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.
Study Type
OBSERVATIONAL
Enrollment
20
The ScanNav, a novel artificial intelligence device designed to assist in Ultrasound guided regional anesthesia.We also aim to see of how it enhances teaching and training of residents by experienced regional anesthesia providers. We intend to use surveys/questionnaires of both resident and regional anesthesia provider as they utilize the device in real time.
Medical College of Wisconsin
Milwaukee, Wisconsin, United States
Qualtric questionaire of participants
Improved teaching and training of anesthesiology residents in the subspecialty of regional anesthesia will be accessed via a questionnaire filled out by participants after use of the device. The questionnaire will access, the type of regional blocks, feasibility of block and ease of teaching with the artificial intelligence Ultrasound.
Time frame: From enrollment to the end of device use at 2 weeks.
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