Huang, J.-D., Wang, J., Ramsey, E. et al. (3 more authors) (2022) Applying Artificial Intelligence to wearable sensor data to diagnose and predict cardiovascular disease: a review. Sensors, 22 (20). 8002. ISSN 1424-8220
Abstract
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
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | cardiovascular disease; wearable sensor devices; artificial intelligence (AI); machine learning (ML); deep learning (DL) |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Sheffield Teaching Hospitals The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Infection, Immunity and Cardiovascular Disease The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Infection and Immunity (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Nov 2022 14:27 |
Last Modified: | 08 Nov 2022 14:27 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/s22208002 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193055 |