Real-Time Vesicular Tracking and Pathway Classification using Multi-Modal Deep Learning for Targeted Drug Delivery Optimization in Receptor-Mediated Endocytosis

**Abstract:** Receptor-mediated endocytosis (RME) is a crucial cellular process involved in nutrient uptake and signaling. Efficient real-time visualization and analysis of this process are vital for optimizing targeted drug delivery strategies. This paper introduces a novel multi-modal deep learning framework for automated vesicular tracking and pathway classification during RME, leveraging fluorescence microscopy, optical coherence tomography (OCT), and machine learning to achieve unprecedented speed and accuracy. Our system outperforms existing methods by dynamically adapting to varying cellular environments and providing actionable insights for targeted drug delivery optimization, with a potential to enhance pharmaceutical efficacy and reduce off-target effects. Specifically, we demonstrate a novel technique achieved through dynamic reinforcement learning and hyper-specific data parsing, improving accuracy and ultimately expanding the possibilities of cell-specific drug delivery precision.