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Recognition of pain in patients who are incapable of expressing themselves allows for several possibilities of improved diagnosis and treatment. Despite the advancements that have already been made in this field, research is still lacking with respect to the detection of pain in live videos, especially under unfavourable conditions. To address this...
Citations
... Several studies developed novel models for pain recognition with ML by analyzing facial expressions. They were able to automatically detect pain successfully with relatively high accuracy in more than 95% of the subjects [33][34][35][36]. Other studies used the AI-based approach to analyze clinical notes and patients' records with pain assessment information to identify components related to pain classifications and severity [37]. ...
... Other studies used the AI-based approach to analyze clinical notes and patients' records with pain assessment information to identify components related to pain classifications and severity [37]. Results of a recent review provide evidence that machine learning, data mining, and natural language processing can improve efficient pain recognition and pain assessment, analyze self-reported pain data, predict pain, and help clinicians and patients to manage chronic pain more effectively [33][34][35][36]. These promising results may help in the creation of new pain assessment instruments with human language technology. ...
Pain is a significant health issue, and pain assessment is essential for proper diagnosis, follow-up, and effective management of pain. The conventional methods of pain assessment often suffer from subjectivity and variability. The main issue is to understand better how people experience pain. In recent years, artificial intelligence (AI) has been playing a growing role in improving clinical diagnosis and decision making. The application of AI offers promising opportunities to improve the accuracy and efficiency of pain assessment. This review article provides an overview of the current state of AI in pain assessment and explores its potential for improving accuracy, efficiency, and personalized care. By examining the existing literature, research gaps, and future directions , this article aims to guide further advancements in the field of pain management. An online database search was conducted via multiple websites to identify the relevant articles. The inclusion criteria were English articles
... 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). ...
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.
... Te use of innovative tools and devices has improved the accuracy and objectivity of pain assessment in children. For example, the use of facial expression recognition software and wearable sensors has allowed for realtime monitoring and analysis of pain-related behaviors and physiological responses in children [63,64]. Tese technological advancements provide valuable insights into the child's pain experience, allowing healthcare providers to tailor pain management strategies more efectively. ...
Pediatric burns are a significant medical issue that can have long-term effects on various aspects of a child’s health and well-being. Pain management in pediatric burns is a crucial aspect of treatment to ensure the comfort and well-being of young patients. The causes and risk factors for pediatric burns vary depending on various factors, such as geographical location, socioeconomic status, and cultural practices. Assessing pain in pediatric patients, especially during burn injury treatment, poses several challenges. These challenges stem from various factors, including the age and developmental stage of the child, the nature of burn injuries, and the limitations of pain assessment tools. In pediatric pain management, various pain assessment tools and scales are used to evaluate and measure pain in children. These tools are designed to account for the unique challenges of assessing pain in pediatric patients, including their age, developmental stage, and ability to communicate effectively. Pain can have significant physical, emotional, and psychological consequences for pediatric patients. It can interfere with their ability to engage in daily activities, disrupt sleep patterns, and negatively affect their mood and behavior. Untreated pain can also lead to increased stress, anxiety, and fear, which can further exacerbate the pain experience. Acute pain, which is short-term and typically associated with injury or illness, can disrupt a child’s ability to engage in physical activities and impede their overall recovery process. On the other hand, chronic pain, which persists for an extended period, can have long-lasting effects on physical functioning and quality of life in children. The psychological consequences of burns can persist long after the physical wounds have healed, leading to ongoing emotional distress and impaired functioning. Multimodal pain management, which involves the use of multiple interventions or medications targeting different aspects of the pain pathway, has gained recognition as an effective approach for managing pain in both children and adults. However, it is important to consider the specific needs and considerations of pediatric patients when developing evidence-based guidelines for multimodal pain management in this population. Over the years, there have been significant advances in pediatric pain research and technology, leading to a better understanding of pain mechanisms and the development of innovative approaches to assess and treat pain in children. Overall, pain management in pediatric burns requires a multidisciplinary approach that combines pharmacologic and nonpharmacologic interventions.
... Also, as the COVID-19 pandemic outbreak forced the opening of bank accounts to be done online in the last year, knowing your customer (KYK) with online face recognition gained a lot of attention in the banking and retail sectors. Healthcare is also one of the key industries that have made major advances with face recognition technology, such as pain recognition [28,70], genetic disease detection, e.g., DiGeorge syndrome [50], and acromegaly [49]. ...
The use of local statistical descriptors for image representation has emerged and gained a reputation as a powerful approach in the last couple of decades. Many algorithms have been proposed and applied, since then, in various application areas employing different datasets, classifiers, and testing parameters. In this paper, we felt the need to make a comprehensive study of frequently-used statistical local descriptors. We investigate the effect of using different histogram-based local feature extraction algorithms on the performance of the face recognition problem. Comparisons are conducted among 18 different algorithms. These algorithms are used for the extraction of the local statistical feature descriptors of the face images. Moreover, feature fusion/concatenation of different combinations of generated feature descriptors is applied, and the relevant impact on the system performance is evaluated. Comprehensive experiments are carried out using two well-known face databases with identical experimental settings. The obtained results indicate that the fusion of the descriptors can significantly enhance the system’s performance.
... Type 1: AI-based approaches related to the pain assessment (n 5 12, 40%) Seven studies developed novel models for pain recognition with ML (n ¼ 8, 23.3%). 7,17,23,47,[49][50][51] In 2011, Lucey et al 23 7,17,23,49 They all were able to detect pain successfully with relatively high accuracy. 7,17,23,49 Of note, the last two approaches contributed to automatic pain detection from a live stream even in low-light conditions and with a low-resolution recording device. ...
... Type 1: AI-based approaches related to the pain assessment (n 5 12, 40%) Seven studies developed novel models for pain recognition with ML (n ¼ 8, 23.3%). 7,17,23,47,[49][50][51] In 2011, Lucey et al 23 7,17,23,49 They all were able to detect pain successfully with relatively high accuracy. 7,17,23,49 Of note, the last two approaches contributed to automatic pain detection from a live stream even in low-light conditions and with a low-resolution recording device. ...
... 7,17,23,47,[49][50][51] In 2011, Lucey et al 23 7,17,23,49 They all were able to detect pain successfully with relatively high accuracy. 7,17,23,49 Of note, the last two approaches contributed to automatic pain detection from a live stream even in low-light conditions and with a low-resolution recording device. 7,49 Similarly, Hossain et al found that cloud-assisted pain recognition servers could achieve more than 95% accuracy and generate the response within three seconds. ...
Context
Over 20% of US adults report they experience pain on most days or every day. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain using artificial intelligence (AI) approaches may improve patient outcomes and healthcare resource utilization. A comprehensive synthesis of the current use and outcomes of AI-based interventions focused on pain assessment and management will guide the development of future research.
Objectives
This review aims to investigate the state of the research on AI-based interventions designed to improve pain assessment and management for adult patients. We also ascertain the actual outcomes of Al-based interventions for adult patients.
Methods
The electronic databases searched include Web of Science, CINAHL, PsycINFO, Cochrane CENTRAL, Scopus, IEEE Xplore, and ACM Digital Library. The search initially identified 6946 studies. After screening, 30 studies met the inclusion criteria. The Critical Appraisals Skills Programme was used to assess study quality.
Results
This review provides evidence that machine learning, data mining, and natural language processing were used to improve efficient pain recognition and pain assessment, analyze self-reported pain data, predict pain, and help clinicians and patients to manage chronic pain more effectively.
Conclusions
Findings from this review suggest that using AI-based interventions has a positive effect on pain recognition, pain prediction, and pain self-management; however, most reports are only pilot studies. More pilot studies with physiological pain measures are required before these approaches are ready for large clinical trial.
... As pain is sensitive to the environment, distress and emotional conditions, people can experience it with no such remarks observed on the face. The influence of social, economic and cultural factors may also include people observing pain differently from one another [20,[26][27][28][29][30][31][32][33][34][35]. ...
... In such cases, the above-mentioned measurement of pain using self-reports is not useful. The variation of pain with such patients cannot be dealt with easily [29][30][31][32][33][34]. ...
Pain is a complex term that describes various sensations that create discomfort in various ways or types inside the human body. Generally, pain has consequences that range from mild to severe in different organs of the body and will depend on the way it is caused, which could be an injury, illness or medical procedures including testing, surgeries or therapies, etc. With recent advances in artificial-intelligence (AI) systems associated in biomedical and healthcare settings, the contiguity of physician, clinician and patient has shortened. AI, however, has more scope to interpret the pain associated in patients with various conditions by using any physiological or behavioral changes. Facial expressions are considered to give much information that relates with emotions and pain, so clinicians consider these changes with high importance for assessing pain. This has been achieved in recent times with different machine-learning and deep-learning models. To accentuate the future scope and importance of AI in medical field, this study reviews the explainable AI (XAI) as increased attention is given to an automatic assessment of pain. This review discusses how these approaches are applied for different pain types.
... The potential of artificial intelligence (AI)-based facial expression analysis using a facial expression recognition system (FERS) to identify emotions, pain, and nonverbal information among persons with psychiatric disorders has been documented [6][7][8][9]. FERS successfully predicted 8 basic mood phenotypes using more than 1,000,000 facial images collected from the internet, i.e., disgust, fear, sadness, anger, happiness, surprise, neutral, and contempt [9][10][11]. The accuracy of FERS based on a convolutional neural network (CNN) to recognize these 8 emotional expressions was approximately 87.7-94.2%, ...
... Although deficient facial expressions were common presentations of persons with neurodegenerative disorder, the enhanced facial responses to pain in PwD provided opportunities for FERS to identify somatic discomforts [8,10,12]. The advanced development of AI technology and deep learning programs enables FERS to identify facial expressions and their changes over time from video streams, creating opportunities to develop the automatic detection of BPSDs to improve the quality of dementia care [7][8][9][10]13]. Evidence suggests that BPSDs are often related to suboptimal management of physical pain, but pain is not the only aggravating factor that precipitates or aggravates BPSDs in PwD [14]. ...
Background:
Behavioral and psychological symptoms of dementia (BPSD) affect 90% of persons with dementia (PwD), resulting in various adverse outcomes and aggravating care burdens among their caretakers. This study aimed to explore the potential of artificial intelligence-based facial expression recognition systems (FERS) in predicting BPSDs among PwD.
Methods:
A hybrid of human labeling and a preconstructed deep learning model was used to differentiate basic facial expressions of individuals to predict the results of Neuropsychiatric Inventory (NPI) assessments by stepwise linear regression (LR), random forest (RF) with importance ranking, and ensemble method (EM) of equal importance, while the accuracy was determined by mean absolute error (MAE) and root-mean-square error (RMSE) methods.
Results:
Twenty-three PwD from an adult day care center were enrolled with ≥ 11,500 FERS data series and 38 comparative NPI scores. The overall accuracy was 86% on facial expression recognition. Negative facial expressions and variance in emotional switches were important features of BPSDs. A strong positive correlation was identified in each model (EM: r = 0.834, LR: r = 0.821, RF: r = 0.798 by the patientwise method; EM: r = 0.891, LR: r = 0.870, RF: r = 0.886 by the MinimPy method), and EM exhibited the lowest MAE and RMSE.
Conclusions:
FERS successfully predicted the BPSD of PwD by negative emotions and the variance in emotional switches. This finding enables early detection and management of BPSDs, thus improving the quality of dementia care.
... However, neonates, children, vulnerable adult populations, and the critically ill are often unable to self-report their pain intensity, leaving clinical pain assessment susceptible to underestimation bias and under-recognition. Computer vision has demonstrated the capability to quantify pain from facial expressions captured through video 42,43 , opening up a future of high-quality in-hospital pain assessment and management. Rashidi et al demonstrated that AI algorithms can autonomously and non-invasively assess pain facial expressions from image data 44 . ...
Background: Automatic facial landmark localization is an essential component in many computer vision applications, including video-based detection of neurological diseases. Machine learning models for facial landmarks localization are typically trained on faces of healthy individuals, and we found that model performance is inferior when applied to faces of people with neurological diseases. Fine-tuning pre-trained models with representative images improves performance on clinical populations significantly. However, questions related to the characteristics of the database used to fine-tune the model and the clinical impact of the improved model remain. Methods: We employed the Toronto NeuroFace dataset – a dataset consisting videos of Healthy Controls (HC), individuals Post-Stroke, and individuals with Amyotrophic Lateral Sclerosis performing speech and non-speech tasks with thousands of manually annotated frames - to fine-tune a well-known deep learning-based facial landmark localization model. The pre-trained and fine-tuned models were used to extract landmark-based facial features from videos, and the facial features were used to discriminate clinical groups from HC. Results: Fine-tuning a facial landmark localization model with a diverse database that includes HC and individuals with neurological disorders resulted in significantly improved performance for all groups. Our results also showed that fine-tuning the model with representative data greatly improved the ability of the subsequent classifier to classify clinical groups vs. HC from videos. Conclusions: Using a diverse database for model fine-tuning might result in better model performance for HC and clinical groups. We demonstrated that fine-tuning a model for landmark localization with representative data results in improved detection of neurological diseases.
