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Lin YT, Lin CS, Tsai CS, Tsai DJ, Lou YS, Fang WH, Lee YT, Lin C. Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases. Aging (Albany NY) 2024; 16:8717-8731. [PMID: 38761181 PMCID: PMC11164519 DOI: 10.18632/aging.205835] [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: 12/26/2023] [Accepted: 04/16/2024] [Indexed: 05/20/2024]
Abstract
BACKGROUND Valvular heart disease (VHD) is becoming increasingly important to manage the risk of future complications. Electrocardiographic (ECG) changes may be related to multiple VHDs, and (AI)-enabled ECG has been able to detect some VHDs. We aimed to develop five deep learning models (DLMs) to identify aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation. METHODS Between 2010 and 2021, 77,047 patients with echocardiography and 12-lead ECG performed within 7 days were identified from an academic medical center to provide DLM development (122,728 ECGs), and internal validation (7,637 ECGs). Additional 11,800 patients from a community hospital were identified to external validation. The ECGs were classified as with or without moderate-to-severe VHDs according to transthoracic echocardiography (TTE) records, and we also collected the other echocardiographic data and follow-up TTE records to identify new-onset valvular heart diseases. RESULTS AI-ECG adjusted for age and sex achieved areas under the curves (AUCs) of >0.84, >0.80, >0.77, >0.83, and >0.81 for detecting aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation, respectively. Since predictions of each DLM shared similar components of ECG rhythms, the positive findings of each DLM were highly correlated with other valvular heart diseases. Of note, a total of 37.5-51.7% of false-positive predictions had at least one significant echocardiographic finding, which may lead to a significantly higher risk of future moderate-to-severe VHDs in patients with initially minimal-to-mild VHDs. CONCLUSION AI-ECG may be used as a large-scale screening tool for detecting VHDs and a basis to undergo an echocardiography.
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Affiliation(s)
- Yu-Ting Lin
- Department of Surgery, Division of Cardiovascular Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chin-Sheng Lin
- Department of Internal Medicine, Division of Cardiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chien-Sung Tsai
- Department of Surgery, Division of Cardiovascular Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Dung-Jang Tsai
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan
- Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Yu-Sheng Lou
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan
| | - Wen-Hui Fang
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Yung-Tsai Lee
- Division of Cardiovascular Surgery, Cheng Hsin Rehabilitation and Medical Center, Taipei, Taiwan
- Department of Exercise and Healthy Science, National Taipei University of Nursing and Healthy Science, Taipei, Taiwan
| | - Chin Lin
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan
- Graduate Institute of Aerospace and Undersea Medicine, National Defense Medical Center, Taipei, Taiwan
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2
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El Hussein MT, Job D, Hakkola J. Aortic Regurgitation: Review of Current Management. Dimens Crit Care Nurs 2024; 43:80-86. [PMID: 38271313 DOI: 10.1097/dcc.0000000000000628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024] Open
Abstract
ABSTRACT Aortic regurgitation (AR) is a valvular disease characterized by retrograde blood flow from the aorta to the left ventricle. Various etiologies result in either an acute or chronic clinical presentation of AR and affect the severity of disease progression. Acute AR is a medical emergency caused by sudden increases in left ventricular volume. Immediate surgical intervention, vasoactive agents, and antibiotics are crucial for management. Chronic AR progresses gradually, leading to heart failure symptoms due to left ventricular remodeling. Diagnoses of both acute and chronic AR rely on electrocardiography, chest radiographs, and echocardiography. Cardiac magnetic resonance imaging may be incorporated in chronic AR diagnosis. Medical management of chronic AR aims to control hypertension and delay left ventricular dysfunction. Angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and potentially calcium-channel blockers are recommended for chronic AR. β-Blockers are cautioned against because of their potential negative effects. This article emphasizes the importance of early diagnosis and prompt surgical intervention in AR. This review provides a comprehensive overview of the pathophysiology and clinical manifestations of acute and chronic AR and a medication regimen for treating chronic AR in the adult population.
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3
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Singh S, Chaudhary R, Bliden KP, Tantry US, Gurbel PA, Visweswaran S, Harinstein ME. Meta-Analysis of the Performance of AI-Driven ECG Interpretation in the Diagnosis of Valvular Heart Diseases. Am J Cardiol 2024; 213:126-131. [PMID: 38103769 PMCID: PMC10842912 DOI: 10.1016/j.amjcard.2023.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 11/17/2023] [Accepted: 12/01/2023] [Indexed: 12/19/2023]
Abstract
Valvular heart diseases (VHDs) significantly impact morbidity and mortality rates worldwide. Early diagnosis improves patient outcomes. Artificial intelligence (AI) applied to electrocardiogram (ECG) interpretation presents a promising approach for early VHD detection. We conducted a meta-analysis on the efficacy of AI models in this context. We reviewed databases including PubMed, MEDLINE, Embase, Scopus, and Cochrane until August 20, 2023, focusing on AI for ECG-based VHD detection. The outcomes included pooled accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value. The pooled proportions were derived using a random-effects model with 95% confidence intervals (CIs). Study heterogeneity was evaluated with the I-squared statistic. Our analysis included 10 studies, involving ECG data from 713,537 patients. The AI algorithms mainly screened for aortic stenosis (n = 6), mitral regurgitation (n = 4), aortic regurgitation (n = 3), mitral stenosis (n = 1), mitral valve prolapse (n = 2), and tricuspid regurgitation (n = 1). A total of 9 studies used convolution neural network models, whereas 1 study combined the strengths of support vector machine logistic regression and multilayer perceptron for ECG interpretation. The collective AI models demonstrated a pooled accuracy of 81% (95% CI 73 to 89, I² = 92%), sensitivity was 83% (95% CI 77 to 88, I² = 86%), specificity was 72% (95% CI 68 to 75, I² = 52%), PPV was 13% (95% CI 7 to 19, I² = 90%), and negative predictive value was 99% (95% CI 97 to 99, I² = 50%). The subgroup analyses for aortic stenosis and mitral regurgitation detection yielded analogous outcomes. In conclusion, AI-driven ECG offers high accuracy in VHD screening. However, its low PPV indicates the need for a combined approach with clinical judgment, especially in primary care settings.
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Affiliation(s)
- Sahib Singh
- Department of Medicine, Sinai Hospital of Baltimore, Baltimore, Maryland
| | - Rahul Chaudhary
- Artificial Intelligence for Holistic Evaluation and Advancement of Cardiovascular Thrombosis (AI-HEART) Lab, Pittsburgh, Pennsylvania; Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania.
| | - Kevin P Bliden
- Department of Cardiology, Sinai Center of Thrombosis Research and Drug Development, Baltimore, Maryland
| | - Udaya S Tantry
- Department of Cardiology, Sinai Center of Thrombosis Research and Drug Development, Baltimore, Maryland
| | - Paul A Gurbel
- Department of Medicine, Sinai Hospital of Baltimore, Baltimore, Maryland; Department of Cardiology, Sinai Center of Thrombosis Research and Drug Development, Baltimore, Maryland
| | - Shyam Visweswaran
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Matthew E Harinstein
- Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
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4
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Choi S, Choi K, Yun HK, Kim SH, Choi HH, Park YS, Joo S. Diagnosis of atrial fibrillation based on AI-detected anomalies of ECG segments. Heliyon 2024; 10:e23597. [PMID: 38187293 PMCID: PMC10770559 DOI: 10.1016/j.heliyon.2023.e23597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 01/09/2024] Open
Abstract
Early detection of atrial fibrillation (AF) is crucial for its effective management and prevention. Various methods for detecting AF using deep learning (DL) based on supervised learning with a large labeled dataset have a remarkable performance. However, supervised learning has several problems, as it is time-consuming for labeling and has a data dependency problem. Moreover, most of the DL methods do not provide any clinical evidence to physicians regarding the analysis of electrocardiography (ECG) for classification or detection of AF. To address these limitations, in this study, we proposed a novel AF diagnosis system using unsupervised learning for anomaly detection with three segments, PreQ, QRS, and PostS, based on the normal ECG. Two independent datasets, PTB-XL and China, were used in three experiments. We used a long short-term memory (LSTM)-based autoencoder to train the segments of the normal ECG. Based on the threshold of anomaly scores using mean squared error (MSE), it distinguished between normal and AF segments. In Experiment A, the best score was that of PreQ, which detected AF with an AUROC score of 0.96. In Experiment B and C for cross validation of each dataset, the best scores were also of PreQ, with AUROC scores of 0.9 and 0.95, respectively. To verify the significance of the anomaly score in distinguishing between AF and normal segments, we utilized an XG-Boosted model after generating anomaly scores in the three segments. The XG-Boosted model achieved an AUROC score of 0.98 and an F1 score of 0.94. AF detection using DL has been controversial among many physicians. However, our study differentiates itself from previous studies in that we can demonstrate evidence that distinguishes AF from normal segments based on the anomaly score.
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Affiliation(s)
- Sanghoon Choi
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Kyungmin Choi
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Hong Kyun Yun
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Su Hyeon Kim
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Hyeon-Hwa Choi
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Yi-Seul Park
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
| | - Segyeong Joo
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea
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5
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Sawano S, Kodera S, Sato M, Shinohara H, Kobayashi A, Takiguchi H, Hirose K, Kamon T, Saito A, Kiriyama H, Miura M, Minatsuki S, Kikuchi H, Takeda N, Morita H, Komuro I. Multimodality Risk Assessment of Patients with Ischemic Heart Disease Using Deep Learning Models Applied to Electrocardiograms and Chest X-rays. Int Heart J 2024; 65:29-38. [PMID: 38296576 DOI: 10.1536/ihj.23-402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
Comprehensive management approaches for patients with ischemic heart disease (IHD) are important aids for prognostication and treatment planning. While single-modality deep neural networks (DNNs) have shown promising performance for detecting cardiac abnormalities, the potential benefits of using DNNs for multimodality risk assessment in patients with IHD have not been reported. The purpose of this study was to investigate the effectiveness of multimodality risk assessment in patients with IHD using a DNN that utilizes 12-lead electrocardiograms (ECGs) and chest X-rays (CXRs), with the prediction of major adverse cardiovascular events (MACEs) being of particular concern.DNN models were applied to detection of left ventricular systolic dysfunction (LVSD) on ECGs and identification of cardiomegaly findings on CXRs. A total of 2107 patients who underwent elective percutaneous coronary intervention were categorized into 4 groups according to the models' outputs: Dual-modality high-risk (n = 105), ECG high-risk (n = 181), CXR high-risk (n = 392), and No-risk (n = 1,429).A total of 342 MACEs were observed. The incidence of a MACE was the highest in the Dual-modality high-risk group (P < 0.001). Multivariate Cox hazards analysis for predicting MACE revealed that the Dual-modality high-risk group had a significantly higher risk of MACE than the No-risk group (hazard ratio (HR): 2.370, P < 0.001), the ECG high-risk group (HR: 1.906, P = 0.010), and the CXR high-risk group (HR: 1.624, P = 0.018), after controlling for confounding factors.The results suggest the usefulness of multimodality risk assessment using DNN models applied to 12-lead ECG and CXR data from patients with IHD.
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Affiliation(s)
- Shinnosuke Sawano
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Satoshi Kodera
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Masataka Sato
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Hiroki Shinohara
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Atsushi Kobayashi
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Hiroshi Takiguchi
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Kazutoshi Hirose
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Tatsuya Kamon
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Akihito Saito
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Hiroyuki Kiriyama
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Mizuki Miura
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Shun Minatsuki
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Hironobu Kikuchi
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Norifumi Takeda
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Hiroyuki Morita
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Issei Komuro
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
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6
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Raileanu G, de Jong JSSG. Electrocardiogram Interpretation Using Artificial Intelligence: Diagnosis of Cardiac and Extracardiac Pathologic Conditions. How Far Has Machine Learning Reached? Curr Probl Cardiol 2024; 49:102097. [PMID: 37739276 DOI: 10.1016/j.cpcardiol.2023.102097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 09/18/2023] [Indexed: 09/24/2023]
Abstract
Artificial intelligence (AI) is already widely used in different fields of medicine, making possible the integration of the paraclinical exams with the clinical findings in patients, for a more accurate and rapid diagnosis and treatment decision. The electrocardiogram remains one of the most important, fastest, cheapest, and noninvasive methods of diagnosis in cardiology, despite the rapid development and progression of the technology. Even if studied a long time ago, it still has a lot of less understood features that, with a better understanding, can give more clues to a correct and prompt diagnosis in a short time. The use of AI in the interpretation of the ECG improved the accuracy and the time to diagnosis in different cardiovascular diseases, and more than this, explaining the decision to make AI diagnosis improved the human understanding of the different features of the ECG that might be considered for a more accurate diagnosis. The purpose of this article is to provide an overview of the most recently published articles about the use of AI in ECG interpretation.
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Affiliation(s)
- Gabriela Raileanu
- Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands.
| | - Jonas S S G de Jong
- Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands
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7
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Denysyuk HV, Pinto RJ, Silva PM, Duarte RP, Marinho FA, Pimenta L, Gouveia AJ, Gonçalves NJ, Coelho PJ, Zdravevski E, Lameski P, Leithardt V, Garcia NM, Pires IM. Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review. Heliyon 2023; 9:e13601. [PMID: 36852052 PMCID: PMC9958295 DOI: 10.1016/j.heliyon.2023.e13601] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 01/31/2023] [Accepted: 02/05/2023] [Indexed: 02/12/2023] Open
Abstract
The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient's autonomy.
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Key Words
- AI, Artificial Intelligence
- BNN, Binarized Neural Network
- CNN, Concolutional Neural Networks
- Cardiovascular diseases
- DL, Deep Learning
- DNN, Deep Neural Networks
- Diagnosis
- ECG sensors
- ECG, Electrocardiography
- GAN, Generative Adversarial Networks
- GMM, Gaussian Mixture Model
- GNB, Gaussian Naive bayes
- GRU, Gated Recurrent Unit
- LASSO, Least Absolute Shrinkage and Selection Operator
- LDA, Linear Discriminant Analysis
- LR, Linear Regression
- LSTM, Long Short-Term Memory
- ML, Machine Learning
- MLP, Multiplayer Perceptron
- MLR, Multiple Linear Regression
- NLP, Natural Language Processing
- POAF, Postoperative Atrial Fibrillation
- RF, Random Forest
- RNN, Recurrent Neural Network
- SHAP, SHapley Additive exPlanations
- SVM, Support Vector Machine
- Systematic review
- WHO, World Health Organization
- kNN, k-nearest neighbors
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Affiliation(s)
| | - Rui João Pinto
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Pedro Miguel Silva
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Rui Pedro Duarte
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Francisco Alexandre Marinho
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Luís Pimenta
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - António Jorge Gouveia
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Norberto Jorge Gonçalves
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Paulo Jorge Coelho
- Polytechnic of Leiria, Leiria, Portugal
- Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra), Coimbra, Portugal
| | - Eftim Zdravevski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia
| | - Petre Lameski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia
| | - Valderi Leithardt
- VALORIZA, Research Center for Endogenous Resources Valorization, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal
- COPELABS, Universidade Lusófona de Humanidades e Tecnologias, Lisboa, Portugal
| | - Nuno M. Garcia
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
| | - Ivan Miguel Pires
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
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Sawano S, Kodera S, Sato M, Katsushika S, Sukeda I, Takeuchi H, Shinohara H, Kobayashi A, Takiguchi H, Hirose K, Kamon T, Saito A, Kiriyama H, Miura M, Minatsuki S, Kikuchi H, Higashikuni Y, Takeda N, Fujiu K, Ando J, Akazawa H, Morita H, Komuro I. Age prediction from coronary angiography using a deep neural network: Age as a potential label to extract prognosis-related imaging features. PLoS One 2022; 17:e0276928. [PMID: 36301966 PMCID: PMC9612526 DOI: 10.1371/journal.pone.0276928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 10/16/2022] [Indexed: 12/03/2022] Open
Abstract
Coronary angiography (CAG) is still considered the reference standard for coronary artery assessment, especially in the treatment of acute coronary syndrome (ACS). Although aging causes changes in coronary arteries, the age-related imaging features on CAG and their prognostic relevance have not been fully characterized. We hypothesized that a deep neural network (DNN) model could be trained to estimate vascular age only using CAG and that this age prediction from CAG could show significant associations with clinical outcomes of ACS. A DNN was trained to estimate vascular age using ten separate frames from each of 5,923 CAG videos from 572 patients. It was then tested on 1,437 CAG videos from 144 patients. Subsequently, 298 ACS patients who underwent percutaneous coronary intervention (PCI) were analysed to assess whether predicted age by DNN was associated with clinical outcomes. Age predicted as a continuous variable showed mean absolute error of 4 years with R squared of 0.72 (r = 0.856). Among the ACS patients stratified by predicted age from CAG images before PCI, major adverse cardiovascular events (MACE) were more frequently observed in the older vascular age group than in the younger vascular age group (p = 0.017). Furthermore, after controlling for actual age, gender, peak creatine kinase, and history of heart failure, the older vascular age group independently suffered from more MACE (hazard ratio 2.14, 95% CI 1.07 to 4.29, p = 0.032). The vascular age estimated based on CAG imaging by DNN showed high predictive value. The age predicted from CAG images by DNN could have significant associations with clinical outcomes in patients with ACS.
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Affiliation(s)
- Shinnosuke Sawano
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Satoshi Kodera
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
- * E-mail:
| | - Masataka Sato
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Susumu Katsushika
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Issei Sukeda
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hirotoshi Takeuchi
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroki Shinohara
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Atsushi Kobayashi
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroshi Takiguchi
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Kazutoshi Hirose
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Tatsuya Kamon
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Akihito Saito
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroyuki Kiriyama
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Mizuki Miura
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Shun Minatsuki
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hironobu Kikuchi
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Yasutomi Higashikuni
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Norifumi Takeda
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Katsuhito Fujiu
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
- Department of Advanced Cardiology, The University of Tokyo, Tokyo, Japan
| | - Jiro Ando
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroshi Akazawa
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroyuki Morita
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Issei Komuro
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
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9
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Kokubo T, Kodera S, Sawano S, Katsushika S, Nakamoto M, Takeuchi H, Kimura N, Shinohara H, Matsuoka R, Nakanishi K, Nakao T, Higashikuni Y, Takeda N, Fujiu K, Daimon M, Akazawa H, Morita H, Matsuyama Y, Komuro I. Automatic Detection of Left Ventricular Dilatation and Hypertrophy from Electrocardiograms Using Deep Learning. Int Heart J 2022; 63:939-947. [PMID: 36104234 DOI: 10.1536/ihj.22-132] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Left ventricular dilatation (LVD) and left ventricular hypertrophy (LVH) are risk factors for heart failure, and their detection improves heart failure screening. This study aimed to investigate the ability of deep learning to detect LVD and LVH from a 12-lead electrocardiogram (ECG). Using ECG and echocardiographic data, we developed deep learning and machine learning models to detect LVD and LVH. We also examined conventional ECG criteria for the diagnosis of LVH. We calculated the area under the receiver operating characteristic (AUROC) curve, sensitivity, specificity, and accuracy of each model and compared the performance of the models. We analyzed data for 18,954 patients (mean age (standard deviation): 64.2 (16.5) years, men: 56.7%). For the detection of LVD, the value (95% confidence interval) of the AUROC was 0.810 (0.801-0.819) for the deep learning model, and this was significantly higher than that of the logistic regression and random forest methods (P < 0.001). The AUROCs for the logistic regression and random forest methods (machine learning models) were 0.770 (0.761-0.779) and 0.757 (0.747-0.767), respectively. For the detection of LVH, the AUROC was 0.784 (0.777-0.791) for the deep learning model, and this was significantly higher than that of the logistic regression and random forest methods and conventional ECG criteria (P < 0.001). The AUROCs for the logistic regression and random forest methods were 0.758 (0.751-0.765) and 0.716 (0.708-0.724), respectively. This study suggests that deep learning is a useful method to detect LVD and LVH from 12-lead ECGs.
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Affiliation(s)
- Takahiro Kokubo
- School of Public Health, Graduate School of Medicine, The University of Tokyo
| | - Satoshi Kodera
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Shinnosuke Sawano
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Susumu Katsushika
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | | | | | - Nisei Kimura
- Department of Technology Management for Innovation, The University of Tokyo
| | - Hiroki Shinohara
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Ryo Matsuoka
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Koki Nakanishi
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Tomoko Nakao
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | | | - Norifumi Takeda
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Katsuhito Fujiu
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Masao Daimon
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Hiroshi Akazawa
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Hiroyuki Morita
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Yutaka Matsuyama
- School of Public Health, Graduate School of Medicine, The University of Tokyo
| | - Issei Komuro
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
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10
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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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11
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Kodera S, Akazawa H, Morita H, Komuro I. Prospects for cardiovascular medicine using artificial intelligence. J Cardiol 2021; 79:319-325. [PMID: 34772574 DOI: 10.1016/j.jjcc.2021.10.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 10/19/2021] [Indexed: 12/19/2022]
Abstract
As the importance of artificial intelligence (AI) in the clinical setting increases, the need for clinicians to understand AI is also increasing. This review focuses on the fundamental principles of AI and the current state of cardiovascular AI. Various types of cardiovascular AI have been developed for evaluating examinations such as X-rays, electrocardiogram, echocardiography, computed tomography, and magnetic resonance imaging. Cardiovascular AI achieves high accuracy in diagnostic support and prognosis prediction. Furthermore, it can even detect abnormalities that were previously difficult for cardiologists to detect. Randomized controlled trials begin to be reported to verify the usefulness of cardiovascular AI. The day is approaching when cardiovascular AI will be commonly used in clinical practice. Various types of medical AI will be used for cardiovascular care; however, it will not replace medical doctors. We need to understand the strengths and weaknesses of medical AI so that cardiologists can effectively use AI to improve the medical care of patients.
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Affiliation(s)
- Satoshi Kodera
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
| | - Hiroshi Akazawa
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Hiroyuki Morita
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Issei Komuro
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
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