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Casarin S, Haelterman NA, Machol K. Transforming personalized chronic pain management with artificial intelligence: A commentary on the current landscape and future directions. Exp Neurol 2024; 382:114980. [PMID: 39353544 DOI: 10.1016/j.expneurol.2024.114980] [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: 06/28/2024] [Revised: 09/05/2024] [Accepted: 09/27/2024] [Indexed: 10/04/2024]
Abstract
Artificial intelligence (AI) has the potential to revolutionize chronic pain management by guiding the development of effective treatment strategies that are tailored to individual patient needs. This potential comes from AI's ability to analyze large and heterogeneous datasets to identify hidden patterns. When applied to clinical datasets of a particular patient population, AI can be used to identify pain subtypes among patients, predict treatment responses, and guide the clinical decision-making process. However, integrating AI into the clinical practice requires overcoming challenges such as data quality, the complexity of human pain physiology, and validation against diverse patient populations. Targeted, collaborative efforts among clinicians, researchers, and AI specialists will be needed to maximize AI's capabilities and advance current management and treatment of chronic pain conditions.
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Affiliation(s)
- Stefano Casarin
- Center for Precision Surgery, Houston Methodist Research Institute, Houston, TX, USA; LaSIE, UMR 7356 CNRS, La Rochelle Université, La Rochelle, France; Department of Surgery, Houston Methodist Hospital, Houston, TX, USA.
| | - Nele A Haelterman
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Keren Machol
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA.
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2
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Gouverneur P, Badura A, Li F, Bieńkowska M, Luebke L, Adamczyk WM, Szikszay TM, Myśliwiec A, Luedtke K, Grzegorzek M, Piętka E. An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition. Sci Data 2024; 11:1051. [PMID: 39333541 PMCID: PMC11436824 DOI: 10.1038/s41597-024-03878-w] [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: 02/05/2024] [Accepted: 09/11/2024] [Indexed: 09/29/2024] Open
Abstract
Access to large amounts of data is essential for successful machine learning research. However, there is insufficient data for many applications, as data collection is often challenging and time-consuming. The same applies to automated pain recognition, where algorithms aim to learn associations between a level of pain and behavioural or physiological responses. Although machine learning models have shown promise in improving the current gold standard of pain monitoring (self-reports) only a handful of datasets are freely accessible to researchers. This paper presents the PainMonit Dataset for automated pain detection using physiological data. The dataset consists of two parts, as pain can be perceived differently depending on its underlying cause. (1) Pain was triggered by heat stimuli in an experimental study during which nine physiological sensor modalities (BVP, 2×EDA, skin temperature, ECG, EMG, IBI, HR, respiration) were recorded from 55 healthy subjects. (2) Eight modalities (2×BVP, 2×EDA, EMG, skin temperature, respiration, grip) were recorded from 49 participants to assess their pain during a physiotherapy session.
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Affiliation(s)
- Philip Gouverneur
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
| | - Aleksandra Badura
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800, Zabrze, Poland
| | - Frédéric Li
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Maria Bieńkowska
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800, Zabrze, Poland
| | - Luisa Luebke
- Institute of Health Sciences, Department of Physiotherapy, Pain and Exercise Research Luebeck (P.E.R.L.), University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Wacław M Adamczyk
- Laboratory of Pain Research, Institute of Physiotherapy and Health Sciences, Academy of Physical Education in Katowice, Mikołowska 72a, 40-065, Katowice, Poland
- Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, US
| | - Tibor M Szikszay
- Institute of Health Sciences, Department of Physiotherapy, Pain and Exercise Research Luebeck (P.E.R.L.), University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Andrzej Myśliwiec
- Laboratory of Physiotherapy and Physioprevention, Institute of Physiotherapy and Health Sciences, Academy of Physical Education in Katowice, Mikołowska 72a, 40-065, Katowice, Poland
| | - Kerstin Luedtke
- Institute of Health Sciences, Department of Physiotherapy, Pain and Exercise Research Luebeck (P.E.R.L.), University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Marcin Grzegorzek
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
- German Research Center for Artificial Intelligence (DFKI), Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Ewa Piętka
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800, Zabrze, Poland
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Luebke L, Gouverneur P, Szikszay TM, Adamczyk WM, Luedtke K, Grzegorzek M. Objective Measurement of Subjective Pain Perception with Autonomic Body Reactions in Healthy Subjects and Chronic Back Pain Patients: An Experimental Heat Pain Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:8231. [PMID: 37837061 PMCID: PMC10575054 DOI: 10.3390/s23198231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023]
Abstract
Multiple attempts to quantify pain objectively using single measures of physiological body responses have been performed in the past, but the variability across participants reduces the usefulness of such methods. Therefore, this study aims to evaluate whether combining multiple autonomic parameters is more appropriate to quantify the perceived pain intensity of healthy subjects (HSs) and chronic back pain patients (CBPPs) during experimental heat pain stimulation. HS and CBPP received different heat pain stimuli adjusted for individual pain tolerance via a CE-certified thermode. Different sensors measured physiological responses. Machine learning models were trained to evaluate performance in distinguishing pain levels and identify key sensors and features for the classification task. The results show that distinguishing between no and severe pain is significantly easier than discriminating lower pain levels. Electrodermal activity is the best marker for distinguishing between low and high pain levels. However, recursive feature elimination showed that an optimal subset of features for all modalities includes characteristics retrieved from several modalities. Moreover, the study's findings indicate that differences in physiological responses to pain in HS and CBPP remain small.
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Affiliation(s)
- Luisa Luebke
- Institute of Health Sciences, Department of Physiotherapy, Pain and Exercise Research Luebeck (P.E.R.L.), Universität zu Lübeck, 23562 Lübeck, Germany; (L.L.); (T.M.S.); (K.L.)
- Center of Brain, Behavior and Metabolism (CBBM), University of Luebeck, 23562 Lübeck, Germany
| | - Philip Gouverneur
- Institute of Medical Informatics, University of Lübeck, 23562 Lübeck, Germany;
| | - Tibor M. Szikszay
- Institute of Health Sciences, Department of Physiotherapy, Pain and Exercise Research Luebeck (P.E.R.L.), Universität zu Lübeck, 23562 Lübeck, Germany; (L.L.); (T.M.S.); (K.L.)
- Center of Brain, Behavior and Metabolism (CBBM), University of Luebeck, 23562 Lübeck, Germany
| | - Wacław M. Adamczyk
- Laboratory of Pain Research, Institute of Physiotherapy and Health Sciences, The Jerzy Kukuczka Academy of Physical Education, 40-065 Katowice, Poland;
- Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229-3026, USA
| | - Kerstin Luedtke
- Institute of Health Sciences, Department of Physiotherapy, Pain and Exercise Research Luebeck (P.E.R.L.), Universität zu Lübeck, 23562 Lübeck, Germany; (L.L.); (T.M.S.); (K.L.)
- Center of Brain, Behavior and Metabolism (CBBM), University of Luebeck, 23562 Lübeck, Germany
| | - Marcin Grzegorzek
- Institute of Medical Informatics, University of Lübeck, 23562 Lübeck, Germany;
- Department of Knowledge Engineering, University of Economics in Katowice, 40-287 Katowice, Poland
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Junaid M, Ali S, Eid F, El-Sappagh S, Abuhmed T. Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 234:107495. [PMID: 37003039 DOI: 10.1016/j.cmpb.2023.107495] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 02/23/2023] [Accepted: 03/17/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND AND OBJECTIVES Parkinson's Disease (PD) is a devastating chronic neurological condition. Machine learning (ML) techniques have been used in the early prediction of PD progression. Fusion of heterogeneous data modalities proved its capability to improve the performance of ML models. Time series data fusion supports the tracking of the disease over time. In addition, the trustworthiness of the resulting models is improved by adding model explainability features. The literature on PD has not sufficiently explored these three points. METHODS In this work, we proposed an ML pipeline for predicting the progression of PD that is both accurate and explainable. We explore the fusion of different combinations of five time series modalities from the Parkinson's Progression Markers Initiative (PPMI) real-world dataset, including patient characteristics, biosamples, medication history, motor, and non-motor function data. Each patient has six visits. The problem has been formulated in two ways: ❶ a three-class based progression prediction with 953 patients in each time series modality, and ❷ a four-class based progression prediction with 1,060 patients in each time series modality. The statistical features of these six visits were calculated from each modality and diverse feature selection methods were applied to select the most informative feature sets. The extracted features were used to train a set of well-known ML models including Support vector machines (SVM), random forests (RF), extra tree classifier (ETC), light gradient boosting machines (LGBM), and stochastic gradient descent (SGD). We examined a number of data-balancing strategies in the pipeline with different combinations of modalities. ML models have been optimized using the Bayesian optimizer. A comprehensive evaluation of various ML methods has been conducted, and the best models have been extended to provide different explainability features. RESULTS We compare the performance of ML models before and after optimization and using and without using feature selection. In the three-class experiment and with various modality fusions, the LGBM model produced the most accurate results with a 10-fold cross-validation (10-CV) accuracy of 90.73% using non-motor function modality. RF produced the best results in the four-class experiment with various modality fusions with a 10-CV accuracy of 94.57% using non-motor modality. With the fused dataset of non-motor and motor function modalities, the LGBM model outperformed the other ML models in both the 3-class and 4-class experiments (i.e., 10-CV accuracy of 94.89% and 93.73%, respectively). Using the Shapely Additive Explanations (SHAP) framework, we employed global and instance-based explanations to explain the behavior of each ML classifier. Moreover, we extended the explainability by implementing the LIME and SHAPASH local explainers. The consistency of these explainers has been explored. The resultant classifiers were accurate, explainable, and thus medically more relevant and applicable. CONCLUSIONS The select modalities and feature sets were confirmed by the literature and medical experts. The various explainers suggest that the bradykinesia (NP3BRADY) feature was the most dominant and consistent. By providing thorough insights into the influence of multiple modalities on the disease risk, the suggested approach is expected to help improve the clinical knowledge of PD progression processes.
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Affiliation(s)
- Muhammad Junaid
- Information Laboratory (InfoLab), Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, South Korea.
| | - Sajid Ali
- Information Laboratory (InfoLab), Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, South Korea.
| | - Fatma Eid
- Technology Management, Stony Brook University, New York 11794, USA.
| | - Shaker El-Sappagh
- Information Laboratory (InfoLab), College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, South Korea; Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt; Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha, 13518, Egypt.
| | - Tamer Abuhmed
- Information Laboratory (InfoLab), College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, South Korea.
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Borna S, Haider CR, Maita KC, Torres RA, Avila FR, Garcia JP, De Sario Velasquez GD, McLeod CJ, Bruce CJ, Carter RE, Forte AJ. A Review of Voice-Based Pain Detection in Adults Using Artificial Intelligence. Bioengineering (Basel) 2023; 10:bioengineering10040500. [PMID: 37106687 PMCID: PMC10135816 DOI: 10.3390/bioengineering10040500] [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: 03/20/2023] [Revised: 04/13/2023] [Accepted: 04/19/2023] [Indexed: 04/29/2023] Open
Abstract
Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared to facial emotions, there is less available evidence linking pain with voice. This literature review synthesizes the current state of research on the use of voice recognition and voice analysis for pain detection in adults, with a specific focus on the role of artificial intelligence (AI) and machine learning (ML) techniques. We describe the previous works on pain recognition using voice and highlight the different approaches to voice as a tool for pain detection, such as a human effect or biosignal. Overall, studies have shown that AI-based voice analysis can be an effective tool for pain detection in adult patients with various types of pain, including chronic and acute pain. We highlight the high accuracy of the ML-based approaches used in studies and their limitations in terms of generalizability due to factors such as the nature of the pain and patient population characteristics. However, there are still potential challenges, such as the need for large datasets and the risk of bias in training models, which warrant further research.
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Affiliation(s)
- Sahar Borna
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Clifton R Haider
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55902, USA
| | - Karla C Maita
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Ricardo A Torres
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Francisco R Avila
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - John P Garcia
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | | | | | - Charles J Bruce
- Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Rickey E Carter
- Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Antonio J Forte
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
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