AI-Enabled Optical Sensing Technologies for Emerging Biomedical Applications
Keywords:
Artificial intelligence, Optical biosensors, Machine learning, Deep learning, Biomedical diagnostics.Abstract
Artificial intelligence (AI)-enabled optical sensing technologies represent a transformative paradigm in biomedical diagnostics, offering unprecedented accuracy and real-time disease detection capabilities. This study investigates the integration of machine learning and deep learning algorithms with optical biosensors for enhanced biomedical applications. The objective was to systematically analyze the performance metrics of AI-enhanced optical sensors across various detection modalities including surface plasmon resonance, fluorescence, Raman spectroscopy, and colorimetric sensing. A comprehensive review methodology was employed, analyzing peer-reviewed literature from 2020-2022 to extract quantitative performance data. The hypothesis posited that AI integration significantly improves sensitivity, specificity, and detection accuracy compared to conventional optical sensing methods. Results demonstrated that AI-enhanced optical biosensors achieved detection accuracies ranging from 91% to 98%, with sensitivities between 5,000-24,000 nm/RIU for SPR-based systems. Discussion revealed that convolutional neural networks and support vector machines emerged as the most effective algorithms for spectral data analysis. The study concludes that AI-enabled optical biosensors represent a promising frontier for point-of-care diagnostics, early cancer detection, and personalized medicine applications.
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