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Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia

INTRODUCTION: Ultrasound-guided regional anesthesia (UGRA) involves the acquisition and interpretation of ultrasound images to delineate sonoanatomy. This study explores the utility of a novel artificial intelligence (AI) device designed to assist in this task (ScanNav Anatomy Peripheral Nerve Block...

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Autores principales: Bowness, James Simeon, El-Boghdadly, Kariem, Woodworth, Glenn, Noble, J Alison, Higham, Helen, Burckett-St Laurent, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9046753/
https://www.ncbi.nlm.nih.gov/pubmed/35091395
http://dx.doi.org/10.1136/rapm-2021-103368
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author Bowness, James Simeon
El-Boghdadly, Kariem
Woodworth, Glenn
Noble, J Alison
Higham, Helen
Burckett-St Laurent, David
author_facet Bowness, James Simeon
El-Boghdadly, Kariem
Woodworth, Glenn
Noble, J Alison
Higham, Helen
Burckett-St Laurent, David
author_sort Bowness, James Simeon
collection PubMed
description INTRODUCTION: Ultrasound-guided regional anesthesia (UGRA) involves the acquisition and interpretation of ultrasound images to delineate sonoanatomy. This study explores the utility of a novel artificial intelligence (AI) device designed to assist in this task (ScanNav Anatomy Peripheral Nerve Block; ScanNav), which applies a color overlay on real-time ultrasound to highlight key anatomical structures. METHODS: Thirty anesthesiologists, 15 non-experts and 15 experts in UGRA, performed 240 ultrasound scans across nine peripheral nerve block regions. Half were performed with ScanNav. After scanning each block region, participants completed a questionnaire on the utility of the device in relation to training, teaching, and clinical practice in ultrasound scanning for UGRA. Ultrasound and color overlay output were recorded from scans performed with ScanNav. Experts present during the scans (real-time experts) were asked to assess potential for increased risk associated with use of the device (eg, needle trauma to safety structures). This was compared with experts who viewed the AI scans remotely. RESULTS: Non-experts were more likely to provide positive and less likely to provide negative feedback than experts (p=0.001). Positive feedback was provided most frequently by non-experts on the potential role for training (37/60, 61.7%); for experts, it was for its utility in teaching (30/60, 50%). Real-time and remote experts reported a potentially increased risk in 12/254 (4.7%) vs 8/254 (3.1%, p=0.362) scans, respectively. DISCUSSION: ScanNav shows potential to support non-experts in training and clinical practice, and experts in teaching UGRA. Such technology may aid the uptake and generalizability of UGRA. TRIAL REGISTRATION NUMBER: NCT04918693.
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spelling pubmed-90467532022-05-11 Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia Bowness, James Simeon El-Boghdadly, Kariem Woodworth, Glenn Noble, J Alison Higham, Helen Burckett-St Laurent, David Reg Anesth Pain Med Brief Technical Report INTRODUCTION: Ultrasound-guided regional anesthesia (UGRA) involves the acquisition and interpretation of ultrasound images to delineate sonoanatomy. This study explores the utility of a novel artificial intelligence (AI) device designed to assist in this task (ScanNav Anatomy Peripheral Nerve Block; ScanNav), which applies a color overlay on real-time ultrasound to highlight key anatomical structures. METHODS: Thirty anesthesiologists, 15 non-experts and 15 experts in UGRA, performed 240 ultrasound scans across nine peripheral nerve block regions. Half were performed with ScanNav. After scanning each block region, participants completed a questionnaire on the utility of the device in relation to training, teaching, and clinical practice in ultrasound scanning for UGRA. Ultrasound and color overlay output were recorded from scans performed with ScanNav. Experts present during the scans (real-time experts) were asked to assess potential for increased risk associated with use of the device (eg, needle trauma to safety structures). This was compared with experts who viewed the AI scans remotely. RESULTS: Non-experts were more likely to provide positive and less likely to provide negative feedback than experts (p=0.001). Positive feedback was provided most frequently by non-experts on the potential role for training (37/60, 61.7%); for experts, it was for its utility in teaching (30/60, 50%). Real-time and remote experts reported a potentially increased risk in 12/254 (4.7%) vs 8/254 (3.1%, p=0.362) scans, respectively. DISCUSSION: ScanNav shows potential to support non-experts in training and clinical practice, and experts in teaching UGRA. Such technology may aid the uptake and generalizability of UGRA. TRIAL REGISTRATION NUMBER: NCT04918693. BMJ Publishing Group 2022-06 2022-01-28 /pmc/articles/PMC9046753/ /pubmed/35091395 http://dx.doi.org/10.1136/rapm-2021-103368 Text en © American Society of Regional Anesthesia & Pain Medicine 2022. Re-use permitted under CC BY-NC. No commercial re-use. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, an indication of whether changes were made, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Brief Technical Report
Bowness, James Simeon
El-Boghdadly, Kariem
Woodworth, Glenn
Noble, J Alison
Higham, Helen
Burckett-St Laurent, David
Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia
title Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia
title_full Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia
title_fullStr Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia
title_full_unstemmed Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia
title_short Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia
title_sort exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia
topic Brief Technical Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9046753/
https://www.ncbi.nlm.nih.gov/pubmed/35091395
http://dx.doi.org/10.1136/rapm-2021-103368
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