Cerebrospinal fluid (CSF) clearance plays a critical role in the brain’s waste removal system. In this chapter, we introduce a supervised deep learning pipeline designed to predict the signal increase ratio (SIR) in the CSF of the human brain 24 hours after the injection of an intrathecal contrast agent. The SIR quantifies the relative increase in magnetic resonance imaging (MRI) signal intensity compared to baseline measurements taken before the tracer injection, thereby providing a visualization of CSF dynamics over time. Our deep learning pipeline enables the prediction and visualization of these dynamics without the need for contrast agent administration, reducing the risk of side effects and lowering both costs and time requirements by relying solely on a baseline MRI scan. This chapter serves a dual purpose: it provides an introduction to deep learning–based image analysis and demonstrates the application of this technology using the nontrivial example of predicting the SIR in CSF.