Exploring the Efficacy of Assistive Artificial Intelligence for Ultrasound Guided Regional Anesthesia in Residency Training

Exploring the Efficacy of Assistive Artificial Intelligence for Ultrasound Guided Regional Anesthesia in Residency Training

The purpose of this study is to investigate the efficacy of a novel artificial intelligence (AI) device designed to assist in Ultrasound guided regional anesthesia (ScanNav Anatomy Peripheral Nerve Block; ScanNav), in the teaching and training of anesthesiology residents in the subspecialty of regional anesthesia.

Ultrasound-guided regional anesthesia (UGRA) relies on the precise acquisition and interpretation of ultrasound images. The necessary skills to attain this is dependent on the knowledge of the underlying anatomy. Notwithstanding, even experienced anesthesiologists can find this challenging, especially in the setting of anatomical variation, obesity and other potential confounders. This study aims to clarify if The ScanNav, a novel artificial intelligence device designed to assist in UGRA, when utilized with trainees, improves their uptake and training. We also aim to see the relationships of how it enhances teaching and training of residents by experienced regional anesthesia providers.

Inclusion Criteria:

• Inclusion criteria include any anesthesia resident with no prior experience with UGRA.

Exclusion Criteria:

• Exclusion criteria include any anesthesia resident who has undergone the regional elective service prior to the inception of the study.