1. Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists’ ultrasound scanning for regional anesthesia
- Author
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Helen Higham, Amit Pawa, Maria Paz Sebastian, Julia Alison Noble, David Burckett-St Laurent, Athmaja Thottungal, Simeon West, James S Bowness, Nat Haslam, Toby Ashken, Chao-Ying Kowa, Megan Morecroft, Alan J R Macfarlane, Steve Margetts, and Jono Womack
- Subjects
Medical technology ,R855-855.5 ,Surgery ,RD1-811 - 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
- Published
- 2024
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