Enhancement and optimization of a graphene-based biosensing platform using machine learning for precise breast cancer...

Area of Science:

• Biomedical Engineering
• Materials Science
• Nanotechnology

Background:

• Early and accurate detection of breast cancer is crucial for improving patient outcomes and treatment efficacy.
• Conventional biosensors often face limitations in sensitivity and reproducibility for early-stage disease detection.

Purpose of the Study:

• To develop and optimize a graphene-based biosensor using machine learning for enhanced early breast cancer detection.
• To improve diagnostic reliability and clinical efficacy in breast cancer screening and monitoring.

Main Methods:

• Utilized a multilayer Ag-SiO₂-Ag architecture to amplify optical response.
• Employed machine learning models for systematic optimization of structural parameters.
• Conducted comprehensive parametric optimization to enhance sensor sensitivity metrics.

Main Results:

• Achieved a peak sensitivity of 1785 nm/RIU.
• Demonstrated superior sensitivity and reproducibility compared to conventional biosensor configurations.
• The optimized design showed enhanced precision and responsiveness for bioanalytical applications.

Conclusions:

• The machine learning-optimized graphene biosensor offers a precise and robust solution for early breast cancer screening.
• The platform shows strong potential for clinical translation in biomedical diagnostics.
• The enhanced sensitivity and accuracy position the sensor as a promising tool for monitoring breast cancer.