Evidence map›Paper›PMID 35987706›Full record

ArticleBritish journal of anaesthesia2023

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

James S Bowness, David Burckett-St Laurent, Nadia Hernandez, Pearse A Keane, Clara Lobo, Steve Margetts, Eleni Moka, Amit Pawa, Meg Rosenblatt, Nick Sleep and 5 more

2 registry-linked trialsOpen access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in British journal of anaesthesia, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 35 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 2 pooled it
14.4field-weighted citation impact, top 1% 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.

NCT04906018 completednot on this map

A Study to Collect Imaging Data for the Validation of the Intelligent Ultrasound's ScanNav Anatomy Peripheral Nerve Block (PNB) - US v1.0

TypeobservationalSponsorIntelligentUltrasound LimitedRan2021 to 2021Enrolled40ConditionsUltrasound Imaging of Anatomical StructuresArmsUltrasound scans
NCT07581678 completednot on this mapstarted 2025, after this paper: background citation

AI-Assisted Evaluation of Anatomical Success of Fascial Plane Blocks Applied in Open Heart Surgery and Investigation of Its Relationship With Postoperative Pain Scores: A Prospective Observational Study

Typeobservational_patient_registrySponsorBursa Yuksek Ihtisas Training and Research HospitalRan2025 to 2026Enrolled120ConditionsPain, Postoperative, Cardiac Surgical Procedures, Nerve Block
3 · Its place in the literature

Who cites it

35 citing papers in PubMed, 2 syntheses or guidelines pooled it, 70 citations in OpenAlex.

  1. Pooled it
  2. Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025
    Pooled it
  3. Article
  4. Review
  5. Article
  6. Article
  7. An adaptive attention U-network for recognizing ultrasound images.The Journal of international medical research · 2026
    Article
  8. Article
  9. Review
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  11. Review
  12. Article
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  18. Artificial intelligence in anesthesiology: a bibliometric analysis.Perioperative medicine (London, England) · 2024
    Review
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  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors at 10 institutions in 3 countries.

James S BownessOxford Simulation, Teaching and Research Centre, University of Oxford, Oxford, UK; Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK. Electronic address: james.bowness@jesus.ox.ac.uk.
David Burckett-St LaurentDepartment of Anaesthesia, Royal Cornwall Hospitals NHS Trust, Truro, UK.
Nadia HernandezDepartment of Anesthesiology, Memorial Hermann Hospital, Texas Medical Centre, Houston, TX, USA.
Pearse A KeaneInstitute of Ophthalmology, Faculty of Brain Sciences, University College London, London, UK; National Institute for Health and Care Research Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK.
Clara LoboAnesthesiology Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates.
Steve MargettsIntelligent Ultrasound, Cardiff, UK.
Eleni MokaAnaesthesiology Department, Creta InterClinic Hospital, Hellenic Healthcare Group, Heraklion, Crete, Greece.
Amit PawaDepartment of Anaesthesia, Guy's and St Thomas' Hospitals NHS Trust, London, UK; Faculty of Life Sciences and Medicine, King's College London, London, UK.
Meg RosenblattDepartment of Anesthesiology, Perioperative and Pain Medicine, Mount Sinai Morningside and West Hospitals, New York, NY, USA.
Nick SleepIntelligent Ultrasound, Cardiff, UK.
Alasdair TaylorDepartment of Anaesthesia, NHS Tayside, Dundee, UK.
Glenn WoodworthDepartment of Anesthesiology and Perioperative Medicine, Oregon Health & Science University, Portland, OR, USA.
Asta VasalauskaiteIntelligent Ultrasound, Cardiff, UK.
J Alison NobleInstitute of Biomedical Engineering, University of Oxford, Oxford, UK.
Helen HighamOxford Simulation, Teaching and Research Centre, University of Oxford, Oxford, UK; Department of Anaesthesia, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Intelligent Health (United Kingdom) · GBUniversity of Oxford · GBAneurin Bevan University Health Board · GBMemorial Hermann · USMoorfields Eye Hospital NHS Foundation Trust · GBNHS Tayside · GBOregon Health & Science University · USPolyclinic General Hospital · GRRoyal Cornwall Hospital Trust · GBSt Thomas' Hospital · GB

Funding

Medical Research Council MR/T019050/1
6 · The paper itself

Abstract

backgroundUltrasonound 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.

methodsUltrasound-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.

resultsThe 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).

conclusionsArtificial 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 registrationNCT04906018.

Indexed as

Anesthesia, ConductionNerve BlockArtificial IntelligenceHumansUltrasonographyUltrasonography, Interventionalanatomyartificial intelligencemachine learningregional anaesthesiatranslational AIultrasonographyultrasound

Identifiers

PMID35987706
PMCPMC9900723
OpenAlexW4292409438

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.