ArticleDiagnostics (Basel, Switzerland)2022
Artificial Intelligence-Enhanced Smartwatch ECG for Heart Failure-Reduced Ejection Fraction Detection by Generating 12-Lead ECG.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06231797 (AI-ECG Screening for Left Ventricular Systolic Dysfunction), which is not on this map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
AI-ECG Screening for Left Ventricular Systolic Dysfunction: A Prospective, Observational, Multicenter Study
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2023Pooled it
- From wrist to heart: clinical validation and real-world translation of wearable and portable AI-enhanced electrocardiography.European heart journal. Digital health · 2026Review
- Prediction of Left Ventricular Systolic Dysfunction Using an Artificial Intelligence-Based Electrocardiogram Analysis Model in Patients Presenting to the Emergency Department.Diagnostics (Basel, Switzerland) · 2026Article
- Automated HFrEF Diagnosis Using an Optimized TimeSformer Model in Echocardiography.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Pulmonary Hypertension: Current State and Future Prospects.Reviews in cardiovascular medicine · 2026Review
- Big Data and Trustworthy AI for Heart Failure: A Review.Circulation. Heart failure · 2026Review
- Real-World Data Needs Real-World Doctors: When Automation Advances Faster Than Clinical Workflow.Journal of participatory medicine · 2026Article
- Redefining endpoints in heart failure clinical trials: the emerging role of wearable technologies in contemporary trial design.Heart failure reviews · 2025Review
- Advancements in Sensor Technology for Monitoring and Management of Chronic Coronary Syndrome.Sensors (Basel, Switzerland) · 2025Review
- Transparent and Robust Artificial Intelligence-Driven Electrocardiogram Model for Left Ventricular Systolic Dysfunction.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence and ECG: A New Frontier in Cardiac Diagnostics and Prevention.Biomedicines · 2025Review
- Artificial intelligence-enhanced six-lead portable electrocardiogram device for detecting left ventricular systolic dysfunction: a prospective single-centre cohort study.European heart journal. Digital health · 2025Article
- AI-Enabled Smartwatch ECG: A Feasibility Study for Early Prediction and Prevention of Heart Failure Rehospitalization.JACC. Basic to translational science · 2025Article
- Efficacy of AI Models in Detecting Heart Failure Using ECG Data: A Systematic Review and Meta-Analysis.Cureus · 2025Review
- Heart disease detection using an acceleration-deceleration curve-based neural network with consumer-grade smartwatch data.Heliyon · 2024Article
- Deep learning algorithm for predicting left ventricular systolic dysfunction in atrial fibrillation with rapid ventricular response.European heart journal. Digital health · 2024Article
- Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future.International journal of heart failure · 2024Review
- A Compact Dual-Band Millimeter Wave Antenna for Smartwatch and IoT Applications with Link Budget Estimation.Sensors (Basel, Switzerland) · 2023Article
- Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy.NPJ digital medicine · 2023Article
- Current and Future Use of Artificial Intelligence in Electrocardiography.Journal of cardiovascular development and disease · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundWe developed and validated an artificial intelligence (AI)-enabled smartwatch ECG to detect heart failure-reduced ejection fraction (HFrEF).
methodsThis was a cohort study involving two hospitals (A and B). We developed the AI in two steps. First, we developed an AI model (ECGT2T) to synthesize ten-lead ECG from the asynchronized 2-lead ECG (Lead I and II). ECGT2T is a deep learning model based on a generative adversarial network, which translates source ECGs to reference ECGs by learning styles of the reference ECGs. For this, we included adult patients aged ≥18 years from hospital A with at least one digitally stored 12-lead ECG. Second, we developed an AI model to detect HFrEF using a 10 s 12-lead ECG. The AI model was based on convolutional neural network. For this, we included adult patients who underwent ECG and echocardiography within 14 days. To validate the AI, we included adult patients from hospital B who underwent two-lead smartwatch ECG and echocardiography on the same day. The AI model generates a 10 s 12-lead ECG from a two-lead smartwatch ECG using ECGT2T and detects HFrEF using the generated 12-lead ECG.
resultsWe included 137,673 patients with 458,745 ECGs and 38,643 patients with 88,900 ECGs from hospital A for developing the ECGT2T and HFrEF detection models, respectively. The area under the receiver operating characteristic curve of AI for detecting HFrEF using smartwatch ECG was 0.934 (95% confidence interval 0.913-0.955) with 755 patients from hospital B. The sensitivity, specificity, positive predictive value, and negative predictive value of AI were 0.897, 0.860, 0.258, and 0.994, respectively.
conclusionsAn AI-enabled smartwatch 2-lead ECG could detect HFrEF with reasonable performance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.