Swain BP, Nag DS, Anand R, Kumar H, Ganguly PK, Singh N. Current evidence on artificial intelligence in regional anesthesia. World J Clin Cases 2024; 12(33): 6613-6619 [DOI: 10.12998/wjcc.v12.i33.6613]
Corresponding Author of This Article
Deb Sanjay Nag, MD, Doctor, Department of Anaesthesiology, Tata Main Hospital, C Road West, Northern Town, Bistupur, Jamshedpur 831001, India. ds.nag@tatasteel.com
Research Domain of This Article
Anesthesiology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Bhanu Pratap Swain, Deb Sanjay Nag, Rishi Anand, Himanshu Kumar, Pradip Kumar Ganguly, Niharika Singh, Department of Anaesthesiology, Tata Main Hospital, Jamshedpur 831001, India
Bhanu Pratap Swain, Rishi Anand, Himanshu Kumar, Department of Anesthesiology, Manipal Tata Medical College, Jamshedpur 831017, India
Co-first authors: Bhanu Pratap Swain and Deb Sanjay Nag.
Author contributions: Swain BP, Nag DS, and Anand R designed the overall concept and outline of the manuscript; Kumar H, Ganguly PG, and Singh N contributed to the discussion and design of the manuscript; Swain BP, Nag DS, Anand R, Kumar H, Ganguly PK, and Singh N contributed to the writing, and editing the manuscript and review of literature; all of the authors read and approved the final version of the manuscript to be published.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Deb Sanjay Nag, MD, Doctor, Department of Anaesthesiology, Tata Main Hospital, C Road West, Northern Town, Bistupur, Jamshedpur 831001, India. ds.nag@tatasteel.com
Received: June 18, 2024 Revised: September 11, 2024 Accepted: September 19, 2024 Published online: November 26, 2024 Processing time: 101 Days and 1.7 Hours
Abstract
The recent advancement in regional anesthesia (RA) has been largely attributed to ultrasound technology. However, the safety and efficiency of ultrasound-guided nerve blocks depend upon the skill and experience of the performer. Even with adequate training, experience, and knowledge, human-related limitations such as fatigue, failure to recognize the correct anatomical structure, and unintentional needle or probe movement can hinder the overall effectiveness of RA. The amalgamation of artificial intelligence (AI) to RA practice has promised to override these human limitations. Machine learning, an integral part of AI can improve its performance through continuous learning and experience, like the human brain. It enables computers to recognize images and patterns specifically useful in anatomic structure identification during the performance of RA. AI can provide real-time guidance to clinicians by highlighting important anatomical structures on ultrasound images, and it can also assist in needle tracking and accurate deposition of local anesthetics. The future of RA with AI integration appears promising, yet obstacles such as device malfunction, data privacy, regulatory barriers, and cost concerns can deter its clinical implementation. The current mini review deliberates the current application, future direction, and barrier to the application of AI in RA practice.
Core Tip: Proficiency in ultrasound-guided regional anesthesia (UGRA) demands an accurate interpretation of sono-anatomy and precise delivery of local anesthetics in the intended location by maneuvering a block needle. Integration of artificial intelligence (AI) can make the job of clinicians a lot easier by deciphering the correct anatomy and providing real-time needle guidance. It promises to improve the success of the UGRA procedures and reduce the complication rate by minimizing human error. Furthermore, AI can be a great tool in education and training. It can help the trainees to learn regional anesthesia techniques faster and more efficiently. Although the future looks promising, the full integration of AI in clinical practice needs user validation and ample data on clinical outcomes.
