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Futuristic biosensors for cardiac health care: an artificial intelligence approach
Open accessAbstract
Biosensor-based devices are pioneering in the modern biomedical applications and will be the future of cardiac health care. The coupling of artificial intelligence (AI) for cardiac monitoring-based biosensors for the point of care (POC) diagnostics is prominently reviewed here. This review deciphers the most significant machine-learning algorithms for the futuristic biosensors along with the internet of things, computational techniques and microchip-based essential cardiac biomarkers for real-time health monitoring and improving patient compliance. The present review also discusses the recently developed cardiac biosensors along with technical strategies involved in their mechanism of working and their applications in healthcare. Additionally, it provides a key for the ontogeny of an effective and supportive hierarchical protocol for clinical decision-making about personalized medicine through combinatory information analysis, and integrated multidisciplinary AI approaches.
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“As the biosensors are still in the development stage in monitoring biochemical signals, the technology is yet to sense biological biomarkers continuously, and they need to achieve higher sensitivity, selectivity, and accuracy towards specific biomolecule sensing [14,17].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [17]
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“DL algorithms have been proven effective in the early detection and preventing of chronic diseases, like diabetes and cardiovascular disease [17,195].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [17]
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“Integration with smartphones and mobile devices enhances the accessibility of wearable ECG monitors for real-time monitoring, data storage, and cloud-based analytics, enabling seamless integration into telemedicine and remote patient monitoring systems [4,16,17].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [17]
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Cites
A Real-Time QRS Detection Algorithm
Pan, J., & Tompkins, W. J. (1985) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1985.325532 · Cited by 7,855
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310
Electrochemical biosensors: recommended definitions and classification
Thévenot, D. R., Tóth, K., Durst, R. A., & Wilson, G. S. (2001) · Biosensors and Bioelectronics · doi:10.1016/s0956-5663(01)00115-4 · Cited by 1,858
Machine learning and data mining methods in diabetes research
Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.12.005 · Cited by 1,445
Wearable Chemical Sensors: Present Challenges and Future Prospects
Bandodkar, A. J., Jeerapan, I., & Wang, J. (2016) · ACS Sensors · doi:10.1021/acssensors.6b00250 · Cited by 778 · cited 1×
“These days, scientists are quite interested and engaged in developing of the novel, smart and advanced devices such that more specific, sensitive and stable biosensors for theranostics purposes can be invented (Bandodkar et al. 2016).”
BackgroundEvolving support vector machines using fruit fly optimization for medical data classification
Shen, L., Chen, H., Yu, Z., Kang, W., Zhang, B., Li, H., Yang, B., & Liu, D. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.01.002 · Cited by 539
Multiplexed Point-of-Care Testing – xPOCT
Dincer, C., Bruch, R. C., Kling, A., Dittrich, P. S., & Urban, G. A. (2017) · Trends in biotechnology · doi:10.1016/j.tibtech.2017.03.013 · Cited by 508
Machine Learning and Decision Support in Critical Care
Johnson, A. E. W., Ghassemi, M. M., Nemati, S., Niehaus, K. E., Clifton, D. A., & Clifford, G. D. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2501978 · Cited by 459
Biosensors for cardiac biomarkers detection: A review
Qureshi, A., Gürbüz, Y., & Niazi, J. H. (2012) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2012.05.077 · Cited by 412
Nanomaterial-based devices for point-of-care diagnostic applications
Quesada-González, D., & Merkoçi, A. (2018) · Chemical Society Reviews · doi:10.1039/c7cs00837f · Cited by 407
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“With integrated AI, biosensors and related point-of-care testing (POCT) systems can diagnose cardiovascular diseases in the early stage [75].”
BackgroundThe emergence of AI-based wearable sensors for digital health technology: a review
Shajari, S., Kuruvinashetti, K., Komeili, A., & Sundararaj, U. (2023) · Sensors · doi:10.3390/s23239498 · Cited by 535
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“[83] In addition to exploiting greater speed and computational efficiency, AI-based health products and tools will likely incorporate greater understanding of biology in their formulations in the future.”
BackgroundArtificial intelligence: a survey on evolution, models, applications and future trends
Lu, Y. (2019) · Journal of Management Analytics · doi:10.1080/23270012.2019.1570365 · Cited by 641
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Metadata
- Authors
- Vashistha, R., Dangi, A. K., Kumar, A., Chhabra, D., & Shukla, P.
- Affiliations
- Maharshi Dayanand University (IN)
- Corresponding author
- Shukla, Pratyoosh
- Venue
- 3 Biotech
- Published
- 2018-08
- Volume
- 8
- Issue
- 8
- Pages
- 358-358
- ISSN
- 2190-572X (ISSN-L)
- 2190-5738
- PMC
- PMC6081842
- OpenAlex
- W2885820762
- Cited by
- 113
- References
- 74
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 18 · 2023: 17 · 2022: 19 · 2021: 12 · 2020: 18 · 2019: 8
- FWCI
- 0.77
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- 3 Biotech (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- artificial intelligence, machine learning, point-of-care testing, cardiac monitoring, Internet of Things, cardiac biomarkers, real-time health monitoring, medication adherence, personalized medicine, clinical decision-making, wearable sensors
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