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Al Moussawy M, Lakkis ZS, Ansari ZA, Cherukuri AR, Abou-Daya KI. The transformative potential of artificial intelligence in solid organ transplantation. FRONTIERS IN TRANSPLANTATION 2024; 3:1361491. [PMID: 38993779 PMCID: PMC11235281 DOI: 10.3389/frtra.2024.1361491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 03/01/2024] [Indexed: 07/13/2024]
Abstract
Solid organ transplantation confronts numerous challenges ranging from donor organ shortage to post-transplant complications. Here, we provide an overview of the latest attempts to address some of these challenges using artificial intelligence (AI). We delve into the application of machine learning in pretransplant evaluation, predicting transplant rejection, and post-operative patient outcomes. By providing a comprehensive overview of AI's current impact, this review aims to inform clinicians, researchers, and policy-makers about the transformative power of AI in enhancing solid organ transplantation and facilitating personalized medicine in transplant care.
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Affiliation(s)
- Mouhamad Al Moussawy
- Department of Surgery, Thomas E. Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, United States
| | - Zoe S Lakkis
- Health Sciences Research Training Program, University of Pittsburgh, Pittsburgh, PA, United States
| | - Zuhayr A Ansari
- Health Sciences Research Training Program, University of Pittsburgh, Pittsburgh, PA, United States
| | - Aravind R Cherukuri
- Department of Surgery, Thomas E. Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, United States
| | - Khodor I Abou-Daya
- Department of Surgery, Thomas E. Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, United States
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2
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Vellvé K, Garcia-Canadilla P, Nogueira M, Youssef L, Arranz A, Nakaki A, Boada D, Blanco I, Faner R, Figueras F, Agustí À, Gratacós E, Crovetto F, Bijnens B, Crispi F. Pulmonary vascular reactivity in growth restricted fetuses using computational modelling and machine learning analysis of fetal Doppler waveforms. Sci Rep 2024; 14:5919. [PMID: 38467666 PMCID: PMC10928161 DOI: 10.1038/s41598-024-54603-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 02/14/2024] [Indexed: 03/13/2024] Open
Abstract
The aim of this study was to investigate the pulmonary vasculature in baseline conditions and after maternal hyperoxygenation in growth restricted fetuses (FGR). A prospective cohort study of singleton pregnancies including 97 FGR and 111 normally grown fetuses was carried out. Ultrasound Doppler of the pulmonary vessels was obtained at 24-37 weeks of gestation and data were acquired before and after oxygen administration. After, Machine Learning (ML) and a computational model were used on the Doppler waveforms to classify individuals and estimate pulmonary vascular resistance (PVR). Our results showed lower mean velocity time integral (VTI) in the main pulmonary and intrapulmonary arteries in baseline conditions in FGR individuals. Delta changes of the main pulmonary artery VTI and intrapulmonary artery pulsatility index before and after hyperoxygenation were significantly greater in FGR when compared with controls. Also, ML identified two clusters: A (including 66% controls and 34% FGR) with similar Doppler traces over time and B (including 33% controls and 67% FGR) with changes after hyperoxygenation. The computational model estimated the ratio of PVR before and after maternal hyperoxygenation which was closer to 1 in cluster A (cluster A 0.98 ± 0.33 vs cluster B 0.78 ± 0.28, p = 0.0156). Doppler ultrasound allows the detection of significant changes in pulmonary vasculature in most FGR at baseline, and distinct responses to hyperoxygenation. Future studies are warranted to assess its potential applicability in the clinical management of FGR.
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Affiliation(s)
- Kilian Vellvé
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Sabino Arana 1, 08028, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Patricia Garcia-Canadilla
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Sabino Arana 1, 08028, Barcelona, Spain
- Interdisciplinary Cardiovascular Research Group, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
| | - Mariana Nogueira
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Lina Youssef
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Sabino Arana 1, 08028, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Angela Arranz
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Sabino Arana 1, 08028, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Ayako Nakaki
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Sabino Arana 1, 08028, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - David Boada
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Sabino Arana 1, 08028, Barcelona, Spain
| | - Isabel Blanco
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Pneumology Department, Respiratory Institute, Hospital Clínic, University of Barcelona, Barcelona, Spain
- Centre for Biomedical Research on Respiratory Diseases (CIBER-ES), Madrid, Spain
| | - Rosa Faner
- Centre for Biomedical Research on Respiratory Diseases (CIBER-ES), Madrid, Spain
| | - Francesc Figueras
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Sabino Arana 1, 08028, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Àlvar Agustí
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Pneumology Department, Respiratory Institute, Hospital Clínic, University of Barcelona, Barcelona, Spain
- Centre for Biomedical Research on Respiratory Diseases (CIBER-ES), Madrid, Spain
- Cathedra Salud Respiratoria, University of Barcelona, Barcelona, Spain
| | - Eduard Gratacós
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Sabino Arana 1, 08028, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centre for Biomedical Research on Rare Diseases (CIBER-ER), Madrid, Spain
| | - Francesca Crovetto
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Sabino Arana 1, 08028, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centre for Biomedical Research on Rare Diseases (CIBER-ER), Madrid, Spain
| | | | - Fàtima Crispi
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Sabino Arana 1, 08028, Barcelona, Spain.
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
- Centre for Biomedical Research on Rare Diseases (CIBER-ER), Madrid, Spain.
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Wieneke H, Voigt I. Principles of artificial intelligence and its application in cardiovascular medicine. Clin Cardiol 2024; 47:e24148. [PMID: 37721424 PMCID: PMC10766001 DOI: 10.1002/clc.24148] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 08/23/2023] [Accepted: 08/29/2023] [Indexed: 09/19/2023] Open
Abstract
Artificial intelligence (AI) represents a rapidly developing field. Its use can improve diagnosis and therapy in many areas of medicine. Despite this enormous progress, many physicians perceive it as a black box and are skeptical about it. This review will present the basics of machine learning. Different classifications of artificial intelligence, such as supervised versus unsupervised and discriminative versus generative AI, are given. Analogies to human intelligence are discussed as far as algorithms are oriented toward it. In the second step, the most common models like random forest, k-means clustering, convolutional neural network, and transformers will be presented in a way that the underlying idea can be understood. Corresponding medical applications in cardiovascular medicine will be named for all models, respectively. The overview is intended to show that the term artificial intelligence covers a wide range of different concepts. It should help physicians understand the principles of AI to make up one's minds about its application in cardiology. It should also enable them to evaluate results obtained with AI's help critically.
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Affiliation(s)
- Heinrich Wieneke
- Department of Cardiology and Angiology, Contilia Heart and Vascular CenterElisabeth‐Krankenhaus EssenEssenGermany
| | - Ingo Voigt
- Department of Cardiology and Angiology, Contilia Heart and Vascular CenterElisabeth‐Krankenhaus EssenEssenGermany
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Palmieri V, Montisci A, Vietri MT, Colombo PC, Sala S, Maiello C, Coscioni E, Donatelli F, Napoli C. Artificial intelligence, big data and heart transplantation: Actualities. Int J Med Inform 2023; 176:105110. [PMID: 37285695 DOI: 10.1016/j.ijmedinf.2023.105110] [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: 03/05/2023] [Revised: 05/21/2023] [Accepted: 05/22/2023] [Indexed: 06/09/2023]
Abstract
BACKGROUND As diagnostic and prognostic models developed by traditional statistics perform poorly in real-world, artificial intelligence (AI) and Big Data (BD) may improve the supply chain of heart transplantation (HTx), allocation opportunities, correct treatments, and finally optimize HTx outcome. We explored available studies, and discussed opportunities and limits of medical application of AI to the field of HTx. METHOD A systematic overview of studies published up to December 31st, 2022, in English on peer-revied journals, have been identified through PUBMED-MEDLINE-WEB of Science, referring to HTx, AI, BD. Studies were grouped in 4 domains based on main studies' objectives and results: etiology, diagnosis, prognosis, treatment. A systematic attempt was made to evaluate studies by the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD). RESULTS Among the 27 publications selected, none used AI applied to BD. Of the selected studies, 4 fell in the domain of etiology, 6 in the domain of diagnosis, 3 in the domain of treatment, and 17 in that of prognosis, as AI was most frequently used for algorithmic prediction and discrimination of survival, but in retrospective cohorts and registries. AI-based algorithms appeared superior to probabilistic functions to predict patterns, but external validation was rarely employed. Indeed, based on PROBAST, selected studies showed, to some extent, significant risk of bias (especially in the domain of predictors and analysis). In addition, as example of applicability in the real-world, a free-use prediction algorithm developed through AI failed to predict 1-year mortality post-HTx in cases from our center. CONCLUSIONS While AI-based prognostic and diagnostic functions performed better than those developed by traditional statistics, risk of bias, lack of external validation, and relatively poor applicability, may affect AI-based tools. More unbiased research with high quality BD meant for AI, transparency and external validations, are needed to have medical AI as a systematic aid to clinical decision making in HTx.
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Affiliation(s)
- Vittorio Palmieri
- Azienda Ospedaliera dei Colli Monaldi-Cotugno-CTO, Department of Cardiac Surgery and Transplantation, Naples, Italy.
| | - Andrea Montisci
- Division of Cardiothoracic Intensive Care, Cardiothoracic Department, ASST Spedali Civili, Brescia, Italy
| | - Maria Teresa Vietri
- Department of Precision Medicine, "Luigi Vanvitelli" University of Campania School of Medicine, Naples, Italy
| | - Paolo C Colombo
- Milstein Division of Cardiology, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Silvia Sala
- Chair of Anesthesia and Intensive Care, University of Brescia, Brescia, Italy
| | - Ciro Maiello
- Azienda Ospedaliera dei Colli Monaldi-Cotugno-CTO, Department of Cardiac Surgery and Transplantation, Naples, Italy
| | - Enrico Coscioni
- Department of Cardiac Surgery, AOU San Giovanni di Dio e Ruggi d'Aragona, Salerno, Italy
| | - Francesco Donatelli
- Department of Cardiac Surgery, Istituto Clinico Sant'Ambrogio, Milan, Italy; Chair of Cardiac Surgery, University of Milan, Milan, Italy
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), "Luigi Vanvitelli" University of Campania School of Medicine, Naples, Italy
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Zhang Y, Xie J, Wu Y, Zhang B, Zhou C, Gao X, Xie X, Li X, Yu J, Wang X, Cheng D, Zhou J, Chen Z, Fan F, Zhou X, Yang B. Novel algorithm for diagnosis of Arrhythmogenic cardiomyopathy and dilated cardiomyopathy: Key gene expression profiling using machine learning. J Gene Med 2023; 25:e3468. [PMID: 36518003 DOI: 10.1002/jgm.3468] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/26/2022] [Accepted: 12/01/2022] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND It is difficult to distinguish between arrhythmogenic cardiomyopathy (ACM) and dilated cardiomyopathy (DCM) because of their similar clinical manifestations. This study aimed to develop a novel diagnostic algorithm for distinguishing ACM from DCM. METHODS Two public datasets containing human ACM and DCM myocardial samples were used. Consensus clustering, non-negative matrix factorization and principal component analysis were applied. Weighted gene co-expression network analysis and machine learning methods, including random forest and the least absolute shrinkage and selection operator, were used to identify candidate genes. Receiver operating characteristic curves and nomograms were performed to estimate diagnostic efficacy, and Spearman's correlation analysis was used to assess the correlation between candidate genes and cardiac function indices. RESULTS Both ACM and DCM showed highly similar gene expression patterns in the clustering analyses. Hub gene modules associated with cardiomyopathy were obtained using weighted gene co-expression network analysis. Thirteen candidate genes were selected using machine learning algorithms, and their combination showed a high diagnostic value (area under the ROC curve = 0.86) for distinguishing ACM from DCM. In addition, TATA-box binding protein associated factor 15 showed a negative correlation with cardiac index (R = -0.54, p = 0.0054) and left ventricular ejection fraction (R = -0.48, p = 0.0015). CONCLUSIONS Our study revealed an effective diagnostic model with key gene signatures, which indicates a potential tool to differentiate between ACM and DCM in clinical practice. In addition, we identified several genes that are highly related to cardiac function, which may contribute to our understanding of ACM and DCM.
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Affiliation(s)
- Youming Zhang
- Center of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jiaxi Xie
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yizhang Wu
- Center of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Baowei Zhang
- Center of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Chunjiang Zhou
- Center of Cardiology, Shanghai East Hospital, Nanjing Medical University, Shanghai, China
| | - Xiaotong Gao
- Center of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xin Xie
- Center of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiaorong Li
- Center of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jinbo Yu
- Center of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xuecheng Wang
- Center of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Dian Cheng
- Center of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jian Zhou
- Center of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zijun Chen
- Center of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Fenghua Fan
- Center of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiujuan Zhou
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Bing Yang
- Center of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
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Application of Machine Learning for Cardiovascular Disease Risk Prediction. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023. [DOI: 10.1155/2023/9418666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
Cardiovascular diseases (CVDs) are a common cause of heart failure globally. The need to explore possible ways to tackle the disease necessitated this study. The study designed a machine learning model for cardiovascular disease risk prediction in accordance with a dataset that contains 11 features which may be used to forecast the disease. The dataset from Kaggle on cardiovascular disease includes approximately 70,000 patient records that were used to determine the outcome. Compared to the UCI dataset, the Kaggle dataset has many more training and validation records. Models created using neural networks, random forests, Bayesian networks, C5.0, and QUEST were compared for this dataset. On training and testing data sets, the results acquired a high accuracy (99.1 percent), which is significantly superior to previous methods. Ahead-of-time detection and diagnosis of cardiac disease, as well as better treatment outcomes, are strong possibilities for the suggested prediction model. Additionally, it may help patients better manage their illness or life forms in order to increase their chances of recovery/survival. The result showed greater accuracy and promising signs that machine-learning algorithms can indeed assist in early identification of the disease and improvement of the treatment outcome.
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Chiarito M, Luceri L, Oliva A, Stefanini G, Condorelli G. Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not Gold. Eur Cardiol 2022; 17:e29. [PMID: 36845218 PMCID: PMC9947926 DOI: 10.15420/ecr.2022.11] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 06/30/2022] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) is a broad term referring to any automated systems that need 'intelligence' to carry out specific tasks. During the last decade, AI-based techniques have been gaining popularity in a vast range of biomedical fields, including the cardiovascular setting. Indeed, the dissemination of cardiovascular risk factors and the better prognosis of patients experiencing cardiovascular events resulted in an increase in the prevalence of cardiovascular disease (CVD), eliciting the need for precise identification of patients at increased risk for development and progression of CVD. AI-based predictive models may overcome some of the limitations that hinder the performance of classic regression models. Nonetheless, the successful application of AI in this field requires knowledge of the potential pitfalls of the AI techniques, to guarantee their safe and effective use in daily clinical practice. The aim of the present review is to summarise the pros and cons of different AI methods and their potential application in the cardiovascular field, with a focus on the development of predictive models and risk assessment tools.
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Affiliation(s)
- Mauro Chiarito
- Department of Biomedical Sciences, Humanitas UniversityPieve Emanuele, Milan, Italy,Center for Interventional Cardiovascular Research and Clinical Trials, The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount SinaiNew York, US
| | - Luca Luceri
- Institute of Information Systems and Networking, University of Applied Sciences and Arts of Southern SwitzerlandLugano, Switzerland
| | - Angelo Oliva
- Department of Biomedical Sciences, Humanitas UniversityPieve Emanuele, Milan, Italy,Cardio Center, Humanitas Research Hospital IRCCSRozzano, Milan, Italy
| | - Giulio Stefanini
- Department of Biomedical Sciences, Humanitas UniversityPieve Emanuele, Milan, Italy,Cardio Center, Humanitas Research Hospital IRCCSRozzano, Milan, Italy
| | - Gianluigi Condorelli
- Department of Biomedical Sciences, Humanitas UniversityPieve Emanuele, Milan, Italy,Cardio Center, Humanitas Research Hospital IRCCSRozzano, Milan, Italy
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Nguyen MB, Villemain O, Friedberg MK, Lovstakken L, Rusin CG, Mertens L. Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes. FRONTIERS IN RADIOLOGY 2022; 2:881777. [PMID: 37492680 PMCID: PMC10365116 DOI: 10.3389/fradi.2022.881777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 08/01/2022] [Indexed: 07/27/2023]
Abstract
Artificial intelligence (AI) is frequently used in non-medical fields to assist with automation and decision-making. The potential for AI in pediatric cardiology, especially in the echocardiography laboratory, is very high. There are multiple tasks AI is designed to do that could improve the quality, interpretation, and clinical application of echocardiographic data at the level of the sonographer, echocardiographer, and clinician. In this state-of-the-art review, we highlight the pertinent literature on machine learning in echocardiography and discuss its applications in the pediatric echocardiography lab with a focus on automation of the pediatric echocardiogram and the use of echo data to better understand physiology and outcomes in pediatric cardiology. We also discuss next steps in utilizing AI in pediatric echocardiography.
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Affiliation(s)
- Minh B. Nguyen
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
- Department of Pediatric Cardiology, Baylor College of Medicine, Houston, TX, United States
| | - Olivier Villemain
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Mark K. Friedberg
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Lasse Lovstakken
- Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Craig G. Rusin
- Department of Pediatric Cardiology, Baylor College of Medicine, Houston, TX, United States
| | - Luc Mertens
- Division of Cardiology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
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Bennis FC, Hoogendoorn M, Aussems C, Korevaar JC. Prediction of heart failure 1 year before diagnosis in general practitioner patients using machine learning algorithms: a retrospective case-control study. BMJ Open 2022; 12:e060458. [PMID: 36041765 PMCID: PMC9438066 DOI: 10.1136/bmjopen-2021-060458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVES Heart failure (HF) is a commonly occurring health problem with high mortality and morbidity. If potential cases could be detected earlier, it may be possible to intervene earlier, which may slow progression in some patients. Preferably, it is desired to reuse already measured data for screening of all persons in an age group, such as general practitioner (GP) data. Furthermore, it is essential to evaluate the number of people needed to screen to find one patient using true incidence rates, as this indicates the generalisability in the true population. Therefore, we aim to create a machine learning model for the prediction of HF using GP data and evaluate the number needed to screen with true incidence rates. DESIGN, SETTINGS AND PARTICIPANTS GP data from 8543 patients (-2 to -1 year before diagnosis) and controls aged 70+ years were obtained retrospectively from 01 January 2012 to 31 December 2019 from the Nivel Primary Care Database. Codes about chronic illness, complaints, diagnostics and medication were obtained. Data were split in a train/test set. Datasets describing demographics, the presence of codes (non-sequential) and upon each other following codes (sequential) were created. Logistic regression, random forest and XGBoost models were trained. Predicted outcome was the presence of HF after 1 year. The ratio case:control in the test set matched true incidence rates (1:45). RESULTS Sole demographics performed average (area under the curve (AUC) 0.692, CI 0.677 to 0.706). Adding non-sequential information combined with a logistic regression model performed best and significantly improved performance (AUC 0.772, CI 0.759 to 0.785, p<0.001). Further adding sequential information did not alter performance significantly (AUC 0.767, CI 0.754 to 0.780, p=0.07). The number needed to screen dropped from 14.11 to 5.99 false positives per true positive. CONCLUSION This study created a model able to identify patients with pending HF a year before diagnosis.
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Affiliation(s)
- Frank C Bennis
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Netherlands Institute for Health Services Research (Nivel), Utrecht, The Netherlands
| | - Mark Hoogendoorn
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Claire Aussems
- Netherlands Institute for Health Services Research (Nivel), Utrecht, The Netherlands
| | - Joke C Korevaar
- Netherlands Institute for Health Services Research (Nivel), Utrecht, The Netherlands
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10
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Dell'Angela L, Nicolosi GL. Artificial intelligence applied to cardiovascular imaging, a critical focus on echocardiography: The point-of-view from "the other side of the coin". JOURNAL OF CLINICAL ULTRASOUND : JCU 2022; 50:772-780. [PMID: 35466409 DOI: 10.1002/jcu.23215] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/16/2022] [Accepted: 04/19/2022] [Indexed: 06/14/2023]
Abstract
Cardiovascular imaging has achieved a crucial role in the management of cardiovascular diseases. In this field, echocardiography advantages include wide availability, portability, and affordability, at a relatively low cost. However, echocardiographic assessment requires highly trained operators, and implies high observer variability, as compared with the other cardiac imaging modalities. Hence, artificial intelligence might be extremely helpful. From the point-of-view of the peripheral "Spoke" Hospital potential user ("the other side of the coin"), artificial intelligence development appears very slow in the clinical arena. Many limitations are still present, and require full involvement, cooperation, and coordination of professional operators into Hub-and-Spoke network.
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Affiliation(s)
- Luca Dell'Angela
- Emergency Department, Cardiology Division, Gorizia & Monfalcone Hospital, ASUGI, Gorizia, Italy
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11
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Garcia-Canadilla P, Isabel-Roquero A, Aurensanz-Clemente E, Valls-Esteve A, Miguel FA, Ormazabal D, Llanos F, Sanchez-de-Toledo J. Machine Learning-Based Systems for the Anticipation of Adverse Events After Pediatric Cardiac Surgery. Front Pediatr 2022; 10:930913. [PMID: 35832588 PMCID: PMC9271800 DOI: 10.3389/fped.2022.930913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/06/2022] [Indexed: 11/18/2022] Open
Abstract
Pediatric congenital heart disease (CHD) patients are at higher risk of postoperative complications and clinical deterioration either due to their underlying pathology or due to the cardiac surgery, contributing significantly to mortality, morbidity, hospital and family costs, and poor quality of life. In current clinical practice, clinical deterioration is detected, in most of the cases, when it has already occurred. Several early warning scores (EWS) have been proposed to assess children at risk of clinical deterioration using vital signs and risk indicators, in order to intervene in a timely manner to reduce the impact of deterioration and risk of death among children. However, EWS are based on measurements performed at a single time point without incorporating trends nor providing information about patient's risk trajectory. Moreover, some of these measurements rely on subjective assessment making them susceptible to different interpretations. All these limitations could explain why the implementation of EWS in high-resource settings failed to show a significant decrease in hospital mortality. By means of machine learning (ML) based algorithms we could integrate heterogeneous and complex data to predict patient's risk of deterioration. In this perspective article, we provide a brief overview of the potential of ML technologies to improve the identification of pediatric CHD patients at high-risk for clinical deterioration after cardiac surgery, and present the CORTEX traffic light, a ML-based predictive system that Sant Joan de Déu Barcelona Children's Hospital is implementing, as an illustration of the application of an ML-based risk stratification system in a relevant hospital setting.
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Affiliation(s)
- Patricia Garcia-Canadilla
- BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine, Hospital Sant Joan de Déu and Hospital Clínic, University of Barcelona, Barcelona, Spain.,Cardiovascular Diseases and Child Development, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Alba Isabel-Roquero
- Department of Pediatric Cardiology, Hospital Sant Joan de Déu Barcelona, Esplugues de Llobregat, Spain.,BCNMedTech, Universitat Pompeu Fabra, Barcelona, Spain
| | - Esther Aurensanz-Clemente
- Cardiovascular Diseases and Child Development, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain.,Department of Pediatric Cardiology, Hospital Sant Joan de Déu Barcelona, Esplugues de Llobregat, Spain
| | - Arnau Valls-Esteve
- Innovation in Health Technologies, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Francesca Aina Miguel
- Department of Engineering, Hospital Sant Joan de Déu Barcelona, Esplugues de Llobregat, Spain
| | - Daniel Ormazabal
- Department of Informatics, Hospital Sant Joan de Déu Barcelona, Esplugues de Llobregat, Spain
| | - Floren Llanos
- Department of Informatics, Hospital Sant Joan de Déu Barcelona, Esplugues de Llobregat, Spain
| | - Joan Sanchez-de-Toledo
- Cardiovascular Diseases and Child Development, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain.,Department of Pediatric Cardiology, Hospital Sant Joan de Déu Barcelona, Esplugues de Llobregat, Spain.,Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
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