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Sadeghi P, Karimi H, Lavafian A, Rashedi R, Samieefar N, Shafiekhani S, Rezaei N. Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future perspective. Expert Rev Clin Immunol 2024:1-18. [PMID: 38771915 DOI: 10.1080/1744666x.2024.2359019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 05/20/2024] [Indexed: 05/23/2024]
Abstract
INTRODUCTION Autoimmune disorders affect 4.5% to 9.4% of children, significantly reducing their quality of life. The diagnosis and prognosis of autoimmune diseases are uncertain because of the variety of onset and development. Machine learning can identify clinically relevant patterns from vast amounts of data. Hence, its introduction has been beneficial in the diagnosis and management of patients. AREAS COVERED This narrative review was conducted through searching various electronic databases, including PubMed, Scopus, and Web of Science. This study thoroughly explores the current knowledge and identifies the remaining gaps in the applications of machine learning specifically in the context of pediatric autoimmune and related diseases. EXPERT OPINION Machine learning algorithms have the potential to completely change how pediatric autoimmune disorders are identified, treated, and managed. Machine learning can assist physicians in making more precise and fast judgments, identifying new biomarkers and therapeutic targets, and personalizing treatment strategies for each patient by utilizing massive datasets and powerful analytics.
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Affiliation(s)
- Parniyan Sadeghi
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hanie Karimi
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Atiye Lavafian
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- School of Medicine, Semnan University of Medical Science, Semnan, Iran
| | - Ronak Rashedi
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- USERN Office, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Noosha Samieefar
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- USERN Office, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sajad Shafiekhani
- Department of Biomedical Engineering, Buein Zahra Technical University, Qazvin, Iran
| | - Nima Rezaei
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
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Fraga LL, Nascimento BR, Haiashi BC, Ferreira AM, Silva MHA, Ribeiro IKDS, Silva GA, Vinhal WC, Coimbra MM, Silva CA, Machado CRL, Pires MC, Diniz MG, Santos LPA, Amaral AM, Diamante LC, Fava HL, Sable C, Nunes MCP, Ribeiro ALP, Cardoso CS. Combination of Tele-Cardiology Tools for Cardiovascular Risk Stratification in Primary Care: Data from the PROVAR+ Study. Arq Bras Cardiol 2024; 121:e20230653. [PMID: 38597537 DOI: 10.36660/abc.20230653] [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: 09/14/2023] [Accepted: 12/13/2023] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Tele-cardiology tools are valuable strategies to improve risk stratification. OBJECTIVE We aimed to evaluate the accuracy of tele-electrocardiography (ECG) to predict abnormalities in screening echocardiography (echo) in primary care (PC). METHODS In 17 months, 6 health providers at 16 PC units were trained on simplified handheld echo protocols. Tele-ECGs were recorded for final diagnosis by a cardiologist. Consented patients with major ECG abnormalities by the Minnesota code, and a 1:5 sample of normal individuals underwent clinical questionnaire and screening echo interpreted remotely. Major heart disease was defined as moderate/severe valve disease, ventricular dysfunction/hypertrophy, pericardial effusion, or wall-motion abnormalities. Association between major ECG and echo abnormalities was assessed by logistic regression as follows: 1) unadjusted model; 2) model 1 adjusted for age/sex; 3) model 2 plus risk factors (hypertension/diabetes); 4) model 3 plus history of cardiovascular disease (Chagas/rheumatic heart disease/ischemic heart disease/stroke/heart failure). P-values < 0.05 were considered significant. RESULTS A total 1,411 patients underwent echo; 1,149 (81%) had major ECG abnormalities. Median age was 67 (IQR 60 to 74) years, and 51.4% were male. Major ECG abnormalities were associated with a 2.4-fold chance of major heart disease on echo in bivariate analysis (OR = 2.42 [95% CI 1.76 to 3.39]), and remained significant after adjustments in models (p < 0.001) 2 (OR = 2.57 [95% CI 1.84 to 3.65]), model 3 (OR = 2.52 [95% CI 1.80 to3.58]), and model 4 (OR = 2.23 [95%CI 1.59 to 3.19]). Age, male sex, heart failure, and ischemic heart disease were also independent predictors of major heart disease on echo. CONCLUSIONS Tele-ECG abnormalities increased the likelihood of major heart disease on screening echo, even after adjustments for demographic and clinical variables.
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Affiliation(s)
- Lucas Leal Fraga
- Hospital das Clínicas da Universidade Federal de Minas Gerais - Serviço de Cardiologia e Cirurgia Carvdiovascular, Belo Horizonte, MG - Brasil
| | - Bruno Ramos Nascimento
- Hospital das Clínicas da Universidade Federal de Minas Gerais - Serviço de Cardiologia e Cirurgia Carvdiovascular, Belo Horizonte, MG - Brasil
- Hospital Madre Teresa - Serviço de Hemodinâmica, Belo Horizonte, MG - Brasil
- Universidade Federal de Minas Gerais - Departamento de Clínica Médica - Faculdade de Medicina, Belo Horizonte, MG - Brasil
| | - Beatriz Costa Haiashi
- Hospital das Clínicas da Universidade Federal de Minas Gerais - Centro de Telessaúde, Belo Horizonte, MG - Brasil
| | - Alexandre Melo Ferreira
- Hospital das Clínicas da Universidade Federal de Minas Gerais - Centro de Telessaúde, Belo Horizonte, MG - Brasil
| | - Mauro Henrique Agapito Silva
- Hospital das Clínicas da Universidade Federal de Minas Gerais - Centro de Telessaúde, Belo Horizonte, MG - Brasil
| | | | - Gabriela Aparecida Silva
- Universidade Federal de São João del Rei - Campus Centro-Oeste Dona Lindu - Campus Divinópolis, Divinópolis, MG - Brasil
| | - Wanessa Campos Vinhal
- Universidade Federal de São João del Rei - Campus Centro-Oeste Dona Lindu - Campus Divinópolis, Divinópolis, MG - Brasil
| | - Mariela Mata Coimbra
- Universidade Federal de São João del Rei - Campus Centro-Oeste Dona Lindu - Campus Divinópolis, Divinópolis, MG - Brasil
| | - Cássia Aparecida Silva
- Hospital das Clínicas da Universidade Federal de Minas Gerais - Serviço de Cardiologia e Cirurgia Carvdiovascular, Belo Horizonte, MG - Brasil
| | - Cristiana Rosa Lima Machado
- Universidade Federal de São João del Rei - Campus Centro-Oeste Dona Lindu - Campus Divinópolis, Divinópolis, MG - Brasil
| | - Magda C Pires
- Universidade Federal de Minas Gerais - Instituto de Ciências Exatas - Departamento de Estatística, Belo Horizonte, MG - Brasil
| | - Marina Gomes Diniz
- Hospital das Clínicas da Universidade Federal de Minas Gerais - Centro de Telessaúde, Belo Horizonte, MG - Brasil
| | | | - Arthur Maia Amaral
- Universidade Federal de Ouro Preto - Departamento de Medicina, Ouro Preto, MG - Brasil
| | - Lucas Chaves Diamante
- Hospital das Clínicas da Universidade Federal de Minas Gerais - Centro de Telessaúde, Belo Horizonte, MG - Brasil
| | - Henrique Leão Fava
- Hospital das Clínicas da Universidade Federal de Minas Gerais - Centro de Telessaúde, Belo Horizonte, MG - Brasil
| | - Craig Sable
- Children's National Health System - Cardiology, Washington, District of Columbia - EUA
| | - Maria Carmo Pereira Nunes
- Universidade Federal de Minas Gerais - Departamento de Clínica Médica - Faculdade de Medicina, Belo Horizonte, MG - Brasil
- Hospital das Clínicas da Universidade Federal de Minas Gerais - Centro de Telessaúde, Belo Horizonte, MG - Brasil
| | - Antonio Luiz P Ribeiro
- Hospital das Clínicas da Universidade Federal de Minas Gerais - Centro de Telessaúde, Belo Horizonte, MG - Brasil
| | - Clareci Silva Cardoso
- Universidade Federal de São João del Rei - Campus Centro-Oeste Dona Lindu - Campus Divinópolis, Divinópolis, MG - Brasil
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Rwebembera J, Marangou J, Mwita JC, Mocumbi AO, Mota C, Okello E, Nascimento B, Thorup L, Beaton A, Kado J, Kaethner A, Kumar RK, Lawrenson J, Marijon E, Mirabel M, Nunes MCP, Piñeiro D, Pinto F, Ralston K, Sable C, Sanyahumbi A, Saxena A, Sliwa K, Steer A, Viali S, Wheaton G, Wilson N, Zühlke L, Reményi B. 2023 World Heart Federation guidelines for the echocardiographic diagnosis of rheumatic heart disease. Nat Rev Cardiol 2024; 21:250-263. [PMID: 37914787 DOI: 10.1038/s41569-023-00940-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/13/2023] [Indexed: 11/03/2023]
Abstract
Rheumatic heart disease (RHD) is an important and preventable cause of morbidity and mortality among children and young adults in low-income and middle-income countries, as well as among certain at-risk populations living in high-income countries. The 2012 World Heart Federation echocardiographic criteria provided a standardized approach for the identification of RHD and facilitated an improvement in early case detection. The 2012 criteria were used to define disease burden in numerous epidemiological studies, but researchers and clinicians have since highlighted limitations that have prompted a revision. In this updated version of the guidelines, we incorporate evidence from a scoping review, an expert panel and end-user feedback and present an approach for active case finding for RHD, including the use of screening and confirmatory criteria. These guidelines also introduce a new stage-based classification for RHD to identify the risk of disease progression. They describe the latest evidence and recommendations on population-based echocardiographic active case finding and risk stratification. Secondary antibiotic prophylaxis, echocardiography equipment and task sharing for RHD active case finding are also discussed. These World Heart Federation 2023 guidelines provide a concise and updated resource for clinical and research applications in RHD-endemic regions.
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Affiliation(s)
| | - James Marangou
- Global and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, Northern Territory, Australia
- Department of Cardiology, Royal Perth Hospital, Perth, Western Australia, Australia
- Department of Cardiology, Fiona Stanley Hospital, Perth, Western Australia, Australia
| | - Julius Chacha Mwita
- Department of Internal Medicine, University of Botswana and Princess Marina Hospital, Gaborone, Botswana
| | | | - Cleonice Mota
- Departamento de Paediatria, Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo, Horizonte, Brazil
- Divisão de Cardiologia Pediátrica e Fetal/Serviço de Cardiologia e Cirurgia Cardiovascular e Serviço de Paediatria, Hospital das Clínicas da Universidade Federal de Minas Gerais, Belo, Horizonte, Brazil
| | - Emmy Okello
- Division of Adult Cardiology, Uganda Heart Institute, Kampala, Uganda
| | - Bruno Nascimento
- Departamento de Clínica Médica, Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo, Horizonte, Brazil
- Serviço de Cardiologia e Cirurgia Cardiovascular, Hospital das Clínicas da Universidade Federal de Minas Gerais, Belo, Horizonte, Brazil
| | - Lene Thorup
- Department of Cardiothoracic Surgery, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Andrea Beaton
- Department of Paediatrics, School of Medicine, University of Cincinnati, Cincinnati, OH, USA
- Division of Cardiology, The Heart Institute, Cincinnati Children's Medical Center, Cincinnati, OH, USA
| | - Joseph Kado
- Wesfarmers Centre of Vaccine and Infectious Diseases, Telethon Kids Institute, Perth, Western Australia, Australia
- School of Medicine, University of Western Australia, Perth, Western Australia, Australia
| | - Alexander Kaethner
- Global and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, Northern Territory, Australia
- NT Cardiac, Darwin, Northern Territory, Australia
| | | | - John Lawrenson
- Paediatric Cardiology Service of the Western Cape, Red Cross War Memorial Children's Hospital and Tygerberg Hospital, Cape Town, South Africa
- Department of Paediatrics and Child Health, Stellenbosch University, Stellenbosch, South Africa
| | - Eloi Marijon
- Division of Cardiology, European Georges Pompidou Hospital, Paris, France
| | | | - Maria Carmo Pereira Nunes
- Departamento de Clínica Médica, Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo, Horizonte, Brazil
- Serviço de Cardiologia e Cirurgia Cardiovascular, Hospital das Clínicas da Universidade Federal de Minas Gerais, Belo, Horizonte, Brazil
| | - Daniel Piñeiro
- Faculty of Medicine, University of Buenos Aires, Buenos Aires, Argentina
| | - Fausto Pinto
- Cardiology Department, Centro Hospitalar Universitário Lisboa Norte, Centro Académico de Medicina de Lisboa, The Cardiovascular Centre of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | | | - Craig Sable
- Division of Cardiology, Children's National Hospital, Washington, DC, USA
| | - Amy Sanyahumbi
- Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA
| | - Anita Saxena
- Pt BD Sharma University of Health Sciences, Rohtak, India
| | - Karen Sliwa
- Cape Heart Institute, Department of Medicine and Cardiology, Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
| | - Andrew Steer
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
- Tropical Diseases Research Group, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of General Medicine, Royal Children's Hospital, Melbourne, Victoria, Australia
| | | | - Gavin Wheaton
- Women's and Children's Hospital, Adelaide, South Australia, Australia
| | - Nigel Wilson
- Green Lane Paediatric and Congenital Cardiac Services, Starship Hospital, Te Whatu Ora, Auckland, New Zealand
| | - Liesl Zühlke
- South African Medical Research Council, Extramural Research & Internal Portfolio, Cape Town, South Africa
- Division of Paediatric Cardiology, Department of Paediatrics and Child Health, University of Cape Town, Cape Town, South Africa
| | - Bo Reményi
- Global and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, Northern Territory, Australia
- NT Cardiac, Darwin, Northern Territory, Australia
- Department of Paediatrics, Royal Darwin Hospital, Darwin, Australia
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Chang A, Wu X, Liu K. Deep learning from latent spatiotemporal information of the heart: Identifying advanced bioimaging markers from echocardiograms. BIOPHYSICS REVIEWS 2024; 5:011304. [PMID: 38559589 PMCID: PMC10978053 DOI: 10.1063/5.0176850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 03/01/2024] [Indexed: 04/04/2024]
Abstract
A key strength of echocardiography lies in its integration of comprehensive spatiotemporal cardiac imaging data in real-time, to aid frontline or bedside patient risk stratification and management. Nonetheless, its acquisition, processing, and interpretation are known to all be subject to heterogeneity from its reliance on manual and subjective human tracings, which challenges workflow and protocol standardization and final interpretation accuracy. In the era of advanced computational power, utilization of machine learning algorithms for big data analytics in echocardiography promises reduction in cost, cognitive errors, and intra- and inter-observer variability. Novel spatiotemporal deep learning (DL) models allow the integration of temporal arm information based on unlabeled pixel echocardiographic data for convolution of an adaptive semantic spatiotemporal calibration to construct personalized 4D heart meshes, assess global and regional cardiac function, detect early valve pathology, and differentiate uncommon cardiovascular disorders. Meanwhile, data visualization on spatiotemporal DL prediction models helps extract latent temporal imaging features to develop advanced imaging biomarkers in early disease stages and advance our understanding of pathophysiology to support the development of personalized prevention or treatment strategies. Since portable echocardiograms have been increasingly used as point-of-care imaging tools to aid rural care delivery, the application of these new spatiotemporal DL techniques show the potentials in streamlining echocardiographic acquisition, processing, and data analysis to improve workflow standardization and efficiencies, and provide risk stratification and decision supporting tools in real-time, to prompt the building of new imaging diagnostic networks to enhance rural healthcare engagement.
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Affiliation(s)
- Amanda Chang
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Iowa, Iowa City, Iowa 52242, USA
| | - Xiaodong Wu
- Department of Electrical and Computer Engineering, College of Engineering, University of Iowa, Iowa City, Iowa 52242, USA
| | - Kan Liu
- Division of Cardiology, Department of Internal Medicine, Washington University in St. Louis, St. Louis, Missouri 63110, USA
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Brown K, Roshanitabrizi P, Rwebembera J, Okello E, Beaton A, Linguraru MG, Sable CA. Using Artificial Intelligence for Rheumatic Heart Disease Detection by Echocardiography: Focus on Mitral Regurgitation. J Am Heart Assoc 2024; 13:e031257. [PMID: 38226515 PMCID: PMC10926790 DOI: 10.1161/jaha.123.031257] [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: 06/02/2023] [Accepted: 10/18/2023] [Indexed: 01/17/2024]
Abstract
BACKGROUND Identification of children with latent rheumatic heart disease (RHD) by echocardiography, before onset of symptoms, provides an opportunity to initiate secondary prophylaxis and prevent disease progression. There have been limited artificial intelligence studies published assessing the potential of machine learning to detect and analyze mitral regurgitation or to detect the presence of RHD on standard portable echocardiograms. METHODS AND RESULTS We used 511 echocardiograms in children, focusing on color Doppler images of the mitral valve. Echocardiograms were independently reviewed by an expert adjudication panel. Among 511 cases, 229 were normal, and 282 had RHD. Our automated method included harmonization of echocardiograms to localize the left atrium during systole using convolutional neural networks and RHD detection using mitral regurgitation jet analysis and deep learning models with an attention mechanism. We identified the correct view with an average accuracy of 0.99 and the correct systolic frame with an average accuracy of 0.94 (apical) and 0.93 (parasternal long axis). It localized the left atrium with an average Dice coefficient of 0.88 (apical) and 0.9 (parasternal long axis). Maximum mitral regurgitation jet measurements were similar to expert manual measurements (P value=0.83) and a 9-feature mitral regurgitation analysis showed an area under the receiver operating characteristics curve of 0.93, precision of 0.83, recall of 0.92, and F1 score of 0.87. Our deep learning model showed an area under the receiver operating characteristics curve of 0.84, precision of 0.78, recall of 0.98, and F1 score of 0.87. CONCLUSIONS Artificial intelligence has the potential to detect RHD as accurately as expert cardiologists and to improve with more data. These innovative approaches hold promise to scale echocardiography screening for RHD.
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Affiliation(s)
- Kelsey Brown
- Department of Pediatric CardiologyChildren’s National HospitalWashingtonDCUSA
| | - Pooneh Roshanitabrizi
- Sheikh Zayed Institute for Pediatric Surgical InnovationChildren’s National HospitalWashingtonDCUSA
| | | | | | - Andrea Beaton
- Department of Pediatric CardiologyCincinnati Children’s Hospital Medical CenterCincinnatiOHUSA
| | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical InnovationChildren’s National HospitalWashingtonDCUSA
- Departments of Radiology and Pediatrics, School of Medicine and Health SciencesGeorge Washington UniversityWashingtonDCUSA
| | - Craig A. Sable
- Department of Pediatric CardiologyChildren’s National HospitalWashingtonDCUSA
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Zha SZ, Rogstadkjernet M, Klæboe LG, Skulstad H, Singstad BJ, Gilbert A, Edvardsen T, Samset E, Brekke PH. Deep learning for automated left ventricular outflow tract diameter measurements in 2D echocardiography. Cardiovasc Ultrasound 2023; 21:19. [PMID: 37833731 PMCID: PMC10571406 DOI: 10.1186/s12947-023-00317-5] [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: 07/16/2023] [Accepted: 10/02/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND Measurement of the left ventricular outflow tract diameter (LVOTd) in echocardiography is a common source of error when used to calculate the stroke volume. The aim of this study is to assess whether a deep learning (DL) model, trained on a clinical echocardiographic dataset, can perform automatic LVOTd measurements on par with expert cardiologists. METHODS Data consisted of 649 consecutive transthoracic echocardiographic examinations of patients with coronary artery disease admitted to a university hospital. 1304 LVOTd measurements in the parasternal long axis (PLAX) and zoomed parasternal long axis views (ZPLAX) were collected, with each patient having 1-6 measurements per examination. Data quality control was performed by an expert cardiologist, and spatial geometry data was preserved for each LVOTd measurement to convert DL predictions into metric units. A convolutional neural network based on the U-Net was used as the DL model. RESULTS The mean absolute LVOTd error was 1.04 (95% confidence interval [CI] 0.90-1.19) mm for DL predictions on the test set. The mean relative LVOTd errors across all data subgroups ranged from 3.8 to 5.1% for the test set. Generally, the DL model had superior performance on the ZPLAX view compared to the PLAX view. DL model precision for patients with repeated LVOTd measurements had a mean coefficient of variation of 2.2 (95% CI 1.6-2.7) %, which was comparable to the clinicians for the test set. CONCLUSION DL for automatic LVOTd measurements in PLAX and ZPLAX views is feasible when trained on a limited clinical dataset. While the DL predicted LVOTd measurements were within the expected range of clinical inter-observer variability, the robustness of the DL model requires validation on independent datasets. Future experiments using temporal information and anatomical constraints could improve valvular identification and reduce outliers, which are challenges that must be addressed before clinical utilization.
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Affiliation(s)
| | | | | | - Helge Skulstad
- University of Oslo, Oslo, Norway
- Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | | | | | - Thor Edvardsen
- University of Oslo, Oslo, Norway
- Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Eigil Samset
- University of Oslo, Oslo, Norway
- GE HealthCare, Oslo, Norway
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Vervoort D, Yilgwan CS, Ansong A, Baumgartner JN, Bansal G, Bukhman G, Cannon JW, Cardarelli M, Cunningham MW, Fenton K, Green-Parker M, Karthikeyan G, Masterson M, Maswime S, Mensah GA, Mocumbi A, Kpodonu J, Okello E, Remenyi B, Williams M, Zühlke LJ, Sable C. Tertiary prevention and treatment of rheumatic heart disease: a National Heart, Lung, and Blood Institute working group summary. BMJ Glob Health 2023; 8:e012355. [PMID: 37914182 PMCID: PMC10619050 DOI: 10.1136/bmjgh-2023-012355] [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: 03/22/2023] [Accepted: 05/14/2023] [Indexed: 11/03/2023] Open
Abstract
Although entirely preventable, rheumatic heart disease (RHD), a disease of poverty and social disadvantage resulting in high morbidity and mortality, remains an ever-present burden in low-income and middle-income countries (LMICs) and rural, remote, marginalised and disenfranchised populations within high-income countries. In late 2021, the National Heart, Lung, and Blood Institute convened a workshop to explore the current state of science, to identify basic science and clinical research priorities to support RHD eradication efforts worldwide. This was done through the inclusion of multidisciplinary global experts, including cardiovascular and non-cardiovascular specialists as well as health policy and health economics experts, many of whom also represented or closely worked with patient-family organisations and local governments. This report summarises findings from one of the four working groups, the Tertiary Prevention Working Group, that was charged with assessing the management of late complications of RHD, including surgical interventions for patients with RHD. Due to the high prevalence of RHD in LMICs, particular emphasis was made on gaining a better understanding of needs in the field from the perspectives of the patient, community, provider, health system and policy-maker. We outline priorities to support the development, and implementation of accessible, affordable and sustainable interventions in low-resource settings to manage RHD and related complications. These priorities and other interventions need to be adapted to and driven by local contexts and integrated into health systems to best meet the needs of local communities.
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Affiliation(s)
- Dominique Vervoort
- Division of Cardiac Surgery, University of Toronto, Toronto, Ontario, Canada
| | | | - Annette Ansong
- Outpatient Cardiology, Children's National Hospital, Washington, District of Columbia, USA
| | | | - Geetha Bansal
- Division of International Training and Research, John E Fogarty International Center, Bethesda, Maryland, USA
| | - Gene Bukhman
- Center for Integration Science, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Program in Global Noncommunicable Disease and Social Change, Harvard Medical School, Boston, Massachusetts, USA
| | - Jeffrey W Cannon
- Department of Global Health and Population, Telethon Kids Institute, Nedlands, Western Australia, Australia
| | - Marcelo Cardarelli
- Pediatric Heart Surgery, Inova Children Hospital, Falls Church, Virginia, USA
| | | | - Kathleen Fenton
- National Heart Lung and Blood Institute, Bethesda, Maryland, USA
| | - Melissa Green-Parker
- National Institutes of Health Office of Disease Prevention, Bethesda, Maryland, USA
| | | | - Mary Masterson
- National Heart Lung and Blood Institute, Bethesda, Maryland, USA
| | - Salome Maswime
- Global Surgery, University of Cape Town Faculty of Health Sciences, Observatory, Western Cape, South Africa
| | - George A Mensah
- National Heart Lung and Blood Institute, Bethesda, Maryland, USA
| | - Ana Mocumbi
- Non Communicable Diseases, Instituto Nacional de Saúde, Maputo, Mozambique
- Universidade Eduardo Mondlane, Maputo, Mozambique
| | - Jacques Kpodonu
- Division of Cardiac Surgery, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Emmy Okello
- Cardiology, Uganda Heart Institute Ltd, Kampala, Uganda
| | - B Remenyi
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory of Australia, Australia
| | - Makeda Williams
- National Heart Lung and Blood Institute, Bethesda, Maryland, USA
| | - Liesl J Zühlke
- South African Medical Research Council, Tygerberg, South Africa
- Department of Medicine, Red Cross War Memorial Children's Hospital, Rondebosch, Western Cape, South Africa
| | - Craig Sable
- Division of Cardiology, Children's National Hospital, Washington, District of Columbia, USA
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The Role of Artificial Intelligence in Echocardiography. J Imaging 2023; 9:jimaging9020050. [PMID: 36826969 PMCID: PMC9962859 DOI: 10.3390/jimaging9020050] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/03/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.
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Galdino BF, Amaral AM, Santos LPA, de Nogueira MAA, Rocha RTL, Nunes MCP, Beaton AZ, Oliveira KKB, Franco J, Barbosa MM, Silva VRH, Reese AT, Ribeiro ALP, Sable CA, Nascimento BR. Reasons for disagreement between screening and standard echocardiography in primary care: data from the PROVAR + study : Disagreement between screening and standard echo. Int J Cardiovasc Imaging 2023; 39:929-937. [PMID: 36680683 DOI: 10.1007/s10554-023-02800-6] [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: 08/26/2022] [Accepted: 01/13/2023] [Indexed: 01/22/2023]
Abstract
We aimed to evaluate the reasons for disagreement between screening echocardiography (echo), acquired by nonexperts, and standard echo in the Brazilian primary care (PC). Over 20 months, 22 PC workers were trained on simplified handheld (GE VSCAN) echo protocols. Screening groups, consisting of patients aged 17-20, 35-40 and 60-65 years, and patients referred for clinical indications underwent focused echo. Studies were remotelyinterpreted in US and Brazil, and those diagnosed with major or severe HD were referred for standard echoperformed by an expert. Major HD was defined as moderate to severe valve disease, ventriculardysfunction/hypertrophy, pericardial effusion or wall-motion abnormalities. A random sample of exams wasselected for evaluation of variables accounting for disagreement. A sample of 768 patients was analyzed, 651(85%) in the referred group. Quality issues were reported in 5.8%, and the random Kappa for major HD between screening and standard echo was 0.51. The most frequent reasons for disagreement were: overestimation of mitral regurgitation (MR) (17.9%, N=138), left ventricular (LV) dysfunction (15.7%, N=121), aortic regurgitation (AR) (15.2%, N=117), LV hypertrophy (13.5%, N=104) and tricuspid regurgitation (12.7%, N=98). Misdiagnosis of mitral and aortic morphological abnormalities was observed in 12.4% and 3.0%, and underestimation of AR and MR occurred in 4.6% and 11.1%. Among 257 patients with suspected mild/moderate MR, 129 were reclassified to normal. In conclusion, although screening echo with task-shifting in PC is a promising tool in low-income areas, estimation of valve regurgitation and LV function and size account for considerable disagreement with standard exams.
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Affiliation(s)
- Bruno F Galdino
- Departamento de Clínica Médica, Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Arthur M Amaral
- Faculdade de Medicina da Universidade Federal de Ouro Preto, Ouro Preto, MG, Brazil
| | - Luiza P A Santos
- Faculdade de Ciências Médicas de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Marcelo Augusto A de Nogueira
- Departamento de Clínica Médica, Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Rodrigo T L Rocha
- Departamento de Clínica Médica, Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Maria Carmo P Nunes
- Departamento de Clínica Médica, Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.,Serviço de Cardiologia e Cirurgia Cardiovascular e Centro de Telessaúde do Hospital das Clínicas da UFMG, Belo Horizonte, MG, Brazil
| | - Andrea Z Beaton
- The Heart Institute, Cincinnati Children's Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, USA
| | - Kaciane K B Oliveira
- Serviço de Cardiologia e Cirurgia Cardiovascular e Centro de Telessaúde do Hospital das Clínicas da UFMG, Belo Horizonte, MG, Brazil
| | - Juliane Franco
- Serviço de Cardiologia e Cirurgia Cardiovascular e Centro de Telessaúde do Hospital das Clínicas da UFMG, Belo Horizonte, MG, Brazil
| | - Márcia M Barbosa
- Serviço de Cardiologia e Cirurgia Cardiovascular e Centro de Telessaúde do Hospital das Clínicas da UFMG, Belo Horizonte, MG, Brazil
| | - Victor R H Silva
- Departamento de Clínica Médica, Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Alison T Reese
- Cardiology, Children's National Health System, Washington, DC, USA
| | - Antonio Luiz P Ribeiro
- Departamento de Clínica Médica, Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.,Serviço de Cardiologia e Cirurgia Cardiovascular e Centro de Telessaúde do Hospital das Clínicas da UFMG, Belo Horizonte, MG, Brazil
| | - Craig A Sable
- Cardiology, Children's National Health System, Washington, DC, USA
| | - Bruno R Nascimento
- Departamento de Clínica Médica, Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil. .,Serviço de Cardiologia e Cirurgia Cardiovascular e Centro de Telessaúde do Hospital das Clínicas da UFMG, Belo Horizonte, MG, Brazil. .,Serviço de Cardiologia e Cirurgia Cardiovascular, Hospital das Clínicas, Universidade Federal de Minas Gerais, Minas Gerais, Rua Muzambinho, 710, apt. 802, CEP 30.210-530, Serra, Belo Horizonte, Brasil.
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10
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Data harnessing to nurture the human mind for a tailored approach to the child. Pediatr Res 2023; 93:357-365. [PMID: 36180585 DOI: 10.1038/s41390-022-02320-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 07/06/2022] [Accepted: 09/12/2022] [Indexed: 11/08/2022]
Abstract
Big data in pediatrics is an ocean of structured and unstructured data. Big data analysis helps to dive into the ocean of data to filter out information that can guide pediatricians in their decision making, precision diagnosis, and targeted therapy. In addition, big data and its analysis have helped in the surveillance, prevention, and performance of the health system. There has been a considerable amount of work in pediatrics that we have tried to highlight in this review and some of it has been already incorporated into the health system. Work in specialties of pediatrics is still forthcoming with the creation of a common data model and amalgamation of the huge "omics" database. The physicians entrusted with the care of children must be aware of the outcome so that they can play a role to ensure that big data algorithms have a clinically relevant effect in improving the health of their patients. They will apply the outcome of big data and its analysis in patient care through clinical algorithms or with the help of embedded clinical support alerts from the electronic medical records. IMPACT: Big data in pediatrics include structured, unstructured data, waveform data, biological, and social data. Big data analytics has unraveled significant information from these databases. This is changing how pediatricians will look at the body of available evidence and translate it into their clinical practice. Data harnessed so far is implemented in certain fields while in others it is in the process of development to become a clinical adjunct to the physician. Common databases are being prepared for future work. Diagnostic and prediction models when incorporated into the health system will guide the pediatrician to a targeted approach to diagnosis and therapy.
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11
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Edwards LA, Feng F, Iqbal M, Fu Y, Sanyahumbi A, Hao S, McElhinney DB, Ling XB, Sable C, Luo J. Machine Learning for Pediatric Echocardiographic Mitral Regurgitation Detection. J Am Soc Echocardiogr 2023; 36:96-104.e4. [PMID: 36191670 DOI: 10.1016/j.echo.2022.09.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 09/23/2022] [Accepted: 09/24/2022] [Indexed: 01/10/2023]
Abstract
BACKGROUND Echocardiography-based screening for valvular disease in at-risk asymptomatic children can result in early diagnosis. These screening programs, however, are resource intensive and may not be feasible in many resource-limited settings. Automated echocardiographic diagnosis may enable more widespread echocardiographic screening, early diagnosis, and improved outcomes. In this feasibility study, the authors sought to build a machine learning model capable of identifying mitral regurgitation (MR) on echocardiography. METHODS Echocardiograms were labeled by clip for view and by frame for the presence of MR. The labeled data were used to build two convolutional neural networks to perform the stepwise tasks of classifying the clips (1) by view and (2) by the presence of any MR, including physiologic, in parasternal long-axis color Doppler views. The view classification model was developed using 66,330 frames, and model performance was evaluated using a hold-out testing data set with 45 echocardiograms (11,730 frames). The MR detection model was developed using 938 frames, and model performance was evaluated using a hold-out testing data set with 42 echocardiograms (182 frames). Metrics to evaluate model performance included accuracy, precision, recall, F1 score (average of precision and recall, ranging from 0 to 1, with 1 suggesting perfect precision and recall), and receiver operating characteristic analysis. RESULTS For the parasternal long-axis view with color Doppler, the view classification convolutional neural network achieved an F1 score of 0.97. The MR detection convolutional neural network achieved testing accuracy of 0.86 and an area under the receiver operating characteristic curve of 0.91. CONCLUSIONS A machine learning model is capable of discerning MR on transthoracic echocardiography. This is an encouraging step toward machine learning-based diagnosis of valvular heart disease on pediatric echocardiography.
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Affiliation(s)
- Lindsay A Edwards
- Department of Pediatrics, Seattle Children's Hospital, Seattle, Washington
| | - Fei Feng
- University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Mehreen Iqbal
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Yong Fu
- University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Amy Sanyahumbi
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas
| | - Shiying Hao
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Palo Alto, California
| | - Doff B McElhinney
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Palo Alto, California
| | - X Bruce Ling
- Department of Surgery, Stanford University School of Medicine, Stanford, California
| | - Craig Sable
- Department of Pediatrics, Children's National Health System, Washington, District of Columbia
| | - Jiajia Luo
- Biomedical Engineering Department, Peking University, Beijing, China.
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12
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Gearhart A, Goto S, Deo RC, Powell AJ. An Automated View Classification Model for Pediatric Echocardiography Using Artificial Intelligence. J Am Soc Echocardiogr 2022; 35:1238-1246. [PMID: 36049595 PMCID: PMC9990955 DOI: 10.1016/j.echo.2022.08.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/10/2022] [Accepted: 08/12/2022] [Indexed: 10/15/2022]
Abstract
BACKGROUND View classification is a key step toward building a fully automated system for interpretation of echocardiograms. However, compared with adult echocardiograms, creating a view classification model for pediatric echocardiograms poses additional challenges, such as greater variation in anatomy, structure size, and views. The aim of this study was to develop a computer vision model to autonomously perform view classification on pediatric echocardiographic images. METHODS Using a training set of 12,067 echocardiographic images from patients aged 0 to 19 years, a convolutional neural network model was trained to identify 27 preselected standard pediatric echocardiographic views which included anatomic sweeps, color Doppler, and Doppler tracings. A validation set of 6,197 images was used for parameter tuning and model selection. A test set of 9,684 images from 100 different patients was then used to evaluate model accuracy. The model was also evaluated on a per study basis using a second test set consisting of 524 echocardiograms from children with leukemia to identify six preselected views pertinent to cardiac dysfunction surveillance. RESULTS The model identified the 27 preselected views with 90.3% accuracy. Accuracy was similar across age groups (89.3% for 0-4 years, 90.8% for 4-9 years, 90.0% for 9-14 years, and 91.2% for 14-19 years; P = .12). Examining the view subtypes, accuracy was 78.3% for the cine one location, 90.5% for sweeps with color Doppler, 82.2% for sweeps without color Doppler, and 91.1% for Doppler tracings. Among the leukemia cohort, the model identified the six preselected views on a per study basis with a positive predictive value of 98.7% to 99.2% and sensitivity of 76.9% to 94.8%. CONCLUSIONS A convolutional neural network model was constructed for view classification of pediatric echocardiograms that was accurate across the spectrum of ages and view types. This work lays the foundation for automated quantitative analysis and diagnostic support to promote efficient, accurate, and scalable analysis of pediatric echocardiograms.
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Affiliation(s)
- Addison Gearhart
- Department of Cardiology, Boston Children's Hospital, and Department of Pediatrics, Harvard Medical School, Boston, Massachusetts.
| | - Shinichi Goto
- One Brave Idea, Division of Cardiovascular Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Rahul C Deo
- One Brave Idea, Division of Cardiovascular Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Andrew J Powell
- Department of Cardiology, Boston Children's Hospital, and Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
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13
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Sethi Y, Patel N, Kaka N, Desai A, Kaiwan O, Sheth M, Sharma R, Huang H, Chopra H, Khandaker MU, Lashin MMA, Hamd ZY, Emran TB. Artificial Intelligence in Pediatric Cardiology: A Scoping Review. J Clin Med 2022; 11:jcm11237072. [PMID: 36498651 PMCID: PMC9738645 DOI: 10.3390/jcm11237072] [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: 11/05/2022] [Revised: 11/22/2022] [Accepted: 11/26/2022] [Indexed: 12/05/2022] Open
Abstract
The evolution of AI and data science has aided in mechanizing several aspects of medical care requiring critical thinking: diagnosis, risk stratification, and management, thus mitigating the burden of physicians and reducing the likelihood of human error. AI modalities have expanded feet to the specialty of pediatric cardiology as well. We conducted a scoping review searching the Scopus, Embase, and PubMed databases covering the recent literature between 2002-2022. We found that the use of neural networks and machine learning has significantly improved the diagnostic value of cardiac magnetic resonance imaging, echocardiograms, computer tomography scans, and electrocardiographs, thus augmenting the clinicians' diagnostic accuracy of pediatric heart diseases. The use of AI-based prediction algorithms in pediatric cardiac surgeries improves postoperative outcomes and prognosis to a great extent. Risk stratification and the prediction of treatment outcomes are feasible using the key clinical findings of each CHD with appropriate computational algorithms. Notably, AI can revolutionize prenatal prediction as well as the diagnosis of CHD using the EMR (electronic medical records) data on maternal risk factors. The use of AI in the diagnostics, risk stratification, and management of CHD in the near future is a promising possibility with current advancements in machine learning and neural networks. However, the challenges posed by the dearth of appropriate algorithms and their nascent nature, limited physician training, fear of over-mechanization, and apprehension of missing the 'human touch' limit the acceptability. Still, AI proposes to aid the clinician tomorrow with precision cardiology, paving a way for extremely efficient human-error-free health care.
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Affiliation(s)
- Yashendra Sethi
- PearResearch, Dehradun 248001, India
- Department of Medicine, Government Doon Medical College, Dehradun 248001, India
| | - Neil Patel
- PearResearch, Dehradun 248001, India
- Department of Medicine, GMERS Medical College, Himmatnagar 383001, India
| | - Nirja Kaka
- PearResearch, Dehradun 248001, India
- Department of Medicine, GMERS Medical College, Himmatnagar 383001, India
| | - Ami Desai
- Department of Medicine, SMIMER Medical College, Surat 395010, India
| | - Oroshay Kaiwan
- PearResearch, Dehradun 248001, India
- Department of Medicine, Northeast Ohio Medical University, Rootstown, OH 44272, USA
- Correspondence: (O.K.); (Z.Y.H.); (T.B.E.)
| | - Mili Sheth
- Department of Medicine, GMERS Gandhinagar, Gandhinagar 382012, India
| | - Rupal Sharma
- Department of Medicine, Government Medical College, Nagpur 440003, India
| | - Helen Huang
- Faculty of Medicine and Health Science, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland
| | - Hitesh Chopra
- Chitkara College of Pharmacy, Chitkara University, Rajpura 140401, India
| | - Mayeen Uddin Khandaker
- Centre for Applied Physics and Radiation Technologies, School of Engineering and Technology, Sunway University, Bandar Sunway 47500, Malaysia
| | - Maha M. A. Lashin
- Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. 84428, Riyadh 11671, Saudi Arabia
| | - Zuhal Y. Hamd
- Department of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. 84428, Riyadh 11671, Saudi Arabia
- Correspondence: (O.K.); (Z.Y.H.); (T.B.E.)
| | - Talha Bin Emran
- Department of Pharmacy, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh
- Correspondence: (O.K.); (Z.Y.H.); (T.B.E.)
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14
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Saikumar K, Rajesh V, Srivastava G, Lin JCW. Heart disease detection based on internet of things data using linear quadratic discriminant analysis and a deep graph convolutional neural network. Front Comput Neurosci 2022; 16:964686. [PMID: 36277609 PMCID: PMC9585537 DOI: 10.3389/fncom.2022.964686] [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: 06/08/2022] [Accepted: 09/09/2022] [Indexed: 11/15/2022] Open
Abstract
Heart disease is an emerging health issue in the medical field, according to WHO every year around 10 billion people are affected with heart abnormalities. Arteries in the heart generate oxygenated blood to all body parts, however sometimes blood vessels become clogged or restrained due to cardiac issues. Past heart diagnosis applications are outdated and suffer from poor performance. Therefore, an intelligent heart disease diagnosis application design is required. In this research work, internet of things (IoT) sensor data with a deep learning-based heart diagnosis application is designed. The heart disease IoT sensor data is collected from the University of California Irvine machine learning repository free open-source dataset which is useful for training the deep graph convolutional network (DG_ConvoNet) deep learning network. The testing data has been collected from the Cleveland Clinic Foundation; it is a collection of 350 real-time clinical instances from heart patients through IoT sensors. The K-means technique is employed to remove noise in sensor data and clustered the unstructured data. The features are extracted to employ Linear Quadratic Discriminant Analysis. DG_ConvoNet is a deep learning process to classify and predict heart diseases. The diagnostic application achieves an accuracy of 96%, sensitivity of 80%, specificity of 73%, precision of 90%, F-Score of 79%, and area under the ROC curve of 75% implementing the proposed model.
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Affiliation(s)
- K. Saikumar
- Department of ECE, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh, India
| | - V. Rajesh
- Department of ECE, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh, India
| | - Gautam Srivastava
- Department of Mathematics and Computer Science, Brandon University, Brandon, MB, Canada
- Research Centre for Interneural Computing, China Medical University, Taichung, Taiwan
- Department of Mathematics and Computer Science, Lebanese American University, Beirut, Lebanon
| | - Jerry Chun-Wei Lin
- Western Norway University of Applied Science, Bergen, Norway
- *Correspondence: Jerry Chun-Wei Lin,
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15
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Nedadur R, Wang B, Tsang W. Artificial intelligence for the echocardiographic assessment of valvular heart disease. Heart 2022; 108:1592-1599. [PMID: 35144983 PMCID: PMC9554049 DOI: 10.1136/heartjnl-2021-319725] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 12/29/2021] [Indexed: 11/18/2022] Open
Abstract
Developments in artificial intelligence (AI) have led to an explosion of studies exploring its application to cardiovascular medicine. Due to the need for training and expertise, one area where AI could be impactful would be in the diagnosis and management of valvular heart disease. This is because AI can be applied to the multitude of data generated from clinical assessments, imaging and biochemical testing during the care of the patient. In the area of valvular heart disease, the focus of AI has been on the echocardiographic assessment and phenotyping of patient populations to identify high-risk groups. AI can assist image acquisition, view identification for review, and segmentation of valve and cardiac structures for automated analysis. Using image recognition algorithms, aortic and mitral valve disease states have been directly detected from the images themselves. Measurements obtained during echocardiographic valvular assessment have been integrated with other clinical data to identify novel aortic valve disease subgroups and describe new predictors of aortic valve disease progression. In the future, AI could integrate echocardiographic parameters with other clinical data for precision medical management of patients with valvular heart disease.
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Affiliation(s)
- Rashmi Nedadur
- Division of Cardiac Surgery, University of Toronto, Toronto, Ontario, Canada.,Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Bo Wang
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Vector Institute of Artificial Intelligence, University of Toronto, Toronto, Ontario, Canada.,Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada
| | - Wendy Tsang
- Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada .,Division of Cardiology, University of Toronto, Toronto, Ontario, Canada
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16
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Impact of Technologic Innovation and COVID-19 Pandemic on Pediatric Cardiology Telehealth. CURRENT TREATMENT OPTIONS IN PEDIATRICS 2022; 8:309-324. [PMID: 36479525 PMCID: PMC9510217 DOI: 10.1007/s40746-022-00258-7] [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] [Accepted: 08/29/2022] [Indexed: 12/14/2022]
Abstract
Purpose of Review Established telehealth practices in pediatrics and pediatric cardiology are evolving rapidly. This review examines several concepts in contemporary telemedicine in our field: recent changes in direct-to-consumer (DTC) pediatric telehealth (TH) and practice based on lessons learned from the pandemic, scientific data from newer technological innovations in pediatric cardiology, and how TH is shaping global pediatric cardiology practice. Recent Findings In 2020, the global pandemic of COVID-19 led to significant changes in healthcare delivery. The lockdown and social distancing guidelines accelerated smart adaptations and pivots to ensure continued pediatric care albeit in a virtual manner. Remote cardiac monitoring technology is continuing to advance at a rapid pace secondary to advances in the areas of Internet access, portable hand-held devices, and artificial intelligence. Summary TH should be approached programmatically by pediatric cardiac healthcare providers with careful selection of patients, technology platforms, infrastructure setup, documentation, and compliance. Payment parity with in-person visits should be advocated and legislated. Newer remote cardiac monitoring technology should be expanded for objective assessment and optimal outcomes. TH continues to be working beyond geographical boundaries in pediatric cardiology and should continue to expand and develop.
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17
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Ramos Nascimento B, Zawacki Beaton A. Improved standardisation of training needed to achieve the potential of handheld echocardiography. Heart 2021; 107:1772-1773. [PMID: 34580139 PMCID: PMC8562304 DOI: 10.1136/heartjnl-2021-319945] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Bruno Ramos Nascimento
- Serviço de Cardiologia e Cirurgia Cardiovascular, Hospital das Clinicas da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil .,Centro de Telessaúde, Hospital das Clínicas da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Andrea Zawacki Beaton
- The Heart Institute, Cincinnati Childrens Hospital Medical Center, Cincinnati, OH, USA.,School of Medicine, University of Cincinnati, Cincinnati, OH, USA
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