Automatic assessment of pain based on deep learning methods: A systematic review

卷:231

Review

关键词: INTENSITY ESTIMATION;    FACIAL EXPRESSION;    VALIDITY;    MODEL;    TIME;   

DOI  :  10.1016/j.cmpb.2023.107365

来源: SCIE

【 摘 要 】

Background and Objective: The automatic assessment of pain is vital in designing optimal pain manage-ment 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 attempt-ing 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.Methods: 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.Results: A total of one hundred and ten publications were identified and categorized by the number of in-formation channels used (unimodal versus multimodal approaches) and whether the temporal dimension was also used.Conclusions: This review demonstrates the importance of multimodal approaches for automatic pain esti-mation, 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 modet development, validation, and application as decision-support tools in real-life scenarioa.(R) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

【 授权许可】

Free