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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| The study will recruit the entire CA1 resident class (n=20) | The study will recruit the entire CA1-2 resident class (n=20-30) who have no prior experience with Ultrasound guided regional anesthesia (UGRA) at the Medical College of Wisconsin/Froedtert Hospital. Inclusion criteria include having no prior experience with UGRA. Exclusion criteria include having undergone the regional elective service prior to the inception of the study (CA-2/3 class). No intervention of interest is noted, the cohort will be accessed based on use of the artificial intelligent Ultrasound. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| The ScanNav, a novel artificial intelligence device designed to assist in Ultrasound guided regional anesthesia | Device | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | From enrollment to the end of device use at 2 weeks. |
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Inclusion Criteria:
Exclusion Criteria:
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The student population includes anesthesia residents in training, and experienced regional anesthesia providers.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Medical College of Wisconsin | Milwaukee | Wisconsin | 53226 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 35091395 | Background | Bowness JS, El-Boghdadly K, Woodworth G, Noble JA, Higham H, Burckett-St Laurent D. Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia. Reg Anesth Pain Med. 2022 Jun;47(6):375-379. doi: 10.1136/rapm-2021-103368. Epub 2022 Jan 28. |
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Quantitative data analysed, results, study information and protocol
When published with ICMJE journal and as requested.
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