Yue JM, Wang Q, Liu B, Zhou L. Postoperative accurate pain assessment of children and artificial intelligence: A medical hypothesis and planned study. World J Clin Cases 2024; 12(4): 681-687 [PMID: 38322690 DOI: 10.12998/wjcc.v12.i4.681]
Corresponding Author of This Article
Leng Zhou, MD, PhD, Associate Professor, Doctor, Department of Anesthesiology, West China Hospital, Sichuan University, No. 37 Guoxue Road, Wuhou District, Chengdu 610041, Sichuan Province, China. zhoulenghx@foxmail.com
Research Domain of This Article
Pediatrics
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/
World J Clin Cases. Feb 6, 2024; 12(4): 681-687 Published online Feb 6, 2024. doi: 10.12998/wjcc.v12.i4.681
Postoperative accurate pain assessment of children and artificial intelligence: A medical hypothesis and planned study
Jian-Ming Yue, Qi Wang, Bin Liu, Leng Zhou
Jian-Ming Yue, Qi Wang, Bin Liu, Leng Zhou, Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Author contributions: Yue JM reviewed the literature and wrote the manuscript; Wang Q reviewed the manuscript and provided some suggestions; Liu B conceived the idea for the manuscript; Zhou L is the corresponding author and coordinate the writing of the paper.
Conflict-of-interest statement: There is no conflict of interest associated with any of the senior author or other coauthors contributed their efforts in this 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: Leng Zhou, MD, PhD, Associate Professor, Doctor, Department of Anesthesiology, West China Hospital, Sichuan University, No. 37 Guoxue Road, Wuhou District, Chengdu 610041, Sichuan Province, China. zhoulenghx@foxmail.com
Received: October 27, 2023 Peer-review started: October 27, 2023 First decision: December 31, 2023 Revised: January 2, 2023 Accepted: January 11, 2024 Article in press: January 11, 2024 Published online: February 6, 2024 Processing time: 89 Days and 20.1 Hours
Abstract
Although the pediatric perioperative pain management has been improved in recent years, the valid and reliable pain assessment tool in perioperative period of children remains a challenging task. Pediatric perioperative pain management is intractable not only because children cannot express their emotions accurately and objectively due to their inability to describe physiological characteristics of feeling which are different from those of adults, but also because there is a lack of effective and specific assessment tool for children. In addition, exposure to repeated painful stimuli early in life is known to have short and long-term adverse sequelae. The short-term sequelae can induce a series of neurological, endocrine, cardiovascular system stress related to psychological trauma, while long-term sequelae may alter brain maturation process, which can lead to impair neurodevelopmental, behavioral, and cognitive function. Children’s facial expressions largely reflect the degree of pain, which has led to the developing of a number of pain scoring tools that will help improve the quality of pain management in children if they are continually studied in depth. The artificial intelligence (AI) technology represented by machine learning has reached an unprecedented level in image processing of deep facial models through deep convolutional neural networks, which can effectively identify and systematically analyze various subtle features of children’s facial expressions. Based on the construction of a large database of images of facial expressions in children with perioperative pain, this study proposes to develop and apply automatic facial pain expression recognition software using AI technology. The study aims to improve the postoperative pain management for pediatric population and the short-term and long-term quality of life for pediatric patients after operational event.
Core Tip: Valid and reliable pain assessment tools in perioperative period of children remain a challenging task by far. The artificial intelligence (AI) technology has reached an unprecedented level in image processing of deep facial models, which can effectively identify and systematically analyze various features of children’s facial expression. Based on the construction of a large database of images of facial expressions in children, we aim to develop an AI tool for pain assessment in order to improve the management of perioperative pain.
