The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review
Open accessAbstract
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.
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Cites
Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association
Benjamin, E. J., Muntner, P., Alonso, A., Bittencourt, M. S., Callaway, C. W., Carson, A. P., Chamberlain, A. M., Chang, A. R., Cheng, S., Das, S. R., et al. (2019) · Circulation · doi:10.1161/cir.0000000000000659 · Cited by 9,036
Burden of valvular heart diseases: a population-based study
Nkomo, V. T., Gardin, J. M., Skelton, T. N., Gottdiener, J. S., Scott, C. G., & Enriquez-Sarano, M. (2006) · The Lancet · doi:10.1016/s0140-6736(06)69208-8 · Cited by 4,881
Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation
Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., Balasubramanian, V., Russo, A. M., Rajmane, A., Cheung, L., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1901183 · Cited by 1,917
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Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365 · cited 2×
“Finally, the use of AI and wearable devices in the detection diagnosis and prediction of CVD will require wider adoption by clinicians and patients if they are to become more mainstream in the field [8,9,18,65,109,110,116].”
BackgroundMethodAutomatic diagnosis of the 12-lead ECG using a deep neural network
Ribeiro, A. H., Ribeiro, M. H., Paixão, G. M. M., Oliveira, D. M., Gomes, P. R., Canazart, J. A., Ferreira, M. P. S., Andersson, C. R., Macfarlane, P. W., Meira, W., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15432-4 · Cited by 894
Smart wearable devices in cardiovascular care: Where we are and how to move forward
Bayoumy, K., Gaber, M., Elshafeey, A., Mhaimeed, O., Dineen, E. H., Marvel, F. A., Martin, S. S., Muse, E. D., Turakhia, M. P., Tarakji, K. G., et al. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-021-00522-7 · Cited by 829
Steinhubl, S. R., Waalen, J., Edwards, A. M., Ariniello, L. M., Mehta, R. R., Ebner, G. S., Carter, C., Baca-Motes, K., Felicione, E., Sarich, T., et al. (2018) · JAMA · doi:10.1001/jama.2018.8102 · Cited by 539 · cited 2×
“The continuous monitoring of physiological parameters then offers a potential solution to more timely access to CVD-based healthcare [9,10,14,47,50,52,53,59,66].”
BackgroundMobile Photoplethysmographic Technology to Detect Atrial Fibrillation
Guo, Y., Wang, H., Zhang, H., Liu, T., Liang, Z., Xia, Y., Yan, L., Xing, Y., Shi, H., Li, S., et al. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.08.019 · Cited by 489
Smartwatch Algorithm for Automated Detection of Atrial Fibrillation
Bumgarner, J. M., Lambert, C. T., Hussein, A. A., Cantillon, D. J., Baranowski, B., Wolski, K., Lindsay, B. D., Wazni, O. M., & Tarakji, K. G. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.03.003 · Cited by 446
Cited by
Hybrid multimodal wearable sensors for comprehensive health monitoring
Mahato, K., Saha, T., Ding, S., Sandhu, S. S., Chang, A.-Y., & Wang, J. (2024) · Nature Electronics · doi:10.1038/s41928-024-01247-4 · Cited by 404
Self-citationWearable Devices in Cardiovascular Medicine
Hughes, A., Shandhi, M. M. H., Master, H., Dunn, J., & Brittain, E. (2023) · Circulation Research · doi:10.1161/circresaha.122.322389 · Cited by 291
Badidi, E. (2023) · Future Internet · doi:10.3390/fi15110370 · Cited by 113
Metadata
- Authors
- Huang, J.-D., Wang, J., Ramsey, E., Leavey, G., Chico, T. J. A., & Condell, J.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-10-20
- Volume
- 22
- Issue
- 20
- Pages
- 8002
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W4306964579
- Cited by
- 175
- References
- 22
- FWCI
- 24.12
- Type
- journal article
- Language
- en
- Open access
- gold
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- Huang, J.-D., Wang, J., Ramsey, E., Leavey, G., Chico, T. J. A., & Condell, J. (2022). Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review. Sensors. https://doi.org/10.3390/s22208002
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