STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Nanoscience, NanoEngineering & Applications Original Research

Nanotechnology-Enhanced Wearable Biosensors for Liver Disease Detection: Integration with AI for Predictive Analytics

  1. C.Sunil Indira Institute of Technology & Sciences

Abstract

The worldwide health burden of liver diseases is substantial, and effective treatment and management depend heavily on early detection. This study investigates the integration of nanotechnology-enhanced wearable biosensors with artificial intelligence (AI) techniques for predictive analytics in liver disease detection. The construction of extremely selective and sensitive biosensors that can identify a variety of biomarkers linked to liver illnesses has been made possible via nanotechnology. These nanotechnology-based biosensors can be integrated into wearable devices, allowing for continuous and noninvasive monitoring of relevant biomarkers. By combining the high-quality data collected from these wearable biosensors with AI-driven predictive analytics, patterns and early signs of liver diseases can be detected, enabling timely interventions and personalized treatment strategies. This study presents a comprehensive review of the current state-of-the-art in nanotechnology-enhanced wearable biosensors for liver disease detection, their integration with AI techniques for predictive analytics, and potential applications in personalized healthcare. It also talks about the difficulties and potential paths for future multidisciplinary study in this area.

Keywords

References (16)

  1. Eswaran U, Eswaran V, Murali K, Eswaran V. Healthcare Smart Sensors. Advances in Medical Diagnosis, Treatment, and Care. 2024:24-48. doi:10.4018/979-8-3693-3679-3.ch002
  2. Thwala LN, Ndlovu SC, Mpofu KT, Lugongolo MY, Mthunzi-Kufa P. Nanotechnology-Based Diagnostics for Diseases Prevalent in Developing Countries: Current Advances in Point-of-Care Tests. Nanomaterials. 2023;13(7):1247. doi:10.3390/nano13071247
  3. Ushaa SM, Madhavilatha M, Rao GM. Design and analysis of nanowire sensor array for prostate cancer detection. International Journal of Nano and Biomaterials. 2011;3(3):239. doi:10.1504/ijnbm.2011.042132
  4. Zhang Y, Ong CM, Lynch K, Waksman J, Wu AHB. Serum microRNA‑122 for assessment of acute liver injury in patients with extensive skeletal muscle damage. Laboratory Medicine. 2024;55(5):609-614. doi:10.1093/labmed/lmae022
  5. Mahmudin L, Wulandani R, Riswan M, Kurnia Sari E, Dwi Jayanti P, Syahrul Ulum M, et al. Silver nanoparticles-based localized surface plasmon resonance biosensor for Escherichia coli detection. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2024;311:123985. doi:10.1016/j.saa.2024.123985
  6. Bhatia D, Paul S, Acharjee T, Ramachairy SS. Biosensors and their widespread impact on human health. Sensors International. 2024;5:100257. doi:10.1016/j.sintl.2023.100257
  7. Eswaran U, Madhavilatha M, Murthy J, Ganji MR. Expert system design for disease detection using pattern recognition techniques. In: Arabnia HR, Mun Y, editors. Proceedings of the 2008 International Conference on Artificial Intelligence, ICAI 2008, July 14-17, 2008, Las Vegas, Nevada, USA. 2 Volumes (includes the 2008 International Conference on Machine Learning; Models, Technologies and Applications). CSREA Press; 2008. p. 902-906.
  8. Eswaran U, Eswaran V, Sudharshan VB. Human like biosensor disease simulator, disease analyzer and drug delivery system. In: 2013 IEEE Conference on Information & Communication Technologies; 2013 Apr 11-12; Thuckalay, India. 2013. p. 1033-1038. doi:10.1109/CICT.2013.
  9. Rakhshaninejad M, Fathian M, Shirkoohi R, Barzinpour F, Gandomi AH. Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach. BMC Bioinformatics. 2024;25(1). doi:10.1186/s12859-024-05657-1
  10. Jawad BN, Shaker SM, Altintas I, Eugen-Olsen J, Nehlin JO, Andersen O, et al. Development and validation of prognostic machine learning models for short- and long-term mortality among acutely admitted patients based on blood tests. Scientific Reports. 2024;14(1). doi:10.1038/s41598-024-56638-6
  11. Mao B, Liu S, Zhu S, Wu F, Yuan W, Yan Y, Wang B. The Janus face of serotonin: Regenerative promoter and chronic liver disease aggravator. Heliyon. 2024;10. doi:10.1016/j.heliyon.2024. PubMed: 38756588.
  12. Alrawi DI, Khalaf M, Jamaludeen MK. Drug-Induced Liver Injury: Mechanisms and Counteracting Herbal-Derived Products. Iraqi Journal of Pharmacy. 2024;21(2):45-56. doi:10.33899/iraqij.p.2024.145444.1078
  13. Thakur S, Kumar V, Das R, Sharma V, Mehta DK. Biomarkers of Hepatic Toxicity: An Overview. Current Therapeutic Research. 2024;100:100737. doi:10.1016/j.curtheres.2024.100737
  14. Lala V, Zubair M, Minter DA. Liver Function Tests. Treasure Island (FL): StatPearls Publishing; 2024.
  15. Eswaran U, Khang A, Eswaran V. Applying Machine Learning for Medical Image Processing. Advances in Medical Technologies and Clinical Practice. 2023:137-154. doi:10.4018/979-8-3693-0876-9.ch009
  16. Eswaran U, Khang A. Augmented reality (AR) and virtual reality (VR) technologies in surgical operating systems. In: Khang A, editor. AI and IoT technology and applications for smart healthcare systems. Boca Raton, FL: Auerbach Publications; 2024.
Support