Facial Pain Expression Recognition in Real-Time Videos

Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review

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 published between January 2014 and January 2024). Articles that were available as full text clinical trials, observational studies, review articles, systemic reviews, and meta-analyses were included in this review. The exclusion criteria were articles that were not in the English language, not available as free full text, those involving pediatric patients, case reports, and editorials. A total of (47) articles were included in this review. In conclusion, the application of AI in pain management could present promising solutions for pain assessment. AI can potentially increase the accuracy, precision, and efficiency of objective pain assessment.

Transforming medicine: artificial intelligence integration in the peripheral nervous system

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.

Pain Management in Pediatric Burns: A Review of the Science behind It

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.

Statistical local descriptors for face recognition: a comprehensive study

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.

Using artificial intelligence to improve pain assessment and pain management: a scoping review

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.

Predicting neuropsychiatric symptoms of persons with dementia in a day care center using a facial expression recognition system

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.