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Ose B, Sattar Z, Gupta A, Toquica C, Harvey C, Noheria A. Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review. Curr Cardiol Rep 2024; 26:561-580. [PMID: 38753291 DOI: 10.1007/s11886-024-02062-1] [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] [Accepted: 04/17/2024] [Indexed: 06/26/2024]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human capabilities, facilitate automated access to nuanced ECG interpretation, and expand the scope of cardiovascular screening in the population. AI can be applied to the standard 12-lead resting ECG and single-lead ECGs in external monitors, implantable devices, and direct-to-consumer smart devices. We summarize the current state of the literature on AI-ECG. RECENT FINDINGS Rhythm classification was the first application of AI-ECG. Subsequently, AI-ECG models have been developed for screening structural heart disease including hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, pulmonary hypertension, and left ventricular systolic dysfunction. Further, AI models can predict future events like development of systolic heart failure and atrial fibrillation. AI-ECG exhibits potential in acute cardiac events and non-cardiac applications, including acute pulmonary embolism, electrolyte abnormalities, monitoring drugs therapy, sleep apnea, and predicting all-cause mortality. Many AI models in the domain of cardiac monitors and smart watches have received Food and Drug Administration (FDA) clearance for rhythm classification, while others for identification of cardiac amyloidosis, pulmonary hypertension and left ventricular dysfunction have received breakthrough device designation. As AI-ECG models continue to be developed, in addition to regulatory oversight and monetization challenges, thoughtful clinical implementation to streamline workflows, avoiding information overload and overwhelming of healthcare systems with false positive results is necessary. Research to demonstrate and validate improvement in healthcare efficiency and improved patient outcomes would be required before widespread adoption of any AI-ECG model.
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Affiliation(s)
- Benjamin Ose
- The University of Kansas School of Medicine, Kansas City, KS, USA
| | - Zeeshan Sattar
- Division of General and Hospital Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
| | - Amulya Gupta
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA
| | | | - Chris Harvey
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA
| | - Amit Noheria
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA.
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA.
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Guo RX, Tian X, Bazoukis G, Tse G, Hong S, Chen KY, Liu T. Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia. Pacing Clin Electrophysiol 2024; 47:789-801. [PMID: 38712484 DOI: 10.1111/pace.14995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 03/29/2024] [Accepted: 04/18/2024] [Indexed: 05/08/2024]
Abstract
The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.
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Affiliation(s)
- Rong-Xin Guo
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Xu Tian
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - George Bazoukis
- Department of Cardiology, Larnaca General Hospital, Inomenon Polition Amerikis, Larnaca, Cyprus
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus
| | - Gary Tse
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
- Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China
- School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, China
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Kang-Yin Chen
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Tong Liu
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
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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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Chou CC, Liu ZY, Chang PC, Liu HT, Wo HT, Lee WC, Wang CC, Chen JS, Kuo CF, Wen MS. Comparing Artificial Intelligence-Enabled Electrocardiogram Models in Identifying Left Atrium Enlargement and Long-term Cardiovascular Risk. Can J Cardiol 2024; 40:585-594. [PMID: 38163477 DOI: 10.1016/j.cjca.2023.12.025] [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: 05/27/2023] [Revised: 10/24/2023] [Accepted: 12/24/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND The role of P-wave in identifying left atrial enlargement (LAE) with the use of artificial intelligence (AI)-enabled electrocardiography (ECG) models is unclear. It is also unknown if AI-enabled single-lead ECG could be used as a diagnostic tool for LAE surveillance. We aimed to build AI-enabled P-wave and single-lead ECG models to identify LAE using sinus rhythm (SR) and non-SR ECGs, and compare the prognostic ability of severe LAE, defined as left atrial diameter ≥ 50 mm, assessed by AI-enabled ECG models vs echocardiography. METHODS This retrospective study used data from 382,594 consecutive adults with paired 12-lead ECG and echocardiography performed within 2 weeks of each other at Chang Gung Memorial Hospital. UNet++ was used for P-wave segmentation. ResNet-18 was used to develop deep convolutional neural network-enabled ECG models for discriminating LAE. External validation was performed with the use of data from 11,753 patients from another hospital. RESULTS The AI-enabled 12-lead ECG model outperformed other ECG models for classifying LAE, but the single-lead ECG models also showed excellent performance at a left atrial diameter cutoff of 50 mm. AI-enabled ECG models had excellent and fair discrimination on LAE using the SR and the non-SR data set, respectively. Severe LAE identified by AI-enabled ECG models was more predictive of future cardiovascular disease than echocardiography; however, the cumulative incidence of new-onset atrial fibrillation and heart failure was higher in patients with echocardiography-severe LAE than with AI-enabled ECG-severe LAE. CONCLUSIONS P-Wave plays a crucial role in discriminating LAE in AI-enabled ECG models. AI-enabled ECG models outperform echocardiography in predicting new-onset cardiovascular diseases associated with severe LAE.
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Affiliation(s)
- Chung-Chuan Chou
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Zhi-Yong Liu
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Po-Cheng Chang
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Hao-Tien Liu
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan
| | - Hung-Ta Wo
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan
| | - Wen-Chen Lee
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan
| | - Chun-Chieh Wang
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Jung-Sheng Chen
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Chang-Fu Kuo
- School of Medicine, Chang Gung University, Taoyuan, Taiwan; Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Division of Rheumatology, Allergy, and Immunology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan.
| | - Ming-Shien Wen
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan.
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Lin C, Kuo FC, Chau T, Shih JH, Lin CS, Chen CC, Lee CC, Lin SH. Artificial intelligence-enabled electrocardiography contributes to hyperthyroidism detection and outcome prediction. COMMUNICATIONS MEDICINE 2024; 4:42. [PMID: 38472334 DOI: 10.1038/s43856-024-00472-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 03/01/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Hyperthyroidism is frequently under-recognized and leads to heart failure and mortality. Timely identification of high-risk patients is a prerequisite to effective antithyroid therapy. Since the heart is very sensitive to hyperthyroidism and its electrical signature can be demonstrated by electrocardiography, we developed an artificial intelligence model to detect hyperthyroidism by electrocardiography and examined its potential for outcome prediction. METHODS The deep learning model was trained using a large dataset of 47,245 electrocardiograms from 33,246 patients at an academic medical center. Patients were included if electrocardiograms and measurements of serum thyroid-stimulating hormone were available that had been obtained within a three day period. Serum thyroid-stimulating hormone and free thyroxine were used to define overt and subclinical hyperthyroidism. We tested the model internally using 14,420 patients and externally using two additional test sets comprising 11,498 and 596 patients, respectively. RESULTS The performance of the deep learning model achieves areas under the receiver operating characteristic curves (AUCs) of 0.725-0.761 for hyperthyroidism detection, AUCs of 0.867-0.876 for overt hyperthyroidism, and AUC of 0.631-0.701 for subclinical hyperthyroidism, superior to a traditional features-based machine learning model. Patients identified as hyperthyroidism-positive by the deep learning model have a significantly higher risk (1.97-2.94 fold) of all-cause mortality and new-onset heart failure compared to hyperthyroidism-negative patients. This cardiovascular disease stratification is particularly pronounced in subclinical hyperthyroidism, surpassing that observed in overt hyperthyroidism. CONCLUSIONS An innovative algorithm effectively identifies overt and subclinical hyperthyroidism and contributes to cardiovascular risk assessment.
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Affiliation(s)
- Chin Lin
- School of Medicine, National Defense Medical Center, Taipei, Taiwan ROC
- Graduate Institute of Aerospace and Undersea Medicine, National Defense Medical Center, Taipei, Taiwan ROC
| | - Feng-Chih Kuo
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC
| | - Tom Chau
- Department of Medicine, Providence St. Vincent Medical Center, Portland, OR, USA
| | - Jui-Hu Shih
- Department of Pharmacy Practice, Tri-Service General Hospital, Taipei, Taiwan ROC
- School of Pharmacy, National Defense Medical Center, Taipei, Taiwan ROC
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC
| | - Chien-Chou Chen
- Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC
| | - Chia-Cheng Lee
- Department of Medical Informatics, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC
- Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC
| | - Shih-Hua Lin
- Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC.
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Wu M, Yu K, Zhao Z, Zhu B. Knowledge structure and global trends of machine learning in stroke over the past decade: A scientometric analysis. Heliyon 2024; 10:e24230. [PMID: 38288018 PMCID: PMC10823080 DOI: 10.1016/j.heliyon.2024.e24230] [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: 04/18/2023] [Revised: 11/23/2023] [Accepted: 01/04/2024] [Indexed: 01/31/2024] Open
Abstract
Objective Machine learning (ML) models have been widely applied in stroke prediction, diagnosis, treatment, and prognosis assessment. We aimed to conduct a comprehensive scientometrics analysis of studies related to ML in stroke and reveal its current status, knowledge structure, and global trends. Methods All documents related to ML in stroke were retrieved from the Web of Science database on March 15, 2023. We refined the documents by including only original articles and reviews in the English language. The literature published over the past decade was imported into scientometrics software for influence detection and collaborative network analysis. Results 2389 related publications were included. The annual publication outputs demonstrated explosive growth, with an average growth rate of 63.99 %. Among the 90 countries/regions involved, the United States (729 articles) and China (636 articles) were the most productive countries. Frontiers in Neurology was the most prolific journal with 94 articles. 234 highly cited articles, each with more than 31 citations, were detected. Keyword analysis revealed a total of 5333 keywords, with a predominant focus on the application of ML models in the early diagnosis, classification, and prediction of "acute ischemic stroke" and "atrial fibrillation-related stroke". The keyword "classification" had the first and longest burst, spanning from 2013 to 2018. 'Upport vector machine' got the strongest burst strength with 6.2. Keywords such as 'mechanical thrombectomy', 'expression', and 'prognosis' experienced bursts in 2022 and have continued to be prominent. Conclusion The applications of ML in stroke are increasingly diverse and extensive, with researchers showing growing interest over the past decade. However, the clinical application of ML in stroke is still in its early stages, and several limitations and challenges need to be addressed for its widespread adoption in clinical practice.
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Affiliation(s)
- Mingfen Wu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Kefu Yu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Zhigang Zhao
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Bin Zhu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
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Sato M, Kodera S, Setoguchi N, Tanabe K, Kushida S, Kanda J, Saji M, Nanasato M, Maki H, Fujita H, Kato N, Watanabe H, Suzuki M, Takahashi M, Sawada N, Yamasaki M, Sawano S, Katsushika S, Shinohara H, Takeda N, Fujiu K, Daimon M, Akazawa H, Morita H, Komuro I. Deep Learning Models for Predicting Left Heart Abnormalities From Single-Lead Electrocardiogram for the Development of Wearable Devices. Circ J 2023; 88:146-156. [PMID: 37967949 DOI: 10.1253/circj.cj-23-0216] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2023]
Abstract
BACKGROUND Left heart abnormalities are risk factors for heart failure. However, echocardiography is not always available. Electrocardiograms (ECGs), which are now available from wearable devices, have the potential to detect these abnormalities. Nevertheless, whether a model can detect left heart abnormalities from single Lead I ECG data remains unclear. METHODS AND RESULTS We developed Lead I ECG models to detect low ejection fraction (EF), wall motion abnormality, left ventricular hypertrophy (LVH), left ventricular dilatation, and left atrial dilatation. We used a dataset comprising 229,439 paired sets of ECG and echocardiography data from 8 facilities, and validated the model using external verification with data from 2 facilities. The area under the receiver operating characteristic curves of our model was 0.913 for low EF, 0.832 for wall motion abnormality, 0.797 for LVH, 0.838 for left ventricular dilatation, and 0.802 for left atrial dilatation. In interpretation tests with 12 cardiologists, the accuracy of the model was 78.3% for low EF and 68.3% for LVH. Compared with cardiologists who read the 12-lead ECGs, the model's performance was superior for LVH and similar for low EF. CONCLUSIONS From a multicenter study dataset, we developed models to predict left heart abnormalities using Lead I on the ECG. The Lead I ECG models show superior or equivalent performance to cardiologists using 12-lead ECGs.
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Affiliation(s)
- Masataka Sato
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Satoshi Kodera
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | | | - Kengo Tanabe
- Division of Cardiology, Mitsui Memorial Hospital
| | | | - Junji Kanda
- Department of Cardiovascular Medicine, Asahi General Hospital
| | - Mike Saji
- Department of Cardiology, Sakakibara Heart Institute
| | | | - Hisataka Maki
- Division of Cardiovascular Medicine, Saitama Medical Center, Jichi Medical University
| | - Hideo Fujita
- Division of Cardiovascular Medicine, Saitama Medical Center, Jichi Medical University
| | - Nahoko Kato
- Department of Cardiology, Tokyo Bay Urayasu Ichikawa Medical Center
| | | | | | | | - Naoko Sawada
- Department of Cardiology, NTT Medical Center Tokyo
| | | | - Shinnosuke Sawano
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Susumu Katsushika
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
| | - Hiroki Shinohara
- 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
- Department of Advanced Cardiology, The University of Tokyo
| | - 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
| | - Issei Komuro
- Department of Cardiovascular Medicine, The University of Tokyo Hospital
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Chen YC, Tsai SC, Lin C, Lin CS, Fang WH, Lou YS, Lee CC, Liu PY. A Deep-Learning Algorithm-Enhanced Electrocardiogram Interpretation for Detecting Pulmonary Embolism. ACTA CARDIOLOGICA SINICA 2023; 39:913-928. [PMID: 38022412 PMCID: PMC10646591 DOI: 10.6515/acs.202311_39(6).20230410b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 04/10/2023] [Indexed: 12/01/2023]
Abstract
Background The early diagnosis of pulmonary embolism (PE) remains a challenge. Electrocardiograms (ECGs) and D-dimer levels are used to screen potential cases. Objective To develop a deep learning model (DLM) to detect PE using ECGs and investigate the clinical value of false detections in patients without PE. Methods Among patients who visited the emergency department between 2011 and 2019, PE cases were identified through a review of medical records. Non-PE ECGs were collected from patients without a diagnostic code for PE. There were 113 PE and 51,456 non-PE ECGs in the training and validation sets for developing the DLM, respectively, and 27 PE and 13,105 non-PE cases in an independent testing set for performance validation. A human-machine competition was conducted from the testing set to compare the performance of the DLM with that of physicians. Receiver operating characteristic (ROC) curves, sensitivity, and specificity were used to determine the diagnostic value. Survival analysis was used to assess the prognosis of the patients without PE, stratified by DLM prediction. Results The DLM was as effective as physicians in diagnosing PE, with 70.8% sensitivity and 69.7% specificity. The area under the ROC curve of DLM was 0.778 in the testing set and up to 0.9 with D-dimer and demographic data. The non-PE patients whose ECG was misclassified as PE by DLM had higher all-cause mortality [hazard ratio (HR) 2.13 (1.51-3.02)] and risk of non-cardiovascular hospitalization [HR 1.55 (1.42-1.68)] than those correctly classified. Conclusions A DLM-enhanced ECG system may prompt PE recognition and provide prognostic outcomes in patients with false-positive predictions.
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Affiliation(s)
| | - Sung-Chiao Tsai
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center
| | - Chin Lin
- Graduate Institute of Life Sciences
- School of Public Health
- School of Medicine, National Defense Medical Center
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center
| | - Wen-Hui Fang
- Department of Family and Community Medicine, Department of Internal Medicine
| | - Yu-Sheng Lou
- Graduate Institute of Life Sciences
- School of Public Health
| | - Chia-Cheng Lee
- Planning and Management Office
- Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Pang-Yen Liu
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center
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Liu PY, Lin C, Lin CS, Fang WH, Lee CC, Wang CH, Tsai DJ. Artificial Intelligence-Enabled Electrocardiography Detects B-Type Natriuretic Peptide and N-Terminal Pro-Brain Natriuretic Peptide. Diagnostics (Basel) 2023; 13:2723. [PMID: 37685262 PMCID: PMC10487184 DOI: 10.3390/diagnostics13172723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/18/2023] [Accepted: 08/20/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND: The B-type natriuretic peptide (BNP) and N-terminal pro-brain natriuretic peptide (pBNP) are predictors of cardiovascular morbidity and mortality. Since the artificial intelligence (AI)-enabled electrocardiogram (ECG) system is widely used in the management of many cardiovascular diseases (CVDs), patients requiring intensive monitoring may benefit from an AI-ECG with BNP/pBNP predictions. This study aimed to develop an AI-ECG to predict BNP/pBNP and compare their values for future mortality. METHODS: The development, tuning, internal validation, and external validation sets included 47,709, 16,249, 4001, and 6042 ECGs, respectively. Deep learning models (DLMs) were trained using a development set for estimating ECG-based BNP/pBNP (ECG-BNP/ECG-pBNP), and the tuning set was used to guide the training process. The ECGs in internal and external validation sets belonging to nonrepeating patients were used to validate the DLMs. We also followed-up all-cause mortality to explore the prognostic value. RESULTS: The DLMs accurately distinguished mild (≥500 pg/mL) and severe (≥1000 pg/mL) an abnormal BNP/pBNP with AUCs of ≥0.85 in the internal and external validation sets, which provided sensitivities of 68.0-85.0% and specificities of 77.9-86.2%. In continuous predictions, the Pearson correlation coefficient between ECG-BNP and ECG-pBNP was 0.93, and they were both associated with similar ECG features, such as the T wave axis and correct QT interval. ECG-pBNP provided a higher all-cause mortality predictive value than ECG-BNP. CONCLUSIONS: The AI-ECG can accurately estimate BNP/pBNP and may be useful for monitoring the risk of CVDs. Moreover, ECG-pBNP may be a better indicator to manage the risk of future mortality.
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Affiliation(s)
- Pang-Yen Liu
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan; (P.-Y.L.); (C.-S.L.)
| | - Chin Lin
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei 114, Taiwan;
- School of Public Health, National Defense Medical Center, Taipei 114, Taiwan
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan;
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan; (P.-Y.L.); (C.-S.L.)
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei 114, Taiwan;
| | - Wen-Hui Fang
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan;
- Department of Family and Community Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Chia-Cheng Lee
- Medical Informatics Office, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan;
- Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Chih-Hung Wang
- Department of Otolaryngology–Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan;
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei 114, Taiwan
| | - Dung-Jang Tsai
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei 114, Taiwan;
- School of Public Health, National Defense Medical Center, Taipei 114, Taiwan
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan;
- Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City 242, Taiwan
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Chen YHJ, Lin CS, Lin C, Tsai DJ, Fang WH, Lee CC, Wang CH, Chen SJ. An AI-Enabled Dynamic Risk Stratification for Emergency Department Patients with ECG and CXR Integration. J Med Syst 2023; 47:81. [PMID: 37523102 DOI: 10.1007/s10916-023-01980-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 07/21/2023] [Indexed: 08/01/2023]
Abstract
Emergency department (ED) triage scale determines the priority of patient care and foretells the prognosis. However, the information retrieved from the initial assessment is limited, hindering the risk identification accuracy of triage. Therefore, we sought to develop a 'dynamic' triage system as secondary screening, using artificial intelligence (AI) techniques to integrate information from initial assessment data and subsequent examinations. This retrospective cohort study included 134,112 ED visits with at least one electrocardiography (ECG) and chest X-ray (CXR) in a medical center from 2012 to 2022. Additionally, an independent community hospital provided 45,614 ED visits as an external validation set. We trained an eXtreme gradient boosting (XGB) model using initial assessment data to predict all-cause mortality in 7 days. Two deep learning models (DLMs) using ECG and CXR were trained to stratify mortality risks. The dynamic triage levels were based on output from the XGB-triage and DLMs from ECG and CXR. During the internal and external validation, the area under the receiver operating characteristic curve (AUC) of the XGB-triage model was >0.866; furthermore, the AUCs of DLMs using ECG and CXR were >0.862 and >0.886, respectively. The dynamic triage scale provided a higher C-index (0.914-0.920 vs. 0.827-0.843) than the original one and demonstrated better predictive ability for 5-year mortality, 30-day ED revisit, and 30-day discharge. The AI-based risk scale provides a more accurate and dynamic stratification of mortality risk in ED patients, particularly in identifying patients who tend to be overlooked due to atypical symptoms.
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Affiliation(s)
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center Taipei, Taipei, Taiwan
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Chin Lin
- School of Public Health, National Defense Medical Center, Taipei, Taiwan
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan
| | - Dung-Jang Tsai
- Center for Artificial Intelligence and Internet of Things, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Wen-Hui Fang
- Center for Artificial Intelligence and Internet of Things, 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
| | - Chia-Cheng Lee
- Medical Informatics Office, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chih-Hung Wang
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Sy-Jou Chen
- Department of Emergency Medicine, Tri-Service General Hospital, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490, Taiwan.
- Graduate Institute of Injury Prevention and Control, College of Public Health and Nutrition, Taipei Medical University, Taipei, Taiwan.
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A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram. J Pers Med 2022; 12:jpm12071150. [PMID: 35887647 PMCID: PMC9324403 DOI: 10.3390/jpm12071150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 07/02/2022] [Accepted: 07/13/2022] [Indexed: 12/20/2022] Open
Abstract
(1) Background: Acute pericarditis is often confused with ST-segment elevation myocardial infarction (STEMI) among patients presenting with acute chest pain in the emergency department (ED). Since a deep learning model (DLM) has been validated to accurately identify STEMI cases via 12-lead electrocardiogram (ECG), this study aimed to develop another DLM for the detection of acute pericarditis in the ED. (2) Methods: This study included 128 ECGs from patients with acute pericarditis and 66,633 ECGs from patients visiting the ED between 1 January 2010 and 31 December 2020. The ECGs were randomly allocated based on patients to the training, tuning, and validation sets, at a 3:1:1 ratio. We used raw ECG signals to train a pericarditis-DLM and used traditional ECG features to train a machine learning model. A human–machine competition was conducted using a subset of the validation set, and the performance of the Philips automatic algorithm was also compared. STEMI cases in the validation set were extracted to analyze the DLM ability of differential diagnosis between acute pericarditis and STEMI using ECG. We also followed the hospitalization events in non-pericarditis cases to explore the meaning of false-positive predictions. (3) Results: The pericarditis-DLM exceeded the performance of all participating human experts and algorithms based on traditional ECG features in the human–machine competition. In the validation set, the pericarditis-DLM could detect acute pericarditis with an area under the receiver operating characteristic curve (AUC) of 0.954, a sensitivity of 78.9%, and a specificity of 97.7%. However, our pericarditis-DLM also misinterpreted 10.2% of STEMI ECGs as pericarditis cases. Therefore, we generated an integrating strategy combining pericarditis-DLM and a previously developed STEMI-DLM, which provided a sensitivity of 73.7% and specificity of 99.4%, to identify acute pericarditis in patients with chest pains. Compared to the true-negative cases, patients with false-positive results using this strategy were associated with higher risk of hospitalization within 3 days due to cardiac disorders (hazard ratio (HR): 8.09; 95% confidence interval (CI): 3.99 to 16.39). (4) Conclusions: The AI-enhanced algorithm may be a powerful tool to assist clinicians in the early detection of acute pericarditis and differentiate it from STEMI using 12-lead ECGs.
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Chen HY, Lin CS, Fang WH, Lee CC, Ho CL, Wang CH, Lin C. Artificial Intelligence-Enabled Electrocardiogram Predicted Left Ventricle Diameter as an Independent Risk Factor of Long-Term Cardiovascular Outcome in Patients With Normal Ejection Fraction. Front Med (Lausanne) 2022; 9:870523. [PMID: 35479951 PMCID: PMC9035739 DOI: 10.3389/fmed.2022.870523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 03/10/2022] [Indexed: 11/13/2022] Open
Abstract
Background Heart failure (HF) is a global disease with increasing prevalence in an aging society. However, the survival rate is poor despite the patient receiving standard treatment. Early identification of patients with a high risk of HF is important but challenging. Left ventricular end-diastolic diameter (LV-D) increase was an independent risk factor of HF and adverse cardiovascular (CV) outcomes. In this study, we aimed to develop an artificial intelligence (AI) enabled electrocardiogram (ECG) system to detect LV-D increase early. Objective We developed a deep learning model (DLM) to predict left ventricular end-diastolic and end-systolic diameter (LV-D and LV-S) with internal and external validations and investigated the relationship between ECG-LV-D and echocardiographic LV-D and explored the contributions of ECG-LV-D on future CV outcomes. Methods Electrocardiograms and corresponding echocardiography data within 7 days were collected and paired for DLM training with 99,692 ECGs in the development set and 20,197 ECGs in the tuning set. The other 7,551 and 11,644 ECGs were collected from two different hospitals to validate the DLM performance in internal and external validation sets. We analyzed the association and prediction ability of ECG-LVD for CV outcomes, including left ventricular (LV) dysfunction, CV mortality, acute myocardial infarction (AMI), and coronary artery disease (CAD). Results The mean absolute errors (MAE) of ECG-LV-D were 5.25/5.29, and the area under the receiver operating characteristic (ROC) curves (AUCs) were 0.8297/0.8072 and 0.9295/0.9148 for the detection of mild (56 ≦ LV-D < 65 mm) and severe (LV-D ≧ 65 mm) LV-D dilation in internal/external validation sets, respectively. Patients with normal ejection fraction (EF) who were identified as high ECHO-LV-D had the higher hazard ratios (HRs) of developing new onset LV dysfunction [HR: 2.34, 95% conference interval (CI): 1.78–3.08], CV mortality (HR 2.30, 95% CI 1.05–5.05), new-onset AMI (HR 2.12, 95% CI 1.36–3.29), and CAD (HR 1.59, 95% CI 1.26–2.00) in the internal validation set. In addition, the ECG-LV-D presents a 1.88-fold risk (95% CI 1.47–2.39) on new-onset LV dysfunction in the external validation set. Conclusion The ECG-LV-D not only identifies high-risk patients with normal EF but also serves as an independent risk factor of long-term CV outcomes.
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Affiliation(s)
- Hung-Yi Chen
- Department of Internal Medicine, National Defense Medical Center, Tri-Service General Hospital, Taipei, Taiwan
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, National Defense Medical Center, Tri-Service General Hospital, Taipei, Taiwan
| | - Wen-Hui Fang
- Department of Family and Community Medicine, National Defense Medical Center, Tri-Service General Hospital, Taipei, Taiwan
- Department of Internal Medicine, National Defense Medical Center, Tri-Service General Hospital, Taipei, Taiwan
- Artificial Intelligence of Things Center, National Defense Medical Center, Tri-Service General Hospital, Taipei, Taiwan
| | - Chia-Cheng Lee
- Medical Informatics Office, National Defense Medical Center, Tri-Service General Hospital, Taipei, Taiwan
- Division of Colorectal Surgery, Department of Surgery, National Defense Medical Center, Tri-Service General Hospital, Taipei, Taiwan
| | - Ching-Liang Ho
- Division of Hematology and Oncology, Department of Internal Medicine, National Defense Medical Center, Tri-Service General Hospital, Taipei, Taiwan
| | - Chih-Hung Wang
- Department of Otolaryngology-Head and Neck Surgery, National Defense Medical Center, Tri-Service General Hospital, Taipei, Taiwan
- National Defense Medical Center, Graduate Institute of Medical Sciences, Taipei, Taiwan
| | - Chin Lin
- Artificial Intelligence of Things Center, National Defense Medical Center, Tri-Service General Hospital, Taipei, Taiwan
- Medical Technology Education Center, National Defense Medical Center, School of Medicine, Taipei, Taiwan
- *Correspondence: Chin Lin,
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