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Mehrabi Nasab E, Sadeghian S, Vasheghani Farahani A, Yamini Sharif A, Masoud Kabir F, Bavanpour Karvane H, Zahedi A, Bozorgi A. Determining the recurrence rate of premature ventricular complexes and idiopathic ventricular tachycardia after radiofrequency catheter ablation with the help of designing a machine-learning model. Regen Ther 2024; 27:32-38. [PMID: 38496010 PMCID: PMC10940794 DOI: 10.1016/j.reth.2024.03.001] [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: 01/27/2024] [Revised: 02/28/2024] [Accepted: 03/03/2024] [Indexed: 03/19/2024] Open
Abstract
Ventricular arrhythmias increase cardiovascular morbidity and mortality. Recurrent PVCs and IVT are generally considered benign in the absence of structural heart abnormalities. Artificial intelligence is a rapidly growing field. In recent years, medical professionals have shown great interest in the potential use of ML, an integral part of AI, in various disciplines, including diagnostic applications, decision-making, prognostic stratification, and solving complex pathophysiological aspects of diseases from these data at extraordinary complexity, scale, and acquisition rate. The aim of this study was to design an ML model to predict the probability of PVC and IVT recurrence after RF ablation. Data of patients were collected and manipulated using traditional analysis and various artificial intelligence models, namely MLP, Gradient Boosting Machines, Random Forest, and Logistic Regression. Hypertension, male sex, and the use of non-irrigate catheters were associated with less freedom from arrhythmia. All these results were obtained through traditional analytic methods, and according to AI, none of the variables had a clear effect on the recurrence of arrhythmia. Each AI model presents unique strengths and weaknesses, and further optimization and fine-tuning of these models are necessary to increase their clinical utility. By expanding the dataset, improved predictions can be fostered to ultimately increase the clinical utility of AI in predicting PVC erosion outcomes.
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Affiliation(s)
- Entezar Mehrabi Nasab
- Department of Cardiology, School of Medicine, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
- Department of Cardiology, School of Medicine, Valiasr Hospital, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Saeed Sadeghian
- Department of Cardiology, School of Medicine, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Vasheghani Farahani
- Department of Cardiology, School of Medicine, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Ahmad Yamini Sharif
- Department of Cardiology, School of Medicine, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Farzad Masoud Kabir
- Department of Cardiology, School of Medicine, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Ahora Zahedi
- Department of Artificial Intelligence in Medical Sciences, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Ali Bozorgi
- Department of Cardiology, School of Medicine, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
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DuBrock HM, Wagner TE, Carlson K, Carpenter CL, Awasthi S, Attia ZI, Frantz RP, Friedman PA, Kapa S, Annis J, Brittain EL, Hemnes AR, Asirvatham SJ, Babu M, Prasad A, Yoo U, Barve R, Selej M, Agron P, Kogan E, Quinn D, Dunnmon P, Khan N, Soundararajan V. An electrocardiogram-based AI algorithm for early detection of pulmonary hypertension. Eur Respir J 2024; 64:2400192. [PMID: 38936966 PMCID: PMC11269769 DOI: 10.1183/13993003.00192-2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 05/19/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Early diagnosis of pulmonary hypertension (PH) is critical for effective treatment and management. We aimed to develop and externally validate an artificial intelligence algorithm that could serve as a PH screening tool, based on analysis of a standard 12-lead ECG. METHODS The PH Early Detection Algorithm (PH-EDA) is a convolutional neural network developed using retrospective ECG voltage-time data, with patients classified as "PH-likely" or "PH-unlikely" (controls) based on right heart catheterisation or echocardiography. In total, 39 823 PH-likely patients and 219 404 control patients from Mayo Clinic were randomly split into training (48%), validation (12%) and test (40%) sets. ECGs taken within 1 month of PH diagnosis (diagnostic dataset) were used to train the PH-EDA at Mayo Clinic. Performance was tested on diagnostic ECGs within the test sets from Mayo Clinic (n=16 175/87 998 PH-likely/controls) and Vanderbilt University Medical Center (VUMC; n=6045/24 256 PH-likely/controls). In addition, performance was tested on ECGs taken 6-18 months (pre-emptive dataset), and up to 5 years prior to a PH diagnosis at both sites. RESULTS Performance testing yielded an area under the receiver operating characteristic curve (AUC) of 0.92 and 0.88 in the diagnostic test sets at Mayo Clinic and VUMC, respectively, and 0.86 and 0.81, respectively, in the pre-emptive test sets. The AUC remained a minimum of 0.79 at Mayo Clinic and 0.73 at VUMC up to 5 years before diagnosis. CONCLUSION The PH-EDA can detect PH at diagnosis and 6-18 months prior, demonstrating the potential to accelerate diagnosis and management of this debilitating disease.
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Affiliation(s)
- Hilary M DuBrock
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA
- Co-first authors
| | - Tyler E Wagner
- nference, Cambridge, MA, USA
- Anumana, Cambridge, MA, USA
- Co-first authors
| | | | | | - Samir Awasthi
- nference, Cambridge, MA, USA
- Anumana, Cambridge, MA, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Robert P Frantz
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jeffrey Annis
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Institute for Clinical and Translational Research, Nashville, TN, USA
| | - Evan L Brittain
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Anna R Hemnes
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Melwin Babu
- Anumana, Cambridge, MA, USA
- nference Labs, Bangalore, India
| | - Ashim Prasad
- Anumana, Cambridge, MA, USA
- nference Labs, Bangalore, India
| | | | - Rakesh Barve
- Anumana, Cambridge, MA, USA
- nference Labs, Bangalore, India
| | - Mona Selej
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
| | - Peter Agron
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
| | - Emily Kogan
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
| | - Deborah Quinn
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
| | - Preston Dunnmon
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
| | - Najat Khan
- Janssen Research and Development, LLC, a Johnson and Johnson company, Raritan, NJ, USA
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Saluja D, Huang Z, Majumder J, Zeldin L, Yarmohammadi H, Biviano A, Wan EY, Ciaccio EJ, Hendon CP, Garan H. Automated prediction of isthmus areas in scar-related atrial tachycardias using artificial intelligence. J Cardiovasc Electrophysiol 2024; 35:1401-1411. [PMID: 38738814 PMCID: PMC11239288 DOI: 10.1111/jce.16299] [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: 10/31/2023] [Revised: 04/22/2024] [Accepted: 04/28/2024] [Indexed: 05/14/2024]
Abstract
INTRODUCTION Ablation of scar-related reentrant atrial tachycardia (SRRAT) involves identification and ablation of a critical isthmus. A graph convolutional network (GCN) is a machine learning structure that is well-suited to analyze the irregularly-structured data obtained in mapping procedures and may be used to identify potential isthmuses. METHODS Electroanatomic maps from 29 SRRATs were collected, and custom electrogram features assessing key tissue and wavefront properties were calculated for each point. Isthmuses were labeled off-line. Training data was used to determine the optimal GCN parameters and train the final model. Putative isthmus points were predicted in the training and test populations and grouped into proposed isthmus areas based on density and distance thresholds. The primary outcome was the distance between the centroids of the true and closest proposed isthmus areas. RESULTS A total of 193 821 points were collected. Thirty isthmuses were detected in 29 tachycardias among 25 patients (median age 65.0, 5 women). The median (IQR) distance between true and the closest proposed isthmus area centroids was 8.2 (3.5, 14.4) mm in the training and 7.3 (2.8, 16.1) mm in the test group. The mean overlap in areas, measured by the Dice coefficient, was 11.5 ± 3.2% in the training group and 13.9 ± 4.6% in the test group. CONCLUSION A GCN can be trained to identify isthmus areas in SRRATs and may help identify critical ablation targets.
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Affiliation(s)
- Deepak Saluja
- Division of Cardiology, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, USA
| | - Ziyi Huang
- Department of Electrical Engineering, Fu Foundation School of Engineering and Applied Science (SEAS), Columbia University, New York, New York, USA
| | - Jonah Majumder
- Department of Biomedical Engineering, Fu Foundation School of Engineering and Applied Science (SEAS), Columbia University, New York, New York, USA
| | - Lawrence Zeldin
- Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, USA
| | - Hirad Yarmohammadi
- Division of Cardiology, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, USA
| | - Angelo Biviano
- Division of Cardiology, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, USA
| | - Elaine Y Wan
- Division of Cardiology, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, USA
| | - Edward J Ciaccio
- Division of Cardiology, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, USA
| | - Christine P Hendon
- Department of Biomedical Engineering, Fu Foundation School of Engineering and Applied Science (SEAS), Columbia University, New York, New York, USA
| | - Hasan Garan
- Division of Cardiology, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, USA
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Rahm AK, Lugenbiel P. [Digital precision medicine in rhythmology : Risk prediction of recurrences, sudden cardiac death, and outcome]. Herzschrittmacherther Elektrophysiol 2024; 35:97-103. [PMID: 38639777 DOI: 10.1007/s00399-024-01015-z] [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: 02/20/2024] [Accepted: 03/08/2024] [Indexed: 04/20/2024]
Abstract
Digital precision medicine is gaining increasing importance in rhythmology, especially in the treatment of cardiac arrhythmias. This trend is driven by the advancing digitization in healthcare and the availability of large amounts of data from various sources such as electrocardiograms (ECGs), implants like pacemakers and implantable cardioverter-defibrillators (ICDs), as well as wearables like smartwatches and fitness trackers. Through the analysis of this data, physicians can develop more precise and individualized diagnoses and treatment strategies for patients with cardiac arrhythmias. For example, subtle changes in ECGs can be identified, indicating potentially dangerous arrhythmias. Genetic analyses and resulting large datasets also play an increasingly significant role, especially in hereditary ion channel disorders such as long QT syndrome (LQTS) and Brugada syndrome (BrS), as well as in lone atrial fibrillation (AF). Precision medicine enables the development of individualized treatment approaches tailored to the specific needs and risk factors of each patient. This can help improve screening strategies, reduce adverse events, and ultimately enhance the quality of life for patients. Technological advancements such as big data, artificial intelligence, machine learning, and predictive analytics play a crucial role in predicting the risk of arrhythmias and sudden cardiac death. These concepts enable more precise and personalized predictions and support physicians in the treatment and monitoring of their patients.
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Affiliation(s)
- Ann-Kathrin Rahm
- Klinik für Kardiologie, Angiologie und Pulmologie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Deutschland.
- HCR - Heidelberger Zentrum für Herzrhythmusstörungen, Heidelberg, Deutschland.
- InformaticsForLife Institute, Heidelberg, Deutschland.
| | - Patrick Lugenbiel
- Klinik für Kardiologie, Angiologie und Pulmologie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Deutschland.
- HCR - Heidelberger Zentrum für Herzrhythmusstörungen, Heidelberg, Deutschland.
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Li ZZ, Zhao W, Mao Y, Bo D, Chen Q, Kojodjojo P, Zhang F. A machine learning approach to differentiate wide QRS tachycardia: distinguishing ventricular tachycardia from supraventricular tachycardia. J Interv Card Electrophysiol 2024:10.1007/s10840-024-01743-9. [PMID: 38246906 DOI: 10.1007/s10840-024-01743-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 01/07/2024] [Indexed: 01/23/2024]
Abstract
BACKGROUND Differential diagnosis of wide QRS tachycardia (WQCT) has been a challenging issue. Published algorithms to distinguish ventricular tachycardia (VT) and supraventricular tachycardia (SVT) have limited diagnostic capabilities. METHODS A total of 278 patients with WQCT from January 2010 to March 2022 were enrolled. The electrophysiological study confirmed SVT in 154 patients and VT in 65 ones. Two hundred nineteen WQCT 12-lead ECGs were randomly divided into development cohort (n = 165) and testing cohort (n = 54) data sets. The development cohort was split into a training group (n = 115) and an internal validation group (n = 50). Forty ECG features extracted from the 219 WQCT ECGs are fed into 9 iteratively trained ML algorithms. This novel ML algorithm was also compared with four published algorithms. RESULTS In the development cohort, the Gradient Boosting Machine (GBM) model displayed the maximum area under curve (AUC) (0.91, 95% confidence interval (CI) 0.81-1.00). In the testing cohort, the GBM model had a higher AUC of 0.97 compared to 4 validated ECG algorithms, namely, Brugada (0.68), avR (0.62), RWPTII (0.72), and LLA algorithms (0.70). Accuracy, sensitivity, specificity, negative predictive value, and positive predictive value of the GBM model were 0.94, 0.97, 0.90, 0.94, and 0.95, respectively. CONCLUSIONS A GBM ML model contributes to distinguishing SVT from VT based on surface ECG features. In addition, we were able to identify important indicators for distinguishing WQCT.
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Affiliation(s)
- Zhen-Zhen Li
- Section of Pacing and Electrophysiology, Division of Cardiology, First Affiliated Hospital of Nanjing Medical University, Guangzhou Road 300, Nanjing, 210006, Jiangsu, China
- Department of Cardiology, Nanjing BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, 210021, Jiangsu, China
| | - Wei Zhao
- Section of Pacing and Electrophysiology, Division of Cardiology, First Affiliated Hospital of Nanjing Medical University, Guangzhou Road 300, Nanjing, 210006, Jiangsu, China
| | - YangMing Mao
- Section of Pacing and Electrophysiology, Division of Cardiology, First Affiliated Hospital of Nanjing Medical University, Guangzhou Road 300, Nanjing, 210006, Jiangsu, China
| | - Dan Bo
- Section of Pacing and Electrophysiology, Division of Cardiology, First Affiliated Hospital of Nanjing Medical University, Guangzhou Road 300, Nanjing, 210006, Jiangsu, China
| | - QiuShi Chen
- Section of Pacing and Electrophysiology, Division of Cardiology, First Affiliated Hospital of Nanjing Medical University, Guangzhou Road 300, Nanjing, 210006, Jiangsu, China
| | | | - FengXiang Zhang
- Section of Pacing and Electrophysiology, Division of Cardiology, First Affiliated Hospital of Nanjing Medical University, Guangzhou Road 300, Nanjing, 210006, Jiangsu, China.
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Raileanu G, de Jong JSSG. Electrocardiogram Interpretation Using Artificial Intelligence: Diagnosis of Cardiac and Extracardiac Pathologic Conditions. How Far Has Machine Learning Reached? Curr Probl Cardiol 2024; 49:102097. [PMID: 37739276 DOI: 10.1016/j.cpcardiol.2023.102097] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 09/18/2023] [Indexed: 09/24/2023]
Abstract
Artificial intelligence (AI) is already widely used in different fields of medicine, making possible the integration of the paraclinical exams with the clinical findings in patients, for a more accurate and rapid diagnosis and treatment decision. The electrocardiogram remains one of the most important, fastest, cheapest, and noninvasive methods of diagnosis in cardiology, despite the rapid development and progression of the technology. Even if studied a long time ago, it still has a lot of less understood features that, with a better understanding, can give more clues to a correct and prompt diagnosis in a short time. The use of AI in the interpretation of the ECG improved the accuracy and the time to diagnosis in different cardiovascular diseases, and more than this, explaining the decision to make AI diagnosis improved the human understanding of the different features of the ECG that might be considered for a more accurate diagnosis. The purpose of this article is to provide an overview of the most recently published articles about the use of AI in ECG interpretation.
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Affiliation(s)
- Gabriela Raileanu
- Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands.
| | - Jonas S S G de Jong
- Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands
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Velraeds A, Strik M, van der Zande J, Fontagne L, Haissaguerre M, Ploux S, Wang Y, Bordachar P. Improving Automatic Smartwatch Electrocardiogram Diagnosis of Atrial Fibrillation by Identifying Regularity within Irregularity. SENSORS (BASEL, SWITZERLAND) 2023; 23:9283. [PMID: 38005669 PMCID: PMC10674836 DOI: 10.3390/s23229283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/06/2023] [Accepted: 11/08/2023] [Indexed: 11/26/2023]
Abstract
Smartwatches equipped with automatic atrial fibrillation (AF) detection through electrocardiogram (ECG) recording are increasingly prevalent. We have recently reported the limitations of the Apple Watch (AW) in correctly diagnosing AF. In this study, we aim to apply a data science approach to a large dataset of smartwatch ECGs in order to deliver an improved algorithm. We included 723 patients (579 patients for algorithm development and 144 patients for validation) who underwent ECG recording with an AW and a 12-lead ECG (21% had AF and 24% had no ECG abnormalities). Similar to the existing algorithm, we first screened for AF by detecting irregularities in ventricular intervals. However, as opposed to the existing algorithm, we included all ECGs (not applying quality or heart rate exclusion criteria) but we excluded ECGs in which we identified regular patterns within the irregular rhythms by screening for interval clusters. This "irregularly irregular" approach resulted in a significant improvement in accuracy compared to the existing AW algorithm (sensitivity of 90% versus 83%, specificity of 92% versus 79%, p < 0.01). Identifying regularity within irregular rhythms is an accurate yet inclusive method to detect AF using a smartwatch ECG.
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Affiliation(s)
- Anouk Velraeds
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Bordeaux, France; (A.V.); (J.v.d.Z.)
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Bordeaux, France
- Biomedical Signals and Systems, TechMed Centre, University of Twente, 7522 NH Enschede, The Netherlands
| | - Marc Strik
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Bordeaux, France; (A.V.); (J.v.d.Z.)
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Bordeaux, France
| | - Joske van der Zande
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Bordeaux, France; (A.V.); (J.v.d.Z.)
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Bordeaux, France
- Biomedical Signals and Systems, TechMed Centre, University of Twente, 7522 NH Enschede, The Netherlands
| | - Leslie Fontagne
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Bordeaux, France; (A.V.); (J.v.d.Z.)
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Bordeaux, France
| | - Michel Haissaguerre
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Bordeaux, France; (A.V.); (J.v.d.Z.)
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Bordeaux, France
| | - Sylvain Ploux
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Bordeaux, France; (A.V.); (J.v.d.Z.)
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Bordeaux, France
| | - Ying Wang
- Biomedical Signals and Systems, TechMed Centre, University of Twente, 7522 NH Enschede, The Netherlands
| | - Pierre Bordachar
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Bordeaux, France; (A.V.); (J.v.d.Z.)
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Bordeaux, France
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Sun S, Wang L, Lin J, Sun Y, Ma C. An effective prediction model based on XGBoost for the 12-month recurrence of AF patients after RFA. BMC Cardiovasc Disord 2023; 23:561. [PMID: 37974062 PMCID: PMC10655386 DOI: 10.1186/s12872-023-03599-9] [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: 05/21/2023] [Accepted: 11/07/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND Atrial fibrillation (AF) is a common heart rhythm disorder that can lead to complications such as stroke and heart failure. Radiofrequency ablation (RFA) is a procedure used to treat AF, but it is not always successful in maintaining a normal heart rhythm. This study aimed to construct a clinical prediction model based on extreme gradient boosting (XGBoost) for AF recurrence 12 months after ablation. METHODS The 27-dimensional data of 359 patients with AF undergoing RFA in the First Affiliated Hospital of Soochow University from October 2018 to November 2021 were retrospectively analysed. We adopted the logistic regression, support vector machine (SVM), random forest (RF) and XGBoost methods to conduct the experiment. To evaluate the performance of the prediction, we used the area under the receiver operating characteristic curve (AUC), the area under the precision-recall curve (AP), and calibration curves of both the training and testing sets. Finally, Shapley additive explanations (SHAP) were utilized to explain the significance of the variables. RESULTS Of the 27-dimensional variables, ejection fraction (EF) of the left atrial appendage (LAA), N-terminal probrain natriuretic peptide (NT-proBNP), global peak longitudinal strain of the LAA (LAAGPLS), left atrial diameter (LAD), diabetes mellitus (DM) history, and female sex had a significant role in the predictive model. The experimental results demonstrated that XGBoost exhibited the best performance among these methods, and the accuracy, specificity, sensitivity, precision and F1 score (a measure of test accuracy) of XGBoost were 86.1%, 89.7%, 71.4%, 62.5% and 0.67, respectively. In addition, SHAP analysis also proved that the 6 parameters were decisive for the effect of the XGBoost-based prediction model. CONCLUSIONS We proposed an effective model based on XGBoost that can be used to predict the recurrence of AF patients after RFA. This prediction result can guide treatment decisions and help to optimize the management of AF.
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Affiliation(s)
- ShiKun Sun
- The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Li Wang
- The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Jia Lin
- The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - YouFen Sun
- The Shengcheng Street Health Center, Shouguang, 262700, China.
| | - ChangSheng Ma
- The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
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Vasconcelos L, Martinez BP, Kent M, Ansari S, Ghanbari H, Nenadic I. Multi-center atrial fibrillation electrocardiogram (ECG) classification using Fourier space convolutional neural networks (FD-CNN) and transfer learning. J Electrocardiol 2023; 81:201-206. [PMID: 37778217 DOI: 10.1016/j.jelectrocard.2023.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 09/05/2023] [Accepted: 09/17/2023] [Indexed: 10/03/2023]
Abstract
There has been a proliferation of machine learning (ML) electrocardiogram (ECG) classification algorithms reaching >85% accuracy for various cardiac pathologies. Despite the high accuracy at individual institutions, challenges remain when it comes to multi-center deployment. Transfer learning (TL) is a technique in which a model trained for a specific task is repurposed for another related task, in this case ECG ML model trained at one institution is fine-tuned to be utilized to classify ECGs at another institution. Models trained at one institution, however, might not be generalizable for accurate classification when deployed broadly due to differences in type, time, and sampling rate of traditional ECG acquisition. In this study, we evaluate the performance of time domain (TD) and frequency domain (FD) convolutional neural network (CNN) classification models in an inter-institutional scenario leveraging three different publicly available datasets. The larger PTB-XL ECG dataset was used to initially train TD and FD CNN models for atrial fibrillation (AFIB) classification. The models were then tested on two different data sets, Lobachevsky University Electrocardiography Database (LUDB) and Korea University Medical Center database (KURIAS). The FD model was able to retain most of its performance (>0.81 F1-score), whereas TD was highly affected (<0.53 F1-score) by the dataset variations, even with TL applied. The FD CNN showed superior robustness to cross-institutional variability and has potential for widespread application with no compromise to ECG classification performance.
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Affiliation(s)
| | | | - Madeline Kent
- Division of Cardiology, Henry Ford Hospital, Detroit, MI, USA
| | - Sardar Ansari
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Hamid Ghanbari
- Cardiovascular Disease, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Ivan Nenadic
- Department of Radiology, Mayo Clinic, Rochester, MN, USA; Duke Cardiology, Duke University Medical Center, Durham, NC, USA
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Kwon S, Lee E, Ju H, Ahn HJ, Lee SR, Choi EK, Suh J, Oh S, Rhee W. Machine Learning Prediction for the Recurrence After Electrical Cardioversion of Patients With Persistent Atrial Fibrillation. Korean Circ J 2023; 53:677-689. [PMID: 37653713 PMCID: PMC10625851 DOI: 10.4070/kcj.2023.0012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 05/25/2023] [Accepted: 06/13/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND AND OBJECTIVES There is limited evidence regarding machine-learning prediction for the recurrence of atrial fibrillation (AF) after electrical cardioversion (ECV). This study aimed to predict the recurrence of AF after ECV using machine learning of clinical features and electrocardiograms (ECGs) in persistent AF patients. METHODS We analyzed patients who underwent successful ECV for persistent AF. Machine learning was designed to predict patients with 1-month recurrence. Individual 12-lead ECGs were collected before and after ECV. Various clinical features were collected and trained the extreme gradient boost (XGBoost)-based model. Ten-fold cross-validation was used to evaluate the performance of the model. The performance was compared to the C-statistics of the selected clinical features. RESULTS Among 718 patients (mean age 63.5±9.3 years, men 78.8%), AF recurred in 435 (60.6%) patients after 1 month. With the XGBoost-based model, the areas under the receiver operating characteristic curves (AUROCs) were 0.57, 0.60, and 0.63 if the model was trained by clinical features, ECGs, and both (the final model), respectively. For the final model, the sensitivity, specificity, and F1-score were 84.7%, 28.2%, and 0.73, respectively. Although the AF duration showed the best predictive performance (AUROC, 0.58) among the clinical features, it was significantly lower than that of the final machine-learning model (p<0.001). Additional training of extended monitoring data of 15-minute single-lead ECG and photoplethysmography in available patients (n=261) did not significantly improve the model's performance. CONCLUSIONS Machine learning showed modest performance in predicting AF recurrence after ECV in persistent AF patients, warranting further validation studies.
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Affiliation(s)
- Soonil Kwon
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - Eunjung Lee
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Hojin Ju
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - Hyo-Jeong Ahn
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - So-Ryoung Lee
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - Eue-Keun Choi
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
| | - Jangwon Suh
- Department of Intelligence and Information, Seoul National University, Seoul, Korea
| | - Seil Oh
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Wonjong Rhee
- Department of Intelligence and Information, Seoul National University, Seoul, Korea.
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Cui S, Traverso A, Niraula D, Zou J, Luo Y, Owen D, El Naqa I, Wei L. Interpretable artificial intelligence in radiology and radiation oncology. Br J Radiol 2023; 96:20230142. [PMID: 37493248 PMCID: PMC10546466 DOI: 10.1259/bjr.20230142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 07/27/2023] Open
Abstract
Artificial intelligence has been introduced to clinical practice, especially radiology and radiation oncology, from image segmentation, diagnosis, treatment planning and prognosis. It is not only crucial to have an accurate artificial intelligence model, but also to understand the internal logic and gain the trust of the experts. This review is intended to provide some insights into core concepts of the interpretability, the state-of-the-art methods for understanding the machine learning models, the evaluation of these methods, identifying some challenges and limits of them, and gives some examples of medical applications.
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Affiliation(s)
- Sunan Cui
- Department of Radiation Oncology, University of Washington, Seattle, WA, United States
| | - Alberto Traverso
- Department of Radiotherapy, Maastro Clinic, Maastricht, Netherlands
| | - Dipesh Niraula
- Department of Machine Learning, Moffitt Cancer Center, FL, United States
| | - Jiaren Zou
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Yi Luo
- Department of Machine Learning, Moffitt Cancer Center, FL, United States
| | - Dawn Owen
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, United States
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, FL, United States
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
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12
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Wang K, Zhao J, Hu J, Liang D, Luo Y. Predicting unmet activities of daily living needs among the oldest old with disabilities in China: a machine learning approach. Front Public Health 2023; 11:1257818. [PMID: 37771828 PMCID: PMC10523409 DOI: 10.3389/fpubh.2023.1257818] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/29/2023] [Indexed: 09/30/2023] Open
Abstract
Background The ageing population in China has led to a significant increase in the number of older persons with disabilities. These individuals face substantial challenges in accessing adequate activities of daily living (ADL) assistance. Unmet ADL needs among this population can result in severe health consequences and strain an already burdened care system. This study aims to identify the factors influencing unmet ADL needs of the oldest old (those aged 80 and above) with disabilities using six machine learning methods. Methods Drawing from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2017-2018 data, we employed six machine learning methods to predict unmet ADL needs among the oldest old with disabilities. The predictive effects of various factors on unmet ADL needs were explored using Shapley Additive exPlanations (SHAP). Results The Random Forest model showed the highest prediction accuracy among the six machine learning methods tested. SHAP analysis based on the Random Forest model revealed that factors such as household registration, disability class, economic rank, self-rated health, caregiver willingness, perceived control, economic satisfaction, pension, educational attainment, financial support given to children, living arrangement, number of children, and primary caregiver played significant roles in the unmet ADL needs of the oldest old with disabilities. Conclusion Our study highlights the importance of socioeconomic factors (e.g., household registration and economic rank), health status (e.g., disability class and self-rated health), and caregiving relationship factors (e.g., caregiver willingness and perceived control) in reducing unmet ADL needs among the oldest old with disabilities in China. Government interventions aimed at bridging the urban-rural divide, targeting groups with deteriorating health status, and enhancing caregiver skills are essential for ensuring the well-being of this vulnerable population. These findings can inform policy decisions and interventions to better address the unmet ADL needs among the oldest old with disabilities.
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Affiliation(s)
- Kun Wang
- Zhongnan University of Economics and Law (School of Philosophy), Wuhan, Hubei, China
- Nankai University (Zhou Enlai School of Government), Tianjin, China
| | - Jinxu Zhao
- Zhongnan University of Economics and Law (School of Philosophy), Wuhan, Hubei, China
| | - Jie Hu
- Wuhan University (School of Physics and Technology), Wuhan, Hubei, China
| | - Dan Liang
- Tongji Medical College of Huazhong University of Science and Technology (School of Medicine and Health Management), Wuhan, Hubei, China
| | - Yansong Luo
- Zhongnan University of Economics and Law (School of Philosophy), Wuhan, Hubei, China
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13
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Lee KCS, Breznen B, Ukhova A, Martin SS, Koehler F. Virtual healthcare solutions in heart failure: a literature review. Front Cardiovasc Med 2023; 10:1231000. [PMID: 37745104 PMCID: PMC10513031 DOI: 10.3389/fcvm.2023.1231000] [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: 05/29/2023] [Accepted: 08/29/2023] [Indexed: 09/26/2023] Open
Abstract
The widespread adoption of mobile technologies offers an opportunity for a new approach to post-discharge care for patients with heart failure (HF). By enabling non-invasive remote monitoring and two-way, real-time communication between the clinic and home-based patients, as well as a host of other capabilities, mobile technologies have a potential to significantly improve remote patient care. This literature review summarizes clinical evidence related to virtual healthcare (VHC), defined as a care team + connected devices + a digital solution in post-release care of patients with HF. Searches were conducted on Embase (06/12/2020). A total of 171 studies were included for data extraction and evidence synthesis: 96 studies related to VHC efficacy, and 75 studies related to AI in HF. In addition, 15 publications were included from the search on studies scaling up VHC solutions in HF within the real-world setting. The most successful VHC interventions, as measured by the number of reported significant results, were those targeting reduction in rehospitalization rates. In terms of relative success rate, the two most effective interventions targeted patient self-care and all-cause hospital visits in their primary endpoint. Among the three categories of VHC identified in this review (telemonitoring, remote patient management, and patient self-empowerment) the integrated approach in remote patient management solutions performs the best in decreasing HF patients' re-admission rates and overall hospital visits. Given the increased amount of data generated by VHC technologies, artificial intelligence (AI) is being investigated as a tool to aid decision making in the context of primary diagnostics, identifying disease phenotypes, and predicting treatment outcomes. Currently, most AI algorithms are developed using data gathered in clinic and only a few studies deploy AI in the context of VHC. Most successes have been reported in predicting HF outcomes. Since the field of VHC in HF is relatively new and still in flux, this is not a typical systematic review capturing all published studies within this domain. Although the standard methodology for this type of reviews was followed, the nature of this review is qualitative. The main objective was to summarize the most promising results and identify potential research directions.
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Affiliation(s)
| | - Boris Breznen
- Evidence Synthesis, Evidinno Outcomes Research Inc., Vancouver, BC, Canada
| | | | - Seth Shay Martin
- Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Friedrich Koehler
- Deutsches Herzzentrum der Charité (DHZC), Centre for Cardiovascular Telemedicine, Campus Charité Mitte, Berlin, Germany
- Division of Cardiology and Angiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
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14
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Wang L, Yang F, Bao X, Bo X, Dang S, Wang R, Pan F. Deep learning-mediated prediction of concealed accessory pathway based on sinus rhythmic electrocardiograms. Ann Noninvasive Electrocardiol 2023; 28:e13072. [PMID: 37530078 PMCID: PMC10475885 DOI: 10.1111/anec.13072] [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: 10/17/2022] [Revised: 06/01/2023] [Accepted: 06/27/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND Concealed accessory pathway (AP) may cause atrial ventricular reentrant tachycardia impacting the health of patients. However, it is asymptomatic and undetectable during sinus rhythm. METHODS To detect concealed AP with electrocardiography (ECG) images, we collected normal sinus rhythmic ECG images of concealed AP patients and healthy subjects. All ECG images were randomly allocated to the training and testing datasets, and were used to train and test six popular convolutional neural networks from ImageNet pre-training and random initialization, respectively. RESULTS We screened 152 ECG recordings in concealed AP group and 600 ECG recordings in control group. There were no statistically significant differences in ECG characteristics between control group and concealed AP group in terms of PR interval and QRS interval. However, the QT interval and QTc were slightly higher in control group than in concealed AP group. In the testing set, ResNet26, SE-ResNet50, MobileNetV3_large_100, and DenseNet169 achieved a sensitivity rate more than 87.0% with a specificity rate above 98.0%. And models trained from random initialization showed similar performance and convergence with models trained from ImageNet pre-training. CONCLUSION Our study suggests that deep learning could be an effective way to predict concealed AP with normal sinus rhythmic ECG images. And our results might encourage people to rethink the possibility of training from random initialization on ECG image tasks.
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Affiliation(s)
- Lei Wang
- Department of CardiologyThe Affiliated Wuxi People's Hospital of Nanjing Medical UniversityWuxiChina
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education)Jiangnan UniversityWuxiChina
| | - Fang Yang
- Department of CardiologyThe Affiliated Wuxi People's Hospital of Nanjing Medical UniversityWuxiChina
| | - Xiao‐Jing Bao
- Department of CardiologyThe Affiliated Wuxi People's Hospital of Nanjing Medical UniversityWuxiChina
| | - Xiao‐Ping Bo
- Department of CardiologyThe Affiliated Wuxi People's Hospital of Nanjing Medical UniversityWuxiChina
| | - Shipeng Dang
- Department of CardiologyThe Affiliated Wuxi People's Hospital of Nanjing Medical UniversityWuxiChina
| | - Ru‐Xing Wang
- Department of CardiologyThe Affiliated Wuxi People's Hospital of Nanjing Medical UniversityWuxiChina
| | - Feng Pan
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education)Jiangnan UniversityWuxiChina
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15
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Kent M, Vasconcelos L, Ansari S, Ghanbari H, Nenadic I. Fourier space approach for convolutional neural network (CNN) electrocardiogram (ECG) classification: A proof-of-concept study. J Electrocardiol 2023; 80:24-33. [PMID: 37141727 DOI: 10.1016/j.jelectrocard.2023.04.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 02/15/2023] [Accepted: 04/04/2023] [Indexed: 05/06/2023]
Abstract
There has been a proliferation of machine learning (ML) electrocardiogram (ECG) classification algorithms reaching > 85% accuracy for various cardiac pathologies. Although the accuracy within institutions might be high, models trained at one institution might not be generalizable enough for accurate detection when deployed in other institutions due to differences in type of signal acquisition, sampling frequency, time of acquisition, device noise characteristics and number of leads. In this proof-of-concept study, we leverage the publicly available PTB-XL dataset to investigate the use of time-domain (TD) and frequency-domain (FD) convolutional neural networks (CNN) to detect myocardial infarction (MI), ST/T-wave changes (STTC), atrial fibrillation (AFIB) and sinus arrhythmia (SARRH). To simulate interinstitutional deployment, the TD and FD implementations were also compared on adapted test sets using different sampling frequencies 50 Hz, 100 Hz and 250 Hz, and acquisition times of 5 s and 10s at 100 Hz sampling frequency from the training dataset. When tested on the original sampling frequency and duration, the FD approach showed comparable results to TD for MI (0.92 FD - 0.93 TD AUROC) and STTC (0.94 FD - 0.95 TD AUROC), and better performance for AFIB (0.99 FD - 0.86 TD AUROC) and SARRH (0.91 FD - 0.65 TD AUROC). Although both methods were robust to changes in sampling frequency, changes in acquisition time were detrimental to the TD MI and STTC AUROCs, at 0.72 and 0.58 respectively. Alternatively, the FD approach was able to maintain the same level of performance, and, therefore, showed better potential for interinstitutional deployment.
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Affiliation(s)
- Madeline Kent
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | | | - Sardar Ansari
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Hamid Ghanbari
- Cardiovascular Disease, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Ivan Nenadic
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA; Department of Radiology, Mayo Clinic, Rochester, MN, USA
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16
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Di Biase L, Zou F, Lin AN, Grupposo V, Marazzato J, Tarantino N, Della Rocca D, Mohanty S, Natale A, Alhuarrat MAD, Haiman G, Haimovich D, Matthew RA, Alcazar J, Costa G, Urman R, Zhang X. Feasibility of three-dimensional artificial intelligence algorithm integration with intracardiac echocardiography for left atrial imaging during atrial fibrillation catheter ablation. Europace 2023; 25:euad211. [PMID: 37477946 PMCID: PMC10403247 DOI: 10.1093/europace/euad211] [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: 03/18/2023] [Accepted: 07/10/2023] [Indexed: 07/22/2023] Open
Abstract
AIMS Intracardiac echocardiography (ICE) is a useful but operator-dependent tool for left atrial (LA) anatomical rendering during atrial fibrillation (AF) ablation. The CARTOSOUND FAM Module, a new deep learning (DL) imaging algorithm, has the potential to overcome this limitation. This study aims to evaluate feasibility of the algorithm compared to cardiac computed tomography (CT) in patients undergoing AF ablation. METHODS AND RESULTS In 28 patients undergoing AF ablation, baseline patient information was recorded, and three-dimensional (3D) shells of LA body and anatomical structures [LA appendage/left superior pulmonary vein/left inferior pulmonary vein/right superior pulmonary vein/right inferior pulmonary vein (RIPV)] were reconstructed using the DL algorithm. The selected ultrasound frames were gated to end-expiration and max LA volume. Ostial diameters of these structures and carina-to-carina distance between left and right pulmonary veins were measured and compared with CT measurements. Anatomical accuracy of the DL algorithm was evaluated by three independent electrophysiologists using a three-anchor scale for LA anatomical structures and a five-anchor scale for LA body. Ablation-related characteristics were summarized. The algorithm generated 3D reconstruction of LA anatomies, and two-dimensional contours overlaid on ultrasound input frames. Average calculation time for LA reconstruction was 65 s. Mean ostial diameters and carina-to-carina distance were all comparable to CT without statistical significance. Ostial diameters and carina-to-carina distance also showed moderate to high correlation (r = 0.52-0.75) except for RIPV (r = 0.20). Qualitative ratings showed good agreement without between-rater differences. Average procedure time was 143.7 ± 43.7 min, with average radiofrequency time 31.6 ± 10.2 min. All patients achieved ablation success, and no immediate complications were observed. CONCLUSION DL algorithm integration with ICE demonstrated considerable accuracy compared to CT and qualitative physician assessment. The feasibility of ICE with this algorithm can potentially further streamline AF ablation workflow.
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Affiliation(s)
- Luigi Di Biase
- Montefiore-Einstein Center for Heart & Vascular Care, Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th street, Bronx, NY, USA
| | - Fengwei Zou
- Montefiore-Einstein Center for Heart & Vascular Care, Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th street, Bronx, NY, USA
| | - Aung N Lin
- Montefiore-Einstein Center for Heart & Vascular Care, Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th street, Bronx, NY, USA
| | | | - Jacopo Marazzato
- Montefiore-Einstein Center for Heart & Vascular Care, Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th street, Bronx, NY, USA
- Department of Medicine and Surgery, University of Insubria, Varese, Italy
| | - Nicola Tarantino
- Montefiore-Einstein Center for Heart & Vascular Care, Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th street, Bronx, NY, USA
| | | | - Sanghamitra Mohanty
- St. David's Medical Center, Texas Cardiac Arrhythmia Institute, Austin, TX, USA
| | - Andrea Natale
- St. David's Medical Center, Texas Cardiac Arrhythmia Institute, Austin, TX, USA
| | - Majd Al Deen Alhuarrat
- Montefiore-Einstein Center for Heart & Vascular Care, Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th street, Bronx, NY, USA
| | | | | | | | | | | | - Roy Urman
- Biosense Webster, Inc., Irvine, CA, USA
| | - Xiaodong Zhang
- Montefiore-Einstein Center for Heart & Vascular Care, Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th street, Bronx, NY, USA
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17
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Tchapmi DP, Agyingi C, Egbe A, Marcus GM, Noubiap JJ. The use of digital health in heart rhythm care. Expert Rev Cardiovasc Ther 2023; 21:553-563. [PMID: 37322576 DOI: 10.1080/14779072.2023.2226868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 06/14/2023] [Indexed: 06/17/2023]
Abstract
INTRODUCTION Digital health is a broad term that includes telecommunication technologies to collect, share and manipulate health information to improve patient health and health care services. With the growing use of wearables, artificial intelligence, machine learning, and other novel technologies, digital health is particularly relevant to the field of cardiac arrhythmias, with roles pertinent to education, prevention, diagnosis, management, prognosis, and surveillance. AREAS COVERED This review summarizes information on the clinical use of digital health technology in arrhythmia care and discusses its opportunities and challenges. EXPERT OPINION Digital health has begun to play an essential role in arrhythmia care regarding diagnostics, long-term monitoring, patient education and shared decision making, management, medication adherence, and research. Despite remarkable advances, integrating digital health technologies into healthcare faces challenges, including patient usability, privacy, system interoperability, physician liability, analysis and incorporation of the huge amount of real-time information from wearables, and reimbursement. Successful implementation of digital health technologies requires clear objectives and deep changes to existing workflows and responsibilities.
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Affiliation(s)
- Donald P Tchapmi
- Department of Medicine, Brookdale University Hospital Medical Center, Brooklyn, NY, USA
| | - Chris Agyingi
- Department of Medicine, Woodhull Medical Center, Brooklyn, NY, USA
| | - Antoine Egbe
- Department of Medicine, Beaumont Hospital, Dearborn, MI, USA
| | - Gregory M Marcus
- Division of Cardiology, Department of Medicine, University of California-San Francisco, San Francisco, CA, USA
| | - Jean Jacques Noubiap
- Division of Cardiology, Department of Medicine, University of California-San Francisco, San Francisco, CA, USA
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18
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Saeed A, AlShafea A, Bin Saeed A, Nasser M, Ali R. Robotics and Artificial Intelligence and Their Impact on the Diagnosis and Treatment of Cardiovascular Diseases. Cureus 2023; 15:e42252. [PMID: 37605683 PMCID: PMC10440146 DOI: 10.7759/cureus.42252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/21/2023] [Indexed: 08/23/2023] Open
Abstract
A new era has begun in the treatment of cardiovascular disorders as a direct result of the significant developments that have been made in robotics and artificial intelligence (AI). This abstract investigates the potential and impact that AI algorithms and robotic systems may have in the diagnosis and treatment of cardiovascular problems. The field of cardiovascular treatments has been completely transformed by robotically assisted surgeries, which have enabled minimally invasive procedures with increased patient safety and decreased recovery time. The incorporation of AI algorithms into cardiovascular care has made early abnormality identification, risk classification, and tailored treatment planning significantly easier. However, problems including patient safety, data privacy, and smooth integration into existing healthcare systems need to be solved. This abstract places an emphasis on the necessity of collaboration and responsible implementation in order to fully harness the promise of robotics and AI in cardiovascular care, which will ultimately lead to improved patient outcomes and an enhanced quality of life.
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Affiliation(s)
| | | | | | | | - Rihana Ali
- Research Unit, Ministry of Health, Abha, SAU
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19
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Vandenberk B, Chew DS, Prasana D, Gupta S, Exner DV. Successes and challenges of artificial intelligence in cardiology. Front Digit Health 2023; 5:1201392. [PMID: 37448836 PMCID: PMC10336354 DOI: 10.3389/fdgth.2023.1201392] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023] Open
Abstract
In the past decades there has been a substantial evolution in data management and data processing techniques. New data architectures made analysis of big data feasible, healthcare is orienting towards personalized medicine with digital health initiatives, and artificial intelligence (AI) is becoming of increasing importance. Despite being a trendy research topic, only very few applications reach the stage where they are implemented in clinical practice. This review provides an overview of current methodologies and identifies clinical and organizational challenges for AI in healthcare.
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Affiliation(s)
- Bert Vandenberk
- Department of Cardiac Sciences, Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Derek S. Chew
- Department of Cardiac Sciences, Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Dinesh Prasana
- Intelense Inc., Markham, ON, Canada
- IOT/AI- Caliber Interconnect Pvt Ltd., Coimbatore, India
| | | | - Derek V. Exner
- Department of Cardiac Sciences, Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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Bawa D, Kabra R, Ahmed A, Bansal S, Darden D, Pothineni NVK, Gopinathannair R, Lakkireddy D. Data deluge from remote monitoring of cardiac implantable electronic devices and importance of clinical stratification. Heart Rhythm O2 2023; 4:374-381. [PMID: 37361614 PMCID: PMC10288027 DOI: 10.1016/j.hroo.2023.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023] Open
Abstract
Background Remote monitoring (RM) has been accepted as a standard of care for follow-up of patients with cardiac implantable electronic devices (CIEDs). However, the resulting data deluge poses major challenge to device clinics. Objective This study aimed to quantify the data deluge from CIED and stratify these data based on clinical relevance. Methods The study included patients from 67 device clinics across the United States being remotely monitored by Octagos Health. The CIEDs included implantable loop recorders, pacemakers, implantable cardioverter-defibrillators, cardiac resynchronization therapy defibrillators, and cardiac resynchronization therapy pacemakers. Transmissions were either dismissed before reaching the clinical practice if they were repetitive or redundant or were forwarded if they were either clinically relevant or actionable transmission (alert). The alerts were further classified as level 1, 2, or 3 based on clinical urgency. Results A total of 32,721 patients with CIEDs were included. There were 14,465 (44.2%) patients with pacemakers, 8381 (25.6%) with implantable loop recorders, 5351 (16.4%) with implantable cardioverter-defibrillators, 3531 (10.8%) with cardiac resynchronization therapy defibrillators, and 993 (3%) with cardiac resynchronization therapy pacemakers. Over a period of 2 years of RM, 384,796 transmissions were received. Of these, 220,049 (57%) transmissions were dismissed, as they were either redundant or repetitive. Only 164,747 (43%) transmissions were transmitted to the clinicians, of which only 13% (n = 50,440) had clinical alerts, while 30.6% (n = 114,307) were routine transmissions. Conclusion Our study shows that data deluge from RM of CIEDs can be streamlined by utilization of appropriate screening strategies that will enhance efficiency of device clinics and provide better patient care.
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Affiliation(s)
- Danish Bawa
- Department of Electrophysiology, Kansas City Heart Rhythm Institute, Overland Park, Kansas
| | - Rajesh Kabra
- Department of Electrophysiology, Kansas City Heart Rhythm Institute, Overland Park, Kansas
| | - Adnan Ahmed
- Department of Electrophysiology, Kansas City Heart Rhythm Institute, Overland Park, Kansas
| | - Shanti Bansal
- Department of Electrophysiology, Houston Heart Rhythm and Octagos Health, Houston, Texas
| | - Douglas Darden
- Department of Electrophysiology, Kansas City Heart Rhythm Institute, Overland Park, Kansas
| | | | - Rakesh Gopinathannair
- Department of Electrophysiology, Kansas City Heart Rhythm Institute, Overland Park, Kansas
| | - Dhanunjaya Lakkireddy
- Department of Electrophysiology, Kansas City Heart Rhythm Institute, Overland Park, Kansas
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Raymond WS, Ghaffari S, Aguilera LU, Ron E, Morisaki T, Fox ZR, May MP, Stasevich TJ, Munsky B. Using mechanistic models and machine learning to design single-color multiplexed nascent chain tracking experiments. Front Cell Dev Biol 2023; 11:1151318. [PMID: 37325568 PMCID: PMC10267835 DOI: 10.3389/fcell.2023.1151318] [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: 01/25/2023] [Accepted: 05/09/2023] [Indexed: 06/17/2023] Open
Abstract
mRNA translation is the ubiquitous cellular process of reading messenger-RNA strands into functional proteins. Over the past decade, large strides in microscopy techniques have allowed observation of mRNA translation at a single-molecule resolution for self-consistent time-series measurements in live cells. Dubbed Nascent chain tracking (NCT), these methods have explored many temporal dynamics in mRNA translation uncaptured by other experimental methods such as ribosomal profiling, smFISH, pSILAC, BONCAT, or FUNCAT-PLA. However, NCT is currently restricted to the observation of one or two mRNA species at a time due to limits in the number of resolvable fluorescent tags. In this work, we propose a hybrid computational pipeline, where detailed mechanistic simulations produce realistic NCT videos, and machine learning is used to assess potential experimental designs for their ability to resolve multiple mRNA species using a single fluorescent color for all species. Our simulation results show that with careful application this hybrid design strategy could in principle be used to extend the number of mRNA species that could be watched simultaneously within the same cell. We present a simulated example NCT experiment with seven different mRNA species within the same simulated cell and use our ML labeling to identify these spots with 90% accuracy using only two distinct fluorescent tags. We conclude that the proposed extension to the NCT color palette should allow experimentalists to access a plethora of new experimental design possibilities, especially for cell Signaling applications requiring simultaneous study of multiple mRNAs.
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Affiliation(s)
- William S Raymond
- School of Biomedical Engineering, Colorado State University, Fort Collins, CO, United States
| | - Sadaf Ghaffari
- Department of Computer Science, Colorado State University, Fort Collins, CO, United States
| | - Luis U Aguilera
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, United States
| | - Eric Ron
- School of Biomedical Engineering, Colorado State University, Fort Collins, CO, United States
| | - Tatsuya Morisaki
- Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, CO, United States
| | - Zachary R Fox
- School of Biomedical Engineering, Colorado State University, Fort Collins, CO, United States
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Michael P May
- School of Biomedical Engineering, Colorado State University, Fort Collins, CO, United States
| | - Timothy J Stasevich
- Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, CO, United States
- World Research Hub Initiative and Cell Biology Unit, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
| | - Brian Munsky
- School of Biomedical Engineering, Colorado State University, Fort Collins, CO, United States
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, United States
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22
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Li W, Guan X, Wang Y, Lv Y, Wu Y, Yu M, Sun Y. Cuproptosis-related gene identification and immune infiltration analysis in systemic lupus erythematosus. Front Immunol 2023; 14:1157196. [PMID: 37313407 PMCID: PMC10258330 DOI: 10.3389/fimmu.2023.1157196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/17/2023] [Indexed: 06/15/2023] Open
Abstract
Background Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by loss of tolerance to self-antigen, autoantibody production, and abnormal immune response. Cuproptosis is a recently reported cell death form correlated with the initiation and development of multiple diseases. This study intended to probe cuproptosis-related molecular clusters in SLE and constructed a predictive model. Methods We analyzed the expression profile and immune features of cuproptosis-related genes (CRGs) in SLE based on GSE61635 and GSE50772 datasets and identified core module genes associated with SLE occurrence using the weighted correlation network analysis (WGCNA). We selected the optimal machine-learning model by comparing the random forest (RF) model, support vector machine (SVM) model, generalized linear model (GLM), and the extreme gradient boosting (XGB) model. The predictive performance of the model was validated by nomogram, calibration curve, decision curve analysis (DCA), and external dataset GSE72326. Subsequently, a CeRNA network based on 5 core diagnostic markers was established. Drugs targeting core diagnostic markers were acquired using the CTD database, and Autodock vina software was employed to perform molecular docking. Results Blue module genes identified using WGCNA were highly related to SLE initiation. Among the four machine-learning models, the SVM model presented the best discriminative performance with relatively low residual and root-mean-square error (RMSE) and high area under the curve (AUC = 0.998). An SVM model was constructed based on 5 genes and performed favorably in the GSE72326 dataset for validation (AUC = 0.943). The nomogram, calibration curve, and DCA validated the predictive accuracy of the model for SLE as well. The CeRNA regulatory network includes 166 nodes (5 core diagnostic markers, 61 miRNAs, and 100 lncRNAs) and 175 lines. Drug detection showed that D00156 (Benzo (a) pyrene), D016604 (Aflatoxin B1), D014212 (Tretinoin), and D009532 (Nickel) could simultaneously act on the 5 core diagnostic markers. Conclusion We revealed the correlation between CRGs and immune cell infiltration in SLE patients. The SVM model using 5 genes was selected as the optimal machine learning model to accurately evaluate SLE patients. A CeRNA network based on 5 core diagnostic markers was constructed. Drugs targeting core diagnostic markers were retrieved with molecular docking performed.
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Affiliation(s)
- Wuquan Li
- College of Pharmacy, Binzhou Medical University, Yantai, China
| | - Xiaoran Guan
- College of Pharmacy, Binzhou Medical University, Yantai, China
| | - Yong Wang
- College of Pharmacy, Binzhou Medical University, Yantai, China
| | - Yan Lv
- College of Life Science, Yantai University, Yantai, China
| | - Yuyong Wu
- College of Pharmacy, Binzhou Medical University, Yantai, China
| | - Min Yu
- College of Pharmacy, Binzhou Medical University, Yantai, China
| | - Yeying Sun
- College of Pharmacy, Binzhou Medical University, Yantai, China
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23
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Neri L, Oberdier MT, van Abeelen KCJ, Menghini L, Tumarkin E, Tripathi H, Jaipalli S, Orro A, Paolocci N, Gallelli I, Dall’Olio M, Beker A, Carrick RT, Borghi C, Halperin HR. Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:4805. [PMID: 37430719 PMCID: PMC10223364 DOI: 10.3390/s23104805] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/09/2023] [Accepted: 05/12/2023] [Indexed: 07/12/2023]
Abstract
Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.
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Affiliation(s)
- Luca Neri
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Matt T. Oberdier
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Kirsten C. J. van Abeelen
- Department of Informatics, Systems, and Communication, University of Milano-Bicocca, 20126 Milan, Italy
- Department of Internal Medicine, Radboud University Medical Center, 6525 AJ Nijmegen, The Netherlands
| | - Luca Menghini
- Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy
| | - Ethan Tumarkin
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Hemantkumar Tripathi
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Sujai Jaipalli
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Alessandro Orro
- Institute of Biomedical Technologies, National Research Council, 20054 Segrate, Italy
| | - Nazareno Paolocci
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Ilaria Gallelli
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Massimo Dall’Olio
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Amir Beker
- AccYouRate Group S.p.A., 67100 L’Aquila, Italy
| | - Richard T. Carrick
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Claudio Borghi
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Henry R. Halperin
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Radiology, Johns Hopkins University, Baltimore, MD 21205, USA
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24
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Ledziński Ł, Grześk G. Artificial Intelligence Technologies in Cardiology. J Cardiovasc Dev Dis 2023; 10:jcdd10050202. [PMID: 37233169 DOI: 10.3390/jcdd10050202] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 05/27/2023] Open
Abstract
As the world produces exabytes of data, there is a growing need to find new methods that are more suitable for dealing with complex datasets. Artificial intelligence (AI) has significant potential to impact the healthcare industry, which is already on the road to change with the digital transformation of vast quantities of information. The implementation of AI has already achieved success in the domains of molecular chemistry and drug discoveries. The reduction in costs and in the time needed for experiments to predict the pharmacological activities of new molecules is a milestone in science. These successful applications of AI algorithms provide hope for a revolution in healthcare systems. A significant part of artificial intelligence is machine learning (ML), of which there are three main types-supervised learning, unsupervised learning, and reinforcement learning. In this review, the full scope of the AI workflow is presented, with explanations of the most-often-used ML algorithms and descriptions of performance metrics for both regression and classification. A brief introduction to explainable artificial intelligence (XAI) is provided, with examples of technologies that have developed for XAI. We review important AI implementations in cardiology for supervised, unsupervised, and reinforcement learning and natural language processing, emphasizing the used algorithm. Finally, we discuss the need to establish legal, ethical, and methodical requirements for the deployment of AI models in medicine.
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Affiliation(s)
- Łukasz Ledziński
- Department of Cardiology and Clinical Pharmacology, Faculty of Health Sciences, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Ujejskiego 75, 85-168 Bydgoszcz, Poland
| | - Grzegorz Grześk
- Department of Cardiology and Clinical Pharmacology, Faculty of Health Sciences, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Ujejskiego 75, 85-168 Bydgoszcz, Poland
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25
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Harmon DM, Sehrawat O, Maanja M, Wight J, Noseworthy PA. Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation. Arrhythm Electrophysiol Rev 2023; 12:e12. [PMID: 37427304 PMCID: PMC10326669 DOI: 10.15420/aer.2022.31] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 12/22/2022] [Indexed: 07/11/2023] Open
Abstract
AF is the most common clinically relevant cardiac arrhythmia associated with multiple comorbidities, cardiovascular complications (e.g. stroke) and increased mortality. As artificial intelligence (AI) continues to transform the practice of medicine, this review article highlights specific applications of AI for the screening, diagnosis and treatment of AF. Routinely used digital devices and diagnostic technology have been significantly enhanced by these AI algorithms, increasing the potential for large-scale population-based screening and improved diagnostic assessments. These technologies have similarly impacted the treatment pathway of AF, identifying patients who may benefit from specific therapeutic interventions. While the application of AI to the diagnostic and therapeutic pathway of AF has been tremendously successful, the pitfalls and limitations of these algorithms must be thoroughly considered. Overall, the multifaceted applications of AI for AF are a hallmark of this emerging era of medicine.
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Affiliation(s)
- David M Harmon
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, US
| | - Ojasav Sehrawat
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, US
| | - Maren Maanja
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, US
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden
| | - John Wight
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, US
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Kawaguchi N, Nakanishi T. Animal Disease Models and Patient-iPS-Cell-Derived In Vitro Disease Models for Cardiovascular Biology-How Close to Disease? BIOLOGY 2023; 12:468. [PMID: 36979160 PMCID: PMC10045735 DOI: 10.3390/biology12030468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 03/15/2023] [Accepted: 03/17/2023] [Indexed: 03/22/2023]
Abstract
Currently, zebrafish, rodents, canines, and pigs are the primary disease models used in cardiovascular research. In general, larger animals have more physiological similarities to humans, making better disease models. However, they can have restricted or limited use because they are difficult to handle and maintain. Moreover, animal welfare laws regulate the use of experimental animals. Different species have different mechanisms of disease onset. Organs in each animal species have different characteristics depending on their evolutionary history and living environment. For example, mice have higher heart rates than humans. Nonetheless, preclinical studies have used animals to evaluate the safety and efficacy of human drugs because no other complementary method exists. Hence, we need to evaluate the similarities and differences in disease mechanisms between humans and experimental animals. The translation of animal data to humans contributes to eliminating the gap between these two. In vitro disease models have been used as another alternative for human disease models since the discovery of induced pluripotent stem cells (iPSCs). Human cardiomyocytes have been generated from patient-derived iPSCs, which are genetically identical to the derived patients. Researchers have attempted to develop in vivo mimicking 3D culture systems. In this review, we explore the possible uses of animal disease models, iPSC-derived in vitro disease models, humanized animals, and the recent challenges of machine learning. The combination of these methods will make disease models more similar to human disease.
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Affiliation(s)
- Nanako Kawaguchi
- Department of Pediatric Cardiology and Adult Congenital Cardiology, Tokyo Women’s Medical University, Tokyo 162-8666, Japan;
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27
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Ginder C, Li J, Halperin JL, Akar JG, Martin DT, Chattopadhyay I, Upadhyay GA. Predicting Malignant Ventricular Arrhythmias Using Real-Time Remote Monitoring. J Am Coll Cardiol 2023; 81:949-961. [PMID: 36889873 DOI: 10.1016/j.jacc.2022.12.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 12/08/2022] [Accepted: 12/15/2022] [Indexed: 03/08/2023]
Abstract
BACKGROUND Although implantable cardioverter-defibrillator (ICD) therapies are associated with increased morbidity and mortality, the prediction of malignant ventricular arrhythmias has remained elusive. OBJECTIVES The purpose of this study was to evaluate whether daily remote-monitoring data may predict appropriate ICD therapies for ventricular tachycardia or ventricular fibrillation. METHODS This was a post hoc analysis of IMPACT (Randomized trial of atrial arrhythmia monitoring to guide anticoagulation in patients with implanted defibrillator and cardiac resynchronization devices), a multicenter, randomized, controlled trial of 2,718 patients evaluating atrial tachyarrhythmias and anticoagulation for patients with heart failure and ICD or cardiac resynchronization therapy with defibrillator devices. All device therapies were adjudicated as either appropriate (to treat ventricular tachycardia or ventricular fibrillation) or inappropriate (all others). Remote monitoring data in the 30 days before device therapy were utilized to develop separate multivariable logistic regression and neural network models to predict appropriate device therapies. RESULTS A total of 59,807 device transmissions were available for 2,413 patients (age 64 ± 11 years, 26% women, 64% ICD). Appropriate device therapies (141 shocks, 10 antitachycardia pacing) were delivered to 151 patients. Logistic regression identified shock lead impedance and ventricular ectopy as significantly associated with increased risk of appropriate device therapy (sensitivity 39%, specificity 91%, AUC: 0.72). Neural network modeling yielded significantly better (P < 0.01 for comparison) predictive performance (sensitivity 54%, specificity 96%, AUC: 0.90), and also identified patterns of change in atrial lead impedance, mean heart rate, and patient activity as predictors of appropriate therapies. CONCLUSIONS Daily remote monitoring data may be utilized to predict malignant ventricular arrhythmias in the 30 days before device therapies. Neural networks complement and enhance conventional approaches to risk stratification.
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Affiliation(s)
- Curtis Ginder
- Division of Cardiovascular Medicine, Department of Internal Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jin Li
- Department of Computer Science, The University of Chicago, Chicago, Illinois, USA
| | - Jonathan L Halperin
- Cardiovascular Institute, Mount Sinai Medical Center, New York, New York, USA
| | - Joseph G Akar
- Cardiac Electrophysiology, Yale University School of Medicine, New Haven, Connecticut, USA
| | - David T Martin
- Division of Cardiovascular Medicine, Department of Internal Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ishanu Chattopadhyay
- Department of Hospital Medicine, The University of Chicago Pritzker School of Medicine, Chicago, Illinois, USA
| | - Gaurav A Upadhyay
- Center for Arrhythmia Care, Heart and Vascular Institute, The University of Chicago Pritzker School of Medicine, Chicago, Illinois, USA. https://twitter.com/gauravaupadhyay
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28
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Ossowska A, Kusiak A, Świetlik D. Progression of Selected Parameters of the Clinical Profile of Patients with Periodontitis Using Kohonen's Self-Organizing Maps. J Pers Med 2023; 13:jpm13020346. [PMID: 36836580 PMCID: PMC9958729 DOI: 10.3390/jpm13020346] [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: 01/25/2023] [Revised: 02/12/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023] Open
Abstract
(1) Background: Periodontitis is an inflammatory condition that affects the tissues surrounding the tooth and causes clinical attachment loss, which is the loss of periodontal attachment (CAL). Periodontitis can advance in various ways, with some patients experiencing severe periodontitis in a short period of time while others may experience mild periodontitis for the rest of their lives. In this study, we have used an alternative methodology to conventional statistics, self-organizing maps (SOM), to group the clinical profiles of patients with periodontitis. (2) Methods: To predict the periodontitis progression and to choose the best treatment plan, we can use artificial intelligence, more precisely Kohonen's self-organizing maps (SOM). In this study, 110 patients, both genders, between the ages of 30 and 60, were included in this retrospective analysis. (3) Results: To discover the pattern of patients according to the periodontitis grade and stage, we grouped the neurons together to form three clusters: Group 1 was made up of neurons 12 and 16 that represented a percentage of slow progression of almost 75%; Group 2 was made up of neurons 3, 4, 6, 7, 11, and 14 in which the percentage of moderate progression was almost 65%; and Group 3 was made up of neurons 1, 2, 5, 8, 9, 10, 13, and 15 that represented a percentage of rapid progression of almost 60%. There were statistically significant differences in the approximate plaque index (API), and bleeding on probing (BoP) versus groups (p < 0.0001). The post-hoc tests showed that API, BoP, pocket depth (PD), and CAL values were significantly lower in Group 1 relative to Group 2 (p < 0.05) and Group 3 (p < 0.05). A detailed statistical analysis showed that the PD value was significantly lower in Group 1 relative to Group 2 (p = 0.0001). Furthermore, the PD was significantly higher in Group 3 relative to Group 2 (p = 0.0068). There was a statistically significant CAL difference between Group 1 relative to Group 2 (p = 0.0370). (4) Conclusions: Self-organizing maps, in contrast to conventional statistics, allow us to view the issue of periodontitis advancement by illuminating how the variables are organized in one or the other of the various suppositions.
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Affiliation(s)
- Agata Ossowska
- Department of Periodontology and Oral Mucosa Diseases, Medical University of Gdansk, 80-208 Gdańsk, Poland
| | - Aida Kusiak
- Department of Periodontology and Oral Mucosa Diseases, Medical University of Gdansk, 80-208 Gdańsk, Poland
| | - Dariusz Świetlik
- Division of Biostatistics and Neural Networks, Medical University of Gdansk, 80-211 Gdańsk, Poland
- Correspondence:
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29
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Deng Y, Cheng S, Huang H, Liu X, Yu Y, Gu M, Cai C, Chen X, Niu H, Hua W. Machine Learning-Based Phenomapping in Patients with Heart Failure and Secondary Prevention Implantable Cardioverter-Defibrillator Implantation: A Proof-of-Concept Study. Rev Cardiovasc Med 2023; 24:37. [PMID: 39077407 PMCID: PMC11273156 DOI: 10.31083/j.rcm2402037] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 11/22/2022] [Accepted: 11/29/2022] [Indexed: 07/31/2024] Open
Abstract
Background Previous studies have failed to implement risk stratification in patients with heart failure (HF) who are eligible for secondary implantable cardioverter-defibrillator (ICD) implantation. We aimed to evaluate whether machine learning-based phenomapping using routinely available clinical data can identify subgroups that differ in characteristics and prognoses. Methods A total of 389 patients with chronic HF implanted with an ICD were included, and forty-four baseline variables were collected. Phenomapping was performed using hierarchical k-means clustering based on factor analysis of mixed data (FAMD). The utility of phenomapping was validated by comparing the baseline features and outcomes of the first appropriate shock and all-cause death among the phenogroups. Results During a median follow-up of 2.7 years for device interrogation and 5.1 years for survival status, 142 (36.5%) first appropriate shocks and 113 (29.0%) all-cause deaths occurred. The first 12 principal components extracted using the FAMD, explaining 60.5% of the total variability, were left for phenomapping. Three mutually exclusive phenogroups were identified. Phenogroup 1 comprised the oldest patients with ischemic cardiomyopathy; had the highest proportion of diabetes mellitus, hypertension, and hyperlipidemia; and had the most favorable cardiac structure and function among the phenogroups. Phenogroup 2 included the youngest patients, mostly those with non-ischemic cardiomyopathy, who had intermediate heart dimensions and function, and the fewest comorbidities. Phenogroup 3 had the worst HF progression. Kaplan-Meier curves revealed significant differences in the first appropriate shock (p = 0.002) and all-cause death (p < 0.001) across the phenogroups. After adjusting for medications in Cox regression, phenogroups 2 and 3 displayed a graded increase in appropriate shock risk (hazard ratio [HR] 1.54, 95% confidence interval [CI] 1.03-2.28, p = 0.033; HR 2.21, 95% CI 1.42-3.43, p < 0.001, respectively; p for trend < 0.001) compared to phenogroup 1. Regarding mortality risk, phenogroup 3 was associated with an increased risk (HR 2.25, 95% CI 1.45-3.49, p < 0.001). In contrast, phenogroup 2 had a risk (p = 0.124) comparable with phenogroup 1. Conclusions Machine-learning-based phenomapping can identify distinct phenotype subgroups in patients with clinically heterogeneous HF with secondary prophylactic ICD therapy. This novel strategy may aid personalized medicine for these patients.
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Affiliation(s)
- Yu Deng
- Cardiac Arrhythmia Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, 100037 Beijing, China
| | - Sijing Cheng
- Cardiac Arrhythmia Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, 100037 Beijing, China
| | - Hao Huang
- Cardiac Arrhythmia Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, 100037 Beijing, China
| | - Xi Liu
- Cardiac Arrhythmia Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, 100037 Beijing, China
| | - Yu Yu
- Cardiac Arrhythmia Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, 100037 Beijing, China
| | - Min Gu
- Cardiac Arrhythmia Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, 100037 Beijing, China
| | - Chi Cai
- Cardiac Arrhythmia Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, 100037 Beijing, China
| | - Xuhua Chen
- Cardiac Arrhythmia Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, 100037 Beijing, China
| | - Hongxia Niu
- Cardiac Arrhythmia Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, 100037 Beijing, China
| | - Wei Hua
- Cardiac Arrhythmia Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, 100037 Beijing, China
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30
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Raymond WS, Ghaffari S, Aguilera LU, Ron E, Morisaki T, Fox ZR, May MP, Stasevich TJ, Munsky B. Using Mechanistic Models and Machine Learning to Design Single-Color Multiplexed Nascent Chain Tracking Experiments. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.25.525583. [PMID: 36747627 PMCID: PMC9900927 DOI: 10.1101/2023.01.25.525583] [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] [Indexed: 01/28/2023]
Abstract
mRNA translation is the ubiquitous cellular process of reading messenger-RNA strands into functional proteins. Over the past decade, large strides in microscopy techniques have allowed observation of mRNA translation at a single-molecule resolution for self-consistent time-series measurements in live cells. Dubbed Nascent chain tracking (NCT), these methods have explored many temporal dynamics in mRNA translation uncaptured by other experimental methods such as ribosomal profiling, smFISH, pSILAC, BONCAT, or FUNCAT-PLA. However, NCT is currently restricted to the observation of one or two mRNA species at a time due to limits in the number of resolvable fluorescent tags. In this work, we propose a hybrid computational pipeline, where detailed mechanistic simulations produce realistic NCT videos, and machine learning is used to assess potential experimental designs for their ability to resolve multiple mRNA species using a single fluorescent color for all species. Through simulation, we show that with careful application, this hybrid design strategy could in principle be used to extend the number of mRNA species that could be watched simultaneously within the same cell. We present a simulated example NCT experiment with seven different mRNA species within the same simulated cell and use our ML labeling to identify these spots with 90% accuracy using only two distinct fluorescent tags. The proposed extension to the NCT color palette should allow experimentalists to access a plethora of new experimental design possibilities, especially for cell signalling applications requiring simultaneous study of multiple mRNAs.
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Affiliation(s)
- William S. Raymond
- School of Biomedical Engineering, Colorado State University, Fort Collins, Colorado, USA
| | - Sadaf Ghaffari
- Department of Computer Science, Colorado State University, Fort Collins, Colorado, USA
| | - Luis U. Aguilera
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, Colorado, USA
| | - Eric Ron
- School of Biomedical Engineering, Colorado State University, Fort Collins, Colorado, USA
| | - Tatsuya Morisaki
- Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, Colorado, USA
| | - Zachary R. Fox
- School of Biomedical Engineering, Colorado State University, Fort Collins, Colorado, USA,Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Michael P. May
- School of Biomedical Engineering, Colorado State University, Fort Collins, Colorado, USA
| | - Timothy J. Stasevich
- Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, Colorado, USA,Cell Biology Unit, Institute of Innovative Research, Tokyo Institute of Technology, Nagatsuta-cho 4259, Midori-ku, Yokohama, Japan
| | - Brian Munsky
- School of Biomedical Engineering, Colorado State University, Fort Collins, Colorado, USA,Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, Colorado, USA,Corresponding Author: Brian Munsky -
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Chang SN, Tseng YH, Chen JJ, Chiu FC, Tsai CF, Hwang JJ, Wang YC, Tsai CT. An artificial intelligence-enabled ECG algorithm for identifying ventricular premature contraction during sinus rhythm. Eur J Med Res 2022; 27:289. [DOI: 10.1186/s40001-022-00929-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 12/03/2022] [Indexed: 12/15/2022] Open
Abstract
Abstract
Background
Ventricular premature complex (VPC) is a common arrhythmia in clinical practice. VPC could trigger ventricular tachycardia/fibrillation or VPC-induced cardiomyopathy in susceptible patients. Existing screening methods require prolonged monitoring and are limited by cost and low yield when the frequency of VPC is low. Twelve-lead electrocardiogram (ECG) is low cost and widely used. We aimed to identify patients with VPC during normal sinus rhythm (NSR) using artificial intelligence (AI) and machine learning-based ECG reading.
Methods
We developed AI-enabled ECG algorithm using a convolutional neural network (CNN) to detect the ECG signature of VPC presented during NSR using standard 12-lead ECGs. A total of 2515 ECG records from 398 patients with VPC were collected. Among them, only ECG records of NSR without VPC (1617 ECG records) were parsed.
Results
A total of 753 normal ECG records from 387 patients under NSR were used for comparison. Both image and time-series datasets were parsed for the training process by the CNN models. The computer architectures were optimized to select the best model for the training process. Both the single-input image model (InceptionV3, accuracy: 0.895, 95% confidence interval [CI] 0.683–0.937) and multi-input time-series model (ResNet50V2, accuracy: 0.880, 95% CI 0.646–0.943) yielded satisfactory results for VPC prediction, both of which were better than the single-input time-series model (ResNet50V2, accuracy: 0.840, 95% CI 0.629–0.952).
Conclusions
AI-enabled ECG acquired during NSR permits rapid identification at point of care of individuals with VPC and has the potential to predict VPC episodes automatically rather than traditional long-time monitoring.
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Huang CB, Tan K, Wu ZY, Yang L. Application of machine learning model to predict lacunar cerebral infarction in elderly patients with femoral neck fracture before surgery. BMC Geriatr 2022; 22:912. [PMID: 36443675 PMCID: PMC9703654 DOI: 10.1186/s12877-022-03631-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 11/17/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Femoral neck fracture and lacunar cerebral infarction (LCI) are the most common diseases in the elderly. When LCI patients undergo a series of traumas such as surgery, their postoperative recovery results are often poor. Moreover, few studies have explored the relationship between LCI and femoral neck fracture in the elderly. Therefore, this study will develop a ML (machine learning)-based model to predict LCI before surgery in elderly patients with a femoral neck fracture. METHODS Professional medical staff retrospectively collected the data of 161 patients with unilateral femoral neck fracture who underwent surgery in the Second Affiliated Hospital of Wenzhou Medical University database from January 1, 2015, to January 1, 2020. Patients were divided into two groups based on LCI (diagnosis based on cranial CT image): the LCI group and the non-LCI group. Preoperative clinical characteristics and preoperative laboratory data were collected for all patients. Features were selected by univariate and multivariate logistic regression analysis, with age, white blood cell (WBC), prealbumin, aspartate aminotransferase (AST), total protein, globulin, serum creatinine (Scr), blood urea nitrogen (Bun)/Scr, lactate dehydrogenase (LDH), serum sodium and fibrinogen as the features of the ML model. Five machine learning algorithms, Logistic regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Decision tree (DT), were used in combination with preoperative clinical characteristics and laboratory data to establish a predictive model of LCI in patients with a femoral neck fracture. Furthermore, indices like the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and accuracy were calculated to test the models' performance. RESULTS The AUROC of 5 ML models ranged from 0.76 to 0.95. It turned out that the RF model demonstrated the highest performance in predicting LCI for femoral neck fracture patients before surgery, whose AUROC was 0.95, sensitivity 1.00, specificity 0.81, and accuracy 0.90 in validation sets. Furthermore, the top 4 high-ranking variables in the RF model were prealbumin, fibrinogen, globulin and Scr, in descending order of importance. CONCLUSION In this study, 5 ML models were developed and validated for patients with femoral neck fracture to predict preoperative LCI. RF model provides an excellent predictive value with an AUROC of 0.95. Clinicians can better conduct multidisciplinary perioperative management for patients with femoral neck fractures through this model and accelerate the postoperative recovery of patients.
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Affiliation(s)
- Cheng-bin Huang
- grid.417384.d0000 0004 1764 2632Department of Orthopaedic Surgery, The Second Affiliated Hospital and Yuying Childrens Hospital of Wenzhou Medical University, Wenzhou, 325000 China ,grid.268099.c0000 0001 0348 3990Key Laboratory of Orthopaedics of Zhejiang Province, Wenzhou, 325000 China
| | - Kai Tan
- grid.417384.d0000 0004 1764 2632Department of Orthopaedic Surgery, The Second Affiliated Hospital and Yuying Childrens Hospital of Wenzhou Medical University, Wenzhou, 325000 China ,grid.268099.c0000 0001 0348 3990Key Laboratory of Orthopaedics of Zhejiang Province, Wenzhou, 325000 China
| | - Zong-yi Wu
- grid.417384.d0000 0004 1764 2632Department of Orthopaedic Surgery, The Second Affiliated Hospital and Yuying Childrens Hospital of Wenzhou Medical University, Wenzhou, 325000 China
| | - Lei Yang
- grid.417384.d0000 0004 1764 2632Department of Orthopaedic Surgery, The Second Affiliated Hospital and Yuying Childrens Hospital of Wenzhou Medical University, Wenzhou, 325000 China ,grid.268099.c0000 0001 0348 3990Key Laboratory of Orthopaedics of Zhejiang Province, Wenzhou, 325000 China
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Escobar F, Gomis-Tena J, Saiz J, Romero L. Automatic modeling of dynamic drug-hERG channel interactions using three voltage protocols and machine learning techniques: A simulation study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107148. [PMID: 36170760 DOI: 10.1016/j.cmpb.2022.107148] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 09/12/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Assessment of drug cardiac safety is critical in the development of new compounds and is commonly addressed by evaluating the half-maximal blocking concentration of the potassium human ether-à-go-go related gene (hERG) channels. However, recent works have evidenced that the modelling of drug-binding dynamics to hERG can help to improve early cardiac safety assessment. Our goal is to develop a methodology to automatically generate Markovian models of the drug-hERG channel interactions. METHODS The training and the test sets consisted of 20800 and 5200 virtual drugs, respectively, distributed into 104 groups with different affinities and kinetics to the conformational states of the channel. In our system, drugs may bind to any state (individually or simultaneously), with different degrees of preference for a conformational state and the change of the conformational state of the drug bound channels may be restricted or allowed. To model such a wide range of possibilities, 12 Markovian chains are considered. Our approach uses the response of the drugs to our three previously developed voltage clamp protocols, which enhance the differences in the probabilities of occupying a certain conformational state of the channel (open, closed and inactivated). The computing tool is comprised of a classifier and a parameter optimizer and uses linear interpolation, support vector machines and a simplex method for function minimization. RESULTS We propose a novel methodology that automatically generates dynamic drug models using Markov model formulations and that elucidates the states where the drug binds and unbinds and the preferential binding state using data obtained from simple voltage clamp protocols that captures the preferential state-dependent binding properties, the relative affinities, trapping and non-trapping dynamics and the onset of IKr block. Overall, the tool correctly predicted the class of 92.04% of the drugs and the model provided by the tool accurately fitted the response of the target compound, the mean accuracy being 97.53%. Moreover, generation of the dynamic model of an IKr blocker from its response to our voltage clamp protocols usually takes less than an hour on a common desktop computer. CONCLUSION Our methodology could be very useful to model and simulate dynamic drug-hERG channel interactions. It would contribute to the improvement of the preclinical assessment of the proarrhythmic risk of drugs that inhibit IKr and the efficacy of antiarrhythmic IKr blockers.
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Affiliation(s)
- Fernando Escobar
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València
| | - Julio Gomis-Tena
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València
| | - Lucía Romero
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València.
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Umar U, Nayab S, Irfan R, Khan MA, Umer A. E-Cardiac Care: A Comprehensive Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:8073. [PMID: 36298423 PMCID: PMC9610906 DOI: 10.3390/s22208073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/02/2022] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
The Internet of Things (IoT) is a complete ecosystem encompassing various communication technologies, sensors, hardware, and software. IoT cutting-edge technologies and Artificial Intelligence (AI) have enhanced the traditional healthcare system considerably. The conventional healthcare system faces many challenges, including avoidable long wait times, high costs, a conventional method of payment, unnecessary long travel to medical centers, and mandatory periodic doctor visits. A Smart healthcare system, Internet of Things (IoT), and AI are arguably the best-suited tailor-made solutions for all the flaws related to traditional healthcare systems. The primary goal of this study is to determine the impact of IoT, AI, various communication technologies, sensor networks, and disease detection/diagnosis in Cardiac healthcare through a systematic analysis of scholarly articles. Hence, a total of 104 fundamental studies are analyzed for the research questions purposefully defined for this systematic study. The review results show that deep learning emerges as a promising technology along with the combination of IoT in the domain of E-Cardiac care with enhanced accuracy and real-time clinical monitoring. This study also pins down the key benefits and significant challenges for E-Cardiology in the domains of IoT and AI. It further identifies the gaps and future research directions related to E-Cardiology, monitoring various Cardiac parameters, and diagnosis patterns.
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Affiliation(s)
- Umara Umar
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44800, Pakistan
| | - Sanam Nayab
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44800, Pakistan
| | - Rabia Irfan
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44800, Pakistan
| | - Muazzam A Khan
- Department of Computer Sciences, Quaid i Azam University, Islamabad 45320, Pakistan
| | - Amna Umer
- Department of Computational Sciences, The University of Faisalabad (TUF), Faisalabad 38000, Pakistan
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Abstract
Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.
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Abstract
The global burden caused by cardiovascular disease is substantial, with heart disease representing the most common cause of death around the world. There remains a need to develop better mechanistic models of cardiac function in order to combat this health concern. Heart rhythm disorders, or arrhythmias, are one particular type of disease which has been amenable to quantitative investigation. Here we review the application of quantitative methodologies to explore dynamical questions pertaining to arrhythmias. We begin by describing single-cell models of cardiac myocytes, from which two and three dimensional models can be constructed. Special focus is placed on results relating to pattern formation across these spatially-distributed systems, especially the formation of spiral waves of activation. Next, we discuss mechanisms which can lead to the initiation of arrhythmias, focusing on the dynamical state of spatially discordant alternans, and outline proposed mechanisms perpetuating arrhythmias such as fibrillation. We then review experimental and clinical results related to the spatio-temporal mapping of heart rhythm disorders. Finally, we describe treatment options for heart rhythm disorders and demonstrate how statistical physics tools can provide insights into the dynamics of heart rhythm disorders.
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Affiliation(s)
- Wouter-Jan Rappel
- Department of Physics, University of California San Diego, La Jolla, CA 92037
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Deng Y, Cheng S, Huang H, Liu X, Yu Y, Gu M, Cai C, Chen X, Niu H, Hua W. Toward Better Risk Stratification for Implantable Cardioverter-Defibrillator Recipients: Implications of Explainable Machine Learning Models. J Cardiovasc Dev Dis 2022; 9:jcdd9090310. [PMID: 36135455 PMCID: PMC9501472 DOI: 10.3390/jcdd9090310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/08/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Current guideline-based implantable cardioverter-defibrillator (ICD) implants fail to meet the demands for precision medicine. Machine learning (ML) designed for survival analysis might facilitate personalized risk stratification. We aimed to develop explainable ML models predicting mortality and the first appropriate shock and compare these to standard Cox proportional hazards (CPH) regression in ICD recipients. Methods and Results: Forty-five routine clinical variables were collected. Four fine-tuned ML approaches (elastic net Cox regression, random survival forests, survival support vector machine, and XGBoost) were applied and compared with the CPH model on the test set using Harrell’s C-index. Of 887 adult patients enrolled, 199 patients died (5.0 per 100 person-years) and 265 first appropriate shocks occurred (12.4 per 100 person-years) during the follow-up. Patients were randomly split into training (75%) and test (25%) sets. Among ML models predicting death, XGBoost achieved the highest accuracy and outperformed the CPH model (C-index: 0.794 vs. 0.760, p < 0.001). For appropriate shock, survival support vector machine showed the highest accuracy, although not statistically different from the CPH model (0.621 vs. 0.611, p = 0.243). The feature contribution of ML models assessed by SHAP values at individual and overall levels was in accordance with established knowledge. Accordingly, a bi-dimensional risk matrix integrating death and shock risk was built. This risk stratification framework further classified patients with different likelihoods of benefiting from ICD implant. Conclusions: Explainable ML models offer a promising tool to identify different risk scenarios in ICD-eligible patients and aid clinical decision making. Further evaluation is needed.
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de Lepper AGW, Buck CMA, van 't Veer M, Huberts W, van de Vosse FN, Dekker LRC. From evidence-based medicine to digital twin technology for predicting ventricular tachycardia in ischaemic cardiomyopathy. JOURNAL OF THE ROYAL SOCIETY, INTERFACE 2022; 19:20220317. [PMID: 36128708 DOI: 10.1098/rsif.2022.0317] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Survivors of myocardial infarction are at risk of life-threatening ventricular tachycardias (VTs) later in their lives. Current guidelines for implantable cardioverter defibrillators (ICDs) implantation to prevent VT-related sudden cardiac death is solely based on symptoms and left ventricular ejection fraction. Catheter ablation of scar-related VTs is performed following ICD therapy, reducing VTs, painful shocks, anxiety, depression and worsening heart failure. We postulate that better prediction of the occurrence and circuit of VT, will improve identification of patients at risk for VT and boost preventive ablation, reducing mortality and morbidity. For this purpose, multiple time-evolving aspects of the underlying pathophysiology, including the anatomical substrate, triggers and modulators, should be part of VT prediction models. We envision digital twins as a solution combining clinical expertise with three prediction approaches: evidence-based medicine (clinical practice), data-driven models (data science) and mechanistic models (biomedical engineering). This paper aims to create a mutual understanding between experts in the different fields by providing a comprehensive description of the clinical problem and the three approaches in an understandable manner, leveraging future collaborations and technological innovations for clinical decision support. Moreover, it defines open challenges and gains for digital twin solutions and discusses the potential of hybrid modelling.
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Affiliation(s)
| | - Carlijn M A Buck
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Marcel van 't Veer
- Department of Cardiology, Catharina Hospital, Eindhoven, The Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Wouter Huberts
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Frans N van de Vosse
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Lukas R C Dekker
- Department of Cardiology, Catharina Hospital, Eindhoven, The Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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Liu J, Li Z, Jin Y, Liu Y, Liu C, Zhao L, Chen X. A review of arrhythmia detection based on electrocardiogram with artificial intelligence. Expert Rev Med Devices 2022; 19:549-560. [DOI: 10.1080/17434440.2022.2115887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- Jinlei Liu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Zhiyuan Li
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Yanrui Jin
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Yunqing Liu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Chengliang Liu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China
| | - Liqun Zhao
- Department of Cardiology, Shanghai First People’s Hospital Affiliated to Shanghai Jiao Tong University, 100 Haining Road, Shanghai 200080, China
| | - Xiaojun Chen
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
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Qing J, Hu X, Li C, Song W, Tirichen H, Yaigoub H, Li Y. Fucose as a potential therapeutic molecule against the immune-mediated inflammation in IgA nepharopathy: An unrevealed link. Front Immunol 2022; 13:929138. [PMID: 36059518 PMCID: PMC9428610 DOI: 10.3389/fimmu.2022.929138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 06/24/2022] [Indexed: 11/29/2022] Open
Abstract
Background IgA nephropathy (IgAN) is an autoimmune disease that affects people of any age and is an important cause of end-stage renal disease. However, the pathogenesis and pathophysiology of IgAN is not clear. This article aimed to explore the immune-mediated inflammation and genetic mechanisms in IgAN. Methods The transcriptome sequencing data of IgAN glomeruli in the Gene Expression Omnibus database were downloaded. Single-sample gene set enrichment analysis was used to estimate the immune microenvironment of the merged microarray data and GSE141295. IgAN samples were divided into two clusters by cluster analysis. “limma” and “DEseq2” package in R were used to identify differentially expressed genes (DEGs). The weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression modules related to inflammation in IgAN. R software package “clusterProfiler” was used for enrichment analysis, whereas Short Time-Series Expression Miner (STEM) analysis was used to identify the trend of gene expression. Machine-learn (ML) was performed using the shiny app. Finally, Drug Signatures Database (DSigDB) was used to identify potential molecules for treating IgAN. Results The infiltration of macrophages in IgAN glomeruli was increased, whereas CD4+ T cells, especially inducedregulatory T cells (iTregs) were decreased. A total of 1,104 common DEGs were identified from the merged data and GSE141295. Brown module was identified to have the highest inflammatory correlation with IgAN using WGCNA, and 15 hub genes were screened from this module. Among these 15 hub genes, 14 increased with the severity of IgAN inflammation based on STEM analysis. Neural network (nnet) is considered as the best model to predict the severity of IgAN. Fucose identified from DSigDB has a potential biological activity to treat IgAN. Conclusion The increase of macrophages and the decrease of iTregs in glomeruli represent the immune-mediated inflammation of IgAN, and fucose may be a potential therapeutic molecule against IgAN because it affects genes involved in the severe inflammation of IgAN.
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Affiliation(s)
- Jianbo Qing
- The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, China
- Department of Nephrology, Shanxi Provincial People’s Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China
| | - Xueli Hu
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Changqun Li
- The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Wenzhu Song
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Hasna Tirichen
- Institutes of Biomedical Sciences, Shanxi University, Taiyuan, China
| | - Hasnaa Yaigoub
- Institutes of Biomedical Sciences, Shanxi University, Taiyuan, China
| | - Yafeng Li
- Department of Nephrology, Shanxi Provincial People’s Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China
- Core Laboratory , Shanxi Provincial People’s Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China
- Shanxi Provincial Key Laboratory of Kidney Disease, Taiyuan, China
- Academy of Microbial Ecology, Shanxi Medical University, Taiyuan, China
- *Correspondence: Yafeng Li,
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Ossowska A, Kusiak A, Świetlik D. Evaluation of the Progression of Periodontitis with the Use of Neural Networks. J Clin Med 2022; 11:jcm11164667. [PMID: 36012906 PMCID: PMC9409699 DOI: 10.3390/jcm11164667] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/01/2022] [Accepted: 08/07/2022] [Indexed: 11/16/2022] Open
Abstract
Periodontitis is an inflammatory disease of the tissues surrounding the tooth that results in loss of periodontal attachment detected as clinical attachment loss (CAL). The mildest form of periodontal disease is gingivitis, which is a necessary condition for periodontitis development. We can distinguish also some modifying factors which have an influence on the rate of development of periodontitis from which the most important are smoking and poorly controlled diabetes. According to the new classification from 2017, we can identify four stages of periodontitis and three grades of periodontitis. Grades tell us about the periodontitis progression risk and may be helpful in treatment planning and motivating the patients. Artificial neural networks (ANN) are widely used in medicine and in dentistry as an additional tool to support clinicians in their work. In this paper, ANN was used to assess grades of periodontitis in the group of patients. Gender, age, nicotinism approximal plaque index (API), bleeding on probing (BoP), clinical attachment loss (CAL), and pocket depth (PD) were taken into consideration. There were no statistically significant differences in the clinical periodontal assessment in relation to the neural network assessment. Based on the definition of the sensitivity and specificity in medicine we obtained 85.7% and 80.0% as a correctly diagnosed and excluded disease, respectively. The quality of the neural network, defined as the percentage of correctly classified patients according to the grade of periodontitis was 84.2% for the training set. The percentage of incorrectly classified patients according to the grade of periodontitis was 15.8%. Artificial neural networks may be useful tool in everyday dental practice to assess the risk of periodontitis development however more studies are needed.
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Affiliation(s)
- Agata Ossowska
- Department of Periodontology and Oral Mucosa Diseases, Medical University of Gdansk, Orzeszkowej 18 St., 80-208 Gdansk, Poland
| | - Aida Kusiak
- Department of Periodontology and Oral Mucosa Diseases, Medical University of Gdansk, Orzeszkowej 18 St., 80-208 Gdansk, Poland
| | - Dariusz Świetlik
- Division of Biostatistics and Neural Networks, Medical University of Gdansk, Debinki 1 St., 80-211 Gdansk, Poland
- Correspondence:
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Wang L, Dang S, Chen S, Sun JY, Wang RX, Pan F. Deep-Learning-Based Detection of Paroxysmal Supraventricular Tachycardia Using Sinus-Rhythm Electrocardiograms. J Clin Med 2022; 11:jcm11154578. [PMID: 35956195 PMCID: PMC9369533 DOI: 10.3390/jcm11154578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/18/2022] [Accepted: 07/31/2022] [Indexed: 11/24/2022] Open
Abstract
Background: Paroxysmal supraventricular tachycardia (PSVT) is a common arrhythmia associated with palpitation and a decline in quality of life. However, it is undetectable with sinus-rhythmic ECGs when patients are not in the symptomatic onset stage. Methods: In the current study, a convolution neural network (CNN) was trained with normal-sinus-rhythm standard 12-lead electrocardiographs (ECGs) of negative control patients and PSVT patients to identify patients with unrecognized PSVT. PSVT refers to atrioventricular nodal reentry tachycardia or atrioventricular reentry tachycardia based on a concealed accessory pathway as confirmed by electrophysiological procedure. Negative control group data were obtained from 5107 patients with at least one normal sinus-rhythmic ECG without any palpitation symptoms. All ECGs were randomly allocated to the training, validation and testing datasets in a 7:1:2 ratio. Model performance was evaluated on the testing dataset through F1 score, overall accuracy, area under the curve, sensitivity, specificity and precision. Results: We retrospectively enrolled 407 sinus-rhythm ECGs of PSVT procedural patients and 1794 ECGs of control patients. A total of 2201 ECGs were randomly divided into training (n = 1541), validation (n = 220) and testing (n = 440) datasets. In the testing dataset, the CNN algorithm showed an overall accuracy of 95.5%, sensitivity of 90.2%, specificity of 96.6% and precision of 86.0%. Conclusion: Our study reveals that a well-trained CNN algorithm may be a rapid, effective, inexpensive and reliable method to contribute to the detection of PSVT.
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Affiliation(s)
- Lei Wang
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
| | - Shipeng Dang
- Department of Cardiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi 214023, China
| | - Shuangxiong Chen
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
| | - Jin-Yu Sun
- Department of Cardiology, The Affiliated Hospital of Nanjing Medical University, Nanjing 210011, China
| | - Ru-Xing Wang
- Department of Cardiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi 214023, China
- Correspondence: (R.-X.W.); (F.P.)
| | - Feng Pan
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
- Correspondence: (R.-X.W.); (F.P.)
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Abstract
In this digital era, artificial intelligence (AI) is establishing a strong foothold in commercial industry and the field of technology. These effects are trickling into the healthcare industry, especially in the clinical arena of cardiology. Machine learning (ML) algorithms are making substantial progress in various subspecialties of cardiology. This will have a positive impact on patient care and move the field towards precision medicine. In this review article, we explore the progress of ML in cardiovascular imaging, electrophysiology, heart failure, and interventional cardiology.
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Brown G, Conway S, Ahmad M, Adegbie D, Patel N, Myneni V, Alradhawi M, Kumar N, Obaid DR, Pimenta D, Bray JJH. Role of artificial intelligence in defibrillators: a narrative review. Open Heart 2022; 9:openhrt-2022-001976. [PMID: 35790317 PMCID: PMC9258481 DOI: 10.1136/openhrt-2022-001976] [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: 01/31/2022] [Accepted: 05/17/2022] [Indexed: 02/01/2023] Open
Abstract
Automated external defibrillators (AEDs) and implantable cardioverter defibrillators (ICDs) are used to treat life-threatening arrhythmias. AEDs and ICDs use shock advice algorithms to classify ECG tracings as shockable or non-shockable rhythms in clinical practice. Machine learning algorithms have recently been assessed for shock decision classification with increasing accuracy. Outside of rhythm classification alone, they have been evaluated in diagnosis of causes of cardiac arrest, prediction of success of defibrillation and rhythm classification without the need to interrupt cardiopulmonary resuscitation. This review explores the many applications of machine learning in AEDs and ICDs. While these technologies are exciting areas of research, there remain limitations to their widespread use including high processing power, cost and the ‘black-box’ phenomenon.
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Affiliation(s)
- Grace Brown
- Cardiology Department, Royal Free Hospital, London, UK
| | - Samuel Conway
- Cardiology Department, Royal Free Hospital, London, UK
| | - Mahmood Ahmad
- Medical Sciences, University College London, London, UK
| | - Divine Adegbie
- Cardiology Department, East and North Hertfordshire NHS Trust, Stevenage, Hertfordshire, UK
| | - Nishil Patel
- Cardiology Department, North Middlesex University Hospital, London, UK
| | | | | | - Niraj Kumar
- Institute of Cardiovascular Science, University College London, London, UK.,Cardiology Department, Barts Health NHS Trust, London, UK
| | - Daniel R Obaid
- Institute of Life Sciences, Swansea University, Swansea, UK
| | - Dominic Pimenta
- Cardiology Department, Richmond Research Institute, London, UK
| | - Jonathan J H Bray
- Cardiff University College of Biomedical and Life Sciences, Cardiff, UK
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Assessment of Soil Fertility Using Induced Fluorescence and Machine Learning. SENSORS 2022; 22:s22124644. [PMID: 35746426 PMCID: PMC9227221 DOI: 10.3390/s22124644] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 02/01/2023]
Abstract
Techniques such as proximal soil sampling are investigated to increase the sampling density and hence the resolution at which nutrient prescription maps are developed. With the advent of a commercial mobile fluorescence sensor, this study assessed the potential of fluorescence to estimate soil chemical properties and fertilizer recommendations. This experiment was conducted over two years at nine sites on 168 soil samples and used random forest regression to estimate soil properties, fertility classes, and recommended N rates for maize production based on induced fluorescence of air-dried soil samples. Results showed that important soil properties such as soil organic matter, pH, and CEC can be estimated with a correlation of 0.74, 0.75, and 0.75, respectively. When attempting to predict fertility classes, this approach yielded an overall accuracy of 0.54, 0.78, and 0.69 for NO3-N, SOM, and Zn, respectively. The N rate recommendation for maize can be directly estimated by fluorescence readings of the soil with an overall accuracy of 0.78. These results suggest that induced fluorescence is a viable approach for assessing soil fertility. More research is required to transpose these laboratory-acquired soil analysis results to in situ readings successfully.
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Atrial fibrillation signatures on intracardiac electrograms identified by deep learning. Comput Biol Med 2022; 145:105451. [PMID: 35429831 PMCID: PMC9951584 DOI: 10.1016/j.compbiomed.2022.105451] [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: 09/02/2021] [Revised: 03/23/2022] [Accepted: 03/23/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy. OBJECTIVE We hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) signatures. METHODS We studied 86 patients in whom the diagnosis of AF or AT was established at electrophysiological study (25 female, 65 ± 11 years). Custom DL architectures were trained to identify AF using N = 29,340 unipolar and N = 23,760 bipolar EGM segments. We compared DL to traditional classifiers based on rate or regularity. We explained DL using computer models to assess the impact of controlled variations in shape, rate and timing on AF/AT classification in 246,067 EGMs reconstructed from clinical data. RESULTS DL identified AF with AUC of 0.97 ± 0.04 (unipolar) and 0.92 ± 0.09 (bipolar). Rule-based classifiers misclassified ∼10-12% of cases. DL classification was explained by regularity in EGM shape (13%) or timing (26%), and rate (60%; p < 0.001), and also by a set of unipolar EGM shapes that classified as AF independent of rate or regularity. Overall, the optimal AF 'fingerprint' comprised these specific EGM shapes, >15% timing variation, <0.48 correlation in beat-to-beat EGM shapes and CL < 190 ms (p < 0.001). CONCLUSIONS Deep learning of intracardiac EGMs can identify AF or AT via signatures of rate, regularity in timing or shape, and specific EGM shapes. Future work should examine if these signatures differ between different clinical subpopulations with AF.
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Ossowska A, Kusiak A, Świetlik D. Artificial Intelligence in Dentistry-Narrative Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19063449. [PMID: 35329136 PMCID: PMC8950565 DOI: 10.3390/ijerph19063449] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/03/2022] [Accepted: 03/11/2022] [Indexed: 12/21/2022]
Abstract
Nowadays, artificial intelligence (AI) is becoming more important in medicine and in dentistry. It can be helpful in many fields where the human may be assisted and helped by new technologies. Neural networks are a part of artificial intelligence, and are similar to the human brain in their work and can solve given problems and make fast decisions. This review shows that artificial intelligence and the use of neural networks has developed very rapidly in recent years, and it may be an ordinary tool in modern dentistry in the near future. The advantages of this process are better efficiency, accuracy, and time saving during the diagnosis and treatment planning. More research and improvements are needed in the use of neural networks in dentistry to put them into daily practice and to facilitate the work of the dentist.
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Affiliation(s)
- Agata Ossowska
- Department of Periodontology and Oral Mucosa Diseases, Medical University of Gdańsk, 80-204 Gdańsk, Poland;
| | - Aida Kusiak
- Department of Biostatistics and Neural Networks, Medical University of Gdańsk, 80-211 Gdańsk, Poland;
| | - Dariusz Świetlik
- Department of Biostatistics and Neural Networks, Medical University of Gdańsk, 80-211 Gdańsk, Poland;
- Correspondence:
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Martinez DSL, Noseworthy PA, Akbilgic O, Herrmann J, Ruddy KJ, Hamid A, Maddula R, Singh A, Davis R, Gunturkun F, Jefferies JL, Brown SA. Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2022; 15:100129. [PMID: 35721662 PMCID: PMC9202996 DOI: 10.1016/j.ahjo.2022.100129] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/20/2022] [Accepted: 03/21/2022] [Indexed: 01/21/2023]
Abstract
Cardiovascular disease is a leading cause of death among cancer survivors, second only to cancer recurrence or development of new tumors. Cardio-oncology has therefore emerged as a relatively new specialty focused on prevention and management of cardiovascular consequences of cancer therapies. Yet challenges remain regarding precision and accuracy with predicting individuals at highest risk for cardiotoxicity. Barriers such as access to care also limit screening and early diagnosis to improve prognosis. Thus, developing innovative approaches for prediction and early detection of cardiovascular illness in this population is critical. In this review, we provide an overview of the present state of machine learning applications in cardio-oncology. We begin by outlining some factors that should be considered while utilizing machine learning algorithms. We then examine research in which machine learning has been applied to improve prediction of cardiac dysfunction in cancer survivors. We also highlight the use of artificial intelligence (AI) in conjunction with electrocardiogram (ECG) to predict cardiac malfunction and also atrial fibrillation (AF), and we discuss the potential role of wearables. Additionally, the article summarizes future prospects and critical takeaways for the application of machine learning in cardio-oncology. This study is the first in a series on artificial intelligence in cardio-oncology, and complements our manuscript on echocardiography and other forms of imaging relevant to cancer survivors cared for in cardiology clinical practice.
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Affiliation(s)
- Daniel Sierra-Lara Martinez
- Coronary Care Unit, National Institute of Cardiology/Instituto Nacional de Cardiologia, Ciudad de Mexico, Mexico
| | | | - Oguz Akbilgic
- Department of Health Informatics and Data Science, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, IL, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Wake Forest, NC, USA
| | - Joerg Herrmann
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Ashima Singh
- Institute of Health and Equity, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Robert Davis
- Center for Biomedical Informatics, University of Tennessee Health Sciences Center, USA
| | - Fatma Gunturkun
- Center for Biomedical Informatics, University of Tennessee Health Sciences Center, USA
| | - John L. Jefferies
- Division of Cardiovascular Diseases, University of Tennessee Health Sciences Center, USA
- Department of Epidemiology, St. Jude Children's Research Hospital, USA
| | - Sherry-Ann Brown
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
- Cardio-Oncology Program, Division of Cardiovascular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
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Prospective evolution of cardiac arrhythmia care: 2030 vision. Arch Cardiovasc Dis 2022; 115:179-189. [DOI: 10.1016/j.acvd.2022.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 02/02/2022] [Accepted: 02/02/2022] [Indexed: 11/21/2022]
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50
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Cumberland MJ, Riebel LL, Roy A, O’Shea C, Holmes AP, Denning C, Kirchhof P, Rodriguez B, Gehmlich K. Basic Research Approaches to Evaluate Cardiac Arrhythmia in Heart Failure and Beyond. Front Physiol 2022; 13:806366. [PMID: 35197863 PMCID: PMC8859441 DOI: 10.3389/fphys.2022.806366] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 01/10/2022] [Indexed: 12/20/2022] Open
Abstract
Patients with heart failure often develop cardiac arrhythmias. The mechanisms and interrelations linking heart failure and arrhythmias are not fully understood. Historically, research into arrhythmias has been performed on affected individuals or in vivo (animal) models. The latter however is constrained by interspecies variation, demands to reduce animal experiments and cost. Recent developments in in vitro induced pluripotent stem cell technology and in silico modelling have expanded the number of models available for the evaluation of heart failure and arrhythmia. An agnostic approach, combining the modalities discussed here, has the potential to improve our understanding for appraising the pathology and interactions between heart failure and arrhythmia and can provide robust and validated outcomes in a variety of research settings. This review discusses the state of the art models, methodologies and techniques used in the evaluation of heart failure and arrhythmia and will highlight the benefits of using them in combination. Special consideration is paid to assessing the pivotal role calcium handling has in the development of heart failure and arrhythmia.
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Affiliation(s)
- Max J. Cumberland
- Institute of Cardiovascular Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Leto L. Riebel
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Ashwin Roy
- Institute of Cardiovascular Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Christopher O’Shea
- Institute of Cardiovascular Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Andrew P. Holmes
- Institute of Cardiovascular Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- Institute of Clinical Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Chris Denning
- Stem Cell Biology Unit, Biodiscovery Institute, British Heart Foundation Centre for Regenerative Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Paulus Kirchhof
- Institute of Cardiovascular Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Blanca Rodriguez
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Katja Gehmlich
- Institute of Cardiovascular Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford and British Heart Foundation Centre of Research Excellence Oxford, Oxford, United Kingdom
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