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

• Published in: Computer methods and programs in biomedicine Vol. 231; p. 107365
• Main Authors: Gkikas, Stefanos, Tsiknakis, Manolis
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.04.2023
• Subjects: Affective computing; Algorithms; Biosignals; Databases, Factual; Deep Learning; Facial expression; Humans; Machine learning; Pain; Pain Measurement - methods; Pain recognition; Biosignals; Affective computing; Pain recognition; Facial expression; Machine learning
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2023.107365

•Automatic pain estimation is great of importance for the pain assessment procedure.•Utilization of deep learning methods for automatic pain estimation.•Pain databases need to include demographics information about the subjects.•Videos and biosignals reveal the pain situation of the patients.•Temporal exploitation of modalities provide greater representation of pain situation. Background and Objective: 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. 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 information 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 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.