Large video dataset enables AI evaluation of nurse central line dressing skills
The Collection of a Real-World, Large-Scale Dataset for Automated Skill Evaluation in Central Line Dressing Change
Computer Vision and Pattern Recognition
Summary
Blood infections caused by central lines can be prevented when nurses perform dressing changes correctly. The authors gathered many videos of nurses doing this procedure and labeled important steps to help computers learn how to judge skill. Their work shows that AI can potentially assist in checking nurse performance more fairly and efficiently. This dataset can help improve nurse training and patient safety in hospitals.
What this means in practice
- •For hospital training staff: Use the dataset to develop AI tools that objectively assess nurse skill during central line dressing changes for better staff certification.
- •For healthcare ai developers: Build deep learning models using the dataset to create automated feedback tools that improve clinical procedure training.
Authors
Ummey Tanin, Shannan K. Hamlin, Hsin-Mei Chen, Peizhu, Qian
Abstract
Central line-associated bloodstream infections (CLABSIs) remain a major source of preventable morbidity and mortality in healthcare settings. Proper execution of the central line dressing change (CLDC) procedure is essential for infection prevention, yet current approaches to competency assessment rely primarily on manual ob- servation by clinical educators, making evaluation time-intensive, difficult to scale, and limited in consistency and granularity. These challenges highlight the need for AI-assisted skill evaluation methods that can support more objective, reproducible, and scalable assessment of clinical performance. However, progress in this area has been constrained by the lack of broad-scale, clinically grounded datasets for developing and evaluating such methods. In this work, we present the collection process of a large-scale video dataset for CLDC skill evaluation, comprising 365 recordings of registered nurses collected during the 2026 annual competency exam at Houston Methodist Hospital. Each video is paired with subtask-level temporal annotations and clinical performance assessments spanning 17 subtasks of the procedure. We describe the data collection and curation pipeline, including pilot testing of record- ing configurations, annotation protocol design, and post-collection processing. We further establish the feasibility of video-based skill assessment through benchmark experiments with spatiotemporal deep learning models. This dataset provides a foundation for future research on automated clinical skill assessment, feedback generation, and AI-assisted training in nursing education.