Advances in Biomedical Optical Sensing through Machine Learning Techniques

Abstract

Biomedical optical sensing has revolutionized medical diagnostics through non-invasive imaging and real-time monitoring capabilities. This paper examines the integration of machine learning (ML) techniques with optical sensing technologies including optical coherence tomography (OCT), spectroscopy, and fluorescence imaging. The primary objective is to analyze how ML algorithms enhance diagnostic accuracy, image processing, and disease detection in biomedical applications. A comprehensive literature review methodology was employed, analyzing 45 peer-reviewed studies from 2018-2023. The hypothesis posited that ML integration significantly improves sensitivity and specificity in optical sensing applications. Results demonstrate that deep learning algorithms achieved 92-97% accuracy in retinal disease detection through OCT, while support vector machines showed 89% accuracy in cancer tissue classification using Raman spectroscopy. Convolutional neural networks reduced image processing time by 75% compared to traditional methods. Discussion reveals that ML techniques address challenges of data interpretation, artifact removal, and real-time analysis in optical sensing. The study concludes that ML-enhanced optical sensing represents a paradigm shift in personalized medicine, offering improved diagnostic capabilities, reduced human error, and faster clinical decision-making in healthcare systems.