Development of AI-Derived, Non-Invasive, Label-Free 3D-Printed Microfluidic SERS Biosensor Platform Utilizing Cu@Ag/Carbon Nanofibers for Detection of Salivary Biomarkers in Mass Screening of Oral Cancer

Developing a non-invasive and reliable tool for the highly sensitive detection of oral cancer is essential for its mass screening and early diagnosis, and improving treatment efficacy. Herein, we utilized a label-free surface enhanced Raman spectroscopy (SERS)-based biosensor composed of Cu@Ag core-shell nanoparticle anchored carbon nanofibers (Cu@Ag/CNFs) for highly sensitive salivary biomarker detection in oral cancer mass screening. This SERS substrate provided a Raman signal enhancement of up to 107 and a detection limit as low as 10-12 M for rhodamine 6G molecules. Finite-difference time-domain (FDTD) simulation studies on Cu@Ag/CNFs indicated an E-field intensity enhancement factor (|E|2/|E0|2) of 250 at the plasmonic hotspot induced between two adjacent Cu@Ag nanoparticles. The interaction of this strong E-field along with the chemical enhancement effects was responsible for such huge enhancement in the Raman signals. To realize the real capability of the developed biosensor in practical scenarios, it was further utilized for the detection of oral cancer biomarkers such as nitrate, nitrite, thiocyanate, proteins, and amino acids with a micro-molar concentration in saliva samples. The integration of SERS substrates with a 3D-printed 12-channel microfluidic platform significantly enhanced the reproducibility and statistical robustness of the analytical process. Moreover, AI-driven techniques were employed to improve the diagnostic accuracy in differentiating the salivary profiles of oral cancer patients (n1 = 56) from those of healthy controls (n2 = 60). Principal component analysis (PCA) was utilized for dimensionality reduction, followed by classification using a random forest (RF) algorithm, yielding a robust classification accuracy of 87.5%, with a specificity of 92% and sensitivity of 88%. These experimental and theoretical findings emphasize the real-world functionality of the present non-invasive diagnostic tool in paving the way for more accurate and early-stage detection of oral cancer in clinical settings.