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Integration of computational optics and machine learning for optimized SPR-based carcinoembryonic antigen detection
Md Al Amin Islam Utshob1, Maymona Binte Juwel1, M M Atiqur Rahman1
1Department of Electrical & Electronic Engineering, Ahsanullah University of Science and Technology (AUST), Tejgaon, Dhaka-1208, Bangladesh.
Biomedical Optics Express
|May 11, 2026
Summary
A novel biosensor enhances Carcinoembryonic antigen (CEA) detection for cancers. This hybrid SPR biosensor utilizes black phosphorus for improved sensitivity, offering precise cancer biomarker measurement.
Area of Science:
- Biomedical Engineering
- Materials Science
- Analytical Chemistry
Background:
- Carcinoembryonic antigen (CEA) is a crucial biomarker for diagnosing and monitoring liver, breast, and colorectal cancers.
- Existing biosensors face limitations in sensitivity and precision for accurate CEA detection.
Purpose of the Study:
- To design and develop a hybrid Surface Plasmon Resonance (SPR) biosensor for enhanced Carcinoembryonic antigen (CEA) detection.
- To leverage black phosphorus and MgO/Cu/MgO multi-layer for improved electric field confinement and sensor sensitivity.
Main Methods:
- A hybrid SPR biosensor integrating black phosphorus and a MgO/Cu/MgO multi-layer was fabricated.
- Brute force algorithm was employed for sensor parameter optimization.
- Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to validate sensor response accuracy.
Main Results:
- The biosensor achieved a high sensitivity of 409.02 deg/RIU.
- A figure of merit (FOM) of 132.25 and a quality factor (Q) of 146.65 RIU-1 were obtained at 633 nm.
- Validated ANN and ANFIS models accurately simulated the biosensor's performance.
Conclusions:
- The developed hybrid SPR biosensor demonstrates significant potential for precise Carcinoembryonic antigen (CEA) measurement.
- The integration of black phosphorus enhances sensor capabilities for cancer biomarker detection.
- This technology offers promising applications in early cancer diagnosis and monitoring.
