No Cover Image

Journal article 22 views 4 downloads

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

  • 68175.VOR.pdf

    PDF | Version of Record

    © Author(s) (or their employer(s)) 2024. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license.

    Download (498.38KB)

Check full text

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...

Full description

Published in: BMJ Surgery, Interventions, & Health Technologies
ISSN: 2631-4940
Published: BMJ 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa68175
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2024-11-05T12:51:26Z
last_indexed 2024-11-05T12:51:26Z
id cronfa68175
recordtype SURis
fullrecord <?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>68175</id><entry>2024-11-05</entry><title>Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists' ultrasound scanning for regional anesthesia</title><swanseaauthors><author><sid>acf71fd45159b2c243880681349e692e</sid><firstname>MEGAN</firstname><surname>MORECROFT</surname><name>MEGAN MORECROFT</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2024-11-05</date><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 &lt;0.01, respectively). Participant confidence and expert rating scores were superior throughout (all p&lt;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.</abstract><type>Journal Article</type><journal>BMJ Surgery, Interventions, &amp; Health Technologies</journal><volume>6</volume><journalNumber>1</journalNumber><paginationStart>e000264</paginationStart><paginationEnd/><publisher>BMJ</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2631-4940</issnElectronic><keywords/><publishedDay>16</publishedDay><publishedMonth>10</publishedMonth><publishedYear>2024</publishedYear><publishedDate>2024-10-16</publishedDate><doi>10.1136/bmjsit-2024-000264</doi><url/><notes/><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>This work was funded by Intelligent Ultrasound (Cardiff, UK). The device studied (ScanNav Anatomy Peripheral Nerve Block) is a product of Intelligent Ultrasound.</funders><projectreference/><lastEdited>2024-11-05T12:54:53.2983459</lastEdited><Created>2024-11-05T12:37:21.2111239</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">School of Health and Social Care - Nursing</level></path><authors><author><firstname>Chao-Ying</firstname><surname>Kowa</surname><order>1</order></author><author><firstname>MEGAN</firstname><surname>MORECROFT</surname><order>2</order></author><author><firstname>Alan J R</firstname><surname>Macfarlane</surname><order>3</order></author><author><firstname>David Burckett-St</firstname><surname>Laurent</surname><order>4</order></author><author><firstname>Amit</firstname><surname>Pawa</surname><order>5</order></author><author><firstname>Simeon</firstname><surname>West</surname><order>6</order></author><author><firstname>Steve</firstname><surname>Margetts</surname><order>7</order></author><author><firstname>Nat</firstname><surname>Haslam</surname><order>8</order></author><author><firstname>Toby</firstname><surname>Ashken</surname><order>9</order></author><author><firstname>Maria Paz</firstname><surname>Sebastian</surname><order>10</order></author><author><firstname>Athmaja</firstname><surname>Thottungal</surname><order>11</order></author><author><firstname>Jono</firstname><surname>Womack</surname><order>12</order></author><author><firstname>Julia Alison</firstname><surname>Noble</surname><order>13</order></author><author><firstname>Helen</firstname><surname>Higham</surname><order>14</order></author><author><firstname>James S</firstname><surname>Bowness</surname><orcid>0000-0002-8665-1984</orcid><order>15</order></author></authors><documents><document><filename>68175__32849__71affe40cedd4694911274b72179fc15.pdf</filename><originalFilename>68175.VOR.pdf</originalFilename><uploaded>2024-11-05T12:46:33.9818546</uploaded><type>Output</type><contentLength>510346</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>© Author(s) (or their employer(s)) 2024. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by-nc/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling v2 68175 2024-11-05 Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists' ultrasound scanning for regional anesthesia acf71fd45159b2c243880681349e692e MEGAN MORECROFT MEGAN MORECROFT true false 2024-11-05 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. Journal Article BMJ Surgery, Interventions, & Health Technologies 6 1 e000264 BMJ 2631-4940 16 10 2024 2024-10-16 10.1136/bmjsit-2024-000264 COLLEGE NANME COLLEGE CODE Swansea University Another institution paid the OA fee This work was funded by Intelligent Ultrasound (Cardiff, UK). The device studied (ScanNav Anatomy Peripheral Nerve Block) is a product of Intelligent Ultrasound. 2024-11-05T12:54:53.2983459 2024-11-05T12:37:21.2111239 Faculty of Medicine, Health and Life Sciences School of Health and Social Care - Nursing Chao-Ying Kowa 1 MEGAN MORECROFT 2 Alan J R Macfarlane 3 David Burckett-St Laurent 4 Amit Pawa 5 Simeon West 6 Steve Margetts 7 Nat Haslam 8 Toby Ashken 9 Maria Paz Sebastian 10 Athmaja Thottungal 11 Jono Womack 12 Julia Alison Noble 13 Helen Higham 14 James S Bowness 0000-0002-8665-1984 15 68175__32849__71affe40cedd4694911274b72179fc15.pdf 68175.VOR.pdf 2024-11-05T12:46:33.9818546 Output 510346 application/pdf Version of Record true © Author(s) (or their employer(s)) 2024. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license. true eng https://creativecommons.org/licenses/by-nc/4.0/
title Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists' ultrasound scanning for regional anesthesia
spellingShingle Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists' ultrasound scanning for regional anesthesia
MEGAN MORECROFT
title_short Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists' ultrasound scanning for regional anesthesia
title_full Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists' ultrasound scanning for regional anesthesia
title_fullStr Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists' ultrasound scanning for regional anesthesia
title_full_unstemmed Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists' ultrasound scanning for regional anesthesia
title_sort Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists' ultrasound scanning for regional anesthesia
author_id_str_mv acf71fd45159b2c243880681349e692e
author_id_fullname_str_mv acf71fd45159b2c243880681349e692e_***_MEGAN MORECROFT
author MEGAN MORECROFT
author2 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
format Journal article
container_title BMJ Surgery, Interventions, & Health Technologies
container_volume 6
container_issue 1
container_start_page e000264
publishDate 2024
institution Swansea University
issn 2631-4940
doi_str_mv 10.1136/bmjsit-2024-000264
publisher BMJ
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str School of Health and Social Care - Nursing{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}School of Health and Social Care - Nursing
document_store_str 1
active_str 0
description 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.
published_date 2024-10-16T12:54:52Z
_version_ 1814887181578141696
score 11.036684

Similar Items