An Integrated Dual-Module Computer Vision System for IV Drip Rate and Fluid Level Monitoring

Manual monitoring of intravenous (IV) therapy is labor-intensive and susceptible to human error, posing risks to patient safety. Existing computer vision-based approaches have addressed either drip rate estimation or fluid level monitoring in isolation, limiting their clinical applicability. This work presents and validates a dual-module computer vision framework for a practical, automated IV monitoring system that simultaneously addresses both tasks. For the critical task of drip rate estimation, we conduct a comparative study of two distinct deep learning paradigms on a diverse custom dataset of 3,458 images, evaluated on a strictly held-out test set: a state-of-the-art YOLOv8 object detector, achieving mAP0.50 of 95.34%, and a novel heatmap-based point-event regression network (ResNet-18 encoder with convolutional decoder), achieving a frame-wise accuracy of 88.44% and recall of 95.16%. The second module leverages a bespoke convolutional neural network (CNN) for fluid level classification, reaching a test accuracy of 95.26% on a public benchmark dataset. Finally, we demonstrate a proof-of-concept prototype that successfully integrates both modules into a unified, automated video-based application, validated on pre-recorded clinical video footage. End-to-end counting evaluation on a 7-video set spanning drip rates from 17 to 94&#x2006;dpm shows the heatmap-based counter achieves 91.0% mean counting accuracy versus 77.5% for the YOLOv8-based counter, with the heatmap demonstrating markedly greater robustness at elevated infusion rates (proof-of-concept evaluation; <inline-formula> <tex-math notation="LaTeX">$n=2$ </tex-math></inline-formula> videos at <inline-formula> <tex-math notation="LaTeX">$\geq 47$ </tex-math></inline-formula>&#x2006;dpm). By validating high-performance solutions for both monitoring tasks and demonstrating a clear path to integration, this study establishes a comprehensive framework for a holistic IV infusion monitoring system with the potential to enhance patient safety and reduce the burden on clinical staff.