Cargando…

Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study

BACKGROUND: Ultrasonound is used to identify anatomical structures during regional anaesthesia and to guide needle insertion and injection of local anaesthetic. ScanNav Anatomy Peripheral Nerve Block (Intelligent Ultrasound, Cardiff, UK) is an artificial intelligence-based device that produces a col...

Descripción completa

Detalles Bibliográficos
Autores principales: Bowness, James S., Burckett-St Laurent, David, Hernandez, Nadia, Keane, Pearse A., Lobo, Clara, Margetts, Steve, Moka, Eleni, Pawa, Amit, Rosenblatt, Meg, Sleep, Nick, Taylor, Alasdair, Woodworth, Glenn, Vasalauskaite, Asta, Noble, J. Alison, Higham, Helen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9900723/
https://www.ncbi.nlm.nih.gov/pubmed/35987706
http://dx.doi.org/10.1016/j.bja.2022.06.031
_version_ 1784882908236873728
author Bowness, James S.
Burckett-St Laurent, David
Hernandez, Nadia
Keane, Pearse A.
Lobo, Clara
Margetts, Steve
Moka, Eleni
Pawa, Amit
Rosenblatt, Meg
Sleep, Nick
Taylor, Alasdair
Woodworth, Glenn
Vasalauskaite, Asta
Noble, J. Alison
Higham, Helen
author_facet Bowness, James S.
Burckett-St Laurent, David
Hernandez, Nadia
Keane, Pearse A.
Lobo, Clara
Margetts, Steve
Moka, Eleni
Pawa, Amit
Rosenblatt, Meg
Sleep, Nick
Taylor, Alasdair
Woodworth, Glenn
Vasalauskaite, Asta
Noble, J. Alison
Higham, Helen
author_sort Bowness, James S.
collection PubMed
description BACKGROUND: Ultrasonound is used to identify anatomical structures during regional anaesthesia and to guide needle insertion and injection of local anaesthetic. ScanNav Anatomy Peripheral Nerve Block (Intelligent Ultrasound, Cardiff, UK) is an artificial intelligence-based device that produces a colour overlay on real-time B-mode ultrasound to highlight anatomical structures of interest. We evaluated the accuracy of the artificial-intelligence colour overlay and its perceived influence on risk of adverse events or block failure. METHODS: Ultrasound-guided regional anaesthesia experts acquired 720 videos from 40 volunteers (across nine anatomical regions) without using the device. The artificial-intelligence colour overlay was subsequently applied. Three more experts independently reviewed each video (with the original unmodified video) to assess accuracy of the colour overlay in relation to key anatomical structures (true positive/negative and false positive/negative) and the potential for highlighting to modify perceived risk of adverse events (needle trauma to nerves, arteries, pleura, and peritoneum) or block failure. RESULTS: The artificial-intelligence models identified the structure of interest in 93.5% of cases (1519/1624), with a false-negative rate of 3.0% (48/1624) and a false-positive rate of 3.5% (57/1624). Highlighting was judged to reduce the risk of unwanted needle trauma to nerves, arteries, pleura, and peritoneum in 62.9–86.4% of cases (302/480 to 345/400), and to increase the risk in 0.0–1.7% (0/160 to 8/480). Risk of block failure was reported to be reduced in 81.3% of scans (585/720) and to be increased in 1.8% (13/720). CONCLUSIONS: Artificial intelligence-based devices can potentially aid image acquisition and interpretation in ultrasound-guided regional anaesthesia. Further studies are necessary to demonstrate their effectiveness in supporting training and clinical practice. CLINICAL TRIAL REGISTRATION: NCT04906018.
format Online
Article
Text
id pubmed-9900723
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-99007232023-02-07 Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study Bowness, James S. Burckett-St Laurent, David Hernandez, Nadia Keane, Pearse A. Lobo, Clara Margetts, Steve Moka, Eleni Pawa, Amit Rosenblatt, Meg Sleep, Nick Taylor, Alasdair Woodworth, Glenn Vasalauskaite, Asta Noble, J. Alison Higham, Helen Br J Anaesth Regional Anaesthesia BACKGROUND: Ultrasonound is used to identify anatomical structures during regional anaesthesia and to guide needle insertion and injection of local anaesthetic. ScanNav Anatomy Peripheral Nerve Block (Intelligent Ultrasound, Cardiff, UK) is an artificial intelligence-based device that produces a colour overlay on real-time B-mode ultrasound to highlight anatomical structures of interest. We evaluated the accuracy of the artificial-intelligence colour overlay and its perceived influence on risk of adverse events or block failure. METHODS: Ultrasound-guided regional anaesthesia experts acquired 720 videos from 40 volunteers (across nine anatomical regions) without using the device. The artificial-intelligence colour overlay was subsequently applied. Three more experts independently reviewed each video (with the original unmodified video) to assess accuracy of the colour overlay in relation to key anatomical structures (true positive/negative and false positive/negative) and the potential for highlighting to modify perceived risk of adverse events (needle trauma to nerves, arteries, pleura, and peritoneum) or block failure. RESULTS: The artificial-intelligence models identified the structure of interest in 93.5% of cases (1519/1624), with a false-negative rate of 3.0% (48/1624) and a false-positive rate of 3.5% (57/1624). Highlighting was judged to reduce the risk of unwanted needle trauma to nerves, arteries, pleura, and peritoneum in 62.9–86.4% of cases (302/480 to 345/400), and to increase the risk in 0.0–1.7% (0/160 to 8/480). Risk of block failure was reported to be reduced in 81.3% of scans (585/720) and to be increased in 1.8% (13/720). CONCLUSIONS: Artificial intelligence-based devices can potentially aid image acquisition and interpretation in ultrasound-guided regional anaesthesia. Further studies are necessary to demonstrate their effectiveness in supporting training and clinical practice. CLINICAL TRIAL REGISTRATION: NCT04906018. Elsevier 2023-02 2022-08-18 /pmc/articles/PMC9900723/ /pubmed/35987706 http://dx.doi.org/10.1016/j.bja.2022.06.031 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Regional Anaesthesia
Bowness, James S.
Burckett-St Laurent, David
Hernandez, Nadia
Keane, Pearse A.
Lobo, Clara
Margetts, Steve
Moka, Eleni
Pawa, Amit
Rosenblatt, Meg
Sleep, Nick
Taylor, Alasdair
Woodworth, Glenn
Vasalauskaite, Asta
Noble, J. Alison
Higham, Helen
Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study
title Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study
title_full Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study
title_fullStr Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study
title_full_unstemmed Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study
title_short Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study
title_sort assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study
topic Regional Anaesthesia
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9900723/
https://www.ncbi.nlm.nih.gov/pubmed/35987706
http://dx.doi.org/10.1016/j.bja.2022.06.031
work_keys_str_mv AT bownessjamess assistiveartificialintelligenceforultrasoundimageinterpretationinregionalanaesthesiaanexternalvalidationstudy
AT burckettstlaurentdavid assistiveartificialintelligenceforultrasoundimageinterpretationinregionalanaesthesiaanexternalvalidationstudy
AT hernandeznadia assistiveartificialintelligenceforultrasoundimageinterpretationinregionalanaesthesiaanexternalvalidationstudy
AT keanepearsea assistiveartificialintelligenceforultrasoundimageinterpretationinregionalanaesthesiaanexternalvalidationstudy
AT loboclara assistiveartificialintelligenceforultrasoundimageinterpretationinregionalanaesthesiaanexternalvalidationstudy
AT margettssteve assistiveartificialintelligenceforultrasoundimageinterpretationinregionalanaesthesiaanexternalvalidationstudy
AT mokaeleni assistiveartificialintelligenceforultrasoundimageinterpretationinregionalanaesthesiaanexternalvalidationstudy
AT pawaamit assistiveartificialintelligenceforultrasoundimageinterpretationinregionalanaesthesiaanexternalvalidationstudy
AT rosenblattmeg assistiveartificialintelligenceforultrasoundimageinterpretationinregionalanaesthesiaanexternalvalidationstudy
AT sleepnick assistiveartificialintelligenceforultrasoundimageinterpretationinregionalanaesthesiaanexternalvalidationstudy
AT tayloralasdair assistiveartificialintelligenceforultrasoundimageinterpretationinregionalanaesthesiaanexternalvalidationstudy
AT woodworthglenn assistiveartificialintelligenceforultrasoundimageinterpretationinregionalanaesthesiaanexternalvalidationstudy
AT vasalauskaiteasta assistiveartificialintelligenceforultrasoundimageinterpretationinregionalanaesthesiaanexternalvalidationstudy
AT noblejalison assistiveartificialintelligenceforultrasoundimageinterpretationinregionalanaesthesiaanexternalvalidationstudy
AT highamhelen assistiveartificialintelligenceforultrasoundimageinterpretationinregionalanaesthesiaanexternalvalidationstudy