Evidence map›Paper›PMID 42364233›Full record

ArticleThe Eurasian journal of medicine2026

Current Status and Future Projections of Artificial Intelligence-Assisted Ultrasonography and Needle Visibility Methods in Regional Anesthesia.

Yasin Tire, Aydın Mermer, Mustafa Aydemir, Ömer Keklicek, Muhammed Nezih Koç, Mehmet Akif Yazar

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Article in The Eurasian journal of medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

6 authors.

Yasin TireDepartment of Anesthesiology and Reanimation, Konya City Hospital, University of Health Sciences, Konya, Türkiye ; Outcomes Research Consortium, Houston, Texas, USA.
Aydın MermerDepartment of Anesthesiology and Reanimation, Konya City Hospital, University of Health Sciences, Konya, Türkiye.
Mustafa AydemirDepartment of Anesthesiology and Reanimation, Konya City Hospital, University of Health Sciences, Konya, Türkiye.
Ömer KeklicekDepartment of Anesthesiology and Reanimation, Konya City Hospital, University of Health Sciences, Konya, Türkiye.
Muhammed Nezih KoçDepartment of Anesthesiology and Reanimation, Konya City Hospital, University of Health Sciences, Konya, Türkiye.
Mehmet Akif YazarDepartment of Anesthesiology and Reanimation, Konya City Hospital, University of Health Sciences, Konya, Türkiye ; Outcomes Research Consortium, Houston, Texas, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ultrasound-guided regional anesthesia (UGRA) has revolutionized regional anesthesia by enabling direct visualization of neural structures, surrounding anatomy, and local anesthetic spread. However, consistent needle visualization remains challenging due to anisotropy, steep insertion angles, tissue deformation, and ultrasound artifacts, potentially increasing procedural difficulty and the risk of complications such as vascular puncture, pneumothorax, or intraneural injection. Recent advances in artificial intelligence (AI) offer promising solutions. Artificial intelligence-assisted ultrasound systems using deep learning and convolutional neural networks can perform real-time anatomical segmentation, automated needle tracking, and image optimization. These platforms highlight nerves, vessels, and fascial planes with color overlays, guide needle trajectory, and provide feedback on image quality and probe positioning. In addition to procedural assistance, AI may improve training by accelerating anatomical recognition and reducing inter-operator variability. Nevertheless, concerns persist regarding automation bias, algorithm performance in atypical anatomy, and the necessity of ongoing clinician oversight. Overall, AI-assisted ultrasonography represents a significant step toward safer, more standardized, and potentially more efficient regional anesthesia practice. Cite this article as: Tire Y, Mermer A, Aydemir M, Keklicek Ö, Koç MN, Yazar MA. Current status and future projections of artificial intelligence-assisted ultrasonography and needle visibility methods in regional anesthesia. Eurasian J Med. 2026, 58(3), 1453, doi: 10.5152/eurasianjmed.2026.261453.

Identifiers

PMID42364233
PMCPMC13202371

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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.