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Sun D, Hu Y, Li Y, Yu X, Chen X, Shen P, Tang X, Wang Y, Lai C, Kang B, Bai Z, Ni Z, Wang N, Wang R, Guan L, Zhou W, Gao Y. Chamber Attention Network (CAN): Towards interpretable diagnosis of pulmonary artery hypertension using echocardiography. J Adv Res 2024; 63:103-115. [PMID: 37926144 PMCID: PMC11380021 DOI: 10.1016/j.jare.2023.10.013] [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] [Revised: 08/20/2023] [Accepted: 10/24/2023] [Indexed: 11/07/2023] Open
Abstract
INTRODUCTION Accurate identification of pulmonary arterial hypertension (PAH) in primary care and rural areas can be a challenging task. However, recent advancements in computer vision offer the potential for automated systems to detect PAH from echocardiography. OBJECTIVES Our aim was to develop a precise and efficient diagnostic model for PAH tailored to the unique requirements of intelligent diagnosis, especially in challenging locales like high-altitude regions. METHODS We proposed the Chamber Attention Network (CAN) for PAH identification from echocardiographic images, trained on a dataset comprising 13,912 individual subjects. A convolutional neural network (CNN) for view classification was used to select the clinically relevant apical four chamber (A4C) and parasternal long axis (PLAX) views for PAH diagnosis. To assess the importance of different heart chambers in PAH diagnosis, we developed a novel Chamber Attention Module. RESULTS The experimental results demonstrated that: 1) The substantial correspondence between our obtained chamber attention vector and clinical expertise suggested that our model was highly interpretable, potentially uncovering diagnostic insights overlooked by the clinical community. 2) The proposed CAN model exhibited superior image-level accuracy and faster convergence on the internal validation dataset compared to the other four models. Furthermore, our CAN model outperformed the others on the external test dataset, with image-level accuracies of 82.53% and 83.32% for A4C and PLAX, respectively. 3) Implementation of the voting strategy notably enhanced the positive predictive value (PPV) and negative predictive value (NPV) of individual-level classification results, enhancing the reliability of our classification outcomes. CONCLUSIONS These findings indicate that CAN is a feasible technique for AI-assisted PAH diagnosis, providing new insights into cardiac structural changes observed in echocardiography.
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Affiliation(s)
- Dezhi Sun
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Yangyi Hu
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Yunming Li
- Department of Information, Medical Support Center, The General Hospital of Western Theater Command, Chengdu 610083, Sichuan, China
| | - Xianbiao Yu
- Department of Ultrasonic Diagnosis, Army 954 Hospital, Shannan 856000, Tibet, China
| | - Xi Chen
- Department of Respiratory Medicine, Army 954 Hospital, Shannan 856000, Tibet, China
| | - Pan Shen
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xianglin Tang
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Yihao Wang
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Chengcai Lai
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Bo Kang
- Department of Academic Affairs, Army 954 Hospital, Shannan 856000, Tibet, China
| | - Zhijie Bai
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Zhexin Ni
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Ningning Wang
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Rui Wang
- General Hospital of Xinjiang Military Region of the Chinese People's Liberation Army, Urumqi 830000, Xinjiang, China
| | - Lina Guan
- General Hospital of Xinjiang Military Region of the Chinese People's Liberation Army, Urumqi 830000, Xinjiang, China
| | - Wei Zhou
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China.
| | - Yue Gao
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, China.
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Fadilah A, Putri VYS, Puling IMDR, Willyanto SE. Assessing the precision of machine learning for diagnosing pulmonary arterial hypertension: a systematic review and meta-analysis of diagnostic accuracy studies. Front Cardiovasc Med 2024; 11:1422327. [PMID: 39257851 PMCID: PMC11385608 DOI: 10.3389/fcvm.2024.1422327] [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: 04/23/2024] [Accepted: 07/30/2024] [Indexed: 09/12/2024] Open
Abstract
Introduction Pulmonary arterial hypertension (PAH) is a severe cardiovascular condition characterized by pulmonary vascular remodeling, increased resistance to blood flow, and eventual right heart failure. Right heart catheterization (RHC) is the gold standard diagnostic technique, but due to its invasiveness, it poses risks such as vessel and valve injury. In recent years, machine learning (ML) technologies have offered non-invasive alternatives combined with ML for improving the diagnosis of PAH. Objectives The study aimed to evaluate the diagnostic performance of various methods, such as electrocardiography (ECG), echocardiography, blood biomarkers, microRNA, chest x-ray, clinical codes, computed tomography (CT) scan, and magnetic resonance imaging (MRI), combined with ML in diagnosing PAH. Methods The outcomes of interest included sensitivity, specificity, area under the curve (AUC), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). This study employed the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool for quality appraisal and STATA V.12.0 for the meta-analysis. Results A comprehensive search across six databases resulted in 26 articles for examination. Twelve articles were categorized as low-risk, nine as moderate-risk, and five as high-risk. The overall diagnostic performance analysis demonstrated significant findings, with sensitivity at 81% (95% CI = 0.76-0.85, p < 0.001), specificity at 84% (95% CI = 0.77-0.88, p < 0.001), and an AUC of 89% (95% CI = 0.85-0.91). In the subgroup analysis, echocardiography displayed outstanding results, with a sensitivity value of 83% (95% CI = 0.72-0.91), specificity value of 93% (95% CI = 0.89-0.96), PLR value of 12.4 (95% CI = 6.8-22.9), and DOR value of 70 (95% CI = 23-231). ECG demonstrated excellent accuracy performance, with a sensitivity of 82% (95% CI = 0.80-0.84) and a specificity of 82% (95% CI = 0.78-0.84). Moreover, blood biomarkers exhibited the highest NLR value of 0.50 (95% CI = 0.42-0.59). Conclusion The implementation of echocardiography and ECG with ML for diagnosing PAH presents a promising alternative to RHC. This approach shows potential, as it achieves excellent diagnostic parameters, offering hope for more accessible and less invasive diagnostic methods. Systematic Review Registration PROSPERO (CRD42024496569).
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Affiliation(s)
- Akbar Fadilah
- Brawijaya Cardiovascular Research Center, Department of Cardiology and Vascular Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, Indonesia
| | - Valerinna Yogibuana Swastika Putri
- Brawijaya Cardiovascular Research Center, Department of Cardiology and Vascular Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, Indonesia
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Salih AM, Galazzo IB, Gkontra P, Rauseo E, Lee AM, Lekadir K, Radeva P, Petersen SE, Menegaz G. A review of evaluation approaches for explainable AI with applications in cardiology. Artif Intell Rev 2024; 57:240. [PMID: 39132011 PMCID: PMC11315784 DOI: 10.1007/s10462-024-10852-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/03/2024] [Indexed: 08/13/2024]
Abstract
Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI models and is important in building trust in model predictions. XAI explanations themselves require evaluation as to accuracy and reasonableness and in the context of use of the underlying AI model. This review details the evaluation of XAI in cardiac AI applications and has found that, of the studies examined, 37% evaluated XAI quality using literature results, 11% used clinicians as domain-experts, 11% used proxies or statistical analysis, with the remaining 43% not assessing the XAI used at all. We aim to inspire additional studies within healthcare, urging researchers not only to apply XAI methods but to systematically assess the resulting explanations, as a step towards developing trustworthy and safe models. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-024-10852-w.
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Affiliation(s)
- Ahmed M. Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
- Department of Population Health Sciences, University of Leicester, University Rd, Leicester, LE1 7RH UK
- Department of Computer Science, University of Zakho, Duhok road, Zakho, Kurdistan Iraq
| | - Ilaria Boscolo Galazzo
- Department of Engineering for Innovative Medicine, University of Verona, S. Francesco, 22, 37129 Verona, Italy
| | - Polyxeni Gkontra
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain
| | - Elisa Rauseo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - Aaron Mark Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - Karim Lekadir
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, Spain
| | - Petia Radeva
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, UK
- Health Data Research, London, UK
- Alan Turing Institute, London, UK
| | - Gloria Menegaz
- Department of Engineering for Innovative Medicine, University of Verona, S. Francesco, 22, 37129 Verona, Italy
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Chinni BK, Manlhiot C. Emerging Analytical Approaches for Personalized Medicine Using Machine Learning In Pediatric and Congenital Heart Disease. Can J Cardiol 2024:S0828-282X(24)00585-3. [PMID: 39097187 DOI: 10.1016/j.cjca.2024.07.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 07/25/2024] [Accepted: 07/26/2024] [Indexed: 08/05/2024] Open
Abstract
Precision and personalized medicine, the process by which patient management is tailored to individual circumstances, are now terms that are familiar to cardiologists, despite it still being an emerging field. Although precision medicine relies most often on the underlying biology and pathophysiology of a patient's condition, personalized medicine relies on digital biomarkers generated through algorithms. Given the complexity of the underlying data, these digital biomarkers are most often generated through machine-learning algorithms. There are a number of analytic considerations regarding the creation of digital biomarkers that are discussed in this review, including data preprocessing, time dependency and gating, dimensionality reduction, and novel methods, both in the realm of supervised and unsupervised machine learning. Some of these considerations, such as sample size requirements and measurements of model performance, are particularly challenging in small and heterogeneous populations with rare outcomes such as children with congenital heart disease. Finally, we review analytic considerations for the deployment of digital biomarkers in clinical settings, including the emerging field of clinical artificial intelligence (AI) operations, computational needs for deployment, efforts to increase the explainability of AI, algorithmic drift, and the needs for distributed surveillance and federated learning. We conclude this review by discussing a recent simulation study that shows that, despite these analytic challenges and complications, the use of digital biomarkers in managing clinical care might have substantial benefits regarding individual patient outcomes.
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Affiliation(s)
- Bhargava K Chinni
- The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Cedric Manlhiot
- The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA; Research Institute, SickKids Hospital, Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada.
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Zeder K, Brittain E, Kovacs G, Maron BA. The Management of Mild Pulmonary Hypertension in Clinical Practice. Ann Am Thorac Soc 2024; 21:1115-1123. [PMID: 38747696 PMCID: PMC11298986 DOI: 10.1513/annalsats.202312-1079fr] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 05/15/2024] [Indexed: 08/02/2024] Open
Abstract
The definition of pulmonary hypertension (PH) has been revised recently, with the mean pulmonary artery pressure (mPAP) threshold (assessed by right heart catheterization) reduced from ⩾25 mm Hg to >20 mm Hg. This change reflects the mPAP upper limit of normal and a lower limit that is independently associated with adverse outcomes. To improve the specificity of diagnosing pathogenic increases in mPAP, however, a diagnosis of precapillary PH now also includes pulmonary vascular resistance >2.0 Wood units (WU) (lowered from >3.0 WU). These changes are positioned to capture approximately 55% more patients with PH. Because all clinical trials showing a benefit of pulmonary vasodilator therapy in precapillary PH used the classical hemodynamic definition, the approach to the diagnosis and management of patients with mild PH (i.e., mPAP 21-24 mm Hg and pulmonary vascular resistance 2-3 WU) requires particular consideration. Here, we use a question/answer format to discuss key areas in the management of mild PH, including practical information tailored to clinicians without training in PH.
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Affiliation(s)
- Katarina Zeder
- Department of Pulmonology, Medical University of Graz and Ludwig Boltzmann Institute for Lung Vascular Research, Graz, Austria
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Division of Cardiovascular Medicine, University of Maryland School of Medicine, Baltimore, Maryland; and
- The University of Maryland-Institute for Health Computing, Bethesda, Maryland
| | - Evan Brittain
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Gabor Kovacs
- Department of Pulmonology, Medical University of Graz and Ludwig Boltzmann Institute for Lung Vascular Research, Graz, Austria
| | - Bradley A. Maron
- Division of Cardiovascular Medicine, University of Maryland School of Medicine, Baltimore, Maryland; and
- The University of Maryland-Institute for Health Computing, Bethesda, Maryland
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Jia H, Liao S, Zhu X, Liu W, Xu Y, Ge R, Zhu Y. Deep learning prediction of survival in patients with heart failure using chest radiographs. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2024:10.1007/s10554-024-03177-w. [PMID: 38969836 DOI: 10.1007/s10554-024-03177-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 06/28/2024] [Indexed: 07/07/2024]
Abstract
Heart failure (HF) is associated with high rates of morbidity and mortality. The value of deep learning survival prediction models using chest radiographs in patients with heart failure is currently unclear. The aim of our study is to develop and validate a deep learning survival prediction model using chest X-ray (DLSPCXR) in patients with HF. The study retrospectively enrolled a cohort of 353 patients with HF who underwent chest X-ray (CXR) at our institution between March 2012 and March 2017. The dataset was randomly divided into training (n = 247) and validation (n = 106) datasets. Univariate and multivariate Cox analysis were conducted on the training dataset to develop clinical and imaging survival prediction models. The DLSPCXR was trained and the selected clinical parameters were incorporated into DLSPCXR to establish a new model called DLSPinteg. Discrimination performance was evaluated using the time-dependent area under the receiver operating characteristic curves (TD AUC) at 1, 3, and 5-years survival. Delong's test was employed for the comparison of differences between two AUCs of different models. The risk-discrimination capability of the optimal model was evaluated by the Kaplan-Meier curve. In multivariable Cox analysis, older age, higher N-terminal pro-B-type natriuretic peptide (NT-ProBNP), systolic pulmonary artery pressure (sPAP) > 50 mmHg, New York Heart Association (NYHA) functional class III-IV and cardiothoracic ratio (CTR) ≥ 0.62 in CXR were independent predictors of poor prognosis in patients with HF. Based on the receiver operating characteristic (ROC) curve analysis, DLSPCXR had better performance at predicting 5-year survival than the imaging Cox model in the validation cohort (AUC: 0.757 vs. 0.561, P = 0.01). DLSPinteg as the optimal model outperforms the clinical Cox model (AUC: 0.826 vs. 0.633, P = 0.03), imaging Cox model (AUC: 0.826 vs. 0.555, P < 0.001), and DLSPCXR (AUC: 0.826 vs. 0.767, P = 0.06). Deep learning models using chest radiographs can predict survival in patients with heart failure with acceptable accuracy.
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Affiliation(s)
- Han Jia
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, Jiangsu, China
| | - Shengen Liao
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Xiaomei Zhu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, Jiangsu, China
| | - Wangyan Liu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, Jiangsu, China
| | - Yi Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, Jiangsu, China.
| | - Rongjun Ge
- School of Instrument Science and Engineering, Southeast University, Nanjing, 210029, Jiangsu, China.
| | - Yinsu Zhu
- Department of Radiology, Jiangsu Institute of Cancer Research, Jiangsu Cancer Hospital, The Affiliated Cancer Hospital of Nanjing Medical University, 42 Baiziting, Nanjing, 210009, China.
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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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Bellfield RAA, Ortega-Martorell S, Lip GYH, Oxborough D, Olier I. Impact of ECG data format on the performance of machine learning models for the prediction of myocardial infarction. J Electrocardiol 2024; 84:17-26. [PMID: 38471239 DOI: 10.1016/j.jelectrocard.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 02/08/2024] [Accepted: 03/04/2024] [Indexed: 03/14/2024]
Abstract
Background We aim to determine which electrocardiogram (ECG) data format is optimal for ML modelling, in the context of myocardial infarction prediction. We will also address the auxiliary objective of evaluating the viability of using digitised ECG signals for ML modelling. Methods Two ECG arrangements displaying 10s and 2.5 s of data for each lead were used. For each arrangement, conservative and speculative data cohorts were generated from the PTB-XL dataset. All ECGs were represented in three different data formats: Signal ECGs, Image ECGs, and Extracted Signal ECGs, with 8358 and 11,621 ECGs in the conservative and speculative cohorts, respectively. ML models were trained using the three data formats in both data cohorts. Results For ECGs that contained 10s of data, Signal and Extracted Signal ECGs were optimal and statistically similar, with AUCs [95% CI] of 0.971 [0.961, 0.981] and 0.974 [0.965, 0.984], respectively, for the conservative cohort; and 0.931 [0.918, 0.945] and 0.919 [0.903, 0.934], respectively, for the speculative cohort. For ECGs that contained 2.5 s of data, the Image ECG format was optimal, with AUCs of 0.960 [0.948, 0.973] and 0.903 [0.886, 0.920], for the conservative and speculative cohorts, respectively. Conclusion When available, the Signal ECG data should be preferred for ML modelling. If not, the optimal format depends on the data arrangement within the ECG: If the Image ECG contains 10s of data for each lead, the Extracted Signal ECG is optimal, however, if it only uses 2.5 s, then using the Image ECG data is optimal for ML performance.
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Affiliation(s)
- Ryan A A Bellfield
- Data Science Research Centre, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK; Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK
| | - Sandra Ortega-Martorell
- Data Science Research Centre, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK; Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK; Department of Clinical Medicine, Aalborg University, Denmark
| | - David Oxborough
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK; School of Sport and Exercise Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
| | - Ivan Olier
- Data Science Research Centre, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK; Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.
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Jia Y, Li Y, Luosang G, Wang J, Peng G, Pu X, Jiang W, Li W, Zhao Z, Peng Y, Feng Y, Wei J, Xu Y, Liu X, Yi Z, Chen M. Electrocardiogram-based prediction of conduction disturbances after transcatheter aortic valve replacement with convolutional neural network. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:219-228. [PMID: 38774374 PMCID: PMC11104474 DOI: 10.1093/ehjdh/ztae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/21/2023] [Accepted: 01/06/2024] [Indexed: 05/24/2024]
Abstract
Aims Permanent pacemaker implantation and left bundle branch block are common complications after transcatheter aortic valve replacement (TAVR) and are associated with impaired prognosis. This study aimed to develop an artificial intelligence (AI) model for predicting conduction disturbances after TAVR using pre-procedural 12-lead electrocardiogram (ECG) images. Methods and results We collected pre-procedural 12-lead ECGs of patients who underwent TAVR at West China Hospital between March 2016 and March 2022. A hold-out testing set comprising 20% of the sample was randomly selected. We developed an AI model using a convolutional neural network, trained it using five-fold cross-validation and tested it on the hold-out testing cohort. We also developed and validated an enhanced model that included additional clinical features. After applying exclusion criteria, we included 1354 ECGs of 718 patients in the study. The AI model predicted conduction disturbances in the hold-out testing cohort with an area under the curve (AUC) of 0.764, accuracy of 0.743, F1 score of 0.752, sensitivity of 0.876, and specificity of 0.624, based solely on pre-procedural ECG images. The performance was better than the Emory score (AUC = 0.704), as well as the logistic (AUC = 0.574) and XGBoost (AUC = 0.520) models built with previously identified high-risk ECG patterns. After adding clinical features, there was an increase in the overall performance with an AUC of 0.779, accuracy of 0.774, F1 score of 0.776, sensitivity of 0.794, and specificity of 0.752. Conclusion Artificial intelligence-enhanced ECGs may offer better predictive value than traditionally defined high-risk ECG patterns.
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Affiliation(s)
- Yuheng Jia
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Yiming Li
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Gaden Luosang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
- Department of Information Science and Technology, Tibet University, No.10 Zangda East Road, Lhasa 850000, Tibet, P. R. China
| | - Jianyong Wang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
| | - Gang Peng
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Xingzhou Pu
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Weili Jiang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
| | - Wenjian Li
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
| | - Zhengang Zhao
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Yong Peng
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Yuan Feng
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Jiafu Wei
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Yuanning Xu
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Xingbin Liu
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Zhang Yi
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
| | - Mao Chen
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
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10
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Singh N, Mehta S. Artificial intelligence to improve the diagnosis of pulmonary hypertension: promises and pitfalls. Heart 2024; 110:541-542. [PMID: 38360056 DOI: 10.1136/heartjnl-2023-323693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/17/2024] Open
Affiliation(s)
- Namisha Singh
- Southwest Ontario PH Clinic, Medicine/Respirology, London Health Sciences Centre, University of Western Ontario, London, Ontario, Canada
| | - Sanjay Mehta
- Southwest Ontario PH Clinic, Medicine/Respirology, London Health Sciences Centre, University of Western Ontario, London, Ontario, Canada
- Pulmonary Hypertension Association of Canada, Vancouver, British Columbia, Canada
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11
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Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform 2024; 12:e52073. [PMID: 38506918 PMCID: PMC10993141 DOI: 10.2196/52073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/12/2023] [Accepted: 01/30/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. OBJECTIVE This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. METHODS We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. RESULTS Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. CONCLUSIONS GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.
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Affiliation(s)
- Dobin Yim
- Loyola University, Maryland, MD, United States
| | - Jiban Khuntia
- University of Colorado Denver, Denver, CO, United States
| | | | - Arlen Meyers
- University of Colorado Denver, Denver, CO, United States
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12
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Ragnarsdottir H, Ozkan E, Michel H, Chin-Cheong K, Manduchi L, Wellmann S, Vogt JE. Deep Learning Based Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms. Int J Comput Vis 2024; 132:2567-2584. [PMID: 38911323 PMCID: PMC11186939 DOI: 10.1007/s11263-024-01996-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 01/04/2024] [Indexed: 06/25/2024]
Abstract
Pulmonary hypertension (PH) in newborns and infants is a complex condition associated with several pulmonary, cardiac, and systemic diseases contributing to morbidity and mortality. Thus, accurate and early detection of PH and the classification of its severity is crucial for appropriate and successful management. Using echocardiography, the primary diagnostic tool in pediatrics, human assessment is both time-consuming and expertise-demanding, raising the need for an automated approach. Little effort has been directed towards automatic assessment of PH using echocardiography, and the few proposed methods only focus on binary PH classification on the adult population. In this work, we present an explainable multi-view video-based deep learning approach to predict and classify the severity of PH for a cohort of 270 newborns using echocardiograms. We use spatio-temporal convolutional architectures for the prediction of PH from each view, and aggregate the predictions of the different views using majority voting. Our results show a mean F1-score of 0.84 for severity prediction and 0.92 for binary detection using 10-fold cross-validation and 0.63 for severity prediction and 0.78 for binary detection on the held-out test set. We complement our predictions with saliency maps and show that the learned model focuses on clinically relevant cardiac structures, motivating its usage in clinical practice. To the best of our knowledge, this is the first work for an automated assessment of PH in newborns using echocardiograms.
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Affiliation(s)
- Hanna Ragnarsdottir
- Department of Computer Science, ETH Zurich, Universitätstrasse 6, 8092 Zürich, Switzerland
| | - Ece Ozkan
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 43 Vassar Street, Cambridge, MA 02139 USA
| | - Holger Michel
- Department of Neonatology, University Children’s Hospital Regensburg (KUNO), Hospital St. Hedwig of the Order of St. John, University of Regensburg, Regensburg, Germany
| | - Kieran Chin-Cheong
- Department of Computer Science, ETH Zurich, Universitätstrasse 6, 8092 Zürich, Switzerland
| | - Laura Manduchi
- Department of Computer Science, ETH Zurich, Universitätstrasse 6, 8092 Zürich, Switzerland
| | - Sven Wellmann
- Department of Neonatology, University Children’s Hospital Regensburg (KUNO), Hospital St. Hedwig of the Order of St. John, University of Regensburg, Regensburg, Germany
| | - Julia E. Vogt
- Department of Computer Science, ETH Zurich, Universitätstrasse 6, 8092 Zürich, Switzerland
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13
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Zargarzadeh A, Javanshir E, Ghaffari A, Mosharkesh E, Anari B. Artificial intelligence in cardiovascular medicine: An updated review of the literature. J Cardiovasc Thorac Res 2023; 15:204-209. [PMID: 38357567 PMCID: PMC10862032 DOI: 10.34172/jcvtr.2023.33031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 12/10/2023] [Indexed: 02/16/2024] Open
Abstract
Screening and early detection of cardiovascular disease (CVD) are crucial for managing progress and preventing related morbidity. In recent years, several studies have reported the important role of Artificial intelligence (AI) technology and its integration into various medical sectors. AI applications are able to deal with the massive amounts of data (medical records, ultrasounds, medications, and experimental results) generated in medicine and identify novel details that would otherwise be forgotten in the mass of healthcare data sets. Nowadays, AI algorithms are currently used to improve diagnosis of some CVDs including heart failure, atrial fibrillation, hypertrophic cardiomyopathy and pulmonary hypertension. This review summarized some AI concepts, critical execution requirements, obstacles, and new applications for CVDs.
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Affiliation(s)
| | - Elnaz Javanshir
- Cardiovascular Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Alireza Ghaffari
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Erfan Mosharkesh
- Faculty of Veterinary Medicine, University of Tabriz, Tabriz, Iran
| | - Babak Anari
- Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
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14
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Averjanovaitė V, Gumbienė L, Zeleckienė I, Šileikienė V. Unmasking a Silent Threat: Improving Pulmonary Hypertension Screening Methods for Interstitial Lung Disease Patients. MEDICINA (KAUNAS, LITHUANIA) 2023; 60:58. [PMID: 38256318 PMCID: PMC10820938 DOI: 10.3390/medicina60010058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 12/23/2023] [Accepted: 12/26/2023] [Indexed: 01/24/2024]
Abstract
This article provides a comprehensive overview of the latest literature on the diagnostics and treatment of pulmonary hypertension (PH) associated with interstitial lung disease (ILD). Heightened suspicion for PH arises when the advancement of dyspnoea in ILD patients diverges from the expected pattern of decline in pulmonary function parameters. The complexity of PH associated with ILD (PH-ILD) diagnostics is emphasized by the limitations of transthoracic echocardiography in the ILD population, necessitating the exploration of alternative diagnostic approaches. Cardiac magnetic resonance imaging (MRI) emerges as a promising tool, offering insights into hemodynamic parameters and providing valuable prognostic information. The potential of biomarkers, alongside pulmonary function and cardiopulmonary exercise tests, is explored for enhanced diagnostic and prognostic precision. While specific treatments for PH-ILD remain limited, recent studies on inhaled treprostinil provide new hope for improved patient outcomes.
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Affiliation(s)
| | - Lina Gumbienė
- Clinic of Cardiac and Vascular Diseases, Faculty of Medicine, Institute of Clinical Medicine, Vilnius University, LT-03101 Vilnius, Lithuania;
| | | | - Virginija Šileikienė
- Clinic of Chest Diseases, Immunology and Allergology, Faculty of Medicine, Institute of Clinical Medicine, Vilnius University, LT-03101 Vilnius, Lithuania;
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15
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Zaidan AM. The leading global health challenges in the artificial intelligence era. Front Public Health 2023; 11:1328918. [PMID: 38089037 PMCID: PMC10711066 DOI: 10.3389/fpubh.2023.1328918] [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: 10/27/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
Millions of people's health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the "digital revolution," characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.
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Affiliation(s)
- Amal Mousa Zaidan
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia
- Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
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16
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Morland K, Gerges C, Elwing J, Visovatti SH, Weatherald J, Gillmeyer KR, Sahay S, Mathai SC, Boucly A, Williams PG, Harikrishnan S, Minty EP, Hobohm L, Jose A, Badagliacca R, Lau EMT, Jing Z, Vanderpool RR, Fauvel C, Leonidas Alves J, Strange G, Pulido T, Qian J, Li M, Mercurio V, Zelt JGE, Moles VM, Cirulis MM, Nikkho SM, Benza RL, Elliott CG. Real-world evidence to advance knowledge in pulmonary hypertension: Status, challenges, and opportunities. A consensus statement from the Pulmonary Vascular Research Institute's Innovative Drug Development Initiative's Real-world Evidence Working Group. Pulm Circ 2023; 13:e12317. [PMID: 38144948 PMCID: PMC10739115 DOI: 10.1002/pul2.12317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/26/2023] [Accepted: 11/21/2023] [Indexed: 12/26/2023] Open
Abstract
This manuscript on real-world evidence (RWE) in pulmonary hypertension (PH) incorporates the broad experience of members of the Pulmonary Vascular Research Institute's Innovative Drug Development Initiative Real-World Evidence Working Group. We aim to strengthen the research community's understanding of RWE in PH to facilitate clinical research advances and ultimately improve patient care. Herein, we review real-world data (RWD) sources, discuss challenges and opportunities when using RWD sources to study PH populations, and identify resources needed to support the generation of meaningful RWE for the global PH community.
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Affiliation(s)
- Kellie Morland
- Global Medical AffairsUnited Therapeutics CorporationResearch Triangle ParkNorth CarolinaUSA
| | - Christian Gerges
- Department of Internal Medicine II, Division of CardiologyMedical University of ViennaViennaAustria
| | - Jean Elwing
- Division of Pulmonary, Critical Care, and Sleep MedicineUniversity of CincinnatiCincinnatiOhioUSA
| | - Scott H. Visovatti
- Division of Cardiovascular MedicineThe Ohio State UniversityColumbusOhioUSA
| | - Jason Weatherald
- Department of Medicine, Division of Pulmonary MedicineUniversity of AlbertaEdmontonCanada
| | - Kari R. Gillmeyer
- The Pulmonary CenterBoston University Chobian & Avedisian School of MedicineBostonMassachusettsUSA
- Center for Healthcare Organization & Implementation ResearchVA Bedford Healthcare System and VA Boston Healthcare SystemBedfordMassachusettsUSA
| | - Sandeep Sahay
- Division of Pulmonary, Critical Care & Sleep MedicineHouston Methodist HospitalHoustonTexasUSA
| | - Stephen C. Mathai
- Division of Pulmonary and Critical Care MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Athénaïs Boucly
- Faculté de MédecineUniversité Paris‐SaclayLe Kremlin‐BicêtreFrance
- Service de Pneumologie et Soins Intensifs Respiratoires, Centre de Référence de l'Hypertension Pulmonaire, Hôpital BicêtreAssistance Publique Hôpitaux de ParisLe Kremlin BicêtreFrance
- National Heart and Lung InstituteImperial CollegeLondonUK
| | - Paul G. Williams
- Center of Chest Diseases & Critical CareMilpark HospitalJohannesburgSouth Africa
| | | | - Evan P. Minty
- Department of Medicine & O'Brien Institute for Public HealthUniversity of CalgaryCalgaryCanada
| | - Lukas Hobohm
- Department of CardiologyUniversity Medical Center of the Johannes Gutenberg University MainzMainzGermany
- Center for Thrombosis and Hemostasis (CTH)University Medical Center of the Johannes Gutenberg University MainzMainzGermany
| | - Arun Jose
- Division of Pulmonary, Critical Care, and Sleep MedicineUniversity of CincinnatiCincinnatiOhioUSA
| | - Roberto Badagliacca
- Department of Clinical, Anesthesiological and Cardiovascular Sciences, Sapienza University of RomePoliclinico Umberto IRomeItaly
| | - Edmund M. T. Lau
- Department of Respiratory Medicine, Royal Prince Alfred HospitalUniversity of SydneyCamperdownNew South WalesAustralia
- Faculty of Medicine and HealthUniversity of SydneyCamperdownNew South WalesAustralia
| | - Zhi‐Cheng Jing
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | | | - Charles Fauvel
- Service de Cardiologie, Centre de Compétence en Hypertension Pulmonaire 27/76, Centre Hospitalier Universitaire Charles Nicolle, INSERM EnVI U1096Université de RouenRouenFrance
| | - Jose Leonidas Alves
- Pulmonary Division, Heart InstituteUniversity of São Paulo Medical SchoolSão PauloBrazil
| | - Geoff Strange
- School of MedicineThe University of Notre Dame AustraliaPerthWestern AustraliaAustralia
| | - Tomas Pulido
- Ignacio Chávez National Heart InstituteMéxico CityMexico
| | - Junyan Qian
- Department of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC‐DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital (PUMCH), Key Laboratory of Rheumatology and Clinical ImmunologyMinistry of EducationBeijingChina
| | - Mengtao Li
- Department of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC‐DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital (PUMCH), Key Laboratory of Rheumatology and Clinical ImmunologyMinistry of EducationBeijingChina
| | - Valentina Mercurio
- Department of Translational Medical SciencesFederico II UniversityNaplesItaly
| | - Jason G. E. Zelt
- Department of Medicine, Faculty of MedicineUniversity of OttawaOttawaCanada
| | - Victor M. Moles
- Division of Cardiovascular MedicineUniversity of MichiganAnn ArborMichiganUSA
| | - Meghan M. Cirulis
- Division of Pulmonary and Critical Care MedicineUniversity of UtahSalt Lake CityUtahUSA
- Department of Pulmonary and Critical Care MedicineIntermountain Medical Center MurraySalt Lake CityUtahUSA
| | | | - Raymond L. Benza
- Mount Sinai HeartIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - C. Gregory Elliott
- Division of Pulmonary and Critical Care MedicineUniversity of UtahSalt Lake CityUtahUSA
- Department of Pulmonary and Critical Care MedicineIntermountain Medical Center MurraySalt Lake CityUtahUSA
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17
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Luo D, Zheng X, Yang Z, Li H, Fei H, Zhang C. Machine learning for clustering and postclosure outcome of adult CHD-PAH patients with borderline hemodynamics. J Heart Lung Transplant 2023; 42:1286-1297. [PMID: 37211333 DOI: 10.1016/j.healun.2023.05.003] [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: 09/14/2022] [Revised: 04/27/2023] [Accepted: 05/08/2023] [Indexed: 05/23/2023] Open
Abstract
BACKGROUND Patients with uncorrected isolated simple shunts associated pulmonary arterial hypertension (PAH) had increased mortality. Treatment strategies for borderline hemodynamics remain controversial. This study aims to investigate preclosure characteristics and its association with postclosure outcome in this group of patients. METHODS Adults with uncorrected isolated simple shunts associated PAH were included. Peak tricuspid regurgitation velocity<2.8 m/sec with normalized cardiac structures was defined as the favorable study outcome. We applied unsupervised and supervised machine learning for clustering analysis and model constructions. RESULTS Finally, 246 patients were included. During a median follow-up of 414days, 58.49% (62/106) of patients with pretricuspid shunts achieved favorable outcome while 32.22% (46/127) of patients with post-tricuspid shunts. In unsupervised learning, two clusters were identified in both types of shunts. Generally, the oxygen saturation, pulmonary blood flow, cardiac index, dimensions of the right and left atrium, were the major features that characterized the identified clusters. Specifically, mean right atrial pressure, right ventricular dimension, and right ventricular outflow tract helped differentiate clusters in pretricuspid shunts while age, aorta dimension, and systemic vascular resistance helped differentiate clusters for post-tricuspid shunts. Notably, cluster 1 had better postclosure outcome than cluster 2 (70.83% vs 32.55%, p < .001 for pretricuspid and 48.10% vs 16.67%, p < .001 for post-tricuspid). However, models constructed from supervised learning methods did not achieve good accuracy for predicting the postclosure outcome. CONCLUSIONS There were two main clusters in patients with borderline hemodynamics, in which one cluster had better postclosure outcome than the other.
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Affiliation(s)
- Dongling Luo
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China
| | - Xinpeng Zheng
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China
| | - Ziyang Yang
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China
| | - Hezhi Li
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China
| | - Hongwen Fei
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China.
| | - Caojin Zhang
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China.
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18
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Bart NK, Pepe S, Gregory AT, Denniss AR. Emerging Roles of Artificial Intelligence (AI) in Cardiology: Benefits and Barriers in a 'Brave New World'. Heart Lung Circ 2023; 32:883-888. [PMID: 37544867 DOI: 10.1016/j.hlc.2023.07.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Affiliation(s)
- Nicole K Bart
- Department of Cardiology, St Vincent's Hospital, Sydney, NSW, Australia; Victor Chang Cardiac Research Institute, Sydney, NSW, Australia; Department of Cardiology, University of New South Wales, Sydney, NSW, Australia; University of Notre Dame, Sydney, NSW, Australia.
| | - Salvatore Pepe
- Heart Research Group, Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, Vic, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Vic, Australia.
| | | | - A Robert Denniss
- Heart, Lung and Circulation, Sydney, NSW, Australia; Department of Cardiology, Westmead Hospital and University of Sydney, Sydney, NSW, Australia; Department of Cardiology, Blacktown Hospital, and Western Sydney University, Sydney NSW, Australia. https://www.twitter.com/heartlungcirc
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19
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Sun X, Yin Y, Yang Q, Huo T. Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives. Eur J Med Res 2023; 28:242. [PMID: 37475050 PMCID: PMC10360247 DOI: 10.1186/s40001-023-01065-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 07/22/2023] Open
Abstract
Artificial intelligence (AI), the technique of extracting information from complex database using sophisticated computer algorithms, has incorporated itself in medical field. AI techniques have shown the potential to accelerate the progression of diagnosis and treatment of cardiovascular diseases (CVDs), including heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease and so on. In clinical scenario, AI have been proved to apply well in CVD diagnosis, enhance effectiveness of auxiliary tools, disease stratification and typing, and outcome prediction. Deeply developed to capture subtle connections from massive amounts of healthcare data, recent AI algorithms are expected to handle even more complex tasks than traditional methods. The aim of this review is to introduce current applications of AI in CVDs, which may allow clinicians who have limited expertise of computer science to better understand the frontier of the subject and put AI algorithms into clinical practice.
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Affiliation(s)
- Xiaoyu Sun
- National Institute of Hospital Administration, National Health Commission, Beijing, China
| | - Yuzhe Yin
- The Sixth Clinical Medical School, Capital Medical University, Beijing, China
| | - Qiwei Yang
- Department of Thorax, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Tianqi Huo
- National Institute of Hospital Administration, National Health Commission, Beijing, China.
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20
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Aras MA, Abreau S, Mills H, Radhakrishnan L, Klein L, Mantri N, Rubin B, Barrios J, Chehoud C, Kogan E, Gitton X, Nnewihe A, Quinn D, Bridges C, Butte AJ, Olgin JE, Tison GH. Electrocardiogram Detection of Pulmonary Hypertension Using Deep Learning. J Card Fail 2023; 29:1017-1028. [PMID: 36706977 PMCID: PMC10363571 DOI: 10.1016/j.cardfail.2022.12.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 11/23/2022] [Accepted: 12/25/2022] [Indexed: 01/26/2023]
Abstract
BACKGROUND Pulmonary hypertension (PH) is life-threatening, and often diagnosed late in its course. We aimed to evaluate if a deep learning approach using electrocardiogram (ECG) data alone can detect PH and clinically important subtypes. We asked: does an automated deep learning approach to ECG interpretation detect PH and its clinically important subtypes? METHODS AND RESULTS Adults with right heart catheterization or an echocardiogram within 90 days of an ECG at the University of California, San Francisco (2012-2019) were retrospectively identified as PH or non-PH. A deep convolutional neural network was trained on patients' 12-lead ECG voltage data. Patients were divided into training, development, and test sets in a ratio of 7:1:2. Overall, 5016 PH and 19,454 patients without PH were used in the study. The mean age at the time of ECG was 62.29 ± 17.58 years and 49.88% were female. The mean interval between ECG and right heart catheterization or echocardiogram was 3.66 and 2.23 days for patients with PH and patients without PH, respectively. In the test dataset, the model achieved an area under the receiver operating characteristic curve, sensitivity, and specificity, respectively of 0.89, 0.79, and 0.84 to detect PH; 0.91, 0.83, and 0.84 to detect precapillary PH; 0.88, 0.81, and 0.81 to detect pulmonary arterial hypertension, and 0.80, 0.73, and 0.76 to detect group 3 PH. We additionally applied the trained model on ECGs from participants in the test dataset that were obtained from up to 2 years before diagnosis of PH; the area under the receiver operating characteristic curve was 0.79 or greater. CONCLUSIONS A deep learning ECG algorithm can detect PH and PH subtypes around the time of diagnosis and can detect PH using ECGs that were done up to 2 years before right heart catheterization/echocardiogram diagnosis. This approach has the potential to decrease diagnostic delays in PH.
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Affiliation(s)
- Mandar A Aras
- UCSF Department of Medicine, Division of Cardiology, San Francisco, California
| | - Sean Abreau
- UCSF Department of Medicine, Division of Cardiology, San Francisco, California
| | - Hunter Mills
- Bakar Computation Health Sciences Institute, University of California, San Francisco, San Francisco, California
| | - Lakshmi Radhakrishnan
- Bakar Computation Health Sciences Institute, University of California, San Francisco, San Francisco, California
| | - Liviu Klein
- UCSF Department of Medicine, Division of Cardiology, San Francisco, California
| | - Neha Mantri
- UCSF Department of Medicine, Division of Cardiology, San Francisco, California
| | - Benjamin Rubin
- Bakar Computation Health Sciences Institute, University of California, San Francisco, San Francisco, California
| | - Joshua Barrios
- UCSF Department of Medicine, Division of Cardiology, San Francisco, California
| | | | - Emily Kogan
- Janssen Pharmaceuticals, Inc, Raritan, New Jersey
| | - Xavier Gitton
- Actelion Pharmaceuticals Ltd., Allschwil, Switzerland
| | | | | | | | - Atul J Butte
- Bakar Computation Health Sciences Institute, University of California, San Francisco, San Francisco, California
| | - Jeffrey E Olgin
- UCSF Department of Medicine, Division of Cardiology, San Francisco, California
| | - Geoffrey H Tison
- UCSF Department of Medicine, Division of Cardiology, San Francisco, California; Bakar Computation Health Sciences Institute, University of California, San Francisco, San Francisco, California; Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California, San Francisco, San Francisco, California.
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21
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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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22
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Tang P, Wang Q, Ouyang H, Yang S, Hua P. The feasibility of early detecting coronary artery disease using deep learning-based algorithm based on electrocardiography. Aging (Albany NY) 2023; 15:3524-3537. [PMID: 37186897 PMCID: PMC10449295 DOI: 10.18632/aging.204688] [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: 03/18/2022] [Accepted: 04/13/2023] [Indexed: 05/17/2023]
Abstract
BACKGROUND Coronary Artery Disease (CAD) is a major cause of morbidity and mortality, yet it is frequently asymptomatic in the early stages and hence goes undetected. OBJECTIVE We aimed to develop a novel artificial intelligence-based approach for early detection of CAD patients based solely on electrocardiogram (ECG). METHODS This study included patients with suspected CAD who had standard 10-s resting 12-lead ECGs and coronary computed tomography angiography (cCTA) results within 4 weeks or less. The ECG and cCTA data from the same patient were matched based on their hospitalization or outpatient ID. All matched data pairs were then randomly divided into training, validation dataset for model development based on convolutional neural network (CNN) and test dataset for model evaluation. The accuracy (Acc), specificity (Spec), sensitivity (Sen), positive predictive value (PPV), negative predictive value (NPV) and area under the receiver operating characteristic curve (AUC) of the model were calculated by using the test dataset. RESULTS In the test dataset, the model for detecting CAD achieved an AUC of 0.75 (95% CI, 0.73 to 0.78) with an accuracy of 70.0%. Using the optimal cut-off point, the CAD detection model had sensitivity of 68.7%, specificity of 70.9%, positive predictive value (PPV) of 61.2%, and negative predictive value (NPV) of 77.2%. Our study demonstrates that a well-trained CNN model based solely on ECG could be considered an efficient, low-cost, and noninvasive method of assisting in CAD detection.
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Affiliation(s)
- Panli Tang
- Department of Cardiovascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Qi Wang
- Department of Cardiovascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Hua Ouyang
- Department of Cardiovascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Songran Yang
- The Biobank of Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Ping Hua
- Department of Cardiovascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
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23
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Lou YS, Lin CS, Fang WH, Lee CC, Lin C. Extensive deep learning model to enhance electrocardiogram application via latent cardiovascular feature extraction from identity identification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107359. [PMID: 36738606 DOI: 10.1016/j.cmpb.2023.107359] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 12/22/2022] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Deep learning models (DLMs) have been successfully applied in biomedicine primarily using supervised learning with large, annotated databases. However, scarce training resources limit the potential of DLMs for electrocardiogram (ECG) analysis. METHODS We have developed a novel pre-training strategy for unsupervised identity identification with an area under the receiver operating characteristic curve (AUC) >0.98. Accordingly, a DLM pre-trained with identity identification can be applied to 70 patient characteristic predictions using transfer learning (TL). These ECG-based patient characteristics were then used for cardiovascular disease (CVD) risk prediction. The DLMs were trained using 507,729 ECGs from 222,473 patients and validated using two independent validation sets (n = 27,824/31,925). RESULTS The DLMs using our method exhibited better performance than directly trained DLMs. Additionally, our DLM performed better than those of previous studies in terms of gender (AUC [internal/external] = 0.982/0.968), age (correlation = 0.886/0.892), low ejection fraction (AUC = 0.942/0.951), and critical markers not addressed previously, including high B-type natriuretic peptide (AUC = 0.921/0.899). Additionally, approximately 50% of the ECG-based characteristics provided significantly more prediction information for cardiovascular risk than real characteristics. CONCLUSIONS This is the first study to use identity identification as a pre-training task for TL in ECG analysis. An extensive exploration of the relationship between ECG and 70 patient characteristics was conducted. Our DLM-enhanced ECG interpretation system extensively advanced ECG-related patient characteristic prediction and mortality risk management for cardiovascular diseases.
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Affiliation(s)
- Yu-Sheng Lou
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan; School of Public Health, National Defense Medical Center, Taipei, Taiwan
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C.; Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Wen-Hui Fang
- Department of Family and Community Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chia-Cheng Lee
- Department of Medical Informatics, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C.; Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chin Lin
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan; School of Public Health, National Defense Medical Center, Taipei, Taiwan; Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C.; School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C..
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24
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Langlais EL, Avram R. Overcoming Diagnostic Delays in Pulmonary Hypertension with Deep Learning ECG Analysis. J Card Fail 2023:S1071-9164(23)00063-5. [PMID: 36878352 DOI: 10.1016/j.cardfail.2023.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 03/07/2023]
Key Words
- 95% CI, 95(th) percentile confidence interval
- AI, artificial intelligence
- AUC, area under the receiver operating characteristic curve
- ECG, electrocardiogram
- ICD, International Classification of Disease
- NPV, negative predictive value
- PH, pulmonary hypertension
- PPV, positive predictive value
- RHC, right heart catheterization
- mPAP, mean pulmonary arterial pressure
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Affiliation(s)
- Elodie Labrecque Langlais
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, QC, Canada
| | - Robert Avram
- Département de Génie Biomédical, Polytechnique Montréal, Montréal, QC, Canada.
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25
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Kogan E, Didden EM, Lee E, Nnewihe A, Stamatiadis D, Mataraso S, Quinn D, Rosenberg D, Chehoud C, Bridges C. A machine learning approach to identifying patients with pulmonary hypertension using real-world electronic health records. Int J Cardiol 2023; 374:95-99. [PMID: 36528138 DOI: 10.1016/j.ijcard.2022.12.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 12/06/2022] [Accepted: 12/08/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND This study aimed to develop a machine learning (ML) model to identify patients who are likely to have pulmonary hypertension (PH), using a large patient-level US-based electronic health record (EHR) database. METHODS A gradient boosting model, XGBoost, was developed using data from Optum's US-based de-identified EHR dataset (2007-2019). PH and disease control adult patients were identified using diagnostic, treatment and procedure codes and were randomly split into the training (90%) or test set (10%). Model features included patient demographics, physician visits, diagnoses, procedures, prescriptions, and laboratory test results. SHapley Additive exPlanations values were used to determine feature importance. RESULTS We identified 11,279,478 control and 115,822 PH patients (mean age, respectively: 62 and 68 years, both 53% female). The final model used 165 features, with the most important predictive features including diagnosis of heart failure, shortness of breath and atrial fibrillation. The model predicted PH with an area under the receiver operating characteristic curve (AUROC) of 0.92. AUROC remained above 0.80 for the prediction of PH up to and beyond 18 months before diagnosis. Among the PH patients, we also identified 955 pulmonary arterial hypertension (PAH) and 1432 chronic thromboembolic pulmonary hypertension (CTEPH) patients, and the range of AUROCs obtained for these cohorts was 0.79-0.90 and 0.87-0.96, respectively. CONCLUSIONS This model to detect PH based on patients' EHR records is viable and performs well in subgroups of PAH and CTEPH patients. This approach has the potential to improve patient outcomes by reducing diagnostic delay in PH.
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Affiliation(s)
- Emily Kogan
- Janssen Pharmaceuticals Inc., Spring House, PA, USA.
| | - Eva-Maria Didden
- Actelion Pharmaceuticals Ltd, a Janssen Pharmaceutical Company of Johnson & Johnson, Allschwil, Switzerland, Global Epidemiology Department
| | - Eileen Lee
- Janssen Pharmaceuticals Inc., Spring House, PA, USA
| | | | - Dimitri Stamatiadis
- Actelion Pharmaceuticals Ltd, a Janssen Pharmaceutical Company of Johnson & Johnson, Allschwil, Switzerland, Global R&D Department
| | | | | | - Daniel Rosenberg
- Actelion Pharmaceuticals Ltd, a Janssen Pharmaceutical Company of Johnson & Johnson, Allschwil, Switzerland, Global Epidemiology Department
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26
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Didden E, Lee E, Wyckmans J, Quinn D, Perchenet L. Time to diagnosis of pulmonary hypertension and diagnostic burden: A retrospective analysis of nationwide US healthcare data. Pulm Circ 2023; 13:e12188. [PMID: 36694845 PMCID: PMC9843478 DOI: 10.1002/pul2.12188] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 12/21/2022] [Accepted: 12/26/2022] [Indexed: 01/09/2023] Open
Abstract
The main aim of this analysis was to investigate time from symptom onset (chronic unexplained dyspnoea [CUD]) to diagnosis of Group 1 pulmonary hypertension (PH)-pulmonary arterial hypertension (PAH)-and to characterize healthcare resource utilization leading up to diagnosis using a nationwide US claims and an electronic health record (EHR) database from Optum©. Eligible patients were ≥18 years old at first CUD diagnosis (index event) and had a PAH diagnosis on or after index date. Based on administrative codes, PAH was defined as right heart catheterization (RHC), ≥ 2 PAH diagnoses (1 within a year of RHC), and ≥1 post-RHC prescription for PAH treatment. All values are median (1st quartile-3rd quartile) unless otherwise stated. Of 854,722 patients with CUD in the claims database, 582 (0.1%) had PAH. Time from CUD to PAH diagnosis was 2.26 (0.73-4.22) years. PAH patients experienced 3 (2-4) transthoracic echocardiograms (TTEs), 6 (3-12) specialist visits, and 2 (1-4) hospitalizations during the diagnostic interval. Almost one-third of patients (29%) waited 10 months or more to have a TTE. Findings from the EHR database were broadly similar. Resource utilization during the diagnostic interval was also analyzed in an overall PH cohort: findings were generally similar to the PAH cohort (2 [1-3] TTEs, 4 [2-9] specialist visits and 2 [1-4] hospitalizations). These data indicate a delay in the diagnostic pathway for PAH, and illustrate the burden associated with PAH diagnosis.
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Affiliation(s)
| | - Eileen Lee
- Janssen Research & DevelopmentSpring HousePennsylvaniaUSA
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27
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Ayano YM, Schwenker F, Dufera BD, Debelee TG. Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review. Diagnostics (Basel) 2022; 13:111. [PMID: 36611403 PMCID: PMC9818170 DOI: 10.3390/diagnostics13010111] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 12/31/2022] Open
Abstract
Heart disease is one of the leading causes of mortality throughout the world. Among the different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non-invasive procedure. However, the following are challenges: the scarcity of medical experts, the complexity of ECG interpretations, the manifestation similarities of heart disease in ECG signals, and heart disease comorbidity. Machine learning algorithms are viable alternatives to the traditional diagnoses of heart disease from ECG signals. However, the black box nature of complex machine learning algorithms and the difficulty in explaining a model's outcomes are obstacles for medical practitioners in having confidence in machine learning models. This observation paves the way for interpretable machine learning (IML) models as diagnostic tools that can build a physician's trust and provide evidence-based diagnoses. Therefore, in this systematic literature review, we studied and analyzed the research landscape in interpretable machine learning techniques by focusing on heart disease diagnosis from an ECG signal. In this regard, the contribution of our work is manifold; first, we present an elaborate discussion on interpretable machine learning techniques. In addition, we identify and characterize ECG signal recording datasets that are readily available for machine learning-based tasks. Furthermore, we identify the progress that has been achieved in ECG signal interpretation using IML techniques. Finally, we discuss the limitations and challenges of IML techniques in interpreting ECG signals.
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Affiliation(s)
| | | | - Bisrat Derebssa Dufera
- Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa 11760, Ethiopia
| | - Taye Girma Debelee
- Ethiopian Artificial Intelligence Institute, Addis Ababa 40782, Ethiopia
- College of Electrical and Computer Engineering, Addis Ababa Science and Technology University, Addis Ababa 16417, Ethiopia
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28
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Barrios JP, Tison GH. Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective. Cell Rep Med 2022; 3:100869. [PMID: 36543095 PMCID: PMC9798021 DOI: 10.1016/j.xcrm.2022.100869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 10/26/2022] [Accepted: 11/21/2022] [Indexed: 12/24/2022]
Abstract
Recent advances in machine learning (ML) have made it possible to analyze high-dimensional and complex data-such as free text, images, waveforms, videos, and sound-in an automated manner by successfully learning complex associations within these data. Cardiovascular medicine is particularly well poised to take advantage of these ML advances, due to the widespread digitization of medical data and the large number of diagnostic tests used to evaluate cardiovascular disease. Various ML approaches have successfully been applied to cardiovascular tests and diseases to automate interpretation, accurately perform measurements, and, in some cases, predict novel diagnoses from less invasive tests, effectively expanding the utility of more widely accessible diagnostic tests. Here, we present examples of some impactful advances in cardiovascular medicine using ML across a variety of modalities, with a focus on deep learning applications.
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Affiliation(s)
- Joshua P. Barrios
- Department of Medicine, Division of Cardiology, University of California, San Francisco, 555 Mission Bay Blvd South Box 3120, San Francisco, CA 94158, USA
| | - Geoffrey H. Tison
- Department of Medicine, Division of Cardiology, University of California, San Francisco, 555 Mission Bay Blvd South Box 3120, San Francisco, CA 94158, USA,Bakar Computational Health Sciences Institute, University of California, San Francisco, 555 Mission Bay Blvd South Box 3120, San Francisco, CA 94158, USA,Corresponding author
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29
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Usmani S, Ahmad M, Bray J. More Than Meets the AI: Electrocardiograms in Heart Failure Prognosis. JACC. ADVANCES 2022; 1:100108. [PMID: 38939717 PMCID: PMC11198073 DOI: 10.1016/j.jacadv.2022.100108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Affiliation(s)
- Saba Usmani
- Faculty of Medical Sciences, University College London, Gower Street, London WC1E 6BT, United Kingdom
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30
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Liu CM, Hsieh ME, Hu YF, Wei TY, Wu IC, Chen PF, Lin YJ, Higa S, Yagi N, Chen SA, Tseng VS. Artificial Intelligence-Enabled Model for Early Detection of Left Ventricular Hypertrophy and Mortality Prediction in Young to Middle-Aged Adults. Circ Cardiovasc Qual Outcomes 2022; 15:e008360. [PMID: 35959675 DOI: 10.1161/circoutcomes.121.008360] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Concealed left ventricular hypertrophy (LVH) is a prevalent condition that is correlated with a substantial risk of cardiovascular events and mortality, especially in young to middle-aged adults. Early identification of LVH is warranted. In this work, we aimed to develop an artificial intelligence (AI)-enabled model for early detection and risk stratification of LVH using 12-lead ECGs. METHODS By deep learning techniques on the ECG recordings from 28 745 patients (20-60 years old), the AI model was developed to detect verified LVH from transthoracic echocardiography and evaluated on an independent cohort. Two hundred twenty-five patients from Japan were externally validated. Cardiologists' diagnosis of LVH was based on conventional ECG criteria. The area under the curve (AUC), sensitivity, and specificity were applied to evaluate the model performance. A Cox regression model estimated the independent effects of AI-predicted LVH on cardiovascular or all-cause death. RESULTS The AUC of the AI model in diagnosing LVH was 0.89 (sensitivity: 90.3%, specificity: 69.3%), which was significantly better than that of the cardiologists' diagnosis (AUC, 0.64). In the second independent cohort, the diagnostic performance of the AI model was consistent (AUC, 0.86; sensitivity: 85.4%, specificity: 67.0%). After a follow-up of 6 years, AI-predicted LVH was independently associated with higher cardiovascular or all-cause mortality (hazard ratio, 1.91 [1.04-3.49] and 1.54 [1.20-1.97], respectively). The predictive power of the AI model for mortality was consistently valid among patients of different ages, sexes, and comorbidities, including hypertension, diabetes, stroke, heart failure, and myocardial infarction. Last, we also validated the model in the international independent cohort from Japan (AUC, 0.83). CONCLUSIONS The AI model improved the detection of LVH and mortality prediction in the young to middle-aged population and represented an attractive tool for risk stratification. Early identification by the AI model gives every chance for timely treatment to reverse adverse outcomes.
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Affiliation(s)
- Chih-Min Liu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan (C.-M.L., Y.-F.H., Y.-J.L., S.-A.C.).,Institute of Clinical Medicine and Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan (C.-M.L., Y.-F.H., Y.-J.L., S.-A.C.)
| | - Ming-En Hsieh
- Institute of Data Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan (M.-E.H., T.-Y.W., V.S.T.)
| | - Yu-Feng Hu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan (C.-M.L., Y.-F.H., Y.-J.L., S.-A.C.).,Institute of Clinical Medicine and Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan (C.-M.L., Y.-F.H., Y.-J.L., S.-A.C.).,Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan (Y.-F.H.)
| | - Tzu-Yin Wei
- Institute of Data Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan (M.-E.H., T.-Y.W., V.S.T.)
| | - I-Chien Wu
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan (I.-C.W., P.-F.C.)
| | - Pei-Fen Chen
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan (I.-C.W., P.-F.C.)
| | - Yenn-Jiang Lin
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan (C.-M.L., Y.-F.H., Y.-J.L., S.-A.C.).,Institute of Clinical Medicine and Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan (C.-M.L., Y.-F.H., Y.-J.L., S.-A.C.)
| | - Satoshi Higa
- Cardiac Electrophysiology and Pacing Laboratory, Division of Cardiovascular Medicine, Makiminato Central Hospital, Okinawa, Japan (S.H.)
| | - Nobumori Yagi
- Division of Cardiovascular Medicine, Nakagami Hospital, Okinawa, Japan (N.Y.)
| | - Shih-Ann Chen
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan (C.-M.L., Y.-F.H., Y.-J.L., S.-A.C.).,Institute of Clinical Medicine and Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan (C.-M.L., Y.-F.H., Y.-J.L., S.-A.C.).,Cardiovascular Center, Taichung Veterans General Hospital, Taiwan (S.-A.C.).,National Chung Hsing University, Taichung, Taiwan (S.-A.C.)
| | - Vincent S Tseng
- Institute of Data Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan (M.-E.H., T.-Y.W., V.S.T.).,Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan (V.S.T.)
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31
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Manzi G, Papa S, Mariani MV, Scoccia G, Filomena D, Malerba C, Adamo FI, Caputo A, De Lazzari C, De Lazzari B, Cedrone N, Madonna R, Recchioni T, Serino G, Vizza CD, Badagliacca R. Telehealth: A winning weapon to face the COVID-19 outbreak for patients with pulmonary arterial hypertension. Vascul Pharmacol 2022; 145:107024. [PMID: 35716991 PMCID: PMC9212864 DOI: 10.1016/j.vph.2022.107024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 06/12/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND COVID-19 pandemic severely affected national health systems, altering the modality and the type of care of patients with acute and chronic diseases. To minimize the risk of exposure to SARS-CoV2 for patients and health professionals, face-to-face visits were cancelled or postponed and the use of telemedicine was strongly encouraged. This reorganization involved especially patients with rare diseases needing periodic comprehensive assessment, such as pulmonary arterial hypertension (PAH). MAIN BODY The paper reports a proposal of strategy adopted for patients followed at our PAH center in Rome, where patients management was diversified based on clinical risk according to the European Society of Cardiology/European Respiratory Society PH guidelines-derived score and the REVEAL 2.0 score. A close monitoring and support of these patients were made possible by policy changes reducing barriers to telehealth access and promoting the use of telemedicine. Synchronous/asynchronous modalities and remote monitoring were used to collect and transfer medical data in order to guide physicians in therapeutic-decision making. Conversely, the use of implantable monitors providing hemodynamic information and echocardiography-mobile devices wirelessly connecting was limited by the poor experience existing in this setting. Large surveys and clinical trials are welcome to test the potential benefit of the optimal balance between traditional PAH management and telemedicine opportunities. CONCLUSION Italy was found unprepared to manage the dramatic effects caused by COVID-19 on healthcare systems. In this emergency situation telemedicine represented a promising tool especially in rare diseases as PAH, but was limited by its scattered availability and legal and ethical issues. Cohesive partnership of health care providers with regional public health officials is needed to prioritize PAH patients for telemedicine by dedicated tools.
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Affiliation(s)
- Giovanna Manzi
- Department of Clinical, Anesthesiological and Cardiovascular Sciences, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy.
| | - Silvia Papa
- Department of Clinical, Anesthesiological and Cardiovascular Sciences, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy.
| | - Marco Valerio Mariani
- Department of Clinical, Anesthesiological and Cardiovascular Sciences, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy.
| | - Gianmarco Scoccia
- Department of Clinical, Anesthesiological and Cardiovascular Sciences, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy.
| | - Domenico Filomena
- Department of Clinical, Anesthesiological and Cardiovascular Sciences, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy.
| | - Claudia Malerba
- Department of Clinical, Anesthesiological and Cardiovascular Sciences, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy.
| | - Francesca Ileana Adamo
- Department of Clinical, Anesthesiological and Cardiovascular Sciences, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy.
| | - Annalisa Caputo
- Department of Clinical, Anesthesiological and Cardiovascular Sciences, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy.
| | - Claudio De Lazzari
- National Research Council, Institute of Clinical Physiology (IFC-CNR), Rome, Italy.
| | - Beatrice De Lazzari
- Università degli Studi di Roma "Foro Italico", P.za Lauro De Bosis, 15, 00135 Rome, Italy.
| | - Nadia Cedrone
- Unità di Medicina Interna, Ospedale S. Pertini, Rome, Italy.
| | - Rosalinda Madonna
- Cardiology Unit, Department of Surgical, Medical and Molecular Pathology and of Critical Sciences, University of Pisa - UNIPI, Pisa, Italy.
| | - Tommaso Recchioni
- Department of Clinical, Anesthesiological and Cardiovascular Sciences, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy.
| | - Giorgia Serino
- Department of Clinical, Anesthesiological and Cardiovascular Sciences, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy.
| | - Carmine Dario Vizza
- Department of Clinical, Anesthesiological and Cardiovascular Sciences, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy.
| | - Roberto Badagliacca
- Department of Clinical, Anesthesiological and Cardiovascular Sciences, Sapienza University of Rome, Viale del Policlinico, 155, 00161 Rome, Italy.
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van der Bijl P, Bax JJ. Using deep learning to diagnose pulmonary hypertension. Eur Heart J Cardiovasc Imaging 2022; 23:1457-1458. [PMID: 35906842 DOI: 10.1093/ehjci/jeac148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Pieter van der Bijl
- Department of Cardiology, Heart Lung Centre, Leiden University Medical Centre, Albinusdreef 2, 2300 RC, Leiden, The Netherlands
| | - Jeroen J Bax
- Department of Cardiology, Heart Lung Centre, Leiden University Medical Centre, Albinusdreef 2, 2300 RC, Leiden, The Netherlands.,Heart Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, FI-20520, Turku, Finland
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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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Liu CM, Shih ES, Chen JY, Huang CH, Wu IC, Chen PF, Higa S, Yagi N, Hu YF, Hwang MJ, Chen SA. Artificial Intelligence-Enabled Electrocardiogram Improves the Diagnosis and Prediction of Mortality in Patients With Pulmonary Hypertension. JACC. ASIA 2022; 2:258-270. [PMID: 36338407 PMCID: PMC9627911 DOI: 10.1016/j.jacasi.2022.02.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 02/22/2022] [Accepted: 02/24/2022] [Indexed: 05/12/2023]
Abstract
BACKGROUND Pulmonary hypertension is a disabling and life-threatening cardiovascular disease. Early detection of elevated pulmonary artery pressure (ePAP) is needed for prompt diagnosis and treatment to avoid detrimental consequences of pulmonary hypertension. OBJECTIVES This study sought to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify patients with ePAP and related prognostic implications. METHODS From a hospital-based ECG database, the authors extracted the first pairs of ECG and transthoracic echocardiography taken within 2 weeks of each other from 41,097 patients to develop an AI model for detecting ePAP (PAP > 50 mm Hg by transthoracic echocardiography). The model was evaluated on independent data sets, including an external cohort of patients from Japan. RESULTS Tests of 10-fold cross-validation neural-network deep learning showed that the area under the receiver-operating characteristic curve of the AI model was 0.88 (sensitivity 81.0%; specificity 79.6%) for detecting ePAP. The diagnostic performance was consistent across age, sex, and various comorbidities (diagnostic odds ratio >8 for most factors examined). At 6-year follow-up, the patients predicted by the AI model to have ePAP were independently associated with higher cardiovascular mortality (HR: 3.69). Similar diagnostic performance and prediction for cardiovascular mortality could be replicated in the external cohort. CONCLUSIONS The ECG-based AI model identified patients with ePAP and predicted their future risk for cardiovascular mortality. This model could serve as a useful clinical test to identify patients with pulmonary hypertension so that treatment can be initiated early to improve their survival prognosis.
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Affiliation(s)
- Chih-Min Liu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Clinical Medicine and Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Edward S.C. Shih
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Jhih-Yu Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Chih-Han Huang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei, Taiwan
| | - I-Chien Wu
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Pei-Fen Chen
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Satoshi Higa
- Cardiac Electrophysiology and Pacing Laboratory, Division of Cardiovascular Medicine, Makiminato Central Hospital, Okinawa, Japan
| | - Nobumori Yagi
- Division of Cardiovascular Medicine, Nakagami Hospital, Okinawa, Japan
| | - Yu-Feng Hu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Clinical Medicine and Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- Address for correspondence: Dr Yu-Feng Hu, Taipei Veterans General Hospital, 201 Sec. 2, Shih-Pai Road, Taipei, Taiwan.
| | - Ming-Jing Hwang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei, Taiwan
- Dr Ming-Jing Hwang, Institute of Biomedical Sciences, Academia Sinica, 128 Sec. 2, Academia Road, Nankang, Taipei, Taiwan.
| | - Shih-Ann Chen
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Clinical Medicine and Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan
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35
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Kwon JM, Kim KH, Jo YY, Jung MS, Cho YH, Shin JH, Lee YJ, Ban JH, Lee SY, Park J, Oh BH. Artificial intelligence assessment for early detection and prediction of renal impairment using electrocardiography. Int Urol Nephrol 2022; 54:2733-2744. [PMID: 35403974 PMCID: PMC9463260 DOI: 10.1007/s11255-022-03165-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 02/28/2022] [Indexed: 11/07/2022]
Abstract
Purpose Although renal failure is a major healthcare burden globally and the cornerstone for preventing its irreversible progression is an early diagnosis, an adequate and noninvasive tool to screen renal impairment (RI) reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance. Methods This retrospective cohort study included two hospitals. We included 115,361 patients who had at least one ECG taken with an estimated glomerular filtration rate measurement within 30 min of the index ECG. A DLM was developed using 96,549 ECGs of 55,222 patients. The internal validation included 22,949 ECGs of 22,949 patients. Furthermore, we conducted an external validation with 37,190 ECGs of 37,190 patients from another hospital. The endpoint was to detect a moderate to severe RI (estimated glomerular filtration rate < 45 ml/min/1.73m2). Results The area under the receiver operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting RI during the internal and external validation was 0.858 (95% confidence interval 0.851–0.866) and 0.906 (0.900–0.912), respectively. In the initial evaluation of 25,536 individuals without RI patients whose DLM was defined as having a higher risk had a significantly higher chance of developing RI than those in the low-risk group (17.2% vs. 2.4%, p < 0.001). The sensitivity map indicated that the DLM focused on the QRS complex and T-wave for detecting RI. Conclusion The DLM demonstrated high performance for RI detection and prediction using 12-, 6-, single-lead ECGs. Supplementary Information The online version contains supplementary material available at 10.1007/s11255-022-03165-w.
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36
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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] [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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Affiliation(s)
- Bradley A Maron
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Cardiology, Veterans Affairs Boston Healthcare System, West Roxbury, Massachusetts
| | - Marc Humbert
- Université Paris-Saclay, INSERM, Assistance Publique Hôpitaux de Paris, Service de Pneumologie et Soins Intensifs Respiratoires, Hôpital Bicêtre, Le Kremlin Bicêtre, France
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38
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Schlesinger DE, Diamant N, Raghu A, Reinertsen E, Young K, Batra P, Pomerantsev E, Stultz CM. A Deep Learning Model for Inferring Elevated Pulmonary Capillary Wedge Pressures From the 12-Lead Electrocardiogram. JACC. ADVANCES 2022; 1:100003. [PMID: 38939079 PMCID: PMC11198366 DOI: 10.1016/j.jacadv.2022.100003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 12/28/2021] [Accepted: 01/19/2022] [Indexed: 06/29/2024]
Abstract
Background Central hemodynamic parameters are typically measured via pulmonary artery catherization-an invasive procedure that involves some risk to the patient and is not routinely available in all settings. Objectives This study sought to develop a noninvasive method to identify elevated mean pulmonary capillary wedge pressure (mPCWP). Methods We leveraged data from 248,955 clinical records at the Massachusetts General Hospital to develop a deep learning model that can infer when the mPCWP >15 mmHg using the 12-lead electrocardiogram (ECG). Of these data, 242,216 records were used to pre-train a model that generates useful ECG representations. The remaining 6,739 records contain encounters with direct measurements of the mPCWP. Eighty percent of these data were used for model development and testing (5,390), and the remaining records comprise a holdout set (1,349) that was used to evaluate the model. We developed an associated unreliability score that identifies when model predictions are likely to be untrustworthy. Results The model achieves an area under the receiver operating characteristic curve (AUC) of 0.80 ± 0.02 (test set) and 0.79 ± 0.01 (holdout set). Model performance varies as a function of the unreliability, where patients with high unreliability scores correspond to a subgroup where model performance is poor: for example, patients in the holdout set with unreliability scores in the highest decile have a reduced AUC of 0.70 ± 0.06. Conclusions The mPCWP can be inferred from the ECG, and the reliability of this inference can be measured. When invasive monitoring cannot be expeditiously performed, deep learning models may provide information that can inform clinical care.
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Affiliation(s)
- Daphne E. Schlesinger
- Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, Massachusetts, USA
- Research Laboratory of Electronics, Computer Science & Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts, USA
| | | | - Aniruddh Raghu
- Research Laboratory of Electronics, Computer Science & Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts, USA
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA
| | - Erik Reinertsen
- Research Laboratory of Electronics, Computer Science & Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts, USA
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Katherine Young
- Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Puneet Batra
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Eugene Pomerantsev
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Collin M. Stultz
- Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, Massachusetts, USA
- Research Laboratory of Electronics, Computer Science & Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts, USA
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA
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Vardas PE, Asselbergs FW, van Smeden M, Friedman P. The year in cardiovascular medicine 2021: digital health and innovation. Eur Heart J 2022; 43:271-279. [PMID: 34974610 DOI: 10.1093/eurheartj/ehab874] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/15/2021] [Accepted: 11/23/2021] [Indexed: 12/15/2022] Open
Abstract
This article presents some of the most important developments in the field of digital medicine that have appeared over the last 12 months and are related to cardiovascular medicine. The article consists of three main sections, as follows: (i) artificial intelligence-enabled cardiovascular diagnostic tools, techniques, and methodologies, (ii) big data and prognostic models for cardiovascular risk protection, and (iii) wearable devices in cardiovascular risk assessment, cardiovascular disease prevention, diagnosis, and management. To conclude the article, the authors present a brief further prospective on this new domain, highlighting existing gaps that are specifically related to artificial intelligence technologies, such as explainability, cost-effectiveness, and, of course, the importance of proper regulatory oversight for each clinical implementation.
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Affiliation(s)
- Panos E Vardas
- Heart Sector, Hygeia Hospitals Group, HHG, 5, Erithrou Stavrou, Marousi, Athens 15123, Greece.,European Heart Agency, ESC, Brussels, Belgium
| | - Folkert W Asselbergs
- Department of Cardiology, Division of Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Paul Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
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Lee S, Chu Y, Ryu J, Park YJ, Yang S, Koh SB. Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis. Yonsei Med J 2022; 63:S93-S107. [PMID: 35040610 PMCID: PMC8790582 DOI: 10.3349/ymj.2022.63.s93] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 10/27/2021] [Accepted: 10/31/2021] [Indexed: 11/27/2022] Open
Abstract
PURPOSE Several artificial intelligence (AI) models for the detection and prediction of cardiovascular-related diseases, including arrhythmias, diabetes, and sleep apnea, have been reported. This systematic review and meta-analysis aimed to identify AI models developed for or applicable to wearable and mobile devices for diverse cardiovascular-related diseases. MATERIALS AND METHODS The searched databases included Medline, Embase, and Cochrane Library. For AI models for atrial fibrillation (AF) detection, a meta-analysis of diagnostic accuracy was performed to summarize sensitivity and specificity. RESULTS A total of 102 studies were included in the qualitative review. There were AI models for the detection of arrythmia (n=62), followed by sleep apnea (n=11), peripheral vascular diseases (n=6), diabetes mellitus (n=5), hyper/hypotension (n=5), valvular heart disease (n=4), heart failure (n=3), myocardial infarction and cardiac arrest (n=2), and others (n=4). For quantitative analysis of 26 studies reporting AI models for AF detection, meta-analyzed sensitivity was 94.80% and specificity was 96.96%. Deep neural networks showed superior performance [meta-analyzed area under receiver operating characteristics curve (AUROC) of 0.981] compared to conventional machine learning algorithms (meta-analyzed AUROC of 0.961). However, AI models tested with proprietary dataset (meta-analyzed AUROC of 0.972) or data acquired from wearable devices (meta-analyzed AUROC of 0.977) showed inferior performance than those with public dataset (meta-analyzed AUROC of 0.986) or data from in-hospital devices (meta-analyzed AUROC of 0.983). CONCLUSION This review found that AI models for diverse cardiovascular-related diseases are being developed, and that they are gradually developing into a form that is suitable for wearable and mobile devices.
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Affiliation(s)
- Solam Lee
- Department of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
- Department of Dermatology, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Yuseong Chu
- Department of Biomedical Engineering, Yonsei University, Wonju, Korea
| | - Jiseung Ryu
- Department of Biomedical Engineering, Yonsei University, Wonju, Korea
| | - Young Jun Park
- Division of Cardiology, Department of Internal Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Sejung Yang
- Department of Biomedical Engineering, Yonsei University, Wonju, Korea.
| | - Sang Baek Koh
- Department of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea.
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Kwon JM, Lee YR, Jung MS, Lee YJ, Jo YY, Kang DY, Lee SY, Cho YH, Shin JH, Ban JH, Kim KH. Deep-learning model for screening sepsis using electrocardiography. Scand J Trauma Resusc Emerg Med 2021; 29:145. [PMID: 34602084 PMCID: PMC8487616 DOI: 10.1186/s13049-021-00953-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 09/13/2021] [Indexed: 12/24/2022] Open
Abstract
Background Sepsis is a life-threatening organ dysfunction and a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, screening for the occurrence of sepsis is difficult. Herein, we propose a deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG). Methods This retrospective cohort study included 46,017 patients who were admitted to two hospitals. A total of 1,548 and 639 patients had sepsis and septic shock, respectively. The DLM was developed using 73,727 ECGs from 18,142 patients, and internal validation was conducted using 7774 ECGs from 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs from 20,101 patients from another hospital to verify the applicability of the DLM across centers.
Results During the internal and external validations, the area under the receiver operating characteristic curve (AUC) of the DLM using 12-lead ECG was 0.901 (95% confidence interval, 0.882–0.920) and 0.863 (0.846–0.879), respectively, for screening sepsis and 0.906 (95% confidence interval (CI), 0.877–0.936) and 0.899 (95% CI, 0.872–0.925), respectively, for detecting septic shock. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs was 0.845–0.882. A sensitivity map revealed that the QRS complex and T waves were associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who were admitted with an infectious disease, and the AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793–0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs. 0.574, p < 0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs. 0.725, p = 0.018).
Conclusions The DLM delivered reasonable performance for sepsis screening using 12-, 6-, and single-lead ECGs. The results suggest that sepsis can be screened using not only conventional ECG devices but also diverse life-type ECG machines employing the DLM, thereby preventing irreversible disease progression and mortality.
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Affiliation(s)
- Joon-Myoung Kwon
- Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, Republic of Korea. .,Medical Research Team, Medical AI, Co., Seoul, Republic of Korea. .,Department of Critical Care and Emergency Medicine, Mediplex Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon, Republic of Korea. .,Medical R&D Center, Body Friend, Co., Seoul, Republic of Korea.
| | - Ye Rang Lee
- Medical Research Team, Medical AI, Co., Seoul, Republic of Korea
| | - Min-Seung Jung
- Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, Republic of Korea
| | - Yoon-Ji Lee
- Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, Republic of Korea
| | - Yong-Yeon Jo
- Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, Republic of Korea
| | - Da-Young Kang
- Medical Research Team, Medical AI, Co., Seoul, Republic of Korea
| | - Soo Youn Lee
- Medical Research Team, Medical AI, Co., Seoul, Republic of Korea.,Division of Cardiology Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Republic of Korea
| | - Yong-Hyeon Cho
- Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, Republic of Korea
| | - Jae-Hyun Shin
- Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, Republic of Korea
| | - Jang-Hyeon Ban
- Medical R&D Center, Body Friend, Co., Seoul, Republic of Korea
| | - Kyung-Hee Kim
- Medical Research Team, Medical AI, Co., Seoul, Republic of Korea.,Division of Cardiology Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Republic of Korea
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Jo YY, Kwon JM, Jeon KH, Cho YH, Shin JH, Lee YJ, Jung MS, Ban JH, Kim KH, Lee SY, Park J, Oh BH. Artificial intelligence to diagnose paroxysmal supraventricular tachycardia using electrocardiography during normal sinus rhythm. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:290-298. [PMID: 36712389 PMCID: PMC9707886 DOI: 10.1093/ehjdh/ztab025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 01/23/2021] [Accepted: 02/05/2021] [Indexed: 02/01/2023]
Abstract
Aims Paroxysmal supraventricular tachycardia (PSVT) is not detected owing to its paroxysmal nature, but it is associated with the risk of cardiovascular disease and worsens the patient quality of life. A deep learning model (DLM) was developed and validated to identify patients with PSVT during normal sinus rhythm in this multicentre retrospective study. Methods and results This study included 12 955 patients with normal sinus rhythm, confirmed by a cardiologist. A DLM was developed using 31 147 electrocardiograms (ECGs) of 9069 patients from one hospital. We conducted an accuracy test with 13 753 ECGs of 3886 patients from another hospital. The DLM was developed based on residual neural network. Digitally stored ECG were used as predictor variables and the outcome of the study was ability of the DLM to identify patients with PSVT using an ECG during sinus rhythm. We employed a sensitivity map method to identify an ECG region that had a significant effect on developing PSVT. During accuracy test, the area under the receiver operating characteristic curve of a DLM using a 12-lead ECG for identifying PSVT patients during sinus rhythm was 0.966 (0.948-0.984). The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of DLM were 0.970, 0.868, 0.972, 0.255, and 0.998, respectively. The DLM showed delta wave and QT interval were important to identify the PSVT. Conclusion The proposed DLM demonstrated a high performance in identifying PSVT during normal sinus rhythm. Thus, it can be used as a rapid, inexpensive, point-of-care means of identifying PSVT in patients.
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Affiliation(s)
- Yong-Yeon Jo
- Department of Medical Research, Medical AI, 163, Yangjaecheon-ro, Gangnam-gu, Seoul, 06302, Republic of Korea
| | - Joon-Myoung Kwon
- Department of Medical Research, Medical AI, 163, Yangjaecheon-ro, Gangnam-gu, Seoul, 06302, Republic of Korea
- Department of artificial intelligence and big data research, Sejong Medical Research Institute, 28, Hohyeon-ro 489beon-gil, Bucheon-si, Gyeonggi-do, 14754, Republic of Korea
- Department of Critical Care and Emergency Medicine, Mediplex Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon, 21080, Republic of Korea
- Department of Medical R&D, Body friend, 163, Yangjaecheon-ro, Gangnam-gu, Seoul, 06302, Republic of Korea
| | - Ki-Hyun Jeon
- Department of artificial intelligence and big data research, Sejong Medical Research Institute, 28, Hohyeon-ro 489beon-gil, Bucheon-si, Gyeonggi-do, 14754, Republic of Korea
- Department of Internal Medicine, Division of Cardiology Cardiovascular Center, Mediplex Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon, 21080, Republic of South Korea
| | - Yong-Hyeon Cho
- Department of Critical Care and Emergency Medicine, Mediplex Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon, 21080, Republic of Korea
| | - Jae-Hyun Shin
- Department of Critical Care and Emergency Medicine, Mediplex Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon, 21080, Republic of Korea
| | - Yoon-Ji Lee
- Department of Critical Care and Emergency Medicine, Mediplex Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon, 21080, Republic of Korea
| | - Min-Seung Jung
- Department of Critical Care and Emergency Medicine, Mediplex Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon, 21080, Republic of Korea
| | - Jang-Hyeon Ban
- Department of Medical R&D, Body friend, 163, Yangjaecheon-ro, Gangnam-gu, Seoul, 06302, Republic of Korea
| | - Kyung-Hee Kim
- Department of artificial intelligence and big data research, Sejong Medical Research Institute, 28, Hohyeon-ro 489beon-gil, Bucheon-si, Gyeonggi-do, 14754, Republic of Korea
- Department of Internal Medicine, Division of Cardiology Cardiovascular Center, Mediplex Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon, 21080, Republic of South Korea
| | - Soo Youn Lee
- Department of artificial intelligence and big data research, Sejong Medical Research Institute, 28, Hohyeon-ro 489beon-gil, Bucheon-si, Gyeonggi-do, 14754, Republic of Korea
- Department of Internal Medicine, Division of Cardiology Cardiovascular Center, Mediplex Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon, 21080, Republic of South Korea
| | - Jinsik Park
- Department of Internal Medicine, Division of Cardiology Cardiovascular Center, Mediplex Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon, 21080, Republic of South Korea
| | - Byung-Hee Oh
- Department of Internal Medicine, Division of Cardiology Cardiovascular Center, Mediplex Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon, 21080, Republic of South Korea
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Shu S, Ren J, Song J. Clinical Application of Machine Learning-Based Artificial Intelligence in the Diagnosis, Prediction, and Classification of Cardiovascular Diseases. Circ J 2021; 85:1416-1425. [PMID: 33883384 DOI: 10.1253/circj.cj-20-1121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
With the rapid development of artificial intelligence (AI) and machine learning (ML), as well as the arrival of the big data era, technological innovations have occurred in the field of cardiovascular medicine. First, the diagnosis of cardiovascular diseases (CVDs) is highly dependent on assistive examinations, the interpretation of which is time consuming and often limited by the knowledge level and clinical experience of doctors; however, AI could be used to automatically interpret the images obtained in auxiliary examinations. Second, some of the predictions of the incidence and prognosis of CVDs are limited in clinical practice by the use of traditional prediction models, but there may be occasions when AI-based prediction models perform well by using ML algorithms. Third, AI has been used to assist precise classification of CVDs by integrating a variety of medical data from patients, which helps better characterize the subgroups of heterogeneous diseases. To help clinicians better understand the applications of AI in CVDs, this review summarizes studies relating to AI-based diagnosis, prediction, and classification of CVDs. Finally, we discuss the challenges of applying AI to cardiovascular medicine.
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Affiliation(s)
- Songren Shu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Jie Ren
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Jiangping Song
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College
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Kwon JM, Jo YY, Lee SY, Kim KH. Artificial intelligence using electrocardiography: strengths and pitfalls. Eur Heart J 2021; 42:2896-2898. [PMID: 33748841 PMCID: PMC8347448 DOI: 10.1093/eurheartj/ehab090] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 02/10/2021] [Indexed: 12/15/2022] Open
Affiliation(s)
- Joon-Myoung Kwon
- Medical research team, Medical AI Co., Seoul, South Korea.,Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, South Korea.,Department of Critical Care and Emergency Medicine, Mediplex Sejong Hospital, Incheon, South Korea.,Medical R&D center, Bodyfriend Co., Seoul, South Korea
| | - Yong-Yeon Jo
- Medical research team, Medical AI Co., Seoul, South Korea
| | - Soo Youn Lee
- Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, South Korea.,Division of Cardiology Cardiovascular Center, Mediplex Sejong Hospital, Incheon, South Korea
| | - Kyung-Hee Kim
- Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, South Korea.,Division of Cardiology Cardiovascular Center, Mediplex Sejong Hospital, Incheon, South Korea
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45
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Kwon JM, Jung MS, Kim KH, Jo YY, Shin JH, Cho YH, Lee YJ, Ban JH, Jeon KH, Lee SY, Park J, Oh BH. Artificial intelligence for detecting electrolyte imbalance using electrocardiography. Ann Noninvasive Electrocardiol 2021; 26:e12839. [PMID: 33719135 PMCID: PMC8164149 DOI: 10.1111/anec.12839] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 01/31/2021] [Accepted: 02/17/2021] [Indexed: 12/28/2022] Open
Abstract
Introduction The detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) for detecting electrolyte imbalance and validated its performance in a multicenter study. Methods and Results This retrospective cohort study included two hospitals: 92,140 patients who underwent a laboratory electrolyte examination and an ECG within 30 min were included in this study. A DLM was developed using 83,449 ECGs of 48,356 patients; the internal validation included 12,091 ECGs of 12,091 patients. We conducted an external validation with 31,693 ECGs of 31,693 patients from another hospital, and the result was electrolyte imbalance detection. During internal, the area under the receiving operating characteristic curve (AUC) of a DLM using a 12‐lead ECG for detecting hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.945, 0.866, 0.944, 0.885, 0.905, and 0.901, respectively. The values during external validation of the AUC of hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.873, 0.857, 0.839, 0.856, 0.831, and 0.813 respectively. The DLM helped to visualize the important ECG region for detecting each electrolyte imbalance, and it showed how the P wave, QRS complex, or T wave differs in importance in detecting each electrolyte imbalance. Conclusion The proposed DLM demonstrated high performance in detecting electrolyte imbalance. These results suggest that a DLM can be used for detecting and monitoring electrolyte imbalance using ECG on a daily basis.
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Affiliation(s)
- Joon-Myoung Kwon
- Medical Research Team, Medical AI Co. Ltd., Seoul, South Korea.,Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, South Korea.,Department of Critical Care and Emergency Medicine, Mediplex Sejong Hospital, Incheon, South Korea.,Medical R&D Center, Bodyfriend Co. Ltd., Seoul, South Korea
| | - Min-Seung Jung
- Medical Research Team, Medical AI Co. Ltd., Seoul, South Korea
| | - Kyung-Hee Kim
- Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, South Korea.,Division of Cardiology Cardiovascular Center, Mediplex Sejong Hospital, Incheon, South Korea
| | - Yong-Yeon Jo
- Medical Research Team, Medical AI Co. Ltd., Seoul, South Korea
| | - Jae-Hyun Shin
- Medical Research Team, Medical AI Co. Ltd., Seoul, South Korea
| | - Yong-Hyeon Cho
- Medical Research Team, Medical AI Co. Ltd., Seoul, South Korea
| | - Yoon-Ji Lee
- Medical Research Team, Medical AI Co. Ltd., Seoul, South Korea
| | - Jang-Hyeon Ban
- Medical R&D Center, Bodyfriend Co. Ltd., Seoul, South Korea
| | - Ki-Hyun Jeon
- Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, South Korea.,Division of Cardiology Cardiovascular Center, Mediplex Sejong Hospital, Incheon, South Korea
| | - Soo Youn Lee
- Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, South Korea.,Division of Cardiology Cardiovascular Center, Mediplex Sejong Hospital, Incheon, South Korea
| | - Jinsik Park
- Division of Cardiology Cardiovascular Center, Mediplex Sejong Hospital, Incheon, South Korea
| | - Byung-Hee Oh
- Division of Cardiology Cardiovascular Center, Mediplex Sejong Hospital, Incheon, South Korea
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Somani S, Russak AJ, Richter F, Zhao S, Vaid A, Chaudhry F, De Freitas JK, Naik N, Miotto R, Nadkarni GN, Narula J, Argulian E, Glicksberg BS. Deep learning and the electrocardiogram: review of the current state-of-the-art. Europace 2021; 23:1179-1191. [PMID: 33564873 PMCID: PMC8350862 DOI: 10.1093/europace/euaa377] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 11/25/2020] [Indexed: 12/22/2022] Open
Abstract
In the recent decade, deep learning, a subset of artificial intelligence and machine learning, has been used to identify patterns in big healthcare datasets for disease phenotyping, event predictions, and complex decision making. Public datasets for electrocardiograms (ECGs) have existed since the 1980s and have been used for very specific tasks in cardiology, such as arrhythmia, ischemia, and cardiomyopathy detection. Recently, private institutions have begun curating large ECG databases that are orders of magnitude larger than the public databases for ingestion by deep learning models. These efforts have demonstrated not only improved performance and generalizability in these aforementioned tasks but also application to novel clinical scenarios. This review focuses on orienting the clinician towards fundamental tenets of deep learning, state-of-the-art prior to its use for ECG analysis, and current applications of deep learning on ECGs, as well as their limitations and future areas of improvement.
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Affiliation(s)
- Sulaiman Somani
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
| | - Adam J Russak
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA.,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Felix Richter
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
| | - Shan Zhao
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA.,Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Akhil Vaid
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
| | - Fayzan Chaudhry
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jessica K De Freitas
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nidhi Naik
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
| | - Riccardio Miotto
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA.,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning. PLoS Negl Trop Dis 2020; 14:e0008960. [PMID: 33362244 PMCID: PMC7757819 DOI: 10.1371/journal.pntd.0008960] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 11/08/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Dengue virus causes a wide spectrum of disease, which ranges from subclinical disease to severe dengue shock syndrome. However, estimating the risk of severe outcomes using clinical presentation or laboratory test results for rapid patient triage remains a challenge. Here, we aimed to develop prognostic models for severe dengue using machine learning, according to demographic information and clinical laboratory data of patients with dengue. METHODOLOGY/PRINCIPAL FINDINGS Out of 1,581 patients in the National Cheng Kung University Hospital with suspected dengue infections and subjected to NS1 antigen, IgM and IgG, and qRT-PCR tests, 798 patients including 138 severe cases were enrolled in the study. The primary target outcome was severe dengue. Machine learning models were trained and tested using the patient dataset that included demographic information and qualitative laboratory test results collected on day 1 when they sought medical advice. To develop prognostic models, we applied various machine learning methods, including logistic regression, random forest, gradient boosting machine, support vector classifier, and artificial neural network, and compared the performance of the methods. The artificial neural network showed the highest average discrimination area under the receiver operating characteristic curve (0.8324 ± 0.0268) and balance accuracy (0.7523 ± 0.0273). According to the model explainer that analyzed the contributions/co-contributions of the different factors, patient age and dengue NS1 antigenemia were the two most important risk factors associated with severe dengue. Additionally, co-existence of anti-dengue IgM and IgG in patients with dengue increased the probability of severe dengue. CONCLUSIONS/SIGNIFICANCE We developed prognostic models for the prediction of dengue severity in patients, using machine learning. The discriminative ability of the artificial neural network exhibited good performance for severe dengue prognosis. This model could help clinicians obtain a rapid prognosis during dengue outbreaks. However, the model requires further validation using external cohorts in future studies.
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