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Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists' ultrasound scanning for regional anesthesia

Chao-Ying Kowa, MEGAN MORECROFT, Alan J R Macfarlane, David Burckett-St Laurent, Amit Pawa, Simeon West, Steve Margetts, Nat Haslam, Toby Ashken, Maria Paz Sebastian, Athmaja Thottungal, Jono Womack, Julia Alison Noble, Helen Higham, James S Bowness Orcid Logo

BMJ Surgery, Interventions, & Health Technologies, Volume: 6, Issue: 1, Start page: e000264

Swansea University Author: MEGAN MORECROFT

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DOI (Published version): 10.1136/bmjsit-2024-000264

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-ge...

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Published in: BMJ Surgery, Interventions, & Health Technologies
ISSN: 2631-4940
Published: BMJ 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa68175
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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 NCT05583032.
College: Faculty of Medicine, Health and Life Sciences
Funders: This work was funded by Intelligent Ultrasound (Cardiff, UK). The device studied (ScanNav Anatomy Peripheral Nerve Block) is a product of Intelligent Ultrasound.
Issue: 1
Start Page: e000264

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