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El-Tallawy SN, Pergolizzi JV, Vasiliu-Feltes I, Ahmed RS, LeQuang JK, El-Tallawy HN, Varrassi G, Nagiub MS. Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review. Pain Ther 2024; 13:293-317. [PMID: 38430433 PMCID: PMC11111436 DOI: 10.1007/s40122-024-00584-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 02/08/2024] [Indexed: 03/03/2024] Open
Abstract
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.
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Affiliation(s)
- Salah N El-Tallawy
- Anesthesia and Pain Department, College of Medicine, King Khalid University Hospital, King Saud University, Riyadh, Saudi Arabia.
- Anesthesia and Pain Department, Faculty of Medicine, Minia University & NCI, Cairo University, Giza, Egypt.
| | | | - Ingrid Vasiliu-Feltes
- Science, Entrepreneurship and Investments Institute, University of Miami, Miami, USA
| | - Rania S Ahmed
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
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Wu H, Chen Z, Gu J, Jiang Y, Gao S, Chen W, Miao C. Predicting Chronic Pain and Treatment Outcomes Using Machine Learning Models Based on High-Dimensional Clinical Data From a Large Retrospective Cohort. Clin Ther 2024:S0149-2918(24)00104-8. [PMID: 38824080 DOI: 10.1016/j.clinthera.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 04/13/2024] [Accepted: 04/27/2024] [Indexed: 06/03/2024]
Abstract
PURPOSE To identify factors and indicators that affect chronic pain and pain relief, and to develop predictive models using machine learning. METHODS We analyzed the data of 67,028 outpatient cases and 11,310 valid samples with pain from a large retrospective cohort. We used decision tree, random forest, AdaBoost, neural network, and logistic regression to discover significant indicators and to predict pain and treatment relief. FINDINGS The random forest model had the highest accuracy, F1 value, precision, and recall rates for predicting pain relief. The main factors affecting pain and treatment relief included body mass index, blood pressure, age, body temperature, heart rate, pulse, and neutrophil/lymphocyte × platelet ratio. The logistic regression model had high sensitivity and specificity for predicting pain occurrence. IMPLICATIONS Machine learning models can be used to analyze the risk factors and predictors of chronic pain and pain relief, and to provide personalized and evidence-based pain management.
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Affiliation(s)
- Han Wu
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key laboratory of Perioperative Stress and Protection, Shanghai, China
| | - Zhaoyuan Chen
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key laboratory of Perioperative Stress and Protection, Shanghai, China
| | - Jiahui Gu
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key laboratory of Perioperative Stress and Protection, Shanghai, China
| | - Yi Jiang
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key laboratory of Perioperative Stress and Protection, Shanghai, China
| | - Shenjia Gao
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key laboratory of Perioperative Stress and Protection, Shanghai, China
| | - Wankun Chen
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key laboratory of Perioperative Stress and Protection, Shanghai, China.
| | - Changhong Miao
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key laboratory of Perioperative Stress and Protection, Shanghai, China.
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Suresh R, Yadalam PK, Ramadoss R, Ramalingam K. Prediction of the Need for Anticonvulsants in the Management of Orofacial Neuropathic Pain Using Machine Learning. Cureus 2024; 16:e58934. [PMID: 38800307 PMCID: PMC11116743 DOI: 10.7759/cureus.58934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 04/23/2024] [Indexed: 05/29/2024] Open
Abstract
Background and aim Orofacial neuropathic pain is a medical condition caused by a lesion or dysfunction of the nervous system and is one of the most challenging for dental clinicians to diagnose. Anticonvulsants, antidepressants, analgesics, nonsteroidal anti-inflammatory drugs, and other classes of medications are frequently used to treat this condition. Our study aimed to build a machine learning-based classifier to predict the need for anticonvulsant drugs in patients with orofacial neuropathic pain. Materials and methods A machine learning tool that was trained and tested on patients for predicting and detecting algorithms, which would in turn predict the need for anticonvulsants in the treatment of orofacial neuropathic pain, was employed in this study. Results Three machine learning algorithms successfully detected and predicted the need for anticonvulsants to treat patients with orofacial neuropathic pain. All three models showed a high accuracy, that is, 97%, 94%, and 89%, in predicting the need for anticonvulsants. Conclusion Machine learning algorithms can accurately predict the need for anticonvulsant drugs for treating orofacial neuropathic pain. Further research is needed to validate these findings using larger sample sizes and imaging modalities.
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Affiliation(s)
- Ramya Suresh
- Oral Biology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND
| | | | - Ramya Ramadoss
- Oral Pathology and Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND
| | - Karthikeyan Ramalingam
- Oral Pathology and Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND
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Chen L, Jiang J, Dou B, Feng H, Liu J, Zhu Y, Zhang B, Zhou T, Wei GW. Machine learning study of the extended drug-target interaction network informed by pain related voltage-gated sodium channels. Pain 2024; 165:908-921. [PMID: 37851391 PMCID: PMC11021136 DOI: 10.1097/j.pain.0000000000003089] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/09/2023] [Indexed: 10/19/2023]
Abstract
ABSTRACT Pain is a significant global health issue, and the current treatment options for pain management have limitations in terms of effectiveness, side effects, and potential for addiction. There is a pressing need for improved pain treatments and the development of new drugs. Voltage-gated sodium channels, particularly Nav1.3, Nav1.7, Nav1.8, and Nav1.9, play a crucial role in neuronal excitability and are predominantly expressed in the peripheral nervous system. Targeting these channels may provide a means to treat pain while minimizing central and cardiac adverse effects. In this study, we construct protein-protein interaction (PPI) networks based on pain-related sodium channels and develop a corresponding drug-target interaction network to identify potential lead compounds for pain management. To ensure reliable machine learning predictions, we carefully select 111 inhibitor data sets from a pool of more than 1000 targets in the PPI network. We employ 3 distinct machine learning algorithms combined with advanced natural language processing (NLP)-based embeddings, specifically pretrained transformer and autoencoder representations. Through a systematic screening process, we evaluate the side effects and repurposing potential of more than 150,000 drug candidates targeting Nav1.7 and Nav1.8 sodium channels. In addition, we assess the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of these candidates to identify leads with near-optimal characteristics. Our strategy provides an innovative platform for the pharmacological development of pain treatments, offering the potential for improved efficacy and reduced side effects.
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Affiliation(s)
- Long Chen
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, P R. China
| | - Jian Jiang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, P R. China
- Department of Mathematics, Michigan State University, East Lansing, MI, United States
| | - Bozheng Dou
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, P R. China
| | - Hongsong Feng
- Department of Mathematics, Michigan State University, East Lansing, MI, United States
| | - Jie Liu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, P R. China
| | - Yueying Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, P R. China
| | - Bengong Zhang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, P R. China
| | - Tianshou Zhou
- Key Laboratory of Computational Mathematics, Guangdong Province, and School of Mathematics, Sun Yat-sen University, Guangzhou, P R. China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, MI, United States
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
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Qian Y, Alhaskawi A, Dong Y, Ni J, Abdalbary S, Lu H. Transforming medicine: artificial intelligence integration in the peripheral nervous system. Front Neurol 2024; 15:1332048. [PMID: 38419700 PMCID: PMC10899496 DOI: 10.3389/fneur.2024.1332048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 02/01/2024] [Indexed: 03/02/2024] Open
Abstract
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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Affiliation(s)
- Yue Qian
- Rehabilitation Center, Hangzhou Wuyunshan Hospital (Hangzhou Institute of Health Promotion), Hangzhou, China
| | - Ahmad Alhaskawi
- Department of Orthopedics, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Yanzhao Dong
- Department of Orthopedics, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Juemin Ni
- Rehabilitation Center, Hangzhou Wuyunshan Hospital (Hangzhou Institute of Health Promotion), Hangzhou, China
| | - Sahar Abdalbary
- Department of Orthopedic Physical Therapy, Faculty of Physical Therapy, Nahda University in Beni Suef, Beni Suef, Egypt
| | - Hui Lu
- Department of Orthopedics, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Zhejiang University, Hangzhou, China
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Salama V, Humbert-Vidan L, Godinich B, Wahid KA, ElHabashy DM, Naser MA, He R, Mohamed ASR, Sahli AJ, Hutcheson KA, Gunn GB, Rosenthal DI, Fuller CD, Moreno AC. Comparison of Machine Leaning Models for Prediction of Acute Pain Severity and On-Treatment Opioid Utilization in Oral Cavity and Oropharyngeal Cancer Patients Receiving Radiation Therapy: Exploratory Analysis from a Large-Scale Retrospective Cohort. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.06.24302341. [PMID: 38370746 PMCID: PMC10871386 DOI: 10.1101/2024.02.06.24302341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Background Acute pain is a common and debilitating symptom experienced by oral cavity and oropharyngeal cancer (OC/OPC) patients undergoing radiation therapy (RT). Uncontrolled pain can result in opioid overuse and increased risks of long-term opioid dependence. The specific aim of this exploratory analysis was the prediction of severe acute pain and opioid use in the acute on-treatment setting, to develop risk-stratification models for pragmatic clinical trials. Materials and Methods A retrospective study was conducted on 900 OC/OPC patients treated with RT during 2017 to 2023. Clinical data including demographics, tumor data, pain scores and medication data were extracted from patient records. On-treatment pain intensity scores were assessed using a numeric rating scale (0-none, 10-worst) and total opioid doses were calculated using morphine equivalent daily dose (MEDD) conversion factors. Analgesics efficacy was assessed based on the combined pain intensity and the total required MEDD. ML models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Model (GBM) were developed and validated using ten-fold cross-validation. Performance of models were evaluated using discrimination and calibration metrics. Feature importance was investigated using bootstrap and permutation techniques. Results For predicting acute pain intensity, the GBM demonstrated superior area under the receiver operating curve (AUC) (0.71), recall (0.39), and F1 score (0.48). For predicting the total MEDD, LR outperformed other models in the AUC (0.67). For predicting the analgesics efficacy, SVM achieved the highest specificity (0.97), and best calibration (ECE of 0.06), while RF and GBM achieved the same highest AUC, 0.68. RF model emerged as the best calibrated model with ECE of 0.02 for pain intensity prediction and 0.05 for MEDD prediction. Baseline pain scores and vital signs demonstrated the most contributed features for the different predictive models. Conclusion These ML models are promising in predicting end-of-treatment acute pain and opioid requirements and analgesics efficacy in OC/OPC patients undergoing RT. Baseline pain score, vital sign changes were identified as crucial predictors. Implementation of these models in clinical practice could facilitate early risk stratification and personalized pain management. Prospective multicentric studies and external validation are essential for further refinement and generalizability.
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Shiferaw KB, Wali P, Waltemath D, Zeleke AA. Navigating the AI frontiers in cardiovascular research: a bibliometric exploration and topic modeling. Front Cardiovasc Med 2024; 10:1308668. [PMID: 38235288 PMCID: PMC10793658 DOI: 10.3389/fcvm.2023.1308668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 12/04/2023] [Indexed: 01/19/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a promising field in cardiovascular disease (CVD) research, offering innovative approaches to enhance diagnosis, treatment, and patient outcomes. In this study, we conducted bibliometric analysis combined with topic modeling to provide a comprehensive overview of the AI research landscape in CVD. Our analysis included 23,846 studies from Web of Science and PubMed, capturing the latest advancements and trends in this rapidly evolving field. By employing LDA (Latent Dirichlet Allocation) we identified key research themes, trends, and collaborations within the AI-CVD domain. The findings revealed the exponential growth of AI-related research in CVD, underscoring its immense potential to revolutionize cardiovascular healthcare. The annual scientific publication of machine learning papers in CVD increases continuously and significantly since 2016, with an overall annual growth rate of 22.8%. Almost half (46.2%) of the growth happened in the last 5 years. USA, China, India, UK and Korea were the top five productive countries in number of publications. UK, Germany and Australia were the most collaborative countries with a multiple country publication (MCP) value of 42.8%, 40.3% and 40.0% respectively. We observed the emergence of twenty-two distinct research topics, including "stroke and robotic rehabilitation therapy," "robotic-assisted cardiac surgery," and "cardiac image analysis," which persisted as major topics throughout the years. Other topics, such as "retinal image analysis and CVD" and "biomarker and wearable signal analyses," have recently emerged as dominant areas of research in cardiovascular medicine. Convolutional neural network appears to be the most mentioned algorithm followed by LSTM (Long Short-Term Memory) and KNN (K-Nearest Neighbours). This indicates that the future direction of AI cardiovascular research is predominantly directing toward neural networks and image analysis. As AI continues to shape the landscape of CVD research, our study serves as a comprehensive guide for researchers, practitioners, and policymakers, providing valuable insights into the current state of AI in CVD research. This study offers a deep understanding of research trends and paves the way for future directions to maximiz the potential of AI to effectively combat cardiovascular diseases.
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Affiliation(s)
- Kirubel Biruk Shiferaw
- Department of Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [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: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Salama V, Godinich B, Geng Y, Humbert-Vidan L, Maule L, Wahid KA, Naser MA, He R, Mohamed ASR, Fuller CD, Moreno AC. Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.06.23299610. [PMID: 38105979 PMCID: PMC10723503 DOI: 10.1101/2023.12.06.23299610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Background/objective Pain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer. Methods A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including "Cancer", "Pain", "Pain Management", "Analgesics", "Opioids", "Artificial Intelligence", "Machine Learning", "Deep Learning", and "Neural Networks" published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies. Results This systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients' pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%). Conclusion Implementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.
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Hassan S, Nesovic K, Babineau J, Furlan AD, Kumbhare D, Carlesso LC. Identifying chronic low back pain phenotypic domains and characteristics accounting for individual variation: a systematic review. Pain 2023; 164:2148-2190. [PMID: 37027149 DOI: 10.1097/j.pain.0000000000002911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 02/27/2023] [Indexed: 04/08/2023]
Abstract
ABSTRACT Interpatient variability is frequently observed among individuals with chronic low back pain (cLBP). This review aimed at identifying phenotypic domains and characteristics that account for interpatient variability in cLBP. We searched MEDLINE ALL (through Ovid), Embase Classic and EMBASE (through Ovid), Scopus, and CINAHL Complete (through EBSCOhost) databases. Studies that aimed to identify or predict cLBP different phenotypes were included. We excluded studies that focused on specific treatments. The methodological quality was assessed using an adaptation of the Downs and Black tool. Forty-three studies were included. Although the patient and pain-related characteristics used to identify phenotypes varied considerably across studies, the following were among the most identified phenotypic domains and characteristics that account for interpatient variability in cLBP: pain-related characteristics (including location, severity, qualities, and duration) and pain impact (including disability, sleep, and fatigue), psychological domains (including anxiety and depression), behavioral domains (including coping, somatization, fear avoidance, and catastrophizing), social domains (including employment and social support), and sensory profiling (including pain sensitivity and sensitization). Despite these findings, our review showed that the evidence on pain phenotyping still requires further investigation. The assessment of the methodological quality revealed several limitations. We recommend adopting a standard methodology to enhance the generalizability of the results and the implementation of a comprehensive and feasible assessment framework to facilitate personalized treatments in clinical settings.
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Affiliation(s)
- Samah Hassan
- Institute of Education Research (TIER), University Health Network, Toronto, ON, Canada
| | - Karlo Nesovic
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Jessica Babineau
- Institute of Education Research (TIER), University Health Network, Toronto, ON, Canada
- Library and Information Services, University Health Network, Toronto, ON, Canada
| | - Andrea D Furlan
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Dinesh Kumbhare
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Lisa C Carlesso
- School of Rehabilitation Science, McMaster University, Hamilton, ON, Canada
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Meng R, Wang H, Si Z, Wang X, Zhao Z, Lu H, Zheng Y, Chen J, Wang H, Hu J, Xue L, Li X, Sun J, Wu J. Analysis of factors affecting nonalcoholic fatty liver disease in Chinese steel workers and risk assessment studies. Lipids Health Dis 2023; 22:123. [PMID: 37559095 PMCID: PMC10411019 DOI: 10.1186/s12944-023-01886-0] [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: 05/30/2023] [Accepted: 07/28/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND The global incidence of nonalcoholic fatty liver disease (NAFLD) is rapidly escalating, positioning it as a principal public health challenge with significant implications for population well-being. Given its status as a cornerstone of China's economic structure, the steel industry employs a substantial workforce, consequently bringing associated health issues under increasing scrutiny. Establishing a risk assessment model for NAFLD within steelworkers aids in disease risk stratification among this demographic, thereby facilitating early intervention measures to protect the health of this significant populace. METHODS Use of cross-sectional studies. A total of 3328 steelworkers who underwent occupational health evaluations between January and September 2017 were included in this study. Hepatic steatosis was uniformly diagnosed via abdominal ultrasound. Influential factors were pinpointed using chi-square (χ2) tests and unconditional logistic regression analysis, with model inclusion variables identified by pertinent literature. Assessment models encompassing logistic regression, random forest, and XGBoost were constructed, and their effectiveness was juxtaposed in terms of accuracy, area under the curve (AUC), and F1 score. Subsequently, a scoring system for NAFLD risk was established, premised on the optimal model. RESULTS The findings indicated that sex, overweight, obesity, hyperuricemia, dyslipidemia, occupational dust exposure, and ALT serve as risk factors for NAFLD in steelworkers, with corresponding odds ratios (OR, 95% confidence interval (CI)) of 0.672 (0.487-0.928), 4.971 (3.981-6.207), 16.887 (12.99-21.953), 2.124 (1.77-2.548), 2.315 (1.63-3.288), 1.254 (1.014-1.551), and 3.629 (2.705-4.869), respectively. The sensitivity of the three models was reported as 0.607, 0.680 and 0.564, respectively, while the precision was 0.708, 0.643, and 0.701, respectively. The AUC measurements were 0.839, 0.839, and 0.832, and the Brier scores were 0.150, 0.153, and 0.155, respectively. The F1 score results were 0.654, 0.661, and 0.625, with log loss measures at 0.460, 0.661, and 0.564, respectively. R2 values were reported as 0.789, 0.771, and 0.778, respectively. Performance was comparable across all three models, with no significant differences observed. The NAFLD risk score system exhibited exceptional risk detection capabilities with an established cutoff value of 86. CONCLUSIONS The study identified sex, BMI, dyslipidemia, hyperuricemia, occupational dust exposure, and ALT as significant risk factors for NAFLD among steelworkers. The traditional logistic regression model proved equally effective as the random forest and XGBoost models in assessing NAFLD risk. The optimal cutoff value for risk assessment was determined to be 86. This study provides clinicians with a visually accessible risk stratification approach to gauge the propensity for NAFLD in steelworkers, thereby aiding early identification and intervention among those at risk.
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Affiliation(s)
- Rui Meng
- School of Public Health, North China University of Science and Technology, Caofeidian New Town, No. 21 Bohai Avenue, Tangshan, 063210, China
| | - Hui Wang
- School of Public Health, North China University of Science and Technology, Caofeidian New Town, No. 21 Bohai Avenue, Tangshan, 063210, China
| | - Zhikang Si
- School of Public Health, North China University of Science and Technology, Caofeidian New Town, No. 21 Bohai Avenue, Tangshan, 063210, China
| | - Xuelin Wang
- School of Public Health, North China University of Science and Technology, Caofeidian New Town, No. 21 Bohai Avenue, Tangshan, 063210, China
| | - Zekun Zhao
- School of Public Health, North China University of Science and Technology, Caofeidian New Town, No. 21 Bohai Avenue, Tangshan, 063210, China
| | - Haipeng Lu
- School of Public Health, North China University of Science and Technology, Caofeidian New Town, No. 21 Bohai Avenue, Tangshan, 063210, China
| | - Yizhan Zheng
- School of Public Health, North China University of Science and Technology, Caofeidian New Town, No. 21 Bohai Avenue, Tangshan, 063210, China
| | - Jiaqi Chen
- School of Public Health, North China University of Science and Technology, Caofeidian New Town, No. 21 Bohai Avenue, Tangshan, 063210, China
| | - Huan Wang
- School of Public Health, North China University of Science and Technology, Caofeidian New Town, No. 21 Bohai Avenue, Tangshan, 063210, China
| | - Jiaqi Hu
- School of Public Health, North China University of Science and Technology, Caofeidian New Town, No. 21 Bohai Avenue, Tangshan, 063210, China
| | - Ling Xue
- School of Public Health, North China University of Science and Technology, Caofeidian New Town, No. 21 Bohai Avenue, Tangshan, 063210, China
| | - Xiaoming Li
- School of Public Health, North China University of Science and Technology, Caofeidian New Town, No. 21 Bohai Avenue, Tangshan, 063210, China
| | - Jian Sun
- School of Public Health, North China University of Science and Technology, Caofeidian New Town, No. 21 Bohai Avenue, Tangshan, 063210, China.
| | - Jianhui Wu
- School of Public Health, North China University of Science and Technology, Caofeidian New Town, No. 21 Bohai Avenue, Tangshan, 063210, China.
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Riganello F, Tonin P, Soddu A. I Feel! Therefore, I Am from Pain to Consciousness in DOC Patients. Int J Mol Sci 2023; 24:11825. [PMID: 37511583 PMCID: PMC10380260 DOI: 10.3390/ijms241411825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/13/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Pain assessment and management in patients with disorders of consciousness (DOC) is a challenging and important aspect of care, with implications for detecting consciousness and promoting recovery. This narrative review explores the role of pain in consciousness, the challenges of pain assessment, pharmacological treatment in DOC, and the implications of pain assessment when detecting changes in consciousness. The review discusses the Nociception Coma Scale and its revised version, which are behavioral scales used to assess pain in DOC patients, and the challenges and controversies surrounding the appropriate pharmacological treatment of pain in these patients. Moreover, we highlight recent evidence suggesting that an accurate pain assessment may predict changes in the level of consciousness in unresponsive wakefulness syndrome/vegetative state patients, underscoring the importance of ongoing pain management in these patients.
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Affiliation(s)
- Francesco Riganello
- Research in Advanced Neurorehabilitation, S. Anna Institute, 88900 Crotone, Italy
| | - Paolo Tonin
- Research in Advanced Neurorehabilitation, S. Anna Institute, 88900 Crotone, Italy
| | - Andrea Soddu
- Physics, and Astronomy Department, Western Institute for Neuroscience, University of Western Ontario, London, ON N6A 3K7, Canada
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13
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Rockholt MM, Kenefati G, Doan LV, Chen ZS, Wang J. In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand? Front Neurosci 2023; 17:1186418. [PMID: 37389362 PMCID: PMC10301750 DOI: 10.3389/fnins.2023.1186418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 05/12/2023] [Indexed: 07/01/2023] Open
Abstract
Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives.
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Affiliation(s)
- Mika M. Rockholt
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
| | - George Kenefati
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
| | - Lisa V. Doan
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
| | - Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, United States
- Department of Neuroscience & Physiology, Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY, United States
| | - Jing Wang
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
- Department of Neuroscience & Physiology, Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY, United States
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14
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Naseri H, Skamene S, Tolba M, Faye MD, Ramia P, Khriguian J, David M, Kildea J. A Scalable Radiomics- and Natural Language Processing-Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study. JMIR AI 2023; 2:e44779. [PMID: 38875572 PMCID: PMC11041487 DOI: 10.2196/44779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 03/12/2023] [Accepted: 04/01/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND The identification of objective pain biomarkers can contribute to an improved understanding of pain, as well as its prognosis and better management. Hence, it has the potential to improve the quality of life of patients with cancer. Artificial intelligence can aid in the extraction of objective pain biomarkers for patients with cancer with bone metastases (BMs). OBJECTIVE This study aimed to develop and evaluate a scalable natural language processing (NLP)- and radiomics-based machine learning pipeline to differentiate between painless and painful BM lesions in simulation computed tomography (CT) images using imaging features (biomarkers) extracted from lesion center point-based regions of interest (ROIs). METHODS Patients treated at our comprehensive cancer center who received palliative radiotherapy for thoracic spine BM between January 2016 and September 2019 were included in this retrospective study. Physician-reported pain scores were extracted automatically from radiation oncology consultation notes using an NLP pipeline. BM center points were manually pinpointed on CT images by radiation oncologists. Nested ROIs with various diameters were automatically delineated around these expert-identified BM center points, and radiomics features were extracted from each ROI. Synthetic Minority Oversampling Technique resampling, the Least Absolute Shrinkage And Selection Operator feature selection method, and various machine learning classifiers were evaluated using precision, recall, F1-score, and area under the receiver operating characteristic curve. RESULTS Radiation therapy consultation notes and simulation CT images of 176 patients (mean age 66, SD 14 years; 95 males) with thoracic spine BM were included in this study. After BM center point identification, 107 radiomics features were extracted from each spherical ROI using pyradiomics. Data were divided into 70% and 30% training and hold-out test sets, respectively. In the test set, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of our best performing model (neural network classifier on an ensemble ROI) were 0.82 (132/163), 0.59 (16/27), 0.85 (116/136), and 0.83, respectively. CONCLUSIONS Our NLP- and radiomics-based machine learning pipeline was successful in differentiating between painful and painless BM lesions. It is intrinsically scalable by using NLP to extract pain scores from clinical notes and by requiring only center points to identify BM lesions in CT images.
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Affiliation(s)
- Hossein Naseri
- Medical Physics Unit, McGill University Health Centre, Montreal, QC, Canada
| | - Sonia Skamene
- Division of Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada
| | - Marwan Tolba
- Division of Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada
| | - Mame Daro Faye
- Division of Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada
| | - Paul Ramia
- Division of Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada
| | - Julia Khriguian
- Division of Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada
| | - Marc David
- Division of Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada
| | - John Kildea
- Medical Physics Unit, McGill University Health Centre, Montreal, QC, Canada
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15
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Antel R, Sahlas E, Gore G, Ingelmo P. Use of artificial intelligence in paediatric anaesthesia: a systematic review. BJA OPEN 2023; 5:100125. [PMID: 37587993 PMCID: PMC10430814 DOI: 10.1016/j.bjao.2023.100125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 01/03/2023] [Indexed: 08/18/2023]
Abstract
Objectives Although the development of artificial intelligence (AI) technologies in medicine has been significant, their application to paediatric anaesthesia is not well characterised. As the paediatric operating room is a data-rich environment that requires critical clinical decision-making, this systematic review aims to characterise the current use of AI in paediatric anaesthesia and to identify barriers to the successful integration of such technologies. Methods This review was registered with PROSPERO (CRD42022304610), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in five electronic databases (Embase, Medline, Central, Scopus, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for paediatric anaesthesia (<18 yr old) within the perioperative setting. Results From 3313 records identified in the initial search, 40 were included in this review. Identified applications of AI were described for patient risk factor prediction (24 studies; 60%), anaesthetic depth estimation (2; 5%), anaesthetic medication/technique decision guidance (2; 5%), intubation assistance (1; 2.5%), airway device selection (3; 7.5%), physiological variable monitoring (6; 15%), and operating room scheduling (2; 5%). Multiple domains of AI were discussed including machine learning, computer vision, fuzzy logic, and natural language processing. Conclusion There is an emerging literature regarding applications of AI for paediatric anaesthesia, and their clinical integration holds potential for ultimately improving patient outcomes. However, multiple barriers to their clinical integration remain including a lack of high-quality input data, lack of external validation/evaluation, and unclear generalisability to diverse settings. Systematic review protocol CRD42022304610 (PROSPERO).
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Affiliation(s)
- Ryan Antel
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
| | - Ella Sahlas
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
| | - Genevieve Gore
- Schulich Library of Physical Sciences, Life Sciences, and Engineering, McGill University, Montreal, Quebec, Canada
| | - Pablo Ingelmo
- Department of Anesthesia, Montreal Children's Hospital, McGill University, Montreal, Quebec, Canada
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16
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Berger SE, Baria AT. Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches. FRONTIERS IN PAIN RESEARCH 2022; 3:896276. [PMID: 35721658 PMCID: PMC9201034 DOI: 10.3389/fpain.2022.896276] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 05/12/2022] [Indexed: 11/13/2022] Open
Abstract
Pain research traverses many disciplines and methodologies. Yet, despite our understanding and field-wide acceptance of the multifactorial essence of pain as a sensory perception, emotional experience, and biopsychosocial condition, pain scientists and practitioners often remain siloed within their domain expertise and associated techniques. The context in which the field finds itself today-with increasing reliance on digital technologies, an on-going pandemic, and continued disparities in pain care-requires new collaborations and different approaches to measuring pain. Here, we review the state-of-the-art in human pain research, summarizing emerging practices and cutting-edge techniques across multiple methods and technologies. For each, we outline foreseeable technosocial considerations, reflecting on implications for standards of care, pain management, research, and societal impact. Through overviewing alternative data sources and varied ways of measuring pain and by reflecting on the concerns, limitations, and challenges facing the field, we hope to create critical dialogues, inspire more collaborations, and foster new ideas for future pain research methods.
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Affiliation(s)
- Sara E. Berger
- Responsible and Inclusive Technologies Research, Exploratory Sciences Division, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, United States
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17
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Zis P, Liampas A, Artemiadis A, Tsalamandris G, Neophytou P, Unwin Z, Kimiskidis VK, Hadjigeorgiou GM, Varrassi G, Zhao Y, Sarrigiannis PG. EEG Recordings as Biomarkers of Pain Perception: Where Do We Stand and Where to Go? Pain Ther 2022; 11:369-380. [PMID: 35322392 PMCID: PMC9098726 DOI: 10.1007/s40122-022-00372-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 03/07/2022] [Indexed: 11/25/2022] Open
Abstract
Introduction The universality and complexity of pain, which is highly prevalent, yield its significance to both patients and researchers. Developing a non-invasive tool that can objectively measure pain is of the utmost importance for clinical and research purposes. Traditionally electroencephalography (EEG) has been mostly used in epilepsy; however, over the recent years EEG has become an important non-invasive clinical tool that has helped increase our understanding of brain network complexities and for the identification of areas of dysfunction. This review aimed to investigate the role of EEG recordings as potential biomarkers of pain perception. Methods A systematic search of the PubMed database led to the identification of 938 papers, of which 919 were excluded as a result of not meeting the eligibility criteria, and one article was identified through screening of the reference lists of the 19 eligible studies. Ultimately, 20 papers were included in this systematic review. Results Changes of the cortical activation have potential, though the described changes are not always consistent. The most consistent finding is the increase in the delta and gamma power activity. Only a limited number of studies have looked into brain networks encoding pain perception. Conclusion Although no robust EEG biomarkers of pain perception have been identified yet, EEG has potential and future research should be attempted. Designing strong research protocols, controlling for potential risk of biases, as well as investigating brain networks rather than isolated cortical changes will be crucial in this attempt. Supplementary Information The online version contains supplementary material available at 10.1007/s40122-022-00372-2.
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Affiliation(s)
- Panagiotis Zis
- Medical School, University of Cyprus, Nicosia, Cyprus
- Medical School, University of Sheffield, Sheffield, UK
- Department of Neurology, Nicosia General Hospital, Nicosia, Cyprus
| | - Andreas Liampas
- Department of Neurology, Nicosia General Hospital, Nicosia, Cyprus
| | - Artemios Artemiadis
- Medical School, University of Cyprus, Nicosia, Cyprus
- Department of Neurology, Nicosia General Hospital, Nicosia, Cyprus
| | | | | | - Zoe Unwin
- Department of Clinical Neurophysiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Vasilios K. Kimiskidis
- 1st Department of Neurology, AHEPA University Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Georgios M. Hadjigeorgiou
- Medical School, University of Cyprus, Nicosia, Cyprus
- Department of Neurology, Nicosia General Hospital, Nicosia, Cyprus
| | | | - Yifan Zhao
- School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, UK
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Dey S, Arora P. Artificial neural network in clinical pain medicine and research. INDIAN JOURNAL OF PAIN 2022. [DOI: 10.4103/ijpn.ijpn_111_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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Singh Bajwa S, Gehdoo R, Jain D, Dogra N. Postoperative pain management: Stepping towards newer frontiers. Indian J Anaesth 2022; 66:481-484. [PMID: 36111086 PMCID: PMC9468995 DOI: 10.4103/ija.ija_605_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 07/14/2022] [Accepted: 07/14/2022] [Indexed: 11/05/2022] Open
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