基于面部表情的术后疼痛人工智能评估系统研究进展
Research Progress of Artificial Intelligence Assessment System for Postoperative Pain Based on Facial Expression
DOI: 10.12677/acm.2024.1441215, PDF,   
作者: 张 妮：西安医学院，陕西 西安；聂 煌*：空军军医大学第一附属医院麻醉与围术期医学科，陕西 西安
关键词: 人工智能；面部表情；疼痛评估；疼痛管理；自动识别；Artificial Intelligence； Facial Expression； Pain Assessment； Pain Management； Automatic Recognition
摘要: 目前，中国近一半的患者术后仍出现中度至重度疼痛。术后疼痛对医疗保健系统和患者满意度构成了重大挑战，人们仍无法对术后疼痛管理的质量进行系统性的评估。疼痛评估是一个复杂的任务，很大程度上依赖于患者的自我报告。然而一些人无法自我报告以及医疗专业人员评估不能保证连续性和客观性，所以对于疼痛自动识别的需求很大。这篇综述旨在提供基于面部表情的术后疼痛人工智能评估系统目前的技术现状，以及用于疼痛检测的技术基础。该综述还强调了人工智能评估系统对临床实践中疼痛评估的潜在影响，在提高疼痛识别效率，分析自我疼痛报告数据，预测疼痛，帮助临床医生有效管理术后疼痛方面提供了更多临床证据，为该领域的进一步研究奠定了基础。
Abstract: Currently, almost half of patients still suffer from moderate-to-severe pain after surgery in China. Postoperative pain poses a significant challenge to the healthcare system and patient satisfaction. However, systemic assessment of the quality of postoperative pain management in China remains unavailable. Pain assessment is a complex task largely dependent on the patient’s self-report. However, some patients can not self-report pain, and professional assessment can not guarantee continuity and objectivity. As a result, there is a growing demand for automatic pain recognition systems. This review aims to provide current state as well as the technical foundations used in pain detection designed to improve pain assessment and management for adult patients. The review highlights the potential impact of AI on pain assessment in clinical practice. This review provides evidence that automatic pain recognition systems were used to improve efficient pain recognition and pain assessment, analyze self-reported pain data, predict pain, and help clinicians and patients to manage chronic pain more effectively and lays the groundwork for further study in this area.
文章引用：张妮, 聂煌. 基于面部表情的术后疼痛人工智能评估系统研究进展[J]. 临床医学进展, 2024, 14(4): 1713-1718. https://doi.org/10.12677/acm.2024.1441215

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