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

Dmitriy Viderman; Mukhit Dossov; Serik Seitenov; Min-Ho Lee

doi:10.3389/fmed.2022.994805

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

Front Med (Lausanne). 2022 Oct 25:9:994805. doi: 10.3389/fmed.2022.994805. eCollection 2022.

Authors

Dmitriy Viderman¹, Mukhit Dossov², Serik Seitenov², Min-Ho Lee³

Affiliations

¹ Department of Biomedical Sciences, Nazarbayev University School of Medicine, Nur-Sultan, Kazakhstan.
² Department of Anesthesiology and Critical Care, Presidential Hospital, Nur-Sultan, Kazakhstan.
³ Department of Computer Sciences, Nazarbayev University School of Engineering and Digital Sciences, Nur-Sultan, Kazakhstan.

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 (AI) has been studied in many medical disciplines with achieving high success, especially in radiology. The purpose of this review was to review 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-S 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; (7) Settings: Any healthcare settings (Medical centers, hospitals, clinics, laboratories.

Results: The systematic searches identified 78 citations. After the removal of the duplicates, 19 full-text articles were assessed; and 15 studies were eligible for inclusion in the review.

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. Although significant progress has been made in the application of AI-guided UGRA, randomized control trials are still missing.

Keywords: artificial intelligence; machine learning; peripheral nerve block; regional anesthesia; sono-anatomy; training; ultrasound; ultrasound-guided regional anesthesia.

Publication types

Review