Convolution Neural Network for Pain Intensity Assessment from Facial Expression

Hosseini, Elahe; Fang, Ruijie; Zhang, Ruoyu; Chuah, Chen-Nee; Orooji, Mahdi; Rafatirad, Soheil; Rafatirad, Setareh; Homayoun, Houman

doi:10.1109/embc48229.2022.9871770

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…For all 315 entries, 39% (124) were assessed as high-risk. In total, 5 studies had the lowest risk of bias, with 6 domains assessed as low risk [26,27,31,32,59].…”

Section: Methodological Quality Of Included Studiesmentioning

confidence: 99%

Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis

Huo,

Yu,

Lin

et al. 2024

J Med Internet Res

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Background The continuous monitoring and recording of patients’ pain status is a major problem in current research on postoperative pain management. In the large number of original or review articles focusing on different approaches for pain assessment, many researchers have investigated how computer vision (CV) can help by capturing facial expressions. However, there is a lack of proper comparison of results between studies to identify current research gaps. Objective The purpose of this systematic review and meta-analysis was to investigate the diagnostic performance of artificial intelligence models for multilevel pain assessment from facial images. Methods The PubMed, Embase, IEEE, Web of Science, and Cochrane Library databases were searched for related publications before September 30, 2023. Studies that used facial images alone to estimate multiple pain values were included in the systematic review. A study quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies, 2nd edition tool. The performance of these studies was assessed by metrics including sensitivity, specificity, log diagnostic odds ratio (LDOR), and area under the curve (AUC). The intermodal variability was assessed and presented by forest plots. Results A total of 45 reports were included in the systematic review. The reported test accuracies ranged from 0.27-0.99, and the other metrics, including the mean standard error (MSE), mean absolute error (MAE), intraclass correlation coefficient (ICC), and Pearson correlation coefficient (PCC), ranged from 0.31-4.61, 0.24-2.8, 0.19-0.83, and 0.48-0.92, respectively. In total, 6 studies were included in the meta-analysis. Their combined sensitivity was 98% (95% CI 96%-99%), specificity was 98% (95% CI 97%-99%), LDOR was 7.99 (95% CI 6.73-9.31), and AUC was 0.99 (95% CI 0.99-1). The subgroup analysis showed that the diagnostic performance was acceptable, although imbalanced data were still emphasized as a major problem. All studies had at least one domain with a high risk of bias, and for 20% (9/45) of studies, there were no applicability concerns. Conclusions This review summarizes recent evidence in automatic multilevel pain estimation from facial expressions and compared the test accuracy of results in a meta-analysis. Promising performance for pain estimation from facial images was established by current CV algorithms. Weaknesses in current studies were also identified, suggesting that larger databases and metrics evaluating multiclass classification performance could improve future studies. Trial Registration PROSPERO CRD42023418181; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=418181

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“…For all 315 entries, 39% (124) were assessed as high-risk. In total, 5 studies had the lowest risk of bias, with 6 domains assessed as low risk [26,27,31,32,59].…”

Section: Methodological Quality Of Included Studiesmentioning

confidence: 99%

Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis

Huo,

Yu,

Lin

et al. 2024

J Med Internet Res

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“…Recent studies have made significant progress using machine learning and deep learning techniques to assess pain intensity from facial expressions. Researchers in [36] [37] developed deep learning algorithms, with the former achieving good accuracy in detecting four pain levels and the latter outperforming existing models in estimating pain intensity across seven levels. Sri et al [38] provided an automatic patient monitoring and alerting system to assist in continuously assessing and detecting pain levels using facial reactions.…”

Section: ) Facial Expressionsmentioning

confidence: 99%

PainMeter: Automatic Assessment of Pain Intensity Levels From Multiple Physiological Signals Using Machine Learning

Albahdal,

Aljebreen,

Ibrahim

2024

IEEE Access

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Pain assessment traditionally relies on self-report, but it is subjective and influenced by various factors. To address this, there's a need for an affordable and scalable objective pain identification method. Current research suggests that pain has physiological markers beyond the brain, such as changes in cardiovascular activity and electrodermal responses. Utilizing these markers, real-time pain detection algorithms were developed using the BioVid Heat Pain dataset, consisting of 86 healthy individuals experiencing acute pain. Three physiological signals were collected (ECG, GSR, EMG). Various machine learning models were employed to lay the foundation for future advancements in creating sophisticated pain categorization algorithms. The goal is to develop a machine learning model capable of accurately classifying levels of pain experienced based solely on physiological signals. The proposed method produced an accuracy score of 87% for binary classification and 52% accuracy for multi-class classification, with the highestperforming machine learning model being Random Forests. These results suggest that the PainMeter can be deployed in field settings using wearable sensors, offering real-time, unbiased pain sensing and management capabilities.

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“…Multiple studies embarked on the creation of innovative models for pain recognition through the utilization of machine learning techniques. And each of these studies achieved remarkable success in accurately detecting instances of pain, showcasing commendable levels of accuracy in their outcomes (49)(50)(51). In a separate investigation, a cutting-edge deep-learning model was harnessed to automate pain assessment by analyzing facial expressions, a particularly valuable application in critically ill patients with a high accuracy rate (52).…”

Section: Ai-based Pain Managementmentioning

confidence: 99%

Transforming medicine: artificial intelligence integration in the peripheral nervous system

Qian,

Alhaskawi,

Dong

et al. 2024

Front. Neurol.

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In recent years, artificial intelligence (AI) has undergone remarkable advancements, exerting a significant influence across a multitude of fields. One area that has particularly garnered attention and witnessed substantial progress is its integration into the realm of the nervous system. This article provides a comprehensive examination of AI’s applications within the peripheral nervous system, with a specific focus on AI-enhanced diagnostics for peripheral nervous system disorders, AI-driven pain management, advancements in neuroprosthetics, and the development of neural network models. By illuminating these facets, we unveil the burgeoning opportunities for revolutionary medical interventions and the enhancement of human capabilities, thus paving the way for a future in which AI becomes an integral component of our nervous system’s interface.

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Convolution Neural Network for Pain Intensity Assessment from Facial Expression

Cited by 10 publications

References 18 publications

Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis

Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis

PainMeter: Automatic Assessment of Pain Intensity Levels From Multiple Physiological Signals Using Machine Learning

Transforming medicine: artificial intelligence integration in the peripheral nervous system

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