ArticleSensors (Basel, Switzerland)2021
Artificial-Intelligence-Enhanced Mobile System for Cardiovascular Health Management.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it, 56 citations in OpenAlex.
- E-Cardiac Care: A Comprehensive Systematic Literature Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review.JMIR mHealth and uHealth · 2022Pooled it
- Practical Lessons on 12-Lead ECG Classification: Meta-Analysis of Methods From PhysioNet/Computing in Cardiology Challenge 2020.Frontiers in physiology · 2021Pooled it
- Multicenter real world validation of on device single lead ECG parameters for remote cardiac assessment.NPJ cardiovascular health · 2026Article
- Integrating Artificial Intelligence with Wearable Sensors for Advanced Health Monitoring and Diagnosis.Biosensors · 2026Review
- Artificial Intelligence-Enhanced Electrocardiogram for Longitudinal Evaluation of Right Ventricular Dysfunction After Acute Pulmonary Embolism: Rational and Design of CURES-CARE Study.Pulmonary circulation · 2026Article
- Empowering chronic disease management with smart healthcare in China: a policy effectiveness evaluation by PMC index model.Frontiers in public health · 2026Article
- ECGomics: An Open Platform for AI-ECG Digital Biomarker Discovery.Health data science · 2026Article
- AI-Driven Real-Time Monitoring of Cardiovascular Conditions With Wearable Devices: Scoping Review.JMIR mHealth and uHealth · 2025Article
- Long-Term Outcomes of Digital Cardiac Rehabilitation.JACC. Asia · 2025Review
- Reliability and validity of a novel single-lead portable electrocardiogram device for pregnant women: a comparative study.BMC medical informatics and decision making · 2025Article
- Screening for severe coronary stenosis in patients with apparently normal electrocardiograms based on deep learning.BMC medical informatics and decision making · 2024Article
- Screening Tool for Paroxysmal Atrial Fibrillation Based on a Deep-Learning Algorithm Using Printed 12-Lead Electrocardiographic Records during Sinus Rhythm.Reviews in cardiovascular medicine · 2024Article
- Post-discharge health education for patients with enterostomy: A nationwide interventional study.Journal of global health · 2023Article
- An Open-Source Graphical User Interface-Embedded Automated Electrocardiogram Quality Assessment: A Balanced Class Representation Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review.Sensors (Basel, Switzerland) · 2023Review
- Estimating critical values from electrocardiogram using a deep ordinal convolutional neural network.BMC medical informatics and decision making · 2022Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score.Journal of medical Internet research · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
The number of patients with cardiovascular diseases is rapidly increasing in the world. The workload of existing clinicians is consequently increasing. However, the number of cardiovascular clinicians is declining. In this paper, we aim to design a mobile and automatic system to improve the abilities of patients' cardiovascular health management while also reducing clinicians' workload. Our system includes both hardware and cloud software devices based on recent advances in Internet of Things (IoT) and Artificial Intelligence (AI) technologies. A small hardware device was designed to collect high-quality Electrocardiogram (ECG) data from the human body. A novel deep-learning-based cloud service was developed and deployed to achieve automatic and accurate cardiovascular disease detection. Twenty types of diagnostic items including sinus rhythm, tachyarrhythmia, and bradyarrhythmia are supported. Experimental results show the effectiveness of our system. Our hardware device can guarantee high-quality ECG data by removing high-/low-frequency distortion and reverse lead detection with 0.9011 Area Under the Receiver Operating Characteristic Curve (ROC-AUC) score. Our deep-learning-based cloud service supports 20 types of diagnostic items, 17 of them have more than 0.98 ROC-AUC score. For a real world application, the system has been used by around 20,000 users in twenty provinces throughout China. As a consequence, using this service, we could achieve both active and passive health management through a lightweight mobile application on the WeChat Mini Program platform. We believe that it can have a broader impact on cardiovascular health management in the world.
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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.