Deep Learning vs. Novice User for Needle Tip Tracking in Ultrasound-Guided Intravenous Access

Abstract

Background:. Ultrasound guided peripheral intravenous (US-pIV) access can reduce patient morbidity and mortality and improve the quality of care. Currently, there are no out-of-plane artificial intelligence tools designed to help learners reach competency with this procedure. Methods: We developed a deep learning (DL) model to identify needle-tip tracking during simulated US-pIV placement with out-of-plane technique. The sensitivity and specificity of this DL model was compared to a novice participant group. Results: Our DL model outperformed our novice group with an increased accuracy score (0.89 vs. 0.82 (95% CI 0.79–0.85), and higher sensitivity (0.91 vs. 0.82 (95% CI 0.79–0.85) and specificity scores (0.85 vs. 0.81 (95% CI 0.74–0.86), although these differences were not statistically significant. Conclusion: DL has the potential to enhance the learning curve associated with performing US-pIV access to ensure safer and more efficient outcomes for patients.