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Machine learning assisted malaria detection using photonic crystal fibre optical sensors
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  • Published: 11 February 2026

Machine learning assisted malaria detection using photonic crystal fibre optical sensors

  • Mohammad Abdullah-Al-Shafi1,
  • Shuvo Sen2 &
  • Mashiyat Mubassera3 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Optics and photonics
  • Physics

Abstract

This study presents a photonic crystal fibre based optical sensor with a simple and practical architecture for malaria detection using refractive index variations in red blood cells. The proposed sensor consists of a hollow central core surrounded by five concentric layers of heptagonal cladding, enabling efficient sample infiltration and enhanced light matter interaction. This configuration provides high sensitivity to subtle refractive index changes while maintaining structural simplicity suitable for real world diagnostic deployment. Refractive index variations corresponding to different Plasmodium developmental stages are converted into distinct wavelength shifts, allowing reliable discrimination between ring trophozoite and schizont stages. The sensor operates over a refractive index range of 1.373 to 1.402, closely matching the optical properties of malaria infected red blood cells. Numerical results demonstrate high relative sensitivity of 97.45% for healthy cells, 96.89% for the ring stage, 96.22% for the trophozoite stage, and 95.45% for the schizont stage. Optical confinement losses remain extremely low, on the order of 10–8 dB per metre at an operating frequency of 2.2 THz. These results highlight the potential of photonic crystal fibre sensors as a cost effective and high-performance platform for early malaria detection and broader biomedical sensing applications.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

The authors would like to thank the anonymous reviewers for reviewing the paper.

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Author information

Authors and Affiliations

  1. Faculty of Science and Engineering, Southern Cross University, Gold Coast, Australia

    Mohammad Abdullah-Al-Shafi

  2. Department of Information and Communication Technology (ICT), Mawlana Bhashani Science and Technology University (MBSTU), Santosh, Tangail, 1902, Bangladesh

    Shuvo Sen

  3. QC Analyst II, Ortec Inc, Piedmont, SC, USA

    Mashiyat Mubassera

Authors
  1. Mohammad Abdullah-Al-Shafi
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  2. Shuvo Sen
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  3. Mashiyat Mubassera
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Contributions

Mohammad Abdullah-Al-Shafi: Conceptualisation, Data curation, Formal analysis, Investigation, Writing - original draft. Shuvo Sen: Methodology, Resources, Software analysis, Validation, Visualisation. Mashiyat Mubassera: Methodology, Investigation, Resources, Visualisation.

Corresponding author

Correspondence to Mohammad Abdullah-Al-Shafi.

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Abdullah-Al-Shafi, M., Sen, S. & Mubassera, M. Machine learning assisted malaria detection using photonic crystal fibre optical sensors. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37709-2

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  • Received: 29 September 2025

  • Accepted: 23 January 2026

  • Published: 11 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-37709-2

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Keywords

  • Photonic crystal fibre
  • Malaria
  • Biosensor
  • Sensitivity
  • Refractive index
  • Terahertz
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