Abstact
Biophotonics has emerged as a transformative interdisciplinary field combining optics, nanotechnology, and biomedical engineering to enable highly sensitive diagnostic and therapeutic technologies. Recent advances in nanostructured photonic materials and artificial intelligence (AI)-driven signal analysis have significantly improved imaging resolution, disease detection accuracy, and targeted therapeutic delivery.
This research proposes an integrated biophotonic platform based on plasmonic nanostructures, multimodal optical sensing, and AI-assisted data interpretation for early-stage disease diagnostics and precision phototherapy. Experimental simulations demonstrate enhanced photon–tissue interaction efficiency, leading to improved sensitivity in detecting cellular-level abnormalities. The proposed system offers a scalable and non-invasive approach suitable for real-time clinical applications, particularly in oncology, cardiovascular disease monitoring, and infectious disease diagnostics.
The findings highlight the potential of next-generation intelligent biophotonic systems to revolutionize personalized medicine through rapid diagnostics, targeted treatment, and continuous health monitoring.
Introduction
Biophotonics integrates photonic science with biological and medical applications to develop advanced technologies for disease detection, imaging, and therapy. Conventional diagnostic methods often face limitations in sensitivity, invasiveness, and response time, creating a demand for rapid and non-destructive biomedical technologies. Optical techniques such as fluorescence imaging, Raman spectroscopy, and plasmonic sensing have demonstrated exceptional capabilities in detecting molecular-level changes in biological tissues.
Recent progress in nanophotonics has enabled the development of engineered photonic nanostructures capable of enhancing light–matter interactions, thereby improving detection sensitivity and imaging contrast. Simultaneously, the integration of artificial intelligence into photonic systems enables automated interpretation of complex optical datasets, significantly improving diagnostic reliability and reducing clinical decision time.
This study explores an AI-assisted biophotonic sensing and imaging framework that combines plasmonic nanostructures, adaptive optical imaging modules, and intelligent signal-processing algorithms to achieve ultra-sensitive disease detection and targeted photonic therapy. The proposed approach aims to advance next-generation biomedical technologies that support early diagnosis, personalized treatment strategies, and continuous health monitoring.
Methodology (Overview)
The research employs computational photonic modeling, nanostructure design optimization, and AI-based spectral data analysis to evaluate system performance. Plasmonic nanoparticle arrays are engineered to maximize optical field enhancement, while machine-learning algorithms process spectral variations associated with pathological biomarkers. Simulation studies assess imaging resolution, sensitivity improvement, and therapeutic light-delivery efficiency.
World Biophotonics Research Awards
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