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Huo J, Yu Y, Lin W, Hu A, Wu C. Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis. J Med Internet Res 2024; 26:e51250. [PMID: 38607660 PMCID: PMC11053395 DOI: 10.2196/51250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/08/2023] [Accepted: 02/28/2024] [Indexed: 04/13/2024] Open
Abstract
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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Affiliation(s)
- Jian Huo
- Boston Intelligent Medical Research Center, Shenzhen United Scheme Technology Company Limited, Boston, MA, United States
| | - Yan Yu
- Department of Anesthesia, Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Key Medical Discipline, Shenzhen, China
| | - Wei Lin
- Shenzhen United Scheme Technology Company Limited, Shenzhen, China
| | - Anmin Hu
- Department of Anesthesia, Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Key Medical Discipline, Shenzhen, China
- Shenzhen United Scheme Technology Company Limited, Shenzhen, China
- The Second Clinical Medical College, Jinan University, Shenzhen, China
| | - Chaoran Wu
- Department of Anesthesia, Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Key Medical Discipline, Shenzhen, China
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Xu J, Smaling HJA, Schoones JW, Achterberg WP, van der Steen JT. Noninvasive monitoring technologies to identify discomfort and distressing symptoms in persons with limited communication at the end of life: a scoping review. BMC Palliat Care 2024; 23:78. [PMID: 38515049 PMCID: PMC10956214 DOI: 10.1186/s12904-024-01371-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 01/29/2024] [Indexed: 03/23/2024] Open
Abstract
BACKGROUND Discomfort and distressing symptoms are common at the end of life, while people in this stage are often no longer able to express themselves. Technologies may aid clinicians in detecting and treating these symptoms to improve end-of-life care. This review provides an overview of noninvasive monitoring technologies that may be applied to persons with limited communication at the end of life to identify discomfort. METHODS A systematic search was performed in nine databases, and experts were consulted. Manuscripts were included if they were written in English, Dutch, German, French, Japanese or Chinese, if the monitoring technology measured discomfort or distressing symptoms, was noninvasive, could be continuously administered for 4 hours and was potentially applicable for bed-ridden people. The screening was performed by two researchers independently. Information about the technology, its clinimetrics (validity, reliability, sensitivity, specificity, responsiveness), acceptability, and feasibility were extracted. RESULTS Of the 3,414 identified manuscripts, 229 met the eligibility criteria. A variety of monitoring technologies were identified, including actigraphy, brain activity monitoring, electrocardiography, electrodermal activity monitoring, surface electromyography, incontinence sensors, multimodal systems, and noncontact monitoring systems. The main indicators of discomfort monitored by these technologies were sleep, level of consciousness, risk of pressure ulcers, urinary incontinence, agitation, and pain. For the end-of-life phase, brain activity monitors could be helpful and acceptable to monitor the level of consciousness during palliative sedation. However, no manuscripts have reported on the clinimetrics, feasibility, and acceptability of the other technologies for the end-of-life phase. CONCLUSIONS Noninvasive monitoring technologies are available to measure common symptoms at the end of life. Future research should evaluate the quality of evidence provided by existing studies and investigate the feasibility, acceptability, and usefulness of these technologies in the end-of-life setting. Guidelines for studies on healthcare technologies should be better implemented and further developed.
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Affiliation(s)
- Jingyuan Xu
- Department of Public Health and Primary Care, Leiden University Medical Center, Hippocratespad 21, Gebouw 3, Postzone V0-P, P.O. Box 9600, 2300 RC, Leiden, The Netherlands.
| | - Hanneke J A Smaling
- Department of Public Health and Primary Care, Leiden University Medical Center, Hippocratespad 21, Gebouw 3, Postzone V0-P, P.O. Box 9600, 2300 RC, Leiden, The Netherlands
- University Network for the Care Sector Zuid-Holland, Leiden University Medical Center, Leiden, The Netherlands
| | - Jan W Schoones
- Directorate of Research Policy, Leiden University Medical Center, Leiden, The Netherlands
| | - Wilco P Achterberg
- Department of Public Health and Primary Care, Leiden University Medical Center, Hippocratespad 21, Gebouw 3, Postzone V0-P, P.O. Box 9600, 2300 RC, Leiden, The Netherlands
- University Network for the Care Sector Zuid-Holland, Leiden University Medical Center, Leiden, The Netherlands
| | - Jenny T van der Steen
- Department of Public Health and Primary Care, Leiden University Medical Center, Hippocratespad 21, Gebouw 3, Postzone V0-P, P.O. Box 9600, 2300 RC, Leiden, The Netherlands
- Department of Primary and Community Care, and Radboudumc Alzheimer Center, Radboud university medical center, Nijmegen, The Netherlands
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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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Gallant NL, Hadjistavropoulos T, Stopyn RJN, Feere EK. Integrating Technology Adoption Models Into Implementation Science Methodologies: A Mixed-Methods Preimplementation Study. THE GERONTOLOGIST 2023; 63:416-427. [PMID: 35810405 PMCID: PMC10028232 DOI: 10.1093/geront/gnac098] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Sustainable implementation of patient-oriented technologies in health care settings is challenging. Preimplementation studies guided by the Consolidated Framework for Implementation Research (CFIR) can provide opportunities to address barriers and leverage facilitators that can maximize the likelihood of successful implementation. When looking to implement patient-oriented technologies, preimplementation studies may also benefit from guidance from a conceptual framework specific to technology adoption such as the Unified Theory of Acceptance and Use of Technology. This study was, therefore, aimed at identifying determinants for the successful implementation of a patient-oriented technology (i.e., automated pain behavior monitoring [APBM] system) within a health care setting (i.e., long-term care [LTC] facility). RESEARCH DESIGN AND METHODS Using a mixed-methods study design, 164 LTC nurses completed a set of questionnaires and 68 LTC staff participated in individual interviews involving their perceptions of an APBM system in LTC environments. Quantitative data were analyzed using a series of mediation analyses and narrative responses were examined using directed content analysis. RESULTS Performance expectancy and effort expectancy partially and fully mediated the influence of implementation, readiness for organizational change, and technology readiness constructs on behavioral intentions to use the APBM system in LTC environments. Findings from the qualitative portion of this study provide guidance for the development of an intervention that is grounded in the CFIR. DISCUSSION AND IMPLICATIONS Based on our results, we offer recommendations for the implementation of patient-oriented technologies in health care settings.
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Affiliation(s)
- Natasha L Gallant
- Department of Psychology, University of Regina, Regina, Saskatchewan, Canada
- Centre on Aging and Health, University of Regina, Regina, Saskatchewan, Canada
| | - Thomas Hadjistavropoulos
- Department of Psychology, University of Regina, Regina, Saskatchewan, Canada
- Centre on Aging and Health, University of Regina, Regina, Saskatchewan, Canada
| | - Rhonda J N Stopyn
- Department of Psychology, University of Regina, Regina, Saskatchewan, Canada
- Centre on Aging and Health, University of Regina, Regina, Saskatchewan, Canada
| | - Emma K Feere
- Department of Psychology, University of Regina, Regina, Saskatchewan, Canada
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Cui Z, Guo Z, Wei L, Zou X, Zhu Z, Liu Y, Wang J, Chen L, Wang D, Ke Z. Altered pain sensitivity in 5×familial Alzheimer disease mice is associated with dendritic spine loss in anterior cingulate cortex pyramidal neurons. Pain 2022; 163:2138-2153. [PMID: 35384934 PMCID: PMC9578529 DOI: 10.1097/j.pain.0000000000002648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 03/25/2022] [Accepted: 03/28/2022] [Indexed: 11/26/2022]
Abstract
ABSTRACT Chronic pain is highly prevalent. Individuals with cognitive disorders such as Alzheimer disease are a susceptible population in which pain is frequently difficult to diagnosis. It is still unclear whether the pathological changes in patients with Alzheimer disease will affect pain processing. Here, we leverage animal behavior, neural activity recording, optogenetics, chemogenetics, and Alzheimer disease modeling to examine the contribution of the anterior cingulate cortex (ACC) neurons to pain response. The 5× familial Alzheimer disease mice show alleviated mechanical allodynia which can be regained by the genetic activation of ACC excitatory neurons. Furthermore, the lower peak neuronal excitation, delayed response initiation, as well as the dendritic spine reduction of ACC pyramidal neurons in 5×familial Alzheimer disease mice can be mimicked by Rac1 or actin polymerization inhibitor in wild-type (WT) mice. These findings indicate that abnormal of pain sensitivity in Alzheimer disease modeling mice is closely related to the variation of neuronal activity and dendritic spine loss in ACC pyramidal neurons, suggesting the crucial role of dendritic spine density in pain processing.
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Affiliation(s)
- Zhengyu Cui
- Academy of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Internal Medicine of Traditional Chinese Medicine, Shanghai East Hospital, Tongji University, Shanghai, China
| | - Zhongzhao Guo
- Academy of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Luyao Wei
- Academy of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiang Zou
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Zilu Zhu
- Department of Physiology, School of Basic Medical Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yuchen Liu
- Department of Physiology, School of Basic Medical Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jie Wang
- Academy of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Liang Chen
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Deheng Wang
- Department of Physiology, School of Basic Medical Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zunji Ke
- Academy of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Hosseini E, Fang R, Zhang R, Chuah CN, Orooji M, Rafatirad S, Rafatirad S, Homayoun H. Convolution Neural Network for Pain Intensity Assessment from Facial Expression. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2697-2702. [PMID: 36085712 DOI: 10.1109/embc48229.2022.9871770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Pain is an unpleasant feeling that can reflect a patient's health situation. Since measuring pain is subjective, time-consuming, and needs continuous monitoring, automated pain intensity detection from facial expression holds great potential for smart healthcare applications. Convolutional Neural Networks (CNNs) are recently being used to identify features, map and model pain intensity from facial images, delivering great promise in helping practitioners detect disease. Limited research has been conducted to determine pain intensity levels across multiple classes. CNNs with simple learning schemes are limited in their ability to extract feature information from images. In order to develop a highly accurate pain intensity estimation system, this study proposes a Deep CNN (DCNN) model using the transfer learning technique, where a pre-trained DCNN model is adopted by replacing its dense upper layers, and the model is tuned using painful facial. We conducted experiments on the UNBC-McMaster shoulder pain archive database to estimate pain intensity in terms of seven-level thresholds using a given facial expression image. The experiments show our method achieves a promising improvement in terms of accuracy and performance to estimate pain intensity and outperform the-state-of-the-arts models.
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Prkachin KM, Hammal Z. Computer mediated automatic detection of pain-related behavior: prospect, progress, perils. FRONTIERS IN PAIN RESEARCH 2022; 2. [PMID: 35174358 PMCID: PMC8846566 DOI: 10.3389/fpain.2021.788606] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Pain is often characterized as a fundamentally subjective phenomenon; however, all pain assessment reduces the experience to observables, with strengths and limitations. Most evidence about pain derives from observations of pain-related behavior. There has been considerable progress in articulating the properties of behavioral indices of pain; especially, but not exclusively those based on facial expression. An abundant literature shows that a limited subset of facial actions, with homologs in several non-human species, encode pain intensity across the lifespan. Unfortunately, acquiring such measures remains prohibitively impractical in many settings because it requires trained human observers and is laborious. The advent of the field of affective computing, which applies computer vision and machine learning (CVML) techniques to the recognition of behavior, raised the prospect that advanced technology might overcome some of the constraints limiting behavioral pain assessment in clinical and research settings. Studies have shown that it is indeed possible, through CVML, to develop systems that track facial expressions of pain. There has since been an explosion of research testing models for automated pain assessment. More recently, researchers have explored the feasibility of multimodal measurement of pain-related behaviors. Commercial products that purport to enable automatic, real-time measurement of pain expression have also appeared. Though progress has been made, this field remains in its infancy and there is risk of overpromising on what can be delivered. Insufficient adherence to conventional principles for developing valid measures and drawing appropriate generalizations to identifiable populations could lead to scientifically dubious and clinically risky claims. There is a particular need for the development of databases containing samples from various settings in which pain may or may not occur, meticulously annotated according to standards that would permit sharing, subject to international privacy standards. Researchers and users need to be sensitive to the limitations of the technology (for e.g., the potential reification of biases that are irrelevant to the assessment of pain) and its potentially problematic social implications.
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Affiliation(s)
- Kenneth M Prkachin
- Department of Psychology, University of Northern British Columbia, Prince George, BC, Canada
| | - Zakia Hammal
- The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States
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