Role of Machine Learning on Surface Plasmon Resonance Sensor for Healthcare: A Comprehensive Review
摘要
Surface plasmon resonance (SPR) sensors have emerged as a transformative technology in healthcare diagnostics, offering real-time, label-free detection of biomolecular interaction with high sensitivity and specificity. This paper explores the latest advancements in SPR technology, highlighting key applications such as cancer diagnostics, kidney disease monitoring, infectious disease detection, and wearable health monitoring. Integrating nanomaterials (AuNPs) like graphene, gold nanoparticles, and molybdenum disulfide (MoS2), has significantly enhanced the performance of SPR sensors by improving detection limits and signal amplification. Additionally, the incorporation of microfluidic systems has enabled the development of point-of-care diagnostics, while flexible and wearable SPR sensors allow for continuous monitoring of critical health parameters, including glucose and cardiovascular biomarkers. Further, the combination of artificial intelligence (AI) and machine learning (ML) with SPR technology has opened new avenues for personalized medicine by improving diagnostic accuracy and enabling predictive analysis. SPR sensors hold great potential for early disease detection, personalized healthcare, and chronic disease management, making them vital tools for the future of medical diagnostics. This version provides a clear overview of viewpoints discussed in this paper, emphasizing the technological advancements and their impact on healthcare diagnostics while maintaining a concise and professional tone.