Article Details

Abstract

This study aims to construct and implement an early diagnosis and warning system for Atrial Fibrillation (AF) patients based on a medical consortium framework, addressing issues such as insufficient ECG diagnostic resources in primary healthcare institutions, delays in manual analysis, and information transmission gaps. The system acquires raw data by interfacing with third-party ECG devices, integrates an AI-assisted diagnostic module based on Convolutional Neural Networks (CNNs) to automatically identify abnormal ECG signals such as AF and atrial flutter, and triggers an alert mechanism via a rule engine to send real-time notifications via SMS and WeChat mini programs to specialists at higher-level hospitals. Physicians can review AI-generated conclusions and confirm diagnoses through the platform, forming a closed-loop management process of “community preliminary screening—AI analysis—alert notification—specialist confirmation—follow-up intervention.”

Keywords

medical consortium; Atrial Fibrillation (AF); early diagnosis; early warning system; ECG signal

References

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About the authors

Wang Lei (1987.04-), male, Han ethnicity, from Zhoushan, Zhejiang, is a senior engineer and a master's student. His research interests include medical artificial intelligence and health big data.