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Kaido M, Arita R, Mitsukura Y, Tsubota K. Electroencephalogram-detected stress levels in the frontal lobe region of patients with dry eye. Ocul Surf 2024; 32:139-144. [PMID: 38458565 DOI: 10.1016/j.jtos.2024.02.007] [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: 10/17/2023] [Revised: 02/17/2024] [Accepted: 02/26/2024] [Indexed: 03/10/2024]
Abstract
PURPOSE To evaluate stress levels extracted from prefrontal electroencephalogram (EEG) signals and investigate their relationship with dry eye symptoms. METHODS This prospective, cross-sectional, comparative study included 25 eyes of 25 patients with aqueous tear-deficient dry eye (low Schirmer group), 25 eyes of 25 patients with short tear breakup time dry eye (short breakup time group), and 24 eyes of 24 individuals without dry eye. An EEG test, the Japanese version of the Ocular Surface Disease Index (OSDI), and a stress questionnaire were administered. EEG-detected stress levels were assessed under three conditions: eyes closed, eyes open, and eyes open under ocular surface anesthesia. RESULTS Stress levels were significantly lower when the eyes were closed than when they were open in all groups (all P < 0.05). Stress levels during eyes open under ocular surface anesthesia were significantly lower than those during eyes open without anesthesia only in the low Schirmer group; no differences were found between the short breakup time and control groups. OSDI scores were associated with EEG-detected stress levels (P = 0.06) and vital staining score (P < 0.05) in the low Schirmer group; they were not associated with EEG-detected stress (P > 0.05), but with subjective stress questionnaire scores and breakup time values in the short breakup time group (P < 0.05). CONCLUSIONS In the low Schirmer group, peripheral nerve stimulation caused by ocular surface damage induced stress reactions in the frontal lobe, resulting in dry eye symptoms. Conversely, in the short breakup time group, the stress response in the frontal lobe was not related to symptom development.
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Affiliation(s)
- Minako Kaido
- Wada Eye Clinic, Chiba, Japan; Tsubota Laboratory, Inc., Tokyo, Japan.
| | - Reiko Arita
- Tsubota Laboratory, Inc., Tokyo, Japan; Itoh Clinic, Saitama, Japan
| | - Yasue Mitsukura
- Faculty of Science and Technology, Keio University, Kanagawa, Japan
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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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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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Mari T, Asgard O, Henderson J, Hewitt D, Brown C, Stancak A, Fallon N. External validation of binary machine learning models for pain intensity perception classification from EEG in healthy individuals. Sci Rep 2023; 13:242. [PMID: 36604453 PMCID: PMC9816165 DOI: 10.1038/s41598-022-27298-1] [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: 03/29/2022] [Accepted: 12/29/2022] [Indexed: 01/07/2023] Open
Abstract
Discrimination of pain intensity using machine learning (ML) and electroencephalography (EEG) has significant potential for clinical applications, especially in scenarios where self-report is unsuitable. However, existing research is limited due to a lack of external validation (assessing performance using novel data). We aimed for the first external validation study for pain intensity classification with EEG. Pneumatic pressure stimuli were delivered to the fingernail bed at high and low pain intensities during two independent EEG experiments with healthy participants. Study one (n = 25) was utilised for training and cross-validation. Study two (n = 15) was used for external validation one (identical stimulation parameters to study one) and external validation two (new stimulation parameters). Time-frequency features of peri-stimulus EEG were computed on a single-trial basis for all electrodes. ML training and analysis were performed on a subset of features, identified through feature selection, which were distributed across scalp electrodes and included frontal, central, and parietal regions. Results demonstrated that ML models outperformed chance. The Random Forest (RF) achieved the greatest accuracies of 73.18, 68.32 and 60.42% for cross-validation, external validation one and two, respectively. Importantly, this research is the first to externally validate ML and EEG for the classification of intensity during experimental pain, demonstrating promising performance which generalises to novel samples and paradigms. These findings offer the most rigorous estimates of ML's clinical potential for pain classification.
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Affiliation(s)
- Tyler Mari
- Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA, UK.
| | - Oda Asgard
- grid.10025.360000 0004 1936 8470Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA UK
| | - Jessica Henderson
- grid.10025.360000 0004 1936 8470Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA UK
| | - Danielle Hewitt
- grid.10025.360000 0004 1936 8470Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA UK
| | - Christopher Brown
- grid.10025.360000 0004 1936 8470Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA UK
| | - Andrej Stancak
- grid.10025.360000 0004 1936 8470Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA UK
| | - Nicholas Fallon
- grid.10025.360000 0004 1936 8470Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA UK
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OA-Pain-Sense: Machine Learning Prediction of Hip and Knee Osteoarthritis Pain from IMU Data. INFORMATICS 2022. [DOI: 10.3390/informatics9040097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Joint pain is a prominent symptom of Hip and Knee Osteoarthritis (OA), impairing patients’ movements and affecting the joint mechanics of walking. Self-report questionnaires are currently the gold standard for Hip OA and Knee OA pain assessment, presenting several problems, including the fact that older individuals often fail to provide accurate self-pain reports. Passive methods to assess pain are desirable. This study aims to explore the feasibility of OA-Pain-Sense, a passive, automatic Machine Learning-based approach that predicts patients’ self-reported pain levels using SpatioTemporal Gait features extracted from the accelerometer signal gathered from an anterior-posterior wearable sensor. To mitigate inter-subject variability, we investigated two types of data rescaling: subject-level and dataset-level. We explored six different binary machine learning classification models for discriminating pain in patients with Hip OA or Knee OA from healthy controls. In rigorous evaluation, OA-Pain-Sense achieved an average accuracy of 86.79% using the Decision Tree and 83.57% using Support Vector Machine classifiers for distinguishing Hip OA and Knee OA patients from healthy subjects, respectively. Our results demonstrate that OA-Pain-Sense is feasible, paving the way for the development of a pain assessment algorithm that can support clinical decision-making and be used on any wearable device, such as smartphones.
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Matsangidou M, Liampas A, Pittara M, Pattichi CS, Zis P. Machine Learning in Pain Medicine: An Up-To-Date Systematic Review. Pain Ther 2021; 10:1067-1084. [PMID: 34568998 PMCID: PMC8586126 DOI: 10.1007/s40122-021-00324-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 09/07/2021] [Indexed: 11/30/2022] Open
Abstract
INTRODUCTION Pain is the unpleasant sensation and emotional experience that leads to poor quality of life for millions of people worldwide. Considering the complexity in understanding the principles of pain and its significant impact on individuals and society, research focuses to deliver innovative pain relief methods and techniques. This review explores the clinical uses of machine learning (ML) for the diagnosis, classification, and management of pain. METHODS A systematic review of the current literature was conducted using the PubMed database library. RESULTS Twenty-six papers related to pain and ML research were included. Most of the studies used ML for effectively classifying the patients' level of pain, followed by use of ML for the prediction of manifestation of pain and for pain management. A less common reason for performing ML analysis was for the diagnosis of pain. The different approaches are thoroughly discussed. CONCLUSION ML is increasingly used in pain medicine and appears to be more effective compared to traditional statistical approaches in the diagnosis, classification, and management of pain.
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Affiliation(s)
| | - Andreas Liampas
- Department of Neurology, Nicosia New General Hospital, Nicosia, Cyprus
| | - Melpo Pittara
- Bernoulli Institute for Mathematics Computer Science and Artificial Intelligent, University of Groningen, Groningen, Netherlands
| | - Constantinos S. Pattichi
- CYENS Centre of Excellence, Nicosia, Cyprus ,Computer Science, University of Cyprus, Nicosia, Cyprus
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Mari T, Henderson J, Maden M, Nevitt S, Duarte R, Fallon N. Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data. THE JOURNAL OF PAIN 2021; 23:349-369. [PMID: 34425248 DOI: 10.1016/j.jpain.2021.07.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/25/2021] [Accepted: 07/27/2021] [Indexed: 11/17/2022]
Abstract
Recent attempts to utilize machine learning (ML) to predict pain-related outcomes from Electroencephalogram (EEG) data demonstrate promising results. The primary aim of this review was to evaluate the effectiveness of ML algorithms for predicting pain intensity, phenotypes or treatment response from EEG. Electronic databases MEDLINE, EMBASE, Web of Science, PsycINFO and The Cochrane Library were searched. A total of 44 eligible studies were identified, with 22 presenting attempts to predict pain intensity, 15 investigating the prediction of pain phenotypes and seven assessing the prediction of treatment response. A meta-analysis was not considered appropriate for this review due to heterogenos methods and reporting. Consequently, data were narratively synthesized. The results demonstrate that the best performing model of the individual studies allows for the prediction of pain intensity, phenotypes and treatment response with accuracies ranging between 62 to 100%, 57 to 99% and 65 to 95.24%, respectively. The results suggest that ML has the potential to effectively predict pain outcomes, which may eventually be used to assist clinical care. However, inadequate reporting and potential bias reduce confidence in the results. Future research should improve reporting standards and externally validate models to decrease bias, which would increase the feasibility of clinical translation. PERSPECTIVE: This systematic review explores the state-of-the-art machine learning methods for predicting pain intensity, phenotype or treatmentresponse from EEG data. Results suggest that machine learning may demonstrate clinical utility, pending further research and development. Areas for improvement, including standardized processing, reporting and the need for better methodological assessment tools, are discussed.
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Affiliation(s)
- Tyler Mari
- Department of Psychology, University of Liverpool, Liverpool, UK.
| | | | - Michelle Maden
- Department of Health Data Science, Liverpool Reviews and Implementation Group, University of Liverpool, Liverpool, UK
| | - Sarah Nevitt
- Department of Health Data Science, Liverpool Reviews and Implementation Group, University of Liverpool, Liverpool, UK
| | - Rui Duarte
- Department of Health Data Science, Liverpool Reviews and Implementation Group, University of Liverpool, Liverpool, UK
| | - Nicholas Fallon
- Department of Psychology, University of Liverpool, Liverpool, UK
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