Background: The continuous monitoring and recording of patients’ pain status is a major problem in current research on postoperative pain management. In the large number of original or review articles focusing on different approaches for pain assessment, many researchers have investigated how computer vision (CV) can help by capturing facial expressions. However, there is a lack of proper comparison of results between studies to identify current research gaps. Objective: The purpose of this systematic review and meta-analysis was to investigate the diagnostic performance of artificial intelligence models for multilevel pain assessment from facial images. Methods: The PubMed, Embase, IEEE, Web of Science, and Cochrane Library databases were searched for related publications before September 30, 2023. Studies that used facial images alone to estimate multiple pain values were included in the systematic review. A study quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies, 2nd edition tool. The performance of these studies was assessed by metrics including sensitivity, specificity, log diagnostic odds ratio (LDOR), and area under the curve (AUC). The intermodal variability was assessed and presented by forest plots. Results: A total of 45 reports were included in the systematic review. The reported test accuracies ranged from 0.27-0.99, and the other metrics, including the mean standard error (MSE), mean absolute error (MAE), intraclass correlation coefficient (ICC), and Pearson correlation coefficient (PCC), ranged from 0.31-4.61, 0.24-2.8, 0.19-0.83, and 0.48-0.92, respectively. In total, 6 studies were included in the meta-analysis. Their combined sensitivity was 98% (95% CI 96%-99%), specificity was 98% (95% CI 97%-99%), LDOR was 7.99 (95% CI 6.73-9.31), and AUC was 0.99 (95% CI 0.99-1). The subgroup analysis showed that the diagnostic performance was acceptable, although imbalanced data were still emphasized as a major problem. All studies had at least one domain with a high risk of bias, and for 20% (9/45) of studies, there were no applicability concerns. Conclusions: This review summarizes recent evidence in automatic multilevel pain estimation from facial expressions and compared the test accuracy of results in a meta-analysis. Promising performance for pain estimation from facial images was established by current CV algorithms. Weaknesses in current studies were also identified, suggesting that larger databases and metrics evaluating multiclass classification performance could improve future studies. Trial Registration: PROSPERO CRD42023418181; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=418181

中文翻译：

---

人工智能在面部图像多级疼痛评估中的应用：系统评价和荟萃分析

背景：持续监测和记录患者疼痛状态是当前术后疼痛管理研究的主要问题。在大量关注不同疼痛评估方法的原创或评论文章中，许多研究人员研究了计算机视觉 (CV) 如何通过捕捉面部表情来提供帮助。然而，缺乏对研究结果进行适当的比较来确定当前的研究差距。目的：本次系统评价和荟萃分析的目的是研究人工智能模型在面部图像多级疼痛评估中的诊断性能。方法：在 PubMed、Embase、IEEE、Web of Science 和 Cochrane 图书馆数据库中搜索 2023 年 9 月 30 日之前的相关出版物。系统评价中纳入了仅使用面部图像来估计多个疼痛值的研究。使用诊断准确性研究质量评估第二版工具进行研究质量评估。这些研究的表现通过敏感性、特异性、对数诊断比值比 (LDOR) 和曲线下面积 (AUC) 等指标进行评估。联运变异性通过森林图进行评估和呈现。结果：共有45篇报告纳入系统评价。报告的测试准确度范围为 0.27-0.99，其他指标，包括平均标准误差 (MSE)、平均绝对误差 (MAE)、组内相关系数 (ICC) 和皮尔逊相关系数 (PCC)，范围为 0.31-分别为 4.61、0.24-2.8、0.19-0.83 和 0.48-0.92。荟萃分析总共纳入 6 项研究。其综合敏感性为 98% (95% CI 96%-99%)，特异性为 98% (95% CI 97%-99%)，LDOR 为 7.99 (95% CI 6.73-9.31)，AUC 为 0.99 (95 % CI 0.99-1)。亚组分析显示诊断性能是可以接受的，尽管数据不平衡仍然被强调为一个主要问题。所有研究都至少有一个领域存在高偏倚风险，并且 20% (9/45) 的研究不存在适用性问题。结论：这篇综述总结了根据面部表情自动进行多级疼痛估计的最新证据，并在荟萃分析中比较了结果的测试准确性。当前的 CV 算法在面部图像疼痛估计方面具有良好的性能。还发现了当前研究的弱点，这表明评估多类分类性能的更大的数据库和指标可以改善未来的研究。试用注册： PROSPERO CRD42023418181； https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=418181