Application of Artificial Intelligence and Machine Learning in Surface Plasmon Resonance Sensor for Blood Sample Diagnosis

Authors

  • Shalini Srivastava Department of Electronics & Communication Engineering, Institute of Engineering & Technology, Dr. Rammanohar Lohia Avadh University, Ayodhya, Uttar Pradesh, India Author
  • Shambhavi Mudra Shukla Department of Electronics & Communication Engineering, Institute of Engineering & Technology, Dr. Rammanohar Lohia Avadh University, Ayodhya, Uttar Pradesh, India Author
  • Parimal Tiwari Department of Electronics & Communication Engineering, Institute of Engineering & Technology, Dr. Rammanohar Lohia Avadh University, Ayodhya, Uttar Pradesh, India Author
  • Ramesh Mishra Department of Electronics & Communication Engineering, Institute of Engineering & Technology, Dr. Rammanohar Lohia Avadh University, Ayodhya, Uttar Pradesh, India Author
  • Sachin Singh Faculty of Physical Sciences, Shri Ramswaroop Memorial University, Lucknow-Deva Road, Barabanki, Uttar Pradesh, India Author

DOI:

https://doi.org/10.32628/IJSRST251278

Keywords:

Surface Plasmon, Artificial intelligence, Machine learning, Algorithms, Nickel

Abstract

In recent years, various optical biosensor technologies are used by researchers to evaluate the conformational changes of biomolecules and their molecular interactions in a wide range of biomedical diagnostic and analysis operations. One of the most widely used techniques among several optical biosensors is surface plasmon resonance biosensors, which are used for label-free, real-time monitoring with exceptional accuracy and precision. In this study, quick and extremely sensitive SPR biosensors based on artificial intelligence (AI) programmed and machine learning (ML) are proposed to optimize the concentration of hemoglobin, plasma, and platelets in blood cells. Prism N-FK51A, graphene, nickel, potassium niobate, silver metal, and glass make up the suggested SPR biosensor device. Kretschmann configuration is the basis for the device structure, and attenuated total reflection (ATR) is the basis for the device function. Blood samples have undergone numerical analysis of the performance characteristics, including angular sensitivity, quality factor, detection accuracy, limit of detection, and electric field. The suggested surface plasmon resonance biosensor can be used for blood sample diagnosis, which opens the new path in biomedical domain.

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References

Buk Cardoso, Lucas, Vanderlei Cunha Parro, Stela Verzinhasse Peres, Maria Paula Curado, Gisele Aparecida Fernandes, Victor Wünsch Filho, and Tatiana Natasha Toporcov. "Machine learning for predicting survival of colorectal cancer patients." Scientific reports 13, no. 1 (2023): 8874. DOI: https://doi.org/10.1038/s41598-023-35649-9

Schinkel, Michiel, Frank C. Bennis, Anneroos W. Boerman, W. Joost Wiersinga, and Prabath WB Nanayakkara. "Embracing cohort heterogeneity in clinical machine learning development: a step toward generalizable models." Scientific reports 13, no. 1 (2023): 8363. DOI: https://doi.org/10.1038/s41598-023-35557-y

Koh, Dow-Mu, Nickolas Papanikolaou, Ulrich Bick, Rowland Illing, Charles E. Kahn Jr, Jayshree Kalpathi-Cramer, Celso Matos et al. "Artificial intelligence and machine learning in cancer imaging." Communications Medicine 2, no. 1 (2022): 133. DOI: https://doi.org/10.1038/s43856-022-00199-0

Lambert, Sophie Isabelle, Murielle Madi, Saša Sopka, Andrea Lenes, Hendrik Stange, Claus-Peter Buszello, and Astrid Stephan. "An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals." NPJ Digital Medicine 6, no. 1 (2023): 111. DOI: https://doi.org/10.1038/s41746-023-00852-5

Zador, Anthony, Sean Escola, Blake Richards, Bence Ölveczky, Yoshua Bengio, Kwabena Boahen, Matthew Botvinick et al. "Catalyzing next-generation artificial intelligence through neuroai." Nature communications 14, no. 1 (2023): 1597. DOI: https://doi.org/10.1038/s41467-023-37180-x

Gerke, Sara, Boris Babic, Theodoros Evgeniou, and I. Glenn Cohen. "The need for a system view to regulate artificial intelligence/machine learning-based software as medical device." NPJ digital medicine 3, no. 1 (2020): 53. DOI: https://doi.org/10.1038/s41746-020-0262-2

Sun, Yi-Kang, Bo-Yang Zhou, Yao Miao, Yi-Lei Shi, Shi-Hao Xu, Dao-Ming Wu, Lei Zhang et al. "Three-dimensional convolutional neural network model to identify clinically significant prostate cancer in transrectal ultrasound videos: a prospective, multi-institutional, diagnostic study." EClinicalMedicine 60 (2023). DOI: https://doi.org/10.1016/j.eclinm.2023.102027

Homma, Yasuhiro, Shun Ito, Xu Zhuang, Tomonori Baba, Kazutoshi Fujibayashi, Kazuo Kaneko, Yu Nishiyama, and Muneaki Ishijima. "Artificial intelligence for distinguishment of hammering sound in total hip arthroplasty." Scientific Reports 12, no. 1 (2022): 9826. DOI: https://doi.org/10.1038/s41598-022-14006-2

Stafford, Imogen S., Melina Kellermann, Enrico Mossotto, Robert Mark Beattie, Ben D. MacArthur, and Sarah Ennis. "A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases." NPJ digital medicine 3, no. 1 (2020): 30. DOI: https://doi.org/10.1038/s41746-020-0229-3

Fanconi, Claudio, Anne de Hond, Dylan Peterson, Angelo Capodici, and Tina Hernandez-Boussard. "A Bayesian approach to predictive uncertainty in chemotherapy patients at risk of acute care utilization." EBioMedicine 92 (2023). DOI: https://doi.org/10.1016/j.ebiom.2023.104632

Hamadani, Ambreen, Nazir A. Ganai, Syed Mudasir, Syed Shanaz, Safeer Alam, and Ishraq Hussain. "Comparison of artificial intelligence algorithms and their ranking for the prediction of genetic merit in sheep." Scientific Reports 12, no. 1 (2022): 18726. DOI: https://doi.org/10.1038/s41598-022-23499-w

Jia, Zhenge, Jianxu Chen, Xiaowei Xu, John Kheir, Jingtong Hu, Han Xiao, Sui Peng, Xiaobo Sharon Hu, Danny Chen, and Yiyu Shi. "The importance of resource awareness in artificial intelligence for healthcare." Nature Machine Intelligence 5, no. 7 (2023): 687-698. DOI: https://doi.org/10.1038/s42256-023-00670-0

Mondal, Himadri Shekhar, Khandaker Asif Ahmed, Nick Birbilis, and Md Zakir Hossain. "Machine learning for detecting DNA attachment on SPR biosensor." Scientific Reports 13, no. 1 (2023): 3742. DOI: https://doi.org/10.1038/s41598-023-29395-1

Sharma, Vipin, Lalit Kumar Dwivedi, Sachin Singh, and Ganga Ram Mishra. "Glucose level monitoring in human blood samples by surface plasmon resonance sensor using cerium oxide and black phosphorus nanomaterials." Journal of Optics 53, no. 4 (2024): 3683-3694. DOI: https://doi.org/10.1007/s12596-023-01597-w

Almawgani, Abdulkarem HM, Malek G. Daher, Sofyan A. Taya, Melad M. Olaimat, Adam RH Alhawari, and Ilhamic Colak. "Detection of blood plasma concentration theoretically using SPR-based biosensor employing black phosphor layers and different metals." Plasmonics 17, no. 4 (2022): 1751-1764. DOI: https://doi.org/10.1007/s11468-022-01662-3

Singh, Shivam, Adarsh Chandra Mishra, Sachin Singh, Pooja Lohia, D. K. Dwivedi, and Sapana Yadav. "Theoretical study of perovskite nano material based surface plasmon resonance biosensor for cancers cell detection." Optik 289 (2023): 171259. DOI: https://doi.org/10.1016/j.ijleo.2023.171259

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Published

07-07-2025

Issue

Vol. 12 No. 4 (2025): July-August

Section

Research Articles

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