School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK.
Department of Global Business & Enterprise, Ulster University at Magee, Londonderry BT48 7JL, UK.
Sensors (Basel). 2022 Oct 20;22(20):8002. doi: 10.3390/s22208002.
Cardiovascular disease (CVD) is the world's leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.
心血管疾病 (CVD) 是全球主要的致死原因。人们对使用人工智能 (AI) 分析新型传感器（如可穿戴设备）的数据非常感兴趣，以期能够更早、更准确地预测和诊断心脏病。融合 AI 和感测设备的数字健康技术可能有助于疾病预防，并减少全球 CVD 导致的大量发病率和死亡率。在本综述中，我们确定并描述了数字健康在 CVD 中的应用的最新进展，重点介绍了通过由可穿戴设备收集的数据驱动的 AI 模型进行 CVD 检测、诊断和预测的 AI 方法。我们总结了关于可穿戴设备和 AI 在心血管疾病诊断中的应用的文献，接着详细描述了应用于从可穿戴设备等传感器获取的数据进行建模和预测的主要 AI 方法。我们讨论了 AI 算法和模型以及临床应用，并发现 AI 和基于机器学习的方法在预测心血管事件方面优于传统或常规统计方法。然而，需要进一步研究评估此类算法在现实世界中的适用性。此外，需要提高可穿戴设备数据的准确性并更好地管理其应用。最后，我们讨论了将这些技术引入常规医疗保健可能面临的挑战。
