A Review of Deep Learning Methods for Automatic Pain Assessment
Pain is a complex phenomenon that incorporates both physical sensations and emotional responses. The use of automated pain assessment is crucial in order to develop effective medical diagnostic systems for pain management. Thus, it is important to conduct a study on the outcomes achieved by utilisin...
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Zhicui L.; Mat Jasin A.B.; Akma Binti Zulkifle F.; Ul-Saufie Bin Mohamad Japeri A.Z. |
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Zhicui L.; Mat Jasin A.B.; Akma Binti Zulkifle F.; Ul-Saufie Bin Mohamad Japeri A.Z. 2-s2.0-86000233085 A Review of Deep Learning Methods for Automatic Pain Assessment 2024 Proceedings - 2024 7th International Conference on Data Science and Information Technology, DSIT 2024 10.1109/DSIT61374.2024.10881665 https://www.scopus.com/inward/record.uri?eid=2-s2.0-86000233085&doi=10.1109%2fDSIT61374.2024.10881665&partnerID=40&md5=ef9db9a5ef917eea5c6502c5d498f88f Pain is a complex phenomenon that incorporates both physical sensations and emotional responses. The use of automated pain assessment is crucial in order to develop effective medical diagnostic systems for pain management. Thus, it is important to conduct a study on the outcomes achieved by utilising deep learning algorithms for the detection of pain expression. This study aims to provide reliable and unbiased methods for the automated assessment of pain. The aim of this systematic review is to discuss the models, methods and data types used to build the foundation of deep learning-based automated pain assessment systems, with a focus on analysing improved strategies and methods based on deep modelling techniques used to enhance feature extraction accuracy and the accuracy of pain level assessment. As a result, the literature explores the application of facial expression recognition techniques in the field environment for addressing various challenges in clinical testing. Consequently, the systematic review identifies the limitations of the current research on automated pain assessment and offers an outlook on the potential future research directions. © 2024 IEEE. Institute of Electrical and Electronics Engineers Inc. English Conference paper |
| author |
2-s2.0-86000233085 |
| spellingShingle |
2-s2.0-86000233085 A Review of Deep Learning Methods for Automatic Pain Assessment |
| author_facet |
2-s2.0-86000233085 |
| author_sort |
2-s2.0-86000233085 |
| title |
A Review of Deep Learning Methods for Automatic Pain Assessment |
| title_short |
A Review of Deep Learning Methods for Automatic Pain Assessment |
| title_full |
A Review of Deep Learning Methods for Automatic Pain Assessment |
| title_fullStr |
A Review of Deep Learning Methods for Automatic Pain Assessment |
| title_full_unstemmed |
A Review of Deep Learning Methods for Automatic Pain Assessment |
| title_sort |
A Review of Deep Learning Methods for Automatic Pain Assessment |
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2024 |
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Proceedings - 2024 7th International Conference on Data Science and Information Technology, DSIT 2024 |
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| doi_str_mv |
10.1109/DSIT61374.2024.10881665 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-86000233085&doi=10.1109%2fDSIT61374.2024.10881665&partnerID=40&md5=ef9db9a5ef917eea5c6502c5d498f88f |
| description |
Pain is a complex phenomenon that incorporates both physical sensations and emotional responses. The use of automated pain assessment is crucial in order to develop effective medical diagnostic systems for pain management. Thus, it is important to conduct a study on the outcomes achieved by utilising deep learning algorithms for the detection of pain expression. This study aims to provide reliable and unbiased methods for the automated assessment of pain. The aim of this systematic review is to discuss the models, methods and data types used to build the foundation of deep learning-based automated pain assessment systems, with a focus on analysing improved strategies and methods based on deep modelling techniques used to enhance feature extraction accuracy and the accuracy of pain level assessment. As a result, the literature explores the application of facial expression recognition techniques in the field environment for addressing various challenges in clinical testing. Consequently, the systematic review identifies the limitations of the current research on automated pain assessment and offers an outlook on the potential future research directions. © 2024 IEEE. |
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Institute of Electrical and Electronics Engineers Inc. |
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| language |
English |
| format |
Conference paper |
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scopus |
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Scopus |
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1828987860982693888 |