Gkikas Stefanos, Tsiknakis Manolis
Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, Heraklion, 71410, Greece; Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research & Technology-Hellas, Vassilika Vouton, Heraklion, 70013, Greece.
Comput Methods Programs Biomed. 2023 Apr;231:107365. doi: 10.1016/j.cmpb.2023.107365. Epub 2023 Feb 8.
The automatic assessment of pain is vital in designing optimal pain management interventions focused on reducing suffering and preventing the functional decline of patients. In recent years, there has been a surge in the adoption of deep learning algorithms by researchers attempting to encode the multidimensional nature of pain into meaningful features. This systematic review aims to discuss the models, the methods, and the types of data employed in establishing the foundation of a deep learning-based automatic pain assessment system.
The systematic review was conducted by identifying original studies searching digital libraries, namely Scopus, IEEE Xplore, and ACM Digital Library. Inclusion and exclusion criteria were applied to retrieve and select those of interest, published until December 2021.
A total of one hundred and ten publications were identified and categorized by the number of information channels used (unimodal versus multimodal approaches) and whether the temporal dimension was also used.
This review demonstrates the importance of multimodal approaches for automatic pain estimation, especially in clinical settings, and also reveals that significant improvements are observed when the temporal exploitation of modalities is included. It provides suggestions regarding better-performing deep architectures and learning methods. Also, it provides suggestions for adopting robust evaluation protocols and interpretation methods to provide objective and comprehensible results. Furthermore, the review presents the limitations of the available pain databases for optimally supporting deep learning model development, validation, and application as decision-support tools in real-life scenarios.
疼痛的自动评估对于设计旨在减轻患者痛苦和预防功能衰退的最佳疼痛管理干预措施至关重要。近年来，研究人员大量采用深度学习算法，试图将疼痛的多维度特性编码为有意义的特征。本系统评价旨在探讨用于建立基于深度学习的自动疼痛评估系统基础的模型、方法和数据类型。
通过检索数字图书馆（即Scopus、IEEE Xplore和ACM数字图书馆）中的原始研究进行系统评价。应用纳入和排除标准检索并选择截至2021年12月发表的相关研究。
共确定了110篇出版物，并根据所使用的信息通道数量（单模态与多模态方法）以及是否使用时间维度进行了分类。
本综述证明了多模态方法在自动疼痛估计中的重要性，尤其是在临床环境中，还表明当纳入模态的时间利用时会观察到显著改进。它提供了关于性能更好的深度架构和学习方法的建议。此外，它还提供了关于采用稳健的评估协议和解释方法以提供客观且可理解结果的建议。此外，该综述指出了现有疼痛数据库在最佳支持深度学习模型开发、验证以及在现实场景中作为决策支持工具应用方面的局限性。
