Artificial Intelligence, Data Sensors and Interconnectivity: Future Opportunities for Heart Failure

Author:

Bachtiger Patrik1,Plymen Carla M2,Pabari Punam A2,Howard James P3,Whinnett Zachary I2,Opoku Felicia4,Janering Stephen4,Faisal Aldo A5,Francis Darrel P3,Peters Nicholas S3

Affiliation:

1. Imperial Centre for Cardiac Engineering, National Heart and Lung Institute, Imperial College London, UK

2. Department of Cardiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, UK

3. Imperial Centre for Cardiac Engineering, National Heart and Lung Institute, Imperial College London, UK; Department of Cardiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, UK

4. IT Department, Imperial College Healthcare NHS, London, UK

5. Departments of Bioengineering and Computing, Data Science Institute, Imperial College London, UK

Abstract

A higher proportion of patients with heart failure have benefitted from a wide and expanding variety of sensor-enabled implantable devices than any other patient group. These patients can now also take advantage of the ever-increasing availability and affordability of consumer electronics. Wearable, on- and near-body sensor technologies, much like implantable devices, generate massive amounts of data. The connectivity of all these devices has created opportunities for pooling data from multiple sensors – so-called interconnectivity – and for artificial intelligence to provide new diagnostic, triage, risk-stratification and disease management insights for the delivery of better, more personalised and cost-effective healthcare. Artificial intelligence is also bringing important and previously inaccessible insights from our conventional cardiac investigations. The aim of this article is to review the convergence of artificial intelligence, sensor technologies and interconnectivity and the way in which this combination is set to change the care of patients with heart failure.

Publisher

Radcliffe Group Ltd

Subject

Cardiology and Cardiovascular Medicine

Reference78 articles.

1. WHO. The top 10 causes of death. 2018. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death (accessed 17 March 2020).

2. Conrad N, Judge A, Tran J, et al. Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals. Lancet 2018;391:572–80. https://doi.org/10.1016/S0140-6736(17)32520-5; PMID: 29174292.

3. De Mauro A, Greco M, Grimaldi M. A formal definition of big data based on its essential features. Library Review 2016;65:122–35. https://doi.org/10.1108/LR-06-2015-0061.

4. Ting DSW, Liu Y, Burlina P, et al. AI for medical imaging goes deep. Nat Med 2018;24:539–40. https://doi.org/10.1038/s41591-018-0029-3; PMID: 29736024.

5. Haenssle HA, Fink C, Schneiderbauer R, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 2018;29:1836–42. https://doi.org/10.1093/annonc/mdy166; PMID: 29846502.

Cited by 28 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3