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Guo RX, Tian X, Bazoukis G, Tse G, Hong S, Chen KY, Liu T. Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia. Pacing Clin Electrophysiol 2024. [PMID: 38712484 DOI: 10.1111/pace.14995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 03/29/2024] [Accepted: 04/18/2024] [Indexed: 05/08/2024]
Abstract
The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.
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Affiliation(s)
- Rong-Xin Guo
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Xu Tian
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - George Bazoukis
- Department of Cardiology, Larnaca General Hospital, Inomenon Polition Amerikis, Larnaca, Cyprus
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus
| | - Gary Tse
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
- Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China
- School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, China
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Kang-Yin Chen
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Tong Liu
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
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Liu CM, Chen WS, Chang SL, Hsieh YC, Hsu YH, Chang HX, Lin YJ, Lo LW, Hu YF, Chung FP, Chao TF, Tuan TC, Liao JN, Lin CY, Chang TY, Kuo L, Wu CI, Wu MH, Chen CK, Chang YY, Shiu YC, Lu HHS, Chen SA. Use of artificial intelligence and I-Score for prediction of recurrence before catheter ablation of atrial fibrillation. Int J Cardiol 2024; 402:131851. [PMID: 38360099 DOI: 10.1016/j.ijcard.2024.131851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/14/2024] [Accepted: 02/10/2024] [Indexed: 02/17/2024]
Abstract
BACKGROUND Based solely on pre-ablation characteristics, previous risk scores have demonstrated variable predictive performance. This study aimed to predict the recurrence of AF after catheter ablation by using artificial intelligence (AI)-enabled pre-ablation computed tomography (PVCT) images and pre-ablation clinical data. METHODS A total of 638 drug-refractory paroxysmal atrial fibrillation (AF) patients undergone ablation were recruited. For model training, we used left atria (LA) acquired from pre-ablation PVCT slices (126,288 images). A total of 29 clinical variables were collected before ablation, including baseline characteristics, medical histories, laboratory results, transthoracic echocardiographic parameters, and 3D reconstructed LA volumes. The I-Score was applied to select variables for model training. For the prediction of one-year AF recurrence, PVCT deep-learning and clinical variable machine-learning models were developed. We then applied machine learning to ensemble the PVCT and clinical variable models. RESULTS The PVCT model achieved an AUC of 0.63 in the test set. Various combinations of clinical variables selected by I-Score can yield an AUC of 0.72, which is significantly better than all variables or features selected by nonparametric statistics (AUCs of 0.66 to 0.69). The ensemble model (PVCT images and clinical variables) significantly improved predictive performance up to an AUC of 0.76 (sensitivity of 86.7% and specificity of 51.0%). CONCLUSIONS Before ablation, AI-enabled PVCT combined with I-Score features was applicable in predicting recurrence in paroxysmal AF patients. Based on all possible predictors, the I-Score is capable of identifying the most influential combination.
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Affiliation(s)
- Chih-Min Liu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Wei-Shiang Chen
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Shih-Lin Chang
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Cheng Hsieh
- Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Yuan-Heng Hsu
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Hao-Xiang Chang
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yenn-Jiang Lin
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Li-Wei Lo
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Feng Hu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Fa-Po Chung
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tze-Fan Chao
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ta-Chuan Tuan
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jo-Nan Liao
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chin-Yu Lin
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ting-Yung Chang
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ling Kuo
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Cheng-I Wu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Mei-Han Wu
- Department of Medical Imaging, Diagnostic Radiology, Cheng Hsin General Hospital, Taipei, Taiwan
| | - Chun-Ku Chen
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Ying-Yueh Chang
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yang-Che Shiu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan
| | - Henry Horng-Shing Lu
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA.
| | - Shih-Ann Chen
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan; National Chung Hsing University, Taichung, Taiwan
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Sigfstead S, Jiang R, Avram R, Davies B, Krahn AD, Cheung CC. Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes. Can J Cardiol 2024:S0828-282X(24)00335-0. [PMID: 38670456 DOI: 10.1016/j.cjca.2024.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 04/08/2024] [Accepted: 04/21/2024] [Indexed: 04/28/2024] Open
Abstract
Inherited arrhythmia disorders account for a significant proportion of sudden cardiac death, particularly among young individuals. Recent advances in our understanding of these syndromes have improved patient diagnosis and care, yet certain clinical gaps remain, particularly within case ascertainment, access to genetic testing and risk stratification. Artificial intelligence (AI), specifically machine learning and its subset deep learning, present promising solutions to these challenges. The capacity of AI to process vast amounts of patient data and identify disease patterns differentiates them from traditional methods, which are time and resource intensive. To date, AI models have demonstrated immense potential in condition detection (including asymptomatic/concealed disease) and genotype and phenotype identification, exceeding expert cardiologists in these tasks. Additionally, they have exhibited applicability for general population screening, improving case ascertainment in a set of conditions that are often asymptomatic such as left ventricular dysfunction. Third, models have displayed ability to improve testing protocols, as through model identification of disease and genotype, specific clinical testing (e.g. drug challenges or further diagnostic imaging) can be avoided, reducing health care expenses, speeding diagnosis, and possibly allowing for more incremental or targeted genetic testing approaches. These significant benefits warrant continued investigation of the field, particularly regarding the development and implementation of clinically applicable screening tools. This review summarizes key developments in the field, including studies in Long QT Syndrome, Brugada Syndrome, Hypertrophic Cardiomyopathy, and Arrhythmogenic Cardiomyopathies, and provides direction for effective future AI implementation in clinical practice.
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Affiliation(s)
- Sophie Sigfstead
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB
| | - River Jiang
- Division of Cardiology, University of British Columbia, Vancouver, BC
| | - Robert Avram
- Heartwise (heartwise.ai), Montreal Heart Institute, Montreal, QC; Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, QC
| | - Brianna Davies
- Center for Cardiovascular Innovation, Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, BC
| | - Andrew D Krahn
- Center for Cardiovascular Innovation, Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, BC.
| | - Christopher C Cheung
- Division of Cardiology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON
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Chauhan VS. Can Left Atrial Enlargement Be Estimated From the Electrocardiogram? An Artificial Intelligence Approach to Screening Risk of Atrial Fibrillation and Heart Disease. Can J Cardiol 2024; 40:595-597. [PMID: 37984780 DOI: 10.1016/j.cjca.2023.11.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/22/2023] Open
Affiliation(s)
- Vijay S Chauhan
- Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada.
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Liu CL, Lee MH, Hsueh SN, Chung CC, Lin CJ, Chang PH, Luo AC, Weng HC, Lee YH, Dai MJ, Tsai MJ. A bagging approach for improved predictive accuracy of intradialytic hypotension during hemodialysis treatment. Comput Biol Med 2024; 172:108244. [PMID: 38457931 DOI: 10.1016/j.compbiomed.2024.108244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 02/24/2024] [Accepted: 03/04/2024] [Indexed: 03/10/2024]
Abstract
The primary objective of this study is to enhance the prediction accuracy of intradialytic hypotension in patients undergoing hemodialysis. A significant challenge in this context arises from the nature of the data derived from the monitoring devices and exhibits an extreme class imbalance problem. Traditional predictive models often display a bias towards the majority class, compromising the accuracy of minority class predictions. Therefore, we introduce a method called UnderXGBoost. This novel methodology combines the under-sampling, bagging, and XGBoost techniques to balance the dataset and improve predictive accuracy for the minority class. This method is characterized by its straightforward implementation and training efficiency. Empirical validation in a real-world dataset confirms the superior performance of UnderXGBoost compared to existing models in predicting intradialytic hypotension. Furthermore, our approach demonstrates versatility, allowing XGBoost to be substituted with other classifiers and still producing promising results. Sensitivity analysis was performed to assess the model's robustness, reinforce its reliability, and indicate its applicability to a broader range of medical scenarios facing similar challenges of data imbalance. Our model aims to enable medical professionals to provide preemptive treatments more effectively, thereby improving patient care and prognosis. This study contributes a novel and effective solution to a critical issue in medical prediction, thus broadening the application spectrum of predictive modeling in the healthcare domain.
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Affiliation(s)
- Chien-Liang Liu
- Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd. East Dist., Hsinchu, 30010, Taiwan, ROC.
| | - Min-Hsuan Lee
- Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd. East Dist., Hsinchu, 30010, Taiwan, ROC
| | - Shan-Ni Hsueh
- Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd. East Dist., Hsinchu, 30010, Taiwan, ROC
| | - Chia-Chen Chung
- Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd. East Dist., Hsinchu, 30010, Taiwan, ROC
| | - Chun-Ju Lin
- Industrial Technology Research Institute, 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu County, 310401, Taiwan, ROC
| | - Po-Han Chang
- Industrial Technology Research Institute, 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu County, 310401, Taiwan, ROC
| | - An-Chun Luo
- Industrial Technology Research Institute, 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu County, 310401, Taiwan, ROC
| | - Hsuan-Chi Weng
- Industrial Technology Research Institute, 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu County, 310401, Taiwan, ROC
| | - Yu-Hsien Lee
- Industrial Technology Research Institute, 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu County, 310401, Taiwan, ROC
| | - Ming-Ji Dai
- Industrial Technology Research Institute, 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu County, 310401, Taiwan, ROC
| | - Min-Juei Tsai
- Department of Nephrology, Chang-Hua Hospital, Ministry of Health and Welfare, Changhua, No. 80, Sec. 2, Zhongzheng Rd., Puxin Township, Changhua County, 513007, Taiwan, ROC.
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Vozzi F, Pedrelli L, Dimitri GM, Micheli A, Persiani E, Piacenti M, Rossi A, Solarino G, Pieragnoli P, Checchi L, Zucchelli G, Mazzocchetti L, De Lucia R, Nesti M, Notarstefano P, Morales MA. Echo state networks for the recognition of type 1 Brugada syndrome from conventional 12-LEAD ECG. Heliyon 2024; 10:e25404. [PMID: 38333823 PMCID: PMC10850578 DOI: 10.1016/j.heliyon.2024.e25404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 10/16/2023] [Accepted: 01/25/2024] [Indexed: 02/10/2024] Open
Abstract
Artificial Intelligence (AI) applications and Machine Learning (ML) methods have gained much attention in recent years for their ability to automatically detect patterns in data without being explicitly taught rules. Specific features characterise the ECGs of patients with Brugada Syndrome (BrS); however, there is still ambiguity regarding the correct diagnosis of BrS and its differentiation from other pathologies. This work presents an application of Echo State Networks (ESN) in the Recurrent Neural Networks (RNN) class for diagnosing BrS from the ECG time series. 12-lead ECGs were obtained from patients with a definite clinical diagnosis of spontaneous BrS Type 1 pattern (Group A), patients who underwent provocative pharmacological testing to induce BrS type 1 pattern, which resulted in positive (Group B) or negative (Group C), and control subjects (Group D). One extracted beat in the V2 lead was used as input, and the dataset was used to train and evaluate the ESN model using a double cross-validation approach. ESN performance was compared with that of 4 cardiologists trained in electrophysiology. The model performance was assessed in the dataset, with a correct global diagnosis observed in 91.5 % of cases compared to clinicians (88.0 %). High specificity (94.5 %), sensitivity (87.0 %) and AUC (94.7 %) for BrS recognition by ESN were observed in Groups A + B vs. C + D. Our results show that this ML model can discriminate Type 1 BrS ECGs with high accuracy comparable to expert clinicians. Future availability of larger datasets may improve the model performance and increase the potential of the ESN as a clinical support system tool for daily clinical practice.
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Affiliation(s)
| | - Luca Pedrelli
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Giovanna Maria Dimitri
- Department of Computer Science, University of Pisa, Pisa, Italy
- Department of Information Engineering and Mathematics, University of Siena, Siena, Italy
| | - Alessio Micheli
- Department of Computer Science, University of Pisa, Pisa, Italy
| | | | | | - Andrea Rossi
- Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | | | | | - Luca Checchi
- Ospedale Careggi, University of Florence, Firenze, Italy
| | - Giulio Zucchelli
- Second Division of Cardiology, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Lorenzo Mazzocchetti
- Second Division of Cardiology, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Raffaele De Lucia
- Second Division of Cardiology, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Martina Nesti
- Cardiovascular and Neurological Department, San Donato Hospital, Arezzo, Italy
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Sellin J, Pantel JT, Börsch N, Conrad R, Mücke M. [Short paths to diagnosis with artificial intelligence: systematic literature review on diagnostic decision support systems]. Schmerz 2024; 38:19-27. [PMID: 38165492 DOI: 10.1007/s00482-023-00777-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/24/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Rare diseases are often recognized late. Their diagnosis is particularly challenging due to the diversity, complexity and heterogeneity of clinical symptoms. Computer-aided diagnostic aids, often referred to as diagnostic decision support systems (DDSS), are promising tools for shortening the time to diagnosis. Despite initial positive evaluations, DDSS are not yet widely used, partly due to a lack of integration with existing clinical or practice information systems. OBJECTIVE This article provides an insight into currently existing diagnostic support systems that function without access to electronic patient records and only require information that is easily obtainable. MATERIALS AND METHODS A systematic literature search identified eight articles on DDSS that can assist in the diagnosis of rare diseases with no need for access to electronic patient records or other information systems in practices and hospitals. The main advantages and disadvantages of the identified rare disease diagnostic support systems were extracted and summarized. RESULTS Symptom checkers and DDSS based on portrait photos and pain drawings already exist. The degree of maturity of these applications varies. CONCLUSION DDSS currently still face a number of challenges, such as concerns about data protection and accuracy, and acceptance and awareness continue to be rather low. On the other hand, there is great potential for faster diagnosis, especially for rare diseases, which are easily overlooked due to their large number and the low awareness of them. The use of DDSS should therefore be carefully considered by doctors on a case-by-case basis.
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Affiliation(s)
- Julia Sellin
- Institut für Digitale Allgemeinmedizin, Universitätsklinikum RWTH Aachen, Aachen, Deutschland.
- Zentrum für Seltene Erkrankungen Aachen (ZSEA), Universitätsklinikum RWTH Aachen, Aachen, Deutschland.
| | - Jean Tori Pantel
- Institut für Digitale Allgemeinmedizin, Universitätsklinikum RWTH Aachen, Aachen, Deutschland
- Zentrum für Seltene Erkrankungen Aachen (ZSEA), Universitätsklinikum RWTH Aachen, Aachen, Deutschland
| | - Natalie Börsch
- Institut für Digitale Allgemeinmedizin, Universitätsklinikum RWTH Aachen, Aachen, Deutschland
- Zentrum für Seltene Erkrankungen Aachen (ZSEA), Universitätsklinikum RWTH Aachen, Aachen, Deutschland
| | - Rupert Conrad
- Klinik für Psychosomatische Medizin und Psychotherapie, Universitätsklinikum Münster, Münster, Deutschland
| | - Martin Mücke
- Institut für Digitale Allgemeinmedizin, Universitätsklinikum RWTH Aachen, Aachen, Deutschland
- Zentrum für Seltene Erkrankungen Aachen (ZSEA), Universitätsklinikum RWTH Aachen, Aachen, Deutschland
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8
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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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Zanchi B, Faraci FD, Gharaviri A, Bergonti M, Monga T, Auricchio A, Conte G. Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach. Europace 2023; 25:euad334. [PMID: 37944131 PMCID: PMC10683037 DOI: 10.1093/europace/euad334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/27/2023] [Accepted: 11/02/2023] [Indexed: 11/12/2023] Open
Abstract
AIMS Brugada syndrome (BrS) is an inherited disease associated with an increased risk of ventricular arrhythmias. Recent studies have reported the presence of an altered atrial phenotype characterized by abnormal P-wave parameters. The aim of this study was to identify BrS based exclusively on P-wave features through an artificial intelligence (AI)-based model. METHODS AND RESULTS Continuous 5 min 12-lead ECG recordings were obtained in sinus rhythm from (i) patients with spontaneous or ajmaline-induced BrS and no history of AF and (ii) subjects with suspected BrS and negative ajmaline challenge. The recorded ECG signals were processed and divided into epochs of 15 s each. Within these epochs, P-waves were first identified and then averaged. From the averaged P-waves, a total of 67 different features considered relevant to the classification task were extracted. These features were then used to train nine different AI-based supervised classifiers. A total of 2228 averaged P-wave observations, resulting from the analysis of 33 420 P-waves, were obtained from 123 patients (79 BrS+ and 44 BrS-). Averaged P-waves were divided using a patient-wise split, allocating 80% for training and 20% for testing, ensuring data integrity and reducing biases in AI-based model training. The BrS+ patients presented with longer P-wave duration (136 ms vs. 124 ms, P < 0.001) and higher terminal force in lead V1 (2.5 au vs. 1.7 au, P < 0.01) compared with BrS- subjects. Among classifiers, AdaBoost model had the highest values of performance for all the considered metrics, reaching an accuracy of over 81% (sensitivity 86%, specificity 73%). CONCLUSION An AI machine-learning model is able to identify patients with BrS based only on P-wave characteristics. These findings confirm the presence of an atrial hallmark and open new horizons for AI-guided BrS diagnosis.
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Affiliation(s)
- Beatrice Zanchi
- Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare of SUPSI, Lugano, Switzerland
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Francesca Dalia Faraci
- Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare of SUPSI, Lugano, Switzerland
| | - Ali Gharaviri
- Center for Computational Medicine in Cardiology, USI, via La Santa 1, 6900, Lugano, Switzerland
- Centre of Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland
| | - Marco Bergonti
- Division of Cardiology, Cardiocentro Ticino Institute, Ente Ospedaliero Cantonale, via Tesserete 64, 6900, Lugano, Switzerland
| | - Tomas Monga
- Faculty of Biomedical Sciences, Università della Svizzera Italiana (USI), Lugano, Switzerland
| | - Angelo Auricchio
- Center for Computational Medicine in Cardiology, USI, via La Santa 1, 6900, Lugano, Switzerland
- Division of Cardiology, Cardiocentro Ticino Institute, Ente Ospedaliero Cantonale, via Tesserete 64, 6900, Lugano, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana (USI), Lugano, Switzerland
| | - Giulio Conte
- Center for Computational Medicine in Cardiology, USI, via La Santa 1, 6900, Lugano, Switzerland
- Division of Cardiology, Cardiocentro Ticino Institute, Ente Ospedaliero Cantonale, via Tesserete 64, 6900, Lugano, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana (USI), Lugano, Switzerland
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10
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Melo L, Ciconte G, Christy A, Vicedomini G, Anastasia L, Pappone C, Grant E. Deep learning unmasks the ECG signature of Brugada syndrome. PNAS Nexus 2023; 2:pgad327. [PMID: 37937270 PMCID: PMC10627411 DOI: 10.1093/pnasnexus/pgad327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/29/2023] [Indexed: 11/09/2023]
Abstract
One in 10 cases of sudden cardiac death strikes without warning as the result of an inherited arrhythmic cardiomyopathy, such as Brugada Syndrome (BrS). Normal physiological variations often obscure visible signs of this and related life-threatening channelopathies in conventional electrocardiograms (ECGs). Sodium channel blockers can reveal previously hidden diagnostic ECG features, however, their use carries the risk of life-threatening proarrhythmic side effects. The absence of a nonintrusive test places a grossly underestimated fraction of the population at risk of SCD. Here, we present a machine-learning algorithm that extracts, aligns, and classifies ECG waveforms for the presence of BrS. This protocol, which succeeds without the use of a sodium channel blocker (88.4% accuracy, 0.934 AUC in validation), can aid clinicians in identifying the presence of this potentially life-threatening heart disease.
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Affiliation(s)
- Luke Melo
- Department of Chemistry, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
| | - Giuseppe Ciconte
- Arrhythmia and Electrophysiology Center, IRCCS Policlinico San Donato, Milan 20097, Italy
| | - Ashton Christy
- Department of Chemistry, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
| | - Gabriele Vicedomini
- Arrhythmia and Electrophysiology Center, IRCCS Policlinico San Donato, Milan 20097, Italy
| | - Luigi Anastasia
- Stem Cell Laboratory for Tissue Engineering, Università Vita-Salute San Raffaele, Milan 20132, Italy
| | - Carlo Pappone
- Arrhythmia and Electrophysiology Center, IRCCS Policlinico San Donato, Milan 20097, Italy
- Department of Cardiology, Università Vita-Salute San Raffaele, Milan 20132, Italy
| | - Edward Grant
- Department of Chemistry, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
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11
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Asatryan B, Bleijendaal H, Wilde AAM. Toward advanced diagnosis and management of inherited arrhythmia syndromes: Harnessing the capabilities of artificial intelligence and machine learning. Heart Rhythm 2023; 20:1399-1407. [PMID: 37442407 DOI: 10.1016/j.hrthm.2023.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/20/2023] [Accepted: 07/02/2023] [Indexed: 07/15/2023]
Abstract
The use of advanced computational technologies, such as artificial intelligence (AI), is now exerting a significant influence on various aspects of life, including health care and science. AI has garnered remarkable public notice with the release of deep learning models that can model anything from artwork to academic papers with minimal human intervention. Machine learning, a method that uses algorithms to extract information from raw data and represent it in a model, and deep learning, a method that uses multiple layers to progressively extract higher-level features from the raw input with minimal human intervention, are increasingly leveraged to tackle problems in the health sector, including utilization for clinical decision support in cardiovascular medicine. Inherited arrhythmia syndromes are a clinical domain where multiple unanswered questions remain despite unprecedented progress over the past 2 decades with the introduction of large panel genetic testing and the first steps in precision medicine. In particular, AI tools can help address gaps in clinical diagnosis by identifying individuals with concealed or transient phenotypes; enhance risk stratification by elevating recognition of underlying risk burden beyond widely recognized risk factors; improve prediction of response to therapy, and further prognostication. In this contemporary review, we provide a summary of the AI models developed to solve challenges in inherited arrhythmia syndromes and also outline gaps that can be filled with the development of intelligent AI models.
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Affiliation(s)
- Babken Asatryan
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
| | - Hidde Bleijendaal
- University of Amsterdam, Heart Center; Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Amsterdam, The Netherlands; Department of Clinical Epidemiology, Biostatistics and Bioinformatics, University of Amsterdam, Amsterdam, The Netherlands
| | - Arthur A M Wilde
- University of Amsterdam, Heart Center; Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Amsterdam, The Netherlands; Department of Clinical Epidemiology, Biostatistics and Bioinformatics, University of Amsterdam, Amsterdam, The Netherlands; European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-Heart)
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12
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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 (Basel) 2023; 12:468. [PMID: 36979160 PMCID: PMC10045735 DOI: 10.3390/biology12030468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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13
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Chui KT, Gupta BB, Zhao M, Malibari A, Arya V, Alhalabi W, Ruiz MT. Enhancing Electrocardiogram Classification with Multiple Datasets and Distant Transfer Learning. Bioengineering (Basel) 2022; 9:683. [PMID: 36421084 PMCID: PMC9687650 DOI: 10.3390/bioengineering9110683] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 10/28/2022] [Accepted: 11/02/2022] [Indexed: 12/26/2023] Open
Abstract
Electrocardiogram classification is crucial for various applications such as the medical diagnosis of cardiovascular diseases, the level of heart damage, and stress. One of the typical challenges of electrocardiogram classification problems is the small size of the datasets, which may lead to limitation in the performance of the classification models, particularly for models based on deep-learning algorithms. Transfer learning has demonstrated effectiveness in transferring knowledge from a source model with a similar domain and can enhance the performance of the target model. Nevertheless, the consideration of datasets with similar domains restricts the selection of source domains. In this paper, electrocardiogram classification was enhanced by distant transfer learning where a generative-adversarial-network-based auxiliary domain with a domain-feature-classifier negative-transfer-avoidance (GANAD-DFCNTA) algorithm was proposed to bridge the knowledge transfer from distant sources to target domains. To evaluate the performance of the proposed algorithm, eight benchmark datasets were chosen, with four from electrocardiogram datasets and four from the following distant domains: ImageNet, COCO, WordNet, and Sentiment140. The results showed an average accuracy improvement of 3.67 to 4.89%. The proposed algorithm was also compared with existing works using traditional transfer learning, revealing an average accuracy improvement of 0.303-5.19%. Ablation studies confirmed the effectiveness of the components of GANAD-DFCNTA.
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Affiliation(s)
- Kwok Tai Chui
- Department of Electronic Engineering and Computer Science, School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
| | - Brij B. Gupta
- International Center for AI and Cyber Security Research and Innovations, Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan
- Lebanese American University, Beirut 1102, Lebanon
- Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun 248007, India
- Department of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Mingbo Zhao
- School of Information Science & Technology, Donghua University, Shanghai 200051, China
| | - Areej Malibari
- Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Varsha Arya
- Lebanese American University, Beirut 1102, Lebanon
- Insights2Techinfo, India
| | - Wadee Alhalabi
- Immersive Virtual Reality Research Group, Department of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Department of Computer Science, Dar Alhekma University, Jeddah 22246, Saudi Arabia
| | - Miguel Torres Ruiz
- Instituto Politécnico Nacional, CIC, UPALM-Zacatenco, Mexico City 07320, Mexico
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14
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Karam CS, Akar FG. Modern Day Wearables to Evade the Widow-Ghost in Brugada Syndrome: From Mythology to Deep-Learning Methodology. JACC Clin Electrophysiol 2022; 8:1021-1023. [PMID: 35981789 DOI: 10.1016/j.jacep.2022.06.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 06/17/2022] [Accepted: 06/28/2022] [Indexed: 11/25/2022]
Affiliation(s)
- Caline S Karam
- Molecular Therapeutics, New York State Psychiatric Institute, New York, New York, USA
| | - Fadi G Akar
- Department of Internal Medicine, Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA; Department of Biomedical Engineering, Yale University Schools of Engineering and Applied Sciences, New Haven, Connecticut, USA.
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15
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Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, Katsaggelos AK, Maglaveras N. State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint). JMIR Med Inform 2022; 10:e38454. [PMID: 35969441 PMCID: PMC9425174 DOI: 10.2196/38454] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/03/2022] [Accepted: 07/03/2022] [Indexed: 11/13/2022] Open
Abstract
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.
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Affiliation(s)
- Georgios Petmezas
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Leandros Stefanopoulos
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilis Kilintzis
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas Tzavelis
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
| | - John A Rogers
- Department of Material Science, Northwestern University, Evanston, IL, United States
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Nicos Maglaveras
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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16
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Bleijendaal H, Wilde AAM. From a Polish 3-Year-Old Boy Who Visited Maastricht to Automatic Detection Using Deep Learning: Brugada Syndrome Is Being Revolutionised. Can J Cardiol 2021; 38:149-151. [PMID: 34571168 DOI: 10.1016/j.cjca.2021.09.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 09/15/2021] [Accepted: 09/16/2021] [Indexed: 11/28/2022] Open
Affiliation(s)
- Hidde Bleijendaal
- Heart Center, Department of Clinical Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
| | - Arthur A M Wilde
- Heart Center, Department of Clinical Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
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