A deep learning approach to automated assessment of peripheral intravenous catheterization skills

Background: Peripheral intravenous catheterization is a fundamental, yet high-risk, nursing procedure that requires precision and repeated practice.However, opportunities for safe, hands-on training are limited, and conventional assessment relies on subjective, resource-intensive instructor judgment.Recent advances in computer vision and deep learning offer the potential for objective and scalable evaluation of clinical procedures in nursing education.Methods: This study aimed to develop and evaluate a deep learning-based framework for the automated assessment of IV insertion performance.The model was designed to recognize procedural stages from video sequences and determine overall pass/fail outcomes.A total of 300 videos were recorded from six nurses performing IV insertion on mannequin arms.Using a pose estimation algorithm, 42 hand keypoints were extracted from each frame.These sequential data were analyzed by a bidirectional long short-term memory (Bi-LSTM) model to classify six procedural stages and predict overall competency. Results:The keypoint detection model demonstrated robust tracking even under partial occlusion and varying illumination.The stage classification achieved an accuracy of 92.9% (F1-score = 0.84; mIoU = 0.83), while procedure-level evaluation reached 84.6% accuracy.Misclassifications primarily occurred at stage transitions, notably during catheter insertion and tourniquet release.Conclusions: This deep learning framework enables objective, automated, and scalable evaluation of IV insertion performance.It reduces faculty workload, enhances feedback efficiency, and promotes student confidence.Integrating AI-driven assessment into nursing education may advance evidence-based teaching practices and improve clinical readiness and patient safety.