AUTHOR=Viderman Dmitriy , Dossov Mukhit , Seitenov Serik , Lee Min-Ho 

TITLE=Artificial intelligence in ultrasound-guided regional anesthesia: A scoping review

JOURNAL=Frontiers in Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2022.994805

DOI=10.3389/fmed.2022.994805

ISSN=2296-858X

ABSTRACT=Background:
Regional anesthesia is increasingly used in acute postoperative pain management. Ultrasound has been used to facilitate the performance of the regional block, increase the percentage of successfully performed procedures and reduce the complication rate. Artificial intelligence has been studied in many medical disciplines with achieving high success, especially in radiology. The purpose of this review was to systematically synthesize the evidence on the application of artificial intelligence for optimization and interpretation of the sonographic image, and visualization of needle advancement and injection of local anesthetic.
Methods:
To conduct this scoping review, we followed the PRISMA guidelines. We included studies if they met the following criteria: 1) Application of Artificial intelligence-assisted in ultrasound-guided regional anesthesia; 2) Any human subject (of any age), object (manikin), or animal; 3) Study design: prospective, retrospective, RCTs; 4) Any method of regional anesthesia (epidural, spinal anesthesia, peripheral nerves); 5) Any anatomical localization of regional anesthesia (any nerve or plexus) 6) Any methods of artificial intelligence.
Results:  The systematic searches identified sixty-five citations. After the removal of the duplicates, fourteen full-text articles were assessed; and eleven studies were included.
Conclusions: AI solutions might be useful in anatomical landmark identification, reducing or even avoiding possible complications. AI-guided solutions can improve the optimization and interpretation of the sonographic image, visualization of needle advancement, and injection of local anesthetic. AI-guided solutions might improve the training process in UGRA.