Evidence map›Paper›PMID 38448269›Full record

ArticleBritish journal of anaesthesia2024

Artificial intelligence for ultrasound scanning in regional anaesthesia: a scoping review of the evidence from multiple disciplines.

James S Bowness, David Metcalfe, Kariem El-Boghdadly, Neal Thurley, Megan Morecroft, Thomas Hartley, Joanna Krawczyk, J Alison Noble, Helen Higham

Open access · hybridAbstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in British journal of anaesthesia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
6.5field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.

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  18. Reshaping Anesthesia with Artificial Intelligence: From Concept to Reality.Anesthesia and critical care (Houston, Tex.) · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 6 institutions in 1 country.

James S BownessNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK. Electronic address: james.bowness@jesus.ox.ac.uk.
David MetcalfeNuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK; Emergency Medicine Research in Oxford (EMROx), Oxford University Hospitals NHS Foundation Trust, Oxford, UK. Electronic address: https://twitter.com/@TraumaDataDoc.
Kariem El-BoghdadlyDepartment of Anaesthesia and Peri-operative Medicine, Guy's & St Thomas's NHS Foundation Trust, London, UK; Centre for Human and Applied Physiological Sciences, King's College London, London, UK. Electronic address: https://twitter.com/@elboghdadly.
Neal ThurleyBodleian Health Care Libraries, University of Oxford, Oxford, UK.
Megan MorecroftFaculty of Medicine, Health & Life Sciences, University of Swansea, Swansea, UK.
Thomas HartleyIntelligent Ultrasound, Cardiff, UK. Electronic address: https://twitter.com/@tomhartley84.
Joanna KrawczykDepartment of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK.
J Alison NobleInstitute of Biomedical Engineering, University of Oxford, Oxford, UK. Electronic address: https://twitter.com/@AlisonNoble_OU.
Helen HighamNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Anaesthesia, Oxford University Hospitals NHS Foundation Trust, Oxford, UK. Electronic address: https://twitter.com/@HelenEHigham.
Aneurin Bevan University Health Board · GBNuffield Orthopaedic Centre · GBUniversity of Oxford · GBIntelligent Health (United Kingdom) · GBKing's College London · GBSwansea University · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) for ultrasound scanning in regional anaesthesia is a rapidly developing interdisciplinary field. There is a risk that work could be undertaken in parallel by different elements of the community but with a lack of knowledge transfer between disciplines, leading to repetition and diverging methodologies. This scoping review aimed to identify and map the available literature on the accuracy and utility of AI systems for ultrasound scanning in regional anaesthesia.

methodsA literature search was conducted using Medline, Embase, CINAHL, IEEE Xplore, and ACM Digital Library. Clinical trial registries, a registry of doctoral theses, regulatory authority databases, and websites of learned societies in the field were searched. Online commercial sources were also reviewed.

resultsIn total, 13,014 sources were identified; 116 were included for full-text review. A marked change in AI techniques was noted in 2016-17, from which point on the predominant technique used was deep learning. Methods of evaluating accuracy are variable, meaning it is impossible to compare the performance of one model with another. Evaluations of utility are more comparable, but predominantly gained from the simulation setting with limited clinical data on efficacy or safety. Study methodology and reporting lack standardisation.

conclusionsThere is a lack of structure to the evaluation of accuracy and utility of AI for ultrasound scanning in regional anaesthesia, which hinders rigorous appraisal and clinical uptake. A framework for consistent evaluation is needed to inform model evaluation, allow comparison between approaches/models, and facilitate appropriate clinical adoption.

Indexed as

Anesthesia, ConductionArtificial IntelligenceComputer SimulationDatabases, FactualHumansUltrasonographyartificial intelligenceevaluationmedical devicesregional anaesthesiaregulationstandardisationultrasound

Identifiers

PMID38448269
PMCPMC11103083
OpenAlexW4392474505

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.