Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives

Advancing the integration of biosignal-based automated pain assessment methods into a comprehensive model for addressing cancer pain

BACKGROUND

Tailoring effective strategies for cancer pain management requires a careful analysis of multiple factors that influence pain phenomena and, ultimately, guide the therapy. While there is a wealth of research on automatic pain assessment (APA), its integration with clinical data remains inadequately explored. This study aimed to address the potential correlations between subjective and APA-derived objectives variables in a cohort of cancer patients.

METHODS

A multidimensional statistical approach was employed. Demographic, clinical, and pain-related variables were examined. Objective measures included electrodermal activity (EDA) and electrocardiogram (ECG) signals. Sensitivity analysis, multiple factorial analysis (MFA), hierarchical clustering on principal components (HCPC), and multivariable regression were used for data analysis.

RESULTS

The study analyzed data from 64 cancer patients. MFA revealed correlations between pain intensity, type, Eastern Cooperative Oncology Group Performance status (ECOG), opioids, and metastases. Clustering identified three distinct patient groups based on pain characteristics, treatments, and ECOG. Multivariable regression analysis showed associations between pain intensity, ECOG, type of breakthrough cancer pain, and opioid dosages. The analyses failed to find a correlation between subjective and objective pain variables.

CONCLUSIONS

The reported pain perception is unrelated to the objective variables of APA. An in-depth investigation of APA is required to understand the variables to be studied, the operational modalities, and above all, strategies for appropriate integration with data obtained from self-reporting.

TRIAL REGISTRATION

This study is registered with ClinicalTrials.gov, number (NCT04726228), registered 27 January 2021, https://classic.

CLINICALTRIALS

gov/ct2/show/NCT04726228?term=nct04726228&draw=2&rank=1.

The Ethical Stewardship of Artificial Intelligence in Chronic Pain and Headache: A Narrative Review

PURPOSE OF REVIEW

As artificial intelligence (AI) and machine learning (ML) are becoming more pervasive in medicine, understanding their ethical considerations for chronic pain and headache management is crucial for optimizing their safety.

RECENT FINDINGS

We reviewed thirty-eight editorial and original research articles published between 2018 and 2023, focusing on the application of AI and ML to chronic pain or headache. The core medical principles of beneficence, non-maleficence, autonomy, and justice constituted the evaluation framework. The AI applications addressed topics such as pain intensity prediction, diagnostic aides, risk assessment for medication misuse, empowering patients to self-manage their conditions, and optimizing access to care. Virtually all AI applications aligned both positively and negatively with specific medical ethics principles. This review highlights the potential of AI to enhance patient outcomes and physicians' experiences in managing chronic pain and headache. We emphasize the importance of carefully considering the advantages, disadvantages, and unintended consequences of utilizing AI tools in chronic pain and headache, and propose the four core principles of medical ethics as an evaluation framework.

The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain

PURPOSE OF REVIEW

This review aims to explore the interface between artificial intelligence (AI) and chronic pain, seeking to identify areas of focus for enhancing current treatments and yielding novel therapies.

RECENT FINDINGS

In the United States, the prevalence of chronic pain is estimated to be upwards of 40%. Its impact extends to increased healthcare costs, reduced economic productivity, and strain on healthcare resources. Addressing this condition is particularly challenging due to its complexity and the significant variability in how patients respond to treatment. Current options often struggle to provide long-term relief, with their benefits rarely outweighing the risks, such as dependency or other side effects. Currently, AI has impacted four key areas of chronic pain treatment and research: (1) predicting outcomes based on clinical information; (2) extracting features from text, specifically clinical notes; (3) modeling 'omic data to identify meaningful patient subgroups with potential for personalized treatments and improved understanding of disease processes; and (4) disentangling complex neuronal signals responsible for pain, which current therapies attempt to modulate. As AI advances, leveraging state-of-the-art architectures will be essential for improving chronic pain treatment. Current efforts aim to extract meaningful representations from complex data, paving the way for personalized medicine. The identification of unique patient subgroups should reveal targets for tailored chronic pain treatments. Moreover, enhancing current treatment approaches is achievable by gaining a more profound understanding of patient physiology and responses. This can be realized by leveraging AI on the increasing volume of data linked to chronic pain.

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.

Post-thoracotomy pain syndrome in the era of minimally invasive thoracic surgery

Post-thoracotomy pain syndrome (PTPS) is defined as pain around the wound that persists for more than 2 months after surgery. Persistent pain not only increases the use of analgesics and their side effects but also causes many social problems, such as decreased activities of daily living, decreased quality of life, and increased medical costs. In particular, thoracic surgery is associated with a higher frequency and severity of chronic pain than is surgery for other diseases. The basic principles of postoperative pain treatment, not limited to thoracic surgery, are multimodal analgesic methods (using combinations of several drugs to minimize opioid use) and around-the-clock treatment (administering analgesics at a fixed time and in sufficient doses). Thoracic surgeons must always be aware of the following three points: acute severe postoperative pain is a major risk factor for chronic pain; neuropathic pain due to intercostal nerve injury is a major cause of postoperative pain after thoracic surgery, and its presence must not be overlooked from the acute stage; and analgesics must be administered in sufficient quantities according to dosage and volume. The frequency of PTPS has decreased compared with that in the standard thoracotomy era because of the development of analgesia and the widespread use of minimally invasive procedures such as thoracoscopic surgery and robot-assisted surgery. However, no consistently effective prevention or treatment strategies for PTPS have yet been established. In this review, we focus on PTPS in the era of minimally invasive surgery and discuss the role of thoracic surgeons in its management.

Ethical issues in pain and palliation

PURPOSE OF REVIEW

Increased public awareness of ethical issues in pain and palliative care, along with patient advocacy groups, put pressure on healthcare systems and professionals to address these concerns.Our aim is to review the ethics dilemmas concerning palliative care in ICU, artificial intelligence applications in pain therapy and palliative care, and the opioids epidemics.

RECENT FINDINGS

In this focus review, we highlighted state of the art papers that were published in the last 18 months, on ethical issues in palliative care within the ICU, artificial intelligence trajectories, and how opioids epidemics has impacted pain management practices (see Visual Abstract).

SUMMARY

Palliative care in the ICU should involve a multidisciplinary team, to mitigate patients suffering and futility. Providing spiritual support in the ICU is an important aspect of holistic patient care too.Increasingly sophisticated tools for diagnosing and treating pain, as those involving artificial intelligence, might favour disparities in access, cause informed consent problems, and surely, they need prudence and reproducibility.Pain clinicians worldwide continue to face the ethical dilemma of prescribing opioids for patients with chronic noncancer pain. Balancing the need for effective pain relief with the risk of opioid misuse, addiction, and overdose is a very controversial task.

Bridging knowledge gaps: a bibliometric analysis of non-invasive ventilation in palliative care studies

BACKGROUND

Despite being a useful strategy for providing respiratory support to patients with advanced or terminal illnesses, non-invasive ventilation (NIV) requires in-depth investigation in several key aspects.

OBJECTIVES

This bibliometric analysis seeks to comprehensively examine the existing research on the subject. Its goal is to uncover valuable insights that can inform the prediction trajectory of studies, guide the implementation of corrective measures, and contribute to the improvement of research networks.

METHODS

A comprehensive review of literature on NIV in the context of palliative care was conducted using the Web of Science core collection online database. The search utilized the key terms "non-invasive ventilation" and "palliative care" to identify the most relevant articles. All data were gathered on November 7, 2023. Relevant information from documents meeting the specified criteria was extracted, and Journal Citation Reports™ 2022 (Clarivate Analytics) served as the data source. The analysis employed literature analysis and knowledge visualization tools, specifically CiteScope (version 6.2.R4) and VOSviewer (version 1.6.20).

RESULTS

A dataset with bibliometric findings from 192 items was analyzed. We found a consistent upward of the scientific output trend over time. Guidelines on amyotrophic lateral sclerosis management received the highest number of citations. Most documents were published in top-ranked journals. Less than one-third of the documents pertain to clinical studies, especially retrospective analyses (25%). Key topics such as "decision making", and "communication" were less addressed.

CONCLUSIONS

Given the substantial clinical implications, further high-quality studies on this subject are recommended. Encouraging international collaborations is needed. Despite the growing volume of documents in the field, this bibliometric analysis indicates a decline in collaborative networks.

Fentanyl in cancer pain management: avoiding hasty judgments and discerning its potential benefits

Cancer pain is an important challenge in treatment and requires a rapid onset of action for its control. In particular, breakthrough cancer pain (BTcP) should be adequately controlled with a stable dose of a short-acting oral opioid. Fentanyl is a synthetic, highly selective opioid with many advantageous chemical properties, including high lipophilicity and distinct pharmacokinetic properties. It is recommended for pain management in a variety of settings, including acute pain, chronic pain and BTcP. To date, its variously designed formulations allow non-invasive administration; amongst others, sublingual fentanyl has proven useful in the management of BTcP and in improving the quality of life of patients with cancer. This review provides an update on the management of BTcP with fentanyl, with consideration of safety, as it remains an important tool in the treatment of cancer pain.

A Quantitative Measure of Pain with Current Perception Threshold, Pain Equivalent Current, and Quantified Pain Degree: A Retrospective Study

Background: As a subjective sensation, pain is difficult to evaluate objectively. The assessment of pain degree is largely dependent on subjective methods such as the numeric rating scale (NRS). The PainVisionTM system has recently been introduced as an objective pain degree measurement tool. The purpose of this study was to analyze correlations between the NRS and the current perception threshold (CPT), pain equivalent current (PEC), and quantified pain degree (QPD). Methods: Medical records of 398 subjects who visited the pain clinic in a university hospital from March 2017 to February 2019 were retrospectively reviewed. To evaluate the pain degree, NRS, CPT, PEC, and QPD were measured. Subjects were categorized into two groups: the Pain group (n = 355) and the No-pain group (n = 43). Results: The NRS showed a negative correlation with CPT (R = -0.10, p = 0.054) and a positive correlation with QPD (R = 0.13, p = 0.008). Among various diseases, only spinal disease patients showed a negative correlation between CPT and NRS (R = -0.22, p = 0.003). Additionally, there were significant differences in CPT and QPD between the Pain and No-pain groups (p = 0.005 and p = 0.002, respectively). Conclusions: CPT and QPD measured using the PainVisionTM system could be used to estimate pain intensity and the presence of pain. These parameters would be considered useful for predicting pain itself and its intensity.