PDF(Pubmed)
Abstract：
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.
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.
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.
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.
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.
PROSPERO CRD42023418181; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=418181.
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.
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.
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.
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.
PROSPERO CRD42023418181; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=418181.
摘要：
背景：患者疼痛状态的连续监测和记录是当前术后疼痛管理研究中的一个主要问题。在大量关注不同疼痛评估方法的原创或评论文章中，许多研究人员已经研究了计算机视觉（CV）如何通过捕获面部表情来提供帮助。然而,研究之间缺乏适当的结果比较，以确定当前的研究差距。
目的：本系统综述和荟萃分析的目的是研究从面部图像中进行多层次疼痛评估的人工智能模型的诊断性能。
方法：PubMed，Embase,IEEE,WebofScience,和Cochrane图书馆数据库在2023年9月30日之前搜索相关出版物。仅使用面部图像来估计多个疼痛值的研究包括在系统评价中。使用诊断准确性研究的质量评估进行研究质量评估，第二版工具。这些研究的性能通过包括敏感性在内的指标进行评估，特异性，对数诊断优势比(LDOR)，和曲线下面积(AUC)。通过森林地块评估并呈现了联运变异性。
结果：系统评价共纳入45份报告。报告的测试精度范围为0.27-0.99，其他指标为，包括平均值标准误差(MSE)，平均绝对误差(MAE)，类内相关系数(ICC)，和皮尔逊相关系数(PCC)，范围分别为0.31-4.61、0.24-2.8、0.19-0.83和0.48-0.92。总的来说,6项研究纳入荟萃分析。他们的综合敏感度为98%（95%CI96%-99%），特异性为98%（95%CI97%-99%），LDOR为7.99（95%CI6.73-9.31），AUC为0.99（95%CI0.99-1）。亚组分析表明，诊断性能是可以接受的，尽管不平衡的数据仍然被强调是一个主要问题。所有研究都至少有一个领域存在高偏倚风险，对于20%(9/45)的研究，没有适用性问题。
结论：这篇综述总结了面部表情自动多层次疼痛评估的最新证据，并在荟萃分析中比较了结果的测试准确性。通过当前的CV算法建立了从面部图像进行疼痛估计的有希望的性能。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数据库和指标可以改善未来的研究。
背景：PROSPEROCRD42023418181;https://www.crd.约克。AC.uk/prospro/display_record.php?RecordID=418181。
目的：本系统综述和荟萃分析的目的是研究从面部图像中进行多层次疼痛评估的人工智能模型的诊断性能。
方法：PubMed，Embase,IEEE,WebofScience,和Cochrane图书馆数据库在2023年9月30日之前搜索相关出版物。仅使用面部图像来估计多个疼痛值的研究包括在系统评价中。使用诊断准确性研究的质量评估进行研究质量评估，第二版工具。这些研究的性能通过包括敏感性在内的指标进行评估，特异性，对数诊断优势比(LDOR)，和曲线下面积(AUC)。通过森林地块评估并呈现了联运变异性。
结果：系统评价共纳入45份报告。报告的测试精度范围为0.27-0.99，其他指标为，包括平均值标准误差(MSE)，平均绝对误差(MAE)，类内相关系数(ICC)，和皮尔逊相关系数(PCC)，范围分别为0.31-4.61、0.24-2.8、0.19-0.83和0.48-0.92。总的来说,6项研究纳入荟萃分析。他们的综合敏感度为98%（95%CI96%-99%），特异性为98%（95%CI97%-99%），LDOR为7.99（95%CI6.73-9.31），AUC为0.99（95%CI0.99-1）。亚组分析表明，诊断性能是可以接受的，尽管不平衡的数据仍然被强调是一个主要问题。所有研究都至少有一个领域存在高偏倚风险，对于20%(9/45)的研究，没有适用性问题。
结论：这篇综述总结了面部表情自动多层次疼痛评估的最新证据，并在荟萃分析中比较了结果的测试准确性。通过当前的CV算法建立了从面部图像进行疼痛估计的有希望的性能。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数据库和指标可以改善未来的研究。
背景：PROSPEROCRD42023418181;https://www.crd.约克。AC.uk/prospro/display_record.php?RecordID=418181。
