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Zhang X, He C, Lu S, Yu H, Li G, Zhang P, Sun Y. Construction and validation of a nomogram to predict left ventricular hypertrophy in low-risk patients with hypertension. J Clin Hypertens (Greenwich) 2024; 26:274-285. [PMID: 38341620 PMCID: PMC10918740 DOI: 10.1111/jch.14780] [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: 12/14/2023] [Revised: 01/08/2024] [Accepted: 01/16/2024] [Indexed: 02/12/2024]
Abstract
Electrocardiography (ECG) is an accessible diagnostic tool for screening patients with hypertensive left ventricular hypertrophy (LVH). However, its diagnostic sensitivity is low, with a high probability of false-negatives. Thus, this study aimed to establish a clinically useful nomogram to supplement the assessment of LVH in patients with hypertension and without ECG-LVH based on Cornell product criteria (low-risk hypertensive population). A cross-sectional dataset was used for model construction and divided into development (n = 2906) and verification (n = 1447) datasets. A multivariable logistic regression risk model and nomogram were developed after screening for risk factors. Of the 4353 low-risk hypertensive patients, 673 (15.4%) had LVH diagnosed by echocardiography (Echo-LVH). Eleven risk factors were identified: hypertension awareness, duration of hypertension, age, sex, high waist-hip ratio, education level, tea consumption, hypochloremia, and other ECG-LVH diagnostic criteria (including Sokolow-Lyon, Sokolow-Lyon products, and Peguero-Lo Presti). For the development and validation datasets, the areas under the curve were 0.724 (sensitivity = 0.606) and 0.700 (sensitivity = 0.663), respectively. After including blood pressure, the areas under the curve were 0.735 (sensitivity = 0.734) and 0.716 (sensitivity = 0.718), respectively. This novel nomogram had a good predictive ability and may be used to assess the Echo-LVH risk in patients with hypertension and without ECG-LVH based on Cornell product criteria.
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Affiliation(s)
- Xueyao Zhang
- Department of CardiologyFirst Hospital of China Medical UniversityShenyangChina
| | - Chuan He
- Department of Laboratory MedicineFirst Hospital of China Medical UniversityShenyangChina
- National Clinical Research Center for Laboratory Medicine CenterFirst Hospital of China Medical UniversityShenyangChina
| | - Saien Lu
- Department of CardiologyFirst Hospital of China Medical UniversityShenyangChina
| | - Haijie Yu
- Department of CardiologyFirst Hospital of China Medical UniversityShenyangChina
| | - Guangxiao Li
- Department of Medical Record Management CenterFirst Hospital of China Medical UniversityShenyangChina
| | - Pengyu Zhang
- Department of CardiologyFirst Hospital of China Medical UniversityShenyangChina
| | - Yingxian Sun
- Department of CardiologyFirst Hospital of China Medical UniversityShenyangChina
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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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Ryu JS, Lee S, Chu Y, Ahn MS, Park YJ, Yang S. CoAt-Mixer: Self-attention deep learning framework for left ventricular hypertrophy using electrocardiography. PLoS One 2023; 18:e0286916. [PMID: 37289800 PMCID: PMC10249819 DOI: 10.1371/journal.pone.0286916] [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: 12/08/2022] [Accepted: 05/25/2023] [Indexed: 06/10/2023] Open
Abstract
Left ventricular hypertrophy is a significant independent risk factor for all-cause mortality and morbidity, and an accurate diagnosis at an early stage of heart change is clinically significant. Electrocardiography is the most convenient, economical, and non-invasive method for screening in primary care. However, the coincidence rate of the actual left ventricular hypertrophy and diagnostic findings was low, consequently increasing the interest in algorithms using big data and deep learning. We attempted to diagnose left ventricular hypertrophy using big data and deep learning algorithms, and aimed to confirm its diagnostic power according to the differences between males and females. This retrospective study used electrocardiographs obtained at Yonsei University Wonju Severance Christian Hospital, Wonju, Korea, from October 2010 to February 2020. Binary classification was performed for primary screening for left ventricular hypertrophy. Three datasets were used for the experiment: the male, female, and entire dataset. A cutoff for binary classification was defined as the meaningful as a screening test (<132 g/m2 vs. ≥132 g/m2, <109 g/m2 vs. ≥109 g/m2). Six types of input were used for the classification tasks. We attempted to determine whether electrocardiography had predictive power for left ventricular hypertrophy diagnosis. For the entire dataset, the model achieved an area under the receiver operating characteristic (AUROC) curve of 0.836 (95% CI, 0.833-838) with a sensitivity of 78.37% (95% CI, 76.79-79.95). For the male dataset, the AUROC was 0.826 (95% CI, 0.822-830) with a sensitivity of 76.73% (95% CI, 75.14-78.33). For the female dataset, the AUROC was 0.772 (95% CI, 0.769-775) with a sensitivity of 72.90% (95% CI, 70.33-75.46). Our model confirmed that left ventricular hypertrophy can be classified to some extent using electrocardiography, demographics, and electrocardiography features. In particular, a learning environment that considered gender differences was constructed. Consequently, the difference in diagnostic power between men and women was confirmed. Our model will help patients with suspected left ventricular hypertrophy to undergo screening tests at a low cost. In addition, our research and attempts will show the expected effect that gender-consideration approaches can help with various currently proposed diagnostic methods.
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Affiliation(s)
- Ji Seung Ryu
- Department of Precision Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - 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
| | - Min-Soo Ahn
- Division of Cardiology, Department of Internal Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, 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 Precision Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
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Sawano S, Kodera S, Katsushika S, Nakamoto M, Ninomiya K, Shinohara H, Higashikuni Y, Nakanishi K, Nakao T, Seki T, Takeda N, Fujiu K, Daimon M, Akazawa H, Morita H, Komuro I. Deep learning model to detect significant aortic regurgitation using electrocardiography: Detection model for aortic regurgitation. J Cardiol 2021; 79:334-341. [PMID: 34544652 DOI: 10.1016/j.jjcc.2021.08.029] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 08/10/2021] [Accepted: 08/20/2021] [Indexed: 12/31/2022]
Abstract
BACKGROUND Aortic regurgitation (AR) is a common heart disease, with a relatively high prevalence of 4.9% in the Framingham Heart Study. Because the prevalence increases with advancing age, an upward shift in the age distribution may increase the burden of AR. To provide an effective screening method for AR, we developed a deep learning-based artificial intelligence algorithm for the diagnosis of significant AR using electrocardiography (ECG). METHODS Our dataset comprised 29,859 paired data of ECG and echocardiography, including 412 AR cases, from January 2015 to December 2019. This dataset was divided into training, validation, and test datasets. We developed a multi-input neural network model, which comprised a two-dimensional convolutional neural network (2D-CNN) using raw ECG data and a fully connected deep neural network (FC-DNN) using ECG features, and compared its performance with the performances of a 2D-CNN model and other machine learning models. In addition, we used gradient-weighted class activation mapping (Grad-CAM) to identify which parts of ECG waveforms had the most effect on algorithm decision making. RESULTS The area under the receiver operating characteristic curve of the multi-input model (0.802; 95% CI, 0.762-0.837) was significantly greater than that of the 2D-CNN model alone (0.734; 95% CI, 0.679-0.783; p<0.001) and those of other machine learning models. Grad-CAM demonstrated that the multi-input model tended to focus on the QRS complex in leads I and aVL when detecting AR. CONCLUSIONS The multi-input deep learning model using 12-lead ECG data could detect significant AR with modest predictive value.
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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.
| | - Susumu Katsushika
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Mitsuhiko Nakamoto
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Kota Ninomiya
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroki Shinohara
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Yasutomi Higashikuni
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Koki Nakanishi
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Tomoko Nakao
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan; Department of Clinical Laboratory, The University of Tokyo Hospital, Tokyo, Japan
| | - Tomohisa Seki
- Department of Healthcare Information Management, 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
| | - Masao Daimon
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan; Department of Clinical Laboratory, 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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Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals. COMPUTERS 2021. [DOI: 10.3390/computers10060082] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.
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