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

Authors

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

Published in

The Eurasian journal of medicine. Volume 58. Issue 3. Pages 1-7. Apr 24, 2026. Epub Apr 24, 2026.

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.

PMID:
42364233
Bibliographic data and abstract were imported from PubMed on 28 Jun 2026.

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