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

Device Evaluation Devices Technology

Journal

BMJ surgery, interventions, & health technologies
ISSN: 2631-4940
Titre abrégé: BMJ Surg Interv Health Technol
Pays: England
ID NLM: 101764673

Informations de publication

Date de publication:
2024
Historique:
received: 30 01 2024
accepted: 10 09 2024
medline: 21 10 2024
pubmed: 21 10 2024
entrez: 21 10 2024
Statut: epublish

Résumé

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. Randomized, partially blinded, prospective cross-over study. 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. 57 junior anesthetists undertook initial assessments and 51 (89.47%) returned at 2 months. 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. 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). 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). 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. www.clinicaltrials.govNCT05583032.

Identifiants

pubmed: 39430867
doi: 10.1136/bmjsit-2024-000264
pii: bmjsit-2024-000264
pmc: PMC11487881
doi:

Banques de données

ClinicalTrials.gov
['NCT05583032']

Types de publication

Journal Article

Langues

eng

Pagination

e000264

Informations de copyright

Copyright © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

JSB is a Senior Clinical Advisor for GE Healthcare (and previously Intelligent Ultrasound, receiving research funding and honoraria). MM was employed by Intelligent Ultrasound as a medical writer during this study. DBSL is a clinical advisor for Intelligent Ultrasound, receiving honoraria. NH is the President of Regional Anaesthesia UK. AJRM is the immediate Past-President of Regional Anaesthesia UK and has received honoraria from Intelligent Ultrasound and GE Healthcare. AP is a Past-President of Regional Anaesthesia UK, has received honoraria from GE Healthcare and has consulted for Pacira Biosciences. TA, MPS, AT and JW are board members of Regional Anaesthesia UK. JAN is a senior scientific advisor for Intelligent Ultrasound.

Auteurs

Chao-Ying Kowa (CY)

Department of Anaesthesia, The Royal London Hospital, London, UK.

Megan Morecroft (M)

Faculty of Medicine, Health & Life Sciences, University of Swansea, Swansea, UK.

Alan J R Macfarlane (AJR)

Department of Anaesthesia, Glasgow Royal Infirmary, Glasgow, UK.
School of Medicine, Dentistry & Nursing, University of Glasgow, Glasgow, UK.

David Burckett-St Laurent (D)

Department of Anaesthesia, Royal Cornwall Hospitals NHS Trust, Truro, UK.

Amit Pawa (A)

Department of Medicine and Perioperative Medicine, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Faculty of Life Sciences and Medicine, King's College London, London, UK.

Simeon West (S)

Department of Anaesthesia, University College London Hospitals NHS Foundation Trust, London, UK.

Steve Margetts (S)

Intelligent Ultrasound, Cardiff, UK.

Nat Haslam (N)

Department of Anaesthesia, South Tyneside and Sunderland NHS Foundation Trust, South Shields, UK.

Toby Ashken (T)

Department of Anaesthesia, University College London Hospitals NHS Foundation Trust, London, UK.

Maria Paz Sebastian (MP)

Department of Anaesthetics, Royal National Orthopaedic Hospital NHS Trust, Stanmore, UK.

Athmaja Thottungal (A)

Department of Anaesthesia and Pain Management, East Kent Hospitals University NHS Foundation Trust, Canterbury, UK.

Jono Womack (J)

Department of Anaesthesia, Royal Victoria Infirmary, Newcastle upon Tyne, UK.

Julia Alison Noble (JA)

Institute of Biomedical Engineering, University of Oxford, Oxford, UK.

Helen Higham (H)

Nuffield Department of Clinical Anaesthesia, University of Oxford, Oxford, UK.
Department of Anaesthesia, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.

James S Bowness (JS)

Department of Anaesthesia, University College London Hospitals NHS Foundation Trust, London, UK.
Department of Targeted Intervention, University College London, London, UK.

Classifications MeSH