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Article Abstract
Objectives: Ultrasound-guided regional anesthesia (UGRA) relies on acquiring and interpreting an appropriate view of sonoanatomy. Artificial intelligence (AI) has the potential to aid this by applying a color overlay to key sonoanatomical structures.The primary aim was to determine whether an AI-generated color overlay was associated with a difference in participants' ability to identify an appropriate block view over a 2-month period after a standardized teaching session (as judged by a blinded assessor). Secondary outcomes included the ability to identify an appropriate block view (unblinded assessor), global rating score and participant confidence scores.
Design: Randomized, partially blinded, prospective cross-over study.
Setting: Simulation scans on healthy volunteers. Initial assessments on 29 November 2022 and 30 November 2022, with follow-up on 25 January 2023 - 27 January 2023.
Participants: 57 junior anesthetists undertook initial assessments and 51 (89.47%) returned at 2 months.
Intervention: Participants performed ultrasound scans for six peripheral nerve blocks, with AI assistance randomized to half of the blocks. Cross-over assignment was employed for 2 months.
Main Outcome Measures: Blinded experts assessed whether the block view acquired was acceptable (yes/no). Unblinded experts also assessed this parameter and provided a global performance rating (0-100). Participants reported scan confidence (0-100).
Results: AI assistance was associated with a higher rate of appropriate block view acquisition in both blinded and unblinded assessments (p=0.02 and <0.01, respectively). Participant confidence and expert rating scores were superior throughout (all p<0.01).
Conclusions: Assistive AI was associated with superior ultrasound scanning performance 2 months after formal teaching. It may aid application of sonoanatomical knowledge and skills gained in teaching, to support delivery of UGRA beyond the immediate post-teaching period.
Trial Registration Number: www.clinicaltrials.govNCT05583032.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11487881 | PMC |
| http://dx.doi.org/10.1136/bmjsit-2024-000264 | DOI Listing |
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