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Gkikas S, Tsiknakis M. Automatic assessment of pain based on deep learning methods: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107365. [PMID: 36764062 DOI: 10.1016/j.cmpb.2023.107365] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 01/06/2023] [Accepted: 01/21/2023] [Indexed: 06/18/2023]
Abstract
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.
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Affiliation(s)
- Stefanos Gkikas
- 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.
| | - Manolis Tsiknakis
- 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.
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Lin Y, Xiao Y, Wang L, Guo Y, Zhu W, Dalip B, Kamarthi S, Schreiber KL, Edwards RR, Urman RD. Experimental Exploration of Objective Human Pain Assessment Using Multimodal Sensing Signals. Front Neurosci 2022; 16:831627. [PMID: 35221908 PMCID: PMC8874020 DOI: 10.3389/fnins.2022.831627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/07/2022] [Indexed: 11/17/2022] Open
Abstract
Optimization of pain assessment and treatment is an active area of research in healthcare. The purpose of this research is to create an objective pain intensity estimation system based on multimodal sensing signals through experimental studies. Twenty eight healthy subjects were recruited at Northeastern University. Nine physiological modalities were utilized in this research, namely facial expressions (FE), electroencephalography (EEG), eye movement (EM), skin conductance (SC), and blood volume pulse (BVP), electromyography (EMG), respiration rate (RR), skin temperature (ST), blood pressure (BP). Statistical analysis and machine learning algorithms were deployed to analyze the physiological data. FE, EEG, SC, BVP, and BP proved to be able to detect different pain states from healthy subjects. Multi-modalities proved to be promising in detecting different levels of painful states. A decision-level multi-modal fusion also proved to be efficient and accurate in classifying painful states.
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Affiliation(s)
- Yingzi Lin
- Intelligent Human Machine Systems Laboratory, College of Engineering, Northeastern University, Boston, MA, United States
- *Correspondence: Yingzi Lin,
| | - Yan Xiao
- College of Nursing and Health Innovation, University of Texas at Arlington, Arlington, TX, United States
| | - Li Wang
- Intelligent Human Machine Systems Laboratory, College of Engineering, Northeastern University, Boston, MA, United States
| | - Yikang Guo
- Intelligent Human Machine Systems Laboratory, College of Engineering, Northeastern University, Boston, MA, United States
| | - Wenchao Zhu
- Intelligent Human Machine Systems Laboratory, College of Engineering, Northeastern University, Boston, MA, United States
| | - Biren Dalip
- Intelligent Human Machine Systems Laboratory, College of Engineering, Northeastern University, Boston, MA, United States
| | - Sagar Kamarthi
- Intelligent Human Machine Systems Laboratory, College of Engineering, Northeastern University, Boston, MA, United States
| | - Kristin L. Schreiber
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard University, Boston, MA, United States
| | - Robert R. Edwards
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard University, Boston, MA, United States
| | - Richard D. Urman
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard University, Boston, MA, United States
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