ArticleBritish journal of anaesthesia2023
Variability between human experts and artificial intelligence in identification of anatomical structures by ultrasound in regional anaesthesia: a framework for evaluation of assistive artificial intelligence.
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. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of artificial intelligence in peripheral neurosurgery: a systematic review.Neurosurgical review · 2026Pooled it
- Effects of convolutional neural network models on the segmentation of the interscalene brachial plexus in ultrasound imaging for radiomics.Quantitative imaging in medicine and surgery · 2026Article
- Artificial intelligence-generated color overlay for ultrasound identification of the brachial plexus in regional anesthesia: an external validation study.Journal of anesthesia · 2026Article
- Masked autoencoder pretraining for peripheral nerve segmentation in ultrasound images.Scientific reports · 2026Article
- A comprehensive review of ultrasonographic imaging in athletic elbow injuries: a compartment-based approach.Skeletal radiology · 2026Review
- Data annotation and its evaluation in artificial intelligence-based anatomy recognition for ultrasound-guided regional anesthesia: a clinical perspective.Frontiers in medicine · 2026Article
- Automated ultrasound in rheumatology: the dawn of a new era.RMD open · 2025Article
- Artificial intelligence in ultrasound-guided regional anesthesia: bridging the gap between potential and practice: a narrative review.Anesthesia and pain medicine · 2025Review
- Regional Anaesthesia, Contemporary Techniques, and Associated Advancements: A Narrative Review.Cureus · 2024Review
- Artificial intelligence for ultrasound scanning in regional anaesthesia: a scoping review of the evidence from multiple disciplines.British journal of anaesthesia · 2024Article
- Research priorities in regional anaesthesia: an international Delphi study.British journal of anaesthesia · 2024Review
- Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists' ultrasound scanning for regional anesthesia.BMJ surgery, interventions, & health technologies · 2024Article
- The evolution of teaching and learning regional anesthesia at every career stage: The U.S. perspective.Saudi journal of anaesthesiaReview
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Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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methodsNineteen experts in ultrasound-guided regional anaesthesia (UGRA) annotated 100 structures in 30 ultrasound videos across six anatomical regions. These annotations were compared with each other to produce a quantitative assessment of the level of agreement amongst human experts. The AI colour overlay was then compared with all expert annotations. Differences in human-human and human-AI agreement are presented for each structure class (artery, muscle, nerve, fascia/serosal plane) and structure. Clinical context is provided through subjective assessment data from UGRA experts.
resultsFor human-human and human-AI annotations, agreement was highest for arteries (mean Dice score 0.88/0.86), then muscles (0.80/0.77), and lowest for nerves (0.48/0.41). Wide discrepancy exists in consistency for different structures, both with human-human and human-AI comparisons; highest for sartorius muscle (0.91/0.92) and lowest for the radial nerve (0.21/0.27).
conclusionsHuman experts and the AI system both showed the same pattern of agreement in sono-anatomical structure identification. The clinical significance of the differences presented must be explored; however the perception that human expert opinion is uniform must be challenged. Elements of this assessment framework could be used for other devices to allow consistent evaluations that inform clinical training and practice. Anaesthetists should be actively engaged in the development and adoption of new AI technology.
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