ArticleJournal of experimental orthopaedics2024
Artificial intelligence-assisted ultrasound-guided regional anaesthesia: An explorative scoping review.
Article in Journal of experimental orthopaedics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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.
Who cites it
6 citing papers in PubMed.
- Current Status and Future Projections of Artificial Intelligence-Assisted Ultrasonography and Needle Visibility Methods in Regional Anesthesia.The Eurasian journal of medicine · 2026Article
- Precision and pitfalls: evolving role of ultrasound-guided nerve blocks in Orthopedic perioperative pathway-a perspective.Frontiers in medicine · 2026Article
- Integrating artificial intelligence into orthopedics: Opportunities, challenges, and future directions.Journal of hand and microsurgery · 2025Review
- Artificial intelligence applications in the management of musculoskeletal disorders of the shoulder: A systematic review.Journal of experimental orthopaedics · 2025Review
- Artificial intelligence-assisted ultrasound-guided regional anaesthesia: An explorative scoping review.Journal of experimental orthopaedics · 2024Article
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The present study reviews the available scientific literature on artificial intelligence (AI)-assisted ultrasound-guided regional anaesthesia (UGRA) and evaluates the reported intraprocedural parameters and postprocedural outcomes. Methods: A literature search was performed on 19 September 2023, using the Medline, EMBASE, CINAHL, Cochrane Library and Google Scholar databases by experts in electronic searching. All study designs were considered with no restrictions regarding patient characteristics or cohort size. Outcomes assessed included the accuracy of AI-model tracking, success at the first attempt, differences in outcomes between AI-assisted and unassisted UGRA, operator feedback and case-report data. Results: A joint adaptive median binary pattern (JAMBP) has been applied to improve the tracking procedure, while a particle filter (PF) is involved in feature extraction. JAMBP combined with PF was most accurate on all images for landmark identification, with accuracy scores of 0.83, 0.93 and 0.93 on original, preprocessed and filtered images, respectively. Evaluation of first-attempt success of spinal needle insertion revealed first-attempt success in most patients. When comparing AI application versus UGRA alone, a significant statistical difference ( Conclusion: AI appears promising to enhance UGRA as well as to positively influence operator training. AI application of UGRA may improve the identification of anatomical structures and provide guidance for needle placement, reducing the risk of complications and improving patient outcomes. Level of Evidence: Level IV.
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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.