1
|
Manetas-Stavrakakis N, Sotiropoulou IM, Paraskevas T, Maneta Stavrakaki S, Bampatsias D, Xanthopoulos A, Papageorgiou N, Briasoulis A. Accuracy of Artificial Intelligence-Based Technologies for the Diagnosis of Atrial Fibrillation: A Systematic Review and Meta-Analysis. J Clin Med 2023; 12:6576. [PMID: 37892714 PMCID: PMC10607777 DOI: 10.3390/jcm12206576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/12/2023] [Accepted: 10/14/2023] [Indexed: 10/29/2023] Open
Abstract
Atrial fibrillation (AF) is the most common arrhythmia with a high burden of morbidity including impaired quality of life and increased risk of thromboembolism. Early detection and management of AF could prevent thromboembolic events. Artificial intelligence (AI)--based methods in healthcare are developing quickly and can be proved as valuable for the detection of atrial fibrillation. In this metanalysis, we aim to review the diagnostic accuracy of AI-based methods for the diagnosis of atrial fibrillation. A predetermined search strategy was applied on four databases, the PubMed on 31 August 2022, the Google Scholar and Cochrane Library on 3 September 2022, and the Embase on 15 October 2022. The identified studies were screened by two independent investigators. Studies assessing the diagnostic accuracy of AI-based devices for the detection of AF in adults against a gold standard were selected. Qualitative and quantitative synthesis to calculate the pooled sensitivity and specificity was performed, and the QUADAS-2 tool was used for the risk of bias and applicability assessment. We screened 14,770 studies, from which 31 were eligible and included. All were diagnostic accuracy studies with case-control or cohort design. The main technologies used were: (a) photoplethysmography (PPG) with pooled sensitivity 95.1% and specificity 96.2%, and (b) single-lead ECG with pooled sensitivity 92.3% and specificity 96.2%. In the PPG group, 0% to 43.2% of the tracings could not be classified using the AI algorithm as AF or not, and in the single-lead ECG group, this figure fluctuated between 0% and 38%. Our analysis showed that AI-based methods for the diagnosis of atrial fibrillation have high sensitivity and specificity for the detection of AF. Further studies should examine whether utilization of these methods could improve clinical outcomes.
Collapse
Affiliation(s)
- Nikolaos Manetas-Stavrakakis
- Department of Clinical Therapeutics, National and Kapodistrian University of Athens, 157 28 Athens, Greece; (I.M.S.); (A.B.)
| | - Ioanna Myrto Sotiropoulou
- Department of Clinical Therapeutics, National and Kapodistrian University of Athens, 157 28 Athens, Greece; (I.M.S.); (A.B.)
| | | | | | | | | | | | - Alexandros Briasoulis
- Department of Clinical Therapeutics, National and Kapodistrian University of Athens, 157 28 Athens, Greece; (I.M.S.); (A.B.)
| |
Collapse
|
2
|
Bacevicius J, Taparauskaite N, Kundelis R, Sokas D, Butkuviene M, Stankeviciute G, Abramikas Z, Pilkiene A, Dvinelis E, Staigyte J, Marinskiene J, Audzijoniene D, Petrylaite M, Jukna E, Karuzas A, Juknevicius V, Jakaite R, Basyte-Bacevice V, Bileisiene N, Badaras I, Kiseliute M, Zarembaite G, Gudauskas M, Jasiunas E, Johnson L, Marozas V, Aidietis A. Six-lead electrocardiography compared to single-lead electrocardiography and photoplethysmography of a wrist-worn device for atrial fibrillation detection controlled by premature atrial or ventricular contractions: six is smarter than one. Front Cardiovasc Med 2023; 10:1160242. [PMID: 37363094 PMCID: PMC10288196 DOI: 10.3389/fcvm.2023.1160242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 05/26/2023] [Indexed: 06/28/2023] Open
Abstract
Background Smartwatches are commonly capable to record a lead-I-like electrocardiogram (ECG) and perform a photoplethysmography (PPG)-based atrial fibrillation (AF) detection. Wearable technologies repeatedly face the challenge of frequent premature beats, particularly in target populations for screening of AF. Objective To investigate the potential diagnostic benefit of six-lead ECG compared to single-lead ECG and PPG-based algorithm for AF detection of the wrist-worn device. Methods and results From the database of DoubleCheck-AF 249 adults were enrolled in AF group (n = 121) or control group of SR with frequent premature ventricular (PVCs) or atrial (PACs) contractions (n = 128). Cardiac rhythm was monitored using a wrist-worn device capable of recording continuous PPG and simultaneous intermittent six-lead standard-limb-like ECG. To display a single-lead ECG, the six-lead ECGs were trimmed to lead-I-like ECGs. Two diagnosis-blinded cardiologists evaluated reference, six-lead and single-lead ECGs as "AF", "SR", or "Cannot be concluded". AF detection based on six-lead ECG, single-lead ECG, and PPG yielded a sensitivity of 99.2%, 95.7%, and 94.2%, respectively. The higher number of premature beats per minute was associated with false positive outcomes of single-lead ECG (18.80 vs. 5.40 beats/min, P < 0.01), six-lead ECG (64.3 vs. 5.8 beats/min, P = 0.018), and PPG-based detector (13.20 vs. 5.60 beats/min, P = 0.05). Single-lead ECG required 3.4 times fewer extrasystoles than six-lead ECG to result in a false positive outcome. In a control subgroup of PACs, the specificity of six-lead ECG, single-lead ECG, and PPG dropped to 95%, 83.8%, and 90%, respectively. The diagnostic value of single-lead ECG (AUC 0.898) was inferior to six-lead ECG (AUC 0.971) and PPG-based detector (AUC 0.921). In a control subgroup of PVCs, the specificity of six-lead ECG, single-lead ECG, and PPG was 100%, 96.4%, and 96.6%, respectively. The diagnostic value of single-lead ECG (AUC 0.961) was inferior to six-lead ECG (AUC 0.996) and non-inferior to PPG-based detector (AUC 0.954). Conclusions A six-lead wearable-recorded ECG demonstrated the superior diagnostic value of AF detection compared to a single-lead ECG and PPG-based AF detection. The risk of type I error due to the widespread use of smartwatch-enabled single-lead ECGs in populations with frequent premature beats is significant.
Collapse
Affiliation(s)
- Justinas Bacevicius
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Neringa Taparauskaite
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Ricardas Kundelis
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Daivaras Sokas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Monika Butkuviene
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Guoste Stankeviciute
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Zygimantas Abramikas
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Aiste Pilkiene
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Ernestas Dvinelis
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Justina Staigyte
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Julija Marinskiene
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Deimile Audzijoniene
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Marija Petrylaite
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Edvardas Jukna
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Albinas Karuzas
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Vytautas Juknevicius
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Rusne Jakaite
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | | | - Neringa Bileisiene
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Ignas Badaras
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Margarita Kiseliute
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Gintare Zarembaite
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Modestas Gudauskas
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Eugenijus Jasiunas
- Center of Informatics and Development, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Linda Johnson
- Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
- Electronics Engineering Department, Kaunas University of Technology, Kaunas, Lithuania
| | - Audrius Aidietis
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| |
Collapse
|
4
|
Niu Y, Wang H, Wang H, Zhang H, Jin Z, Guo Y. Diagnostic validation of smart wearable device embedded with single-lead electrocardiogram for arrhythmia detection. Digit Health 2023; 9:20552076231198682. [PMID: 37667685 PMCID: PMC10475230 DOI: 10.1177/20552076231198682] [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: 02/20/2023] [Accepted: 08/12/2023] [Indexed: 09/06/2023] Open
Abstract
Objective To validate a single-lead electrocardiogram algorithm for identifying atrial fibrillation, atrial premature beats, ventricular premature beats, and sinus rhythm. Methods A total of 656 subjects aged 19 to 94 years were enrolled. Participants were simultaneously tested with a wristwatch (Huawei Watch GT2 Pro, Huawei Technologies Co., Ltd, Shenzhen, China) and a 12-lead electrocardiogram for 3 minutes. A total of 1926 electrocardiogram signals from 628 subjects (282 men and 346 women) aged 19 to 94 years (median 64 years) were analyzed using an algorithm. Results The numbers of subjects with atrial fibrillation, atrial premature beats, ventricular premature beats, and sinus rhythm were 129, 141, 107, and 251, respectively, and together they had a total of 1926 electrocardiogram signals. For the three-class classification system, the recall, precision, and F1 score were 97.6%, 96.5%, 97.0% for sinus rhythm; 96.7%, 96.9%, 96.8% for atrial fibrillation; and 92.8%, 94.2%, 93.5% for ectopic beats, respectively. The macro-F1 score of the three-class classification system was 95.8%. For the four-class classification system, the recall, precision, and F1 score were 97.6%, 96.5%, 97.0% for sinus rhythm; 96.7%, 96.9%, 96.8% for atrial fibrillation; 90.5%, 89.4%, 89.9% for atrial premature beats; and 86.1%, 89.6%, 87.8% for ventricular premature beats, respectively. The macro-F1 score of the four-class classification system was 92.9%. Conclusions The single-lead electrocardiogram algorithm embedded into smart wearables demonstrated good performance in detecting atrial fibrillation, atrial/ventricular premature beats, and sinus rhythm, and thus would facilitate atrial fibrillation screening and management.
Collapse
Affiliation(s)
- Yonghong Niu
- Department of Cardiology, The First Affiliated Hospital of Tsinghua University, Beijing, China
| | - Hao Wang
- Department of Cardiology, Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Hong Wang
- Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Centre, Chinese PLA General Hospital, Beijing, China
- Graduate School of PLA General Hospital, Beijing, China
| | - Hui Zhang
- Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Zhigeng Jin
- Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Yutao Guo
- Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Centre, Chinese PLA General Hospital, Beijing, China
| |
Collapse
|
5
|
Santos Rodrigues A, Augustauskas R, Lukoševičius M, Laguna P, Marozas V. Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs. SENSORS (BASEL, SWITZERLAND) 2022; 22:5414. [PMID: 35891094 PMCID: PMC9328169 DOI: 10.3390/s22145414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/16/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Abstract
The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to estimate QRS-T angles from reduced-lead ECGs registered with consumer healthcare devices would, therefore, facilitate ambulatory monitoring. (1) Objective: Develop a method to estimate spatial QRS-T angles from reduced-lead ECGs. (2) Approach: We designed a deep learning model to locate the QRS and T wave vectors necessary for computing the QRS-T angle. We implemented an original loss function to guide the model in the 3D space to search for each vector's coordinates. A gradual reduction of ECG leads from the largest publicly available dataset of clinical 12-lead ECG recordings (PTB-XL) is used for training and validation. (3) Results: The spatial QRS-T angle can be estimated from leads {I, II, aVF, V2} with sufficient accuracy (absolute mean and median errors of 11.4° and 7.3°) for detecting abnormal angles without sacrificing patient comfortability. (4) Significance: Our model could enable ambulatory monitoring of spatial QRS-T angles using patch- or textile-based ECG devices. Populations at risk of SCD, like chronic cardiac and kidney disease patients, might benefit from this technology.
Collapse
Affiliation(s)
- Ana Santos Rodrigues
- Biomedical Engineering Institute, Kaunas University of Technology, 51423 Kaunas, Lithuania;
| | - Rytis Augustauskas
- Department of Automation, Kaunas University of Technology, 51367 Kaunas, Lithuania;
| | - Mantas Lukoševičius
- Faculty of Informatics, Kaunas University of Technology, 51368 Kaunas, Lithuania;
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain;
- Biomedical Research Networking Center (CIBER), 50018 Zaragoza, Spain
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, 51423 Kaunas, Lithuania;
- Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, 51367 Kaunas, Lithuania
| |
Collapse
|