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Artificial-Intelligence-Enhanced Mobile System for Cardiovascular Health Management

机译：用于心血管健康管理的人工智能增强的移动系统

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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.
机译：患有心血管疾病的患者的数量在世界上迅速增加。因此，现有临床医生的工作量正在增加。然而，心血管临床医生的数量正在下降。在本文中，我们的目的是设计一种移动和自动系统，以提高患者心血管健康管理的能力，同时降低临床医生的工作量。我们的系统包括基于事物互联网（IOT）和人工智能（AI）技术的最近进步的硬件和云软件设备。小型硬件设备旨在从人体收集高质量的心电图（ECG）数据。开发并部署了一种新型的深建云服务，实现了自动和准确的心血管疾病检测。支持20种类型的诊断项目，包括窦性心律，心律失常和Bradyarrhalthmia。实验结果表明了我们系统的有效性。我们的硬件设备可以通过在接收器操作特性曲线（ROC-AUC）得分下，通过删除高/低频失真和反向引线检测来保证高质量的ECG数据。我们深入的基于学习的云服务支持20种类型的诊断项目，其中17项具有超过0.98 Roc-AUC的分数。对于现实世界的应用，该系统已在中国20个省份的大约20,000名用户使用。因此，使用此服务，我们可以通过微信迷你程序平台上的轻量级移动应用来实现主动和被动健康管理。我们认为它可以对世界上的心血管健康管理产生更广泛的影响。

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