مقاله ترویجی: بررسی نقش یادگیری ماشین در بهبود عملکرد حسگرهای زیستی پلاسمونیکی

حسگرهای زیستی پلاسمونیکی، با بهره‌گیری از ویژگی‌های منحصربه‌فرد نانوساختارهای فلزی و مواد دوبعدی، به ابزاری قدرتمند در تشخیص دقیق و سریع رویدادهای زیستی تبدیل شده‌اند. حساسیت بالای این حسگرها به تغییرات بسیار کوچک در محیط اطراف، بدون نیاز به برچسب یا گزارشگر، آن‌ها را به گزینه‌ای ایده‌آل برای کاربردهایی همچون تشخیص زودهنگام بیماری‌ها، نظارت بر فرآیندهای زیستی و گسترش داروهای جدید تبدیل کرده است. با این حال، پیچیدگی داده‌های تولید شده با استفاده از این حسگرها و نیاز به بررسی دقیق آنها، چالشی اساسی در استفاده از این فناوری به شمار می‌آید. یادگیری ماشین با توانایی استخراج الگوهای پیچیده از داده‌ها، می‌تواند عملکرد این حسگرها را به صورت چشمگیری بهبود بخشد. این مقاله به بررسی جامع کاربردهای یادگیری ماشین در حوزه حسگرهای زیستی پلاسمونیکی می‌پردازد و نشان می‌دهد که چگونه این فناوری می‌تواند در تحلیل سیگنال‌ها، کاهش نوفه، بهبود دقت تشخیص و بهینه‌سازی نانوساختارها موثر باشد.

investigating the role of machine learning in enhancing the performance of plasmonic biosensors

1. introductionsurface plasmon resonance (spr) biosensors have emerged as powerful tools for real-time, label-free detection of biomolecular interactions. by detecting shifts in the refractive index at metal–dielectric interfaces, typically involving gold or other plasmonic nanostructures, spr systems enable high-sensitivity monitoring across a range of applications, including clinical diagnostics, drug development, and environmental sensing. despite their utility, conventional spr systems face several limitations. these include high signal noise, particularly when detecting low-molecular-weight analytes, limited sensitivity in complex biological media, and the analytical burden of interpreting high-dimensional spr imaging data . to address these challenges, machine learning (ml) and deep learning (dl) methods are being increasingly adopted. their integration has enhanced signal clarity, optimized sensor configurations, and enabled rapid, high-accuracy diagnostics. this short review summarizes the current landscape of ml-enhanced spr biosensors, focusing on signal processing, sensor design, and real-world clinical applications.2. machine learning applications in spr biosensing2.1 signal processing and feature extraction ml algorithms play a crucial role in improving the signal quality of spr systems. supervised learning techniques, such as support vector machines (svms) and random forests, have been used to identify analyte-specific patterns within noisy spectra, enhancing classification and quantification tasks. in spr imaging (spri), convolutional neural networks (cnns) excel at extracting diagnostic features from complex images, achieving disease detection accuracies exceeding 90% . dimensionality reduction techniques like principal component analysis (pca) and t-distributed stochastic neighbor embedding (t-sne) further support the visualization and clustering of high-dimensional data, facilitating interpretation and downstream analysis. 2.2 sensor design and optimization ml techniques have revolutionized the design of spr sensors by enabling predictive modeling and inverse design. neural networks, including multilayer perceptrons (mlps) and cnns, have been trained to predict optical responses (e.g., resonance wavelength shifts) from nanostructure geometry and material parameters. these models have reached prediction accuracies of over 99% when combined with statistical techniques like bayesian ridge regression. advanced generative approaches, such as generative adversarial networks (gans) and reinforcement learning, allow for the inverse design of nanostructures. these models generate configurations that maximize field enhancement and sensitivity. metaheuristic algorithms, including particle swarm optimization (pso) and genetic algorithms (gas), have also been combined with ml models to accelerate multi-parameter optimization, leading to a 75% reduction in design time and sensitivity enhancements up to 5,200 nm/riu. 2.3 clinical and diagnostic applications ml-powered spr biosensors have demonstrated remarkable promise in clinical diagnostics. wearable spr devices, integrated with lightweight ml models, are capable of real-time monitoring of biomarkers such as cardiac enzymes and inflammatory mediators.in infectious disease diagnostics, dl-enhanced spr systems have achieved femtomolar-level detection of sars-cov-2 in under 30 minutes—surpassing the performance of standard rt-pcr tests. cnn-based systems have also been used for cancer detection, enabling identification of exosomes and multiple protein biomarkers (e.g., eight breast cancer indicators) at picogram-per-milliliter sensitivity.3.advantages and persistent challengesml integration has substantially improved the sensitivity, speed, and multiplexing capacity of spr biosensors. optimized sensor designs using hybrid materials (e.g., au–tio₂–cds) have improved sensitivity by 30–45%, and cnns have significantly reduced noise in complex biological media such as serum. however, several challenges remain. the development of high-performing ml models requires large, annotated datasets, which are difficult to generate in biomedical settings. computational resource demands, particularly for training deep networks, limit the portability of these systems. moreover, the black-box nature of many dl models hampers their interpretability, raising concerns in clinical settings. lastly, spr’s inherent limitations in detecting low-molecular-weight analytes (<500 da) and its susceptibility to interference in biological matrices persist as obstacles. 4. future directionsseveral promising strategies are emerging to address these limitations. lightweight dl models suitable for edge computing are being developed for use in point-of-care devices. transfer learning techniques reduce the dependence on large training datasets by repurposing pre-trained models for spr-specific tasks . hybrid physics-informed ml models combine domain-specific physical principles with data-driven learning, enhancing both performance and interpretability. additionally, federated learning offers a path toward collaborative model training across institutions while preserving data privacy, a critical consideration in clinical applications. 5. conclusionmachine learning has significantly advanced the capabilities of spr biosensors. by improving signal interpretation, guiding sensor design, and enabling rapid multiplexed diagnostics, ml is pushing the limits of what spr can achieve. although challenges related to data availability, model transparency, and sensor physics remain, ongoing developments in computational modeling and materials science are steadily bridging these gaps. as the convergence between artificial intelligence and nanophotonics continues to evolve, spr biosensors are poised to play a transformative role in personalized medicine, real-time diagnostics, and global health monitoring.