www.cdigoias.com.br
| | - Ronaldo Martins da Costa
- Federal University of Goias, Alameda Palmeiras, Quadra D, Câmpus Samambaia, Goiânia, GO, Brazil.
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43
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Li Y, Liang L, Guo D, Yang Y, Gong J, Zhang X, Zhang D, Jiang Z, Lu X. Right Ventricular Function Predicts Adverse Clinical Outcomes in Patients With Chronic Thromboembolic Pulmonary Hypertension: A Three-Dimensional Echocardiographic Study. Front Med (Lausanne) 2021; 8:697396. [PMID: 34497813 PMCID: PMC8419302 DOI: 10.3389/fmed.2021.697396] [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: 04/19/2021] [Accepted: 07/12/2021] [Indexed: 01/29/2023] Open
Abstract
Background: Right ventricular (RV) function plays a vital role in the prognosis of patients with chronic thromboembolic pulmonary hypertension (CTEPH). We used new machine learning (ML)-based fully automated software to quantify RV function using three-dimensional echocardiography (3DE) to predict adverse clinical outcomes in CTEPH patients. Methods: A total of 151 consecutive CTEPH patients were registered in this prospective study between April 2015 and July 2019. New ML-based methods were used for data management, and quantitative analysis of RV volume and ejection fraction (RVEF) was performed offline. RV structural and functional parameters were recorded using 3DE. CTEPH was diagnosed using right heart catheterization, and 62 patients underwent cardiac magnetic resonance to assess right heart function. Adverse clinical outcomes were defined as PH-related hospitalization with hemoptysis or increased RV failure, including conditions requiring balloon pulmonary angioplasty or pulmonary endarterectomy, as well as death. Results: The median follow-up time was 19.7 months (interquartile range, 0.5–54 months). Among the 151 CTEPH patients, 72 experienced adverse clinical outcomes. Multivariate Cox proportional-hazard analysis showed that ML-based 3DE analysis of RVEF was a predictor of adverse clinical outcomes (hazard ratio, 1.576; 95% confidence interval (CI), 1.046~2.372; P = 0.030). Conclusions: The new ML-based 3DE algorithm is a promising technique for rapid 3D quantification of RV function in CTEPH patients.
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Affiliation(s)
- Yidan Li
- Department of Echocardiography, Heart Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Lirong Liang
- Clinical Epidemiology & Tobacco Dependence Treatment Research Department, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Dichen Guo
- Department of Echocardiography, Heart Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Yuanhua Yang
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Juanni Gong
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Xinyuan Zhang
- Department of Echocardiography, Heart Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Di Zhang
- Clinical Epidemiology & Tobacco Dependence Treatment Research Department, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Zhe Jiang
- Department of Echocardiography, Heart Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Xiuzhang Lu
- Department of Echocardiography, Heart Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
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Labus J, Uhlig C. Role of Echocardiography for the Perioperative Assessment of the Right Ventricle. CURRENT ANESTHESIOLOGY REPORTS 2021. [DOI: 10.1007/s40140-021-00474-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Abstract
Purpose of Review
This review aims to highlight the perioperative echocardiographic evaluation of right ventricular (RV) function with strengths and limitations of commonly used and evolving techniques. It explains the value of transthoracic echocardiography (TTE) and transesophageal echocardiography (TEE) and describes the perioperative changes of RV function echocardiographers should be aware of.
Recent Findings
RV dysfunction is an entity with strong influence on outcome. However, its definition and assessment in the perioperative interval are not well-defined. Moreover, values assessed by TTE and TEE are not interchangeable; while some parameters seem to correlate well, others do not. Myocardial strain analysis and three-dimensional echocardiography may overcome the limitations of conventional echocardiographic measures and provide further insight into perioperative cardiac mechanics.
Summary
Echocardiography has become an essential part of modern anesthesiology in patients with RV dysfunction. It offers the opportunity to evaluate not only global but also regional RV function and distinguish alterations of RV contraction.
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Li Y, Guo D, Gong J, Wang J, Huang Q, Yang S, Zhang X, Hu H, Jiang Z, Yang Y, Lu X. Right Ventricular Function and Its Coupling With Pulmonary Circulation in Precapillary Pulmonary Hypertension: A Three-Dimensional Echocardiographic Study. Front Cardiovasc Med 2021; 8:690606. [PMID: 34277739 PMCID: PMC8282926 DOI: 10.3389/fcvm.2021.690606] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Accepted: 06/09/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: To assess right ventricular (RV) function and RV-pulmonary arterial (PA) coupling by three-dimensions echocardiography and investigate the ability of RV-PA coupling to predict adverse clinical outcomes in patients with precapillary pulmonary hypertension (PH). Methods: We retrospectively collected a longitudinal cohort of 203 consecutive precapillary PH patients. RV volume, RV ejection fraction (RVEF), and RV longitudinal strain (RVLS) were quantitatively determined offline by 3D echocardiography. RV-PA coupling parameters including the RVEF/PA systolic pressure (PASP) ratio, pulmonary arterial compliance (PAC), and total pulmonary resistance (TPR) were recorded. Results: Over a median follow-up period of 20.9 months (interquartile range, 0.1-67.4 months), 87 (42.9%) of 203 patients experienced adverse clinical outcomes. With increasing World Health Organization functional class (WHO-FC), significant trends were observed in increasing RV volume, decreasing RVEF, and worsening RVLS. RV arterial coupling (RVAC) and PAC were lower and TPR was higher for WHO-FC III+IV than WHO-FC I or II. The RVEF/PASP ratio showed a significant correlation with RVLS. RVAC had a stronger correlation with the RVEF/PASP ratio than other indices. Multivariate Cox proportional-hazard analysis identified a lower 3D RVEF and worse RVLS as strong predictors of adverse clinical events. RVAC, TPR, and PAC had varying degrees of predictive value, with optimal cutoff values of 0.74, 11.64, and 1.18, respectively. Conclusions: Precapillary-PH with RV-PA uncoupling as expressed by a RVEF/PASP ratio <0.44 was associated with adverse clinical outcomes. PAC decreased and TPR increased with increasing WHO-FC, with TPR showing better independent predictive value.
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Affiliation(s)
- Yidan Li
- Department of Echocardiography, Heart Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Dichen Guo
- Department of Echocardiography, Heart Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Juanni Gong
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Jianfeng Wang
- Department of Intervention, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Qiang Huang
- Department of Intervention, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Shu Yang
- Philips (China) Investment Co. Ltd., Beijing, China
| | - Xinyuan Zhang
- Department of Echocardiography, Heart Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Huimin Hu
- Department of Echocardiography, Heart Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Zhe Jiang
- Department of Echocardiography, Heart Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Yuanhua Yang
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Xiuzhang Lu
- Department of Echocardiography, Heart Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
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Otani K, Nabeshima Y, Kitano T, Takeuchi M. Accuracy of fully automated right ventricular quantification software with 3D echocardiography: direct comparison with cardiac magnetic resonance and semi-automated quantification software. Eur Heart J Cardiovasc Imaging 2021; 21:787-795. [PMID: 31549722 DOI: 10.1093/ehjci/jez236] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 08/30/2019] [Indexed: 11/14/2022] Open
Abstract
AIMS The aim of this study was to determine the accuracy and reproducibility of a novel, fully automated 3D echocardiography (3DE) right ventricular (RV) quantification software compared with cardiac magnetic resonance (CMR) and semi-automated 3DE RV quantification software. METHODS AND RESULTS RV volumes and the RV ejection fraction (RVEF) were measured using a fully automated software (Philips), a semi-automated software (TomTec), and CMR in 100 patients who had undergone both CMR and 3DE examinations on the same day. The feasibility of the fully automated software was 91%. Although the fully automated software, without any manual editing, significantly underestimated RV end-diastolic volume (bias: -12.6 mL, P < 0.001) and stroke volume (-5.1 mL, P < 0.001) compared with CMR, there were good correlations between the two modalities (r = 0.82 and 0.78). No significant differences in RVEF between the fully automated software and CMR were observed, and there was a fair correlation (r = 0.72). The RVEF determined by the semi-automated software was significantly larger than that by CMR or the fully automated software (P < 0.001). The fully automated software had a shorter analysis time compared with the semi-automated software (15 s vs. 120 s, P < 0.001) and had a good reproducibility. CONCLUSION A novel, fully automated 3DE RV quantification software underestimated RV volumes but successfully approximated RVEF when compared with CMR. No inferiority of this software was observed when compared with the semi-automated software. Rapid analysis and higher reproducibility also support the routine adoption of this method in the daily clinical workflow.
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Affiliation(s)
- Kyoko Otani
- Department of Laboratory and Transfusion Medicine, University of Occupational and Environmental Health Hospital, Iseigaoka, Yahatanishi-ku, Kitakyushu 807-8556, Japan
| | - Yosuke Nabeshima
- Second Department of Internal Medicine, University of Occupational and Environmental Health, School of Medicine, Iseigaoka, Yahatanishi-ku, Kitakyushu 807-8555, Japan
| | - Tetsuji Kitano
- Second Department of Internal Medicine, University of Occupational and Environmental Health, School of Medicine, Iseigaoka, Yahatanishi-ku, Kitakyushu 807-8555, Japan
| | - Masaaki Takeuchi
- Department of Laboratory and Transfusion Medicine, University of Occupational and Environmental Health Hospital, Iseigaoka, Yahatanishi-ku, Kitakyushu 807-8556, Japan
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Malagoli A, Albini A, Mandoli GE, Baggiano A, Vinco G, Bandera F, D'Andrea A, Esposito R, D'Ascenzi F, Sorrentino R, Santoro C, Benfari G, Contorni F, Cameli M. Multimodality imaging of the ischemic right ventricle: an overview and proposal of a diagnostic algorithm. Int J Cardiovasc Imaging 2021; 37:3343-3354. [PMID: 34114150 DOI: 10.1007/s10554-021-02309-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 06/04/2021] [Indexed: 10/21/2022]
Abstract
Right ventricular (RV) involvement is frequently detected in patients presenting with acute left ventricular myocardial infarction. The ischemic right ventricle carries a dismal outcome by predisposing the heart to arrhythmic events and mechanical or hemodynamic complications. A comprehensive RV evaluation by multimodality imaging could guide clinical practice but has always been a conundrum for the imagers. Two-dimensional echocardiography is the best first-line tool due to its availability of bedside capabilities. More advanced imaging techniques provide a more comprehensive evaluation of the complex RV geometry but are mostly reserved for the post-acute setting. Three-dimensional echocardiography has improved the evaluation of RV volumes and function. The recent application of speckle-tracking echocardiography to the right ventricle appears promising, allowing the earlier detection of subtle RV dysfunction. Cardiac magnetic resonance imaging is considered the gold standard for the RV assessment. Cardiac multidetector computed tomography could be a reliable alternative. The aim of this review is to focus on the growing importance of multimodality imaging of the ischemic right ventricle and to propose a diagnostic algorithm, in order to reach a comprehensive assessment of this too frequently neglected chamber.
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Affiliation(s)
- A Malagoli
- Division of Cardiology, Nephro-Cardiovascular Department, Baggiovara Hospital, University of Modena and Reggio Emilia, Modena, Italy.
| | - A Albini
- Division of Cardiology, Nephro-Cardiovascular Department, Policlinico University Hospital of Modena, University of Modena and Reggio Emilia, Modena, Italy
| | - G E Mandoli
- Department of Cardiovascular Diseases, University of Siena, Siena, Italy
| | - A Baggiano
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Milan, Italy.,Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - G Vinco
- Department of Medicine, University of Verona, Verona, Italy
| | - F Bandera
- Cardiology University Department, Heart Failure Unit, Department of Biomedical Sciences for Health, IRCCS, Policlinico San Donato, San Donato Milanese, Milan, Italy.,Department of Biomedical Sciences for Health, University of Milano, Milan, Italy
| | - A D'Andrea
- Division of Cardiology, Umberto I' Hospital Nocera Inferiore (Salerno), Luigi Vanvitelli University, Caserta, Italy
| | - R Esposito
- Department of Advanced Biomedical Science, Federico II University Hospital Naples, Naples, Italy
| | - F D'Ascenzi
- Department of Cardiovascular Diseases, University of Siena, Siena, Italy
| | - R Sorrentino
- Department of Advanced Biomedical Science, Federico II University Hospital Naples, Naples, Italy
| | - C Santoro
- Department of Advanced Biomedical Science, Federico II University Hospital Naples, Naples, Italy
| | - G Benfari
- Division of Cardiology, Department of Medicine, University of Verona, Verona, Italy
| | - F Contorni
- Department of Cardiovascular Diseases, University of Siena, Siena, Italy
| | - M Cameli
- Department of Cardiovascular Diseases, University of Siena, Siena, Italy
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48
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Narang A, Bae R, Hong H, Thomas Y, Surette S, Cadieu C, Chaudhry A, Martin RP, McCarthy PM, Rubenson DS, Goldstein S, Little SH, Lang RM, Weissman NJ, Thomas JD. Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use. JAMA Cardiol 2021; 6:624-632. [PMID: 33599681 PMCID: PMC8204203 DOI: 10.1001/jamacardio.2021.0185] [Citation(s) in RCA: 150] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 01/20/2021] [Indexed: 12/14/2022]
Abstract
Importance Artificial intelligence (AI) has been applied to analysis of medical imaging in recent years, but AI to guide the acquisition of ultrasonography images is a novel area of investigation. A novel deep-learning (DL) algorithm, trained on more than 5 million examples of the outcome of ultrasonographic probe movement on image quality, can provide real-time prescriptive guidance for novice operators to obtain limited diagnostic transthoracic echocardiographic images. Objective To test whether novice users could obtain 10-view transthoracic echocardiographic studies of diagnostic quality using this DL-based software. Design, Setting, and Participants This prospective, multicenter diagnostic study was conducted in 2 academic hospitals. A cohort of 8 nurses who had not previously conducted echocardiograms was recruited and trained with AI. Each nurse scanned 30 patients aged at least 18 years who were scheduled to undergo a clinically indicated echocardiogram at Northwestern Memorial Hospital or Minneapolis Heart Institute between March and May 2019. These scans were compared with those of sonographers using the same echocardiographic hardware but without AI guidance. Interventions Each patient underwent paired limited echocardiograms: one from a nurse without prior echocardiography experience using the DL algorithm and the other from a sonographer without the DL algorithm. Five level 3-trained echocardiographers independently and blindly evaluated each acquisition. Main Outcomes and Measures Four primary end points were sequentially assessed: qualitative judgement about left ventricular size and function, right ventricular size, and the presence of a pericardial effusion. Secondary end points included 6 other clinical parameters and comparison of scans by nurses vs sonographers. Results A total of 240 patients (mean [SD] age, 61 [16] years old; 139 men [57.9%]; 79 [32.9%] with body mass indexes >30) completed the study. Eight nurses each scanned 30 patients using the DL algorithm, producing studies judged to be of diagnostic quality for left ventricular size, function, and pericardial effusion in 237 of 240 cases (98.8%) and right ventricular size in 222 of 240 cases (92.5%). For the secondary end points, nurse and sonographer scans were not significantly different for most parameters. Conclusions and Relevance This DL algorithm allows novices without experience in ultrasonography to obtain diagnostic transthoracic echocardiographic studies for evaluation of left ventricular size and function, right ventricular size, and presence of a nontrivial pericardial effusion, expanding the reach of echocardiography to clinical settings in which immediate interrogation of anatomy and cardiac function is needed and settings with limited resources.
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Affiliation(s)
- Akhil Narang
- Bluhm Cardiovascular Institute, Northwestern University, Chicago, Illinois
| | - Richard Bae
- Division of Cardiology, Minneapolis Heart Institute, Minneapolis, Minnesota
| | - Ha Hong
- Caption Health, Brisbane, California
| | | | | | | | | | | | | | | | - Steven Goldstein
- Division of Cardiology, MedStar Washington Hospital Center, Washington, DC
| | | | - Roberto M. Lang
- Section of Cardiology, The University of Chicago, Chicago, Illinois
| | | | - James D. Thomas
- Bluhm Cardiovascular Institute, Northwestern University, Chicago, Illinois
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Volpato V, Mantegazza V, Tamborini G, Gripari P, Muratori M, Fusini L, Zanobini M, Alamanni F, Pepi M. Tricuspid annular dilation in patients undergoing early mitral valve surgery: is it an old story? Int J Cardiovasc Imaging 2021; 37:2439-2446. [PMID: 33899135 DOI: 10.1007/s10554-021-02223-1] [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: 12/26/2020] [Accepted: 03/09/2021] [Indexed: 11/29/2022]
Abstract
Patients with mitral valve prolapse (MVP), undergoing early surgery for severe regurgitation, are usually characterized by a low degree of right chambers' remodeling. In this selected population, the mechanisms leading to tricuspid annular (TA) dilatation (TAD) are not well understood. In this setting, we aimed to evaluate, using three-dimensional echocardiography (3DE), how right chambers affect TA size and might contribute to functional tricuspid regurgitation (FTR) progression. We studied 159 patients treated with early isolated surgery for MVP, characterized by: sinus rhythm; normal biventricular function; normal or elevated pulmonary artery pressure; tricuspid regurgitation (TR) ≤ mild; no concomitant cardiac disease. All patients reached a 3-year echocardiographic follow-up. Based on two-dimensional echocardiography, patients were divided in Group 1 (N = 68, 43%, TAD, TA ≥ 21 mm/m2) and Group 2 (N = 91, 57%, no TAD, TA < 21 mm/m2). By 3DE, Group 1 showed larger TA size, right atrial (RA) volume and right ventricular (RV) conical remodeling compared to Group 2 (p < 0.05). The multivariate analysis revealed that RA volume, RV basal diameter and function were independently correlated to TA size (p < 0.05). At the 3-year follow-up there was a low incidence of FTR, with a trend towards FTR progression in Group 1 (p = 0.07). In patients undergoing early surgery for MVP, TAD seems to result from distinctive early-onset geometrical changes of the right chambers, preceding TR, RV dilatation and pulmonary hypertension at rest. An integrated approach, including right chambers' assessment by 3DE, might help to better recognized patients at higher risk for TAD and, potentially for FTR.
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Affiliation(s)
- Valentina Volpato
- Department of Cardiovascular Imaging - Centro, Cardiologico Fondazione Monzino IRCCS, via Carlo Parea 4, 20138, Milan, MI, Italy.
| | - Valentina Mantegazza
- Department of Cardiovascular Imaging - Centro, Cardiologico Fondazione Monzino IRCCS, via Carlo Parea 4, 20138, Milan, MI, Italy
| | - Gloria Tamborini
- Department of Cardiovascular Imaging - Centro, Cardiologico Fondazione Monzino IRCCS, via Carlo Parea 4, 20138, Milan, MI, Italy
| | - Paola Gripari
- Department of Cardiovascular Imaging - Centro, Cardiologico Fondazione Monzino IRCCS, via Carlo Parea 4, 20138, Milan, MI, Italy
| | - Manuela Muratori
- Department of Cardiovascular Imaging - Centro, Cardiologico Fondazione Monzino IRCCS, via Carlo Parea 4, 20138, Milan, MI, Italy
| | - Laura Fusini
- Department of Cardiovascular Imaging - Centro, Cardiologico Fondazione Monzino IRCCS, via Carlo Parea 4, 20138, Milan, MI, Italy
| | - Marco Zanobini
- Department of Cardiovascular Imaging - Centro, Cardiologico Fondazione Monzino IRCCS, via Carlo Parea 4, 20138, Milan, MI, Italy
| | - Francesco Alamanni
- Department of Cardiovascular Imaging - Centro, Cardiologico Fondazione Monzino IRCCS, via Carlo Parea 4, 20138, Milan, MI, Italy
| | - Mauro Pepi
- Department of Cardiovascular Imaging - Centro, Cardiologico Fondazione Monzino IRCCS, via Carlo Parea 4, 20138, Milan, MI, Italy
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50
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Vijiiac A, Onciul S, Guzu C, Scarlatescu A, Petre I, Zamfir D, Onut R, Deaconu S, Dorobantu M. Forgotten No More-The Role of Right Ventricular Dysfunction in Heart Failure with Reduced Ejection Fraction: An Echocardiographic Perspective. Diagnostics (Basel) 2021; 11:diagnostics11030548. [PMID: 33808566 PMCID: PMC8003573 DOI: 10.3390/diagnostics11030548] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 03/09/2021] [Accepted: 03/15/2021] [Indexed: 12/28/2022] Open
Abstract
During the last decade, studies have raised awareness of the crucial role that the right ventricle plays in various clinical settings, including diseases primarily linked to the left ventricle. The assessment of right ventricular performance with conventional echocardiography is challenging. Novel echocardiographic techniques improve the functional assessment of the right ventricle and they show good correlation with the gold standard represented by cardiac magnetic resonance. This review summarizes the traditional and innovative echocardiographic techniques used in the functional assessment of the right ventricle, focusing on the role of right ventricular dysfunction in heart failure with reduced ejection fraction and providing a perspective on recent evidence from literature.
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Affiliation(s)
- Aura Vijiiac
- Department of Cardiology, Carol Davila University of Medicine and Pharmacy, 050513 Bucharest, Romania; (S.O.); (I.P.); (S.D.); (M.D.)
- Department of Cardiology, Emergency Clinical Hospital, 014461 Bucharest, Romania; (C.G.); (A.S.); (D.Z.); (R.O.)
- Correspondence: ; Tel.: +40-(75)-2298-189
| | - Sebastian Onciul
- Department of Cardiology, Carol Davila University of Medicine and Pharmacy, 050513 Bucharest, Romania; (S.O.); (I.P.); (S.D.); (M.D.)
- Department of Cardiology, Emergency Clinical Hospital, 014461 Bucharest, Romania; (C.G.); (A.S.); (D.Z.); (R.O.)
| | - Claudia Guzu
- Department of Cardiology, Emergency Clinical Hospital, 014461 Bucharest, Romania; (C.G.); (A.S.); (D.Z.); (R.O.)
| | - Alina Scarlatescu
- Department of Cardiology, Emergency Clinical Hospital, 014461 Bucharest, Romania; (C.G.); (A.S.); (D.Z.); (R.O.)
| | - Ioana Petre
- Department of Cardiology, Carol Davila University of Medicine and Pharmacy, 050513 Bucharest, Romania; (S.O.); (I.P.); (S.D.); (M.D.)
- Department of Cardiology, Emergency Clinical Hospital, 014461 Bucharest, Romania; (C.G.); (A.S.); (D.Z.); (R.O.)
| | - Diana Zamfir
- Department of Cardiology, Emergency Clinical Hospital, 014461 Bucharest, Romania; (C.G.); (A.S.); (D.Z.); (R.O.)
| | - Roxana Onut
- Department of Cardiology, Emergency Clinical Hospital, 014461 Bucharest, Romania; (C.G.); (A.S.); (D.Z.); (R.O.)
| | - Silvia Deaconu
- Department of Cardiology, Carol Davila University of Medicine and Pharmacy, 050513 Bucharest, Romania; (S.O.); (I.P.); (S.D.); (M.D.)
- Department of Cardiology, Emergency Clinical Hospital, 014461 Bucharest, Romania; (C.G.); (A.S.); (D.Z.); (R.O.)
| | - Maria Dorobantu
- Department of Cardiology, Carol Davila University of Medicine and Pharmacy, 050513 Bucharest, Romania; (S.O.); (I.P.); (S.D.); (M.D.)
- Department of Cardiology, Emergency Clinical Hospital, 014461 Bucharest, Romania; (C.G.); (A.S.); (D.Z.); (R.O.)
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