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Hage G, Buisseret F, Brismée JM, Dierick F, Detrembleur C, Hage R. Evaluating the additive diagnostic value of DidRen LaserTest: Correlating temporal and kinematic predictors and patient-reported outcome measures in acute-subacute non-specific neck pain. J Bodyw Mov Ther 2024; 39:201-208. [PMID: 38876626 DOI: 10.1016/j.jbmt.2024.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 02/28/2024] [Accepted: 03/03/2024] [Indexed: 06/16/2024]
Affiliation(s)
- Guillaume Hage
- Laboratoire de Neuro Musculo Squelettique (NMSK), Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, 1200 Brussels, Belgium
| | - Fabien Buisseret
- Centre de Recherche et de Formation de la HELHa (CeREF), Chaussée de Binche 159, 7000 Mons, Belgium; Service de Physique Nucléaire et Subnucléaire, UMONS, Research Institute for Complex Systems, Place Du Parc 20, 7000 Mons, Belgium
| | - Jean-Michel Brismée
- Center for Rehabilitation Research, Texas Tech University Health Sciences Center, Lubbock, TX, USA
| | - Frédéric Dierick
- Centre de Recherche et de Formation de la HELHa (CeREF), Chaussée de Binche 159, 7000 Mons, Belgium; Laboratoire d'Analyse du Mouvement et de la Posture (LAMP), Centre National de Rééducation Fonctionnelle et de Réadaptation (Rehazenter), Rue André Vésale 1, 2674 Luxembourg, Luxembourg; Faculté des Sciences de La Motricité, UCLouvain, Place Pierre de Coubertin 1-2, 1348 Louvain-la-Neuve, Belgium
| | - Christine Detrembleur
- Laboratoire de Neuro Musculo Squelettique (NMSK), Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, 1200 Brussels, Belgium
| | - Renaud Hage
- Centre de Recherche et de Formation de la HELHa (CeREF), Chaussée de Binche 159, 7000 Mons, Belgium; Faculté des Sciences de La Motricité, UCLouvain, Place Pierre de Coubertin 1-2, 1348 Louvain-la-Neuve, Belgium; Traitement Formation Thérapie Manuelle (TFTM), Private Physiotherapy/Manual Therapy Center, Avenue des Cerisiers 211A, 1200 Brussels, Belgium; Haute école Libre de Bruxelles Ilya Prigogine, Section Kinésithérapie, 808, Route de Lennik, Bâtiment P, 1070 Brussels, Belgium.
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Kim JH. Comparative analysis of machine learning models for efficient low back pain prediction using demographic and lifestyle factors. J Back Musculoskelet Rehabil 2024:BMR240059. [PMID: 39031340 DOI: 10.3233/bmr-240059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/22/2024]
Abstract
BACKGROUND Low back pain (LBP) is one of the most frequently occurring musculoskeletal disorders, and factors such as lifestyle as well as individual characteristics are associated with LBP. OBJECTIVE The purpose of this study was to develop and compare efficient low back pain prediction models using easily obtainable demographic and lifestyle factors. METHODS Data from adult men and women aged 50 years or older collected from the Korean National Health and Nutrition Examination Survey (KNHANES) were used. The dataset included 22 predictor variables, including demographic, physical activity, occupational, and lifestyle factors. Four machine learning algorithms, including XGBoost, LGBM, CatBoost, and RandomForest, were used to develop predictive models. RESULTS All models achieved an accuracy greater than 0.8, with the LGBM model outperforming the others with an accuracy of 0.830. The CatBoost model had the highest sensitivity (0.804), while the LGBM model showed the highest specificity (0.884) and F1-Score (0.821). Feature importance analysis revealed that EQ-5D was the most critical variable across all models. CONCLUSION In this study, an efficient LBP prediction model was developed using easily accessible variables. Using this model, it may be helpful to identify the risk of LBP in advance or establish prevention strategies in subjects who have difficulty accessing medical facilities.
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Kalanjiyam GP, Chandramohan T, Raman M, Kalyanasundaram H. Artificial intelligence: a new cutting-edge tool in spine surgery. Asian Spine J 2024; 18:458-471. [PMID: 38917854 PMCID: PMC11222879 DOI: 10.31616/asj.2023.0382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/07/2024] [Accepted: 01/11/2024] [Indexed: 06/27/2024] Open
Abstract
The purpose of this narrative review was to comprehensively elaborate the various components of artificial intelligence (AI), their applications in spine surgery, practical concerns, and future directions. Over the years, spine surgery has been continuously transformed in various aspects, including diagnostic strategies, surgical approaches, procedures, and instrumentation, to provide better-quality patient care. Surgeons have also augmented their surgical expertise with rapidly growing technological advancements. AI is an advancing field that has the potential to revolutionize many aspects of spine surgery. We performed a comprehensive narrative review of the various aspects of AI and machine learning in spine surgery. To elaborate on the current role of AI in spine surgery, a review of the literature was performed using PubMed and Google Scholar databases for articles published in English in the last 20 years. The initial search using the keywords "artificial intelligence" AND "spine," "machine learning" AND "spine," and "deep learning" AND "spine" extracted a total of 78, 60, and 37 articles and 11,500, 4,610, and 2,270 articles on PubMed and Google Scholar. After the initial screening and exclusion of unrelated articles, duplicates, and non-English articles, 405 articles were identified. After the second stage of screening, 93 articles were included in the review. Studies have shown that AI can be used to analyze patient data and provide personalized treatment recommendations in spine care. It also provides valuable insights for planning surgeries and assisting with precise surgical maneuvers and decisionmaking during the procedures. As more data become available and with further advancements, AI is likely to improve patient outcomes.
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Affiliation(s)
- Guna Pratheep Kalanjiyam
- Spine Surgery Unit, Department of Orthopaedics, Meenakshi Mission Hospital and Research Centre, Madurai,
India
| | - Thiyagarajan Chandramohan
- Department of Orthopaedics, Government Stanley Medical College, Chennai,
India
- Department of Emergency Medicine, Government Stanley Medical College, Chennai,
India
| | - Muthu Raman
- Department of Orthopaedics, Tenkasi Government Hospital, Tenkasi,
India
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Suo M, Zhou L, Wang J, Huang H, Zhang J, Sun T, Liu X, Chen X, Song C, Li Z. The Application of Surface Electromyography Technology in Evaluating Paraspinal Muscle Function. Diagnostics (Basel) 2024; 14:1086. [PMID: 38893614 PMCID: PMC11172025 DOI: 10.3390/diagnostics14111086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 05/01/2024] [Accepted: 05/07/2024] [Indexed: 06/21/2024] Open
Abstract
Surface electromyography (sEMG) has emerged as a valuable tool for assessing muscle activity in various clinical and research settings. This review focuses on the application of sEMG specifically in the context of paraspinal muscles. The paraspinal muscles play a critical role in providing stability and facilitating movement of the spine. Dysfunctions or alterations in paraspinal muscle activity can lead to various musculoskeletal disorders and spinal pathologies. Therefore, understanding and quantifying paraspinal muscle activity is crucial for accurate diagnosis, treatment planning, and monitoring therapeutic interventions. This review discusses the clinical applications of sEMG in paraspinal muscles, including the assessment of low back pain, spinal disorders, and rehabilitation interventions. It explores how sEMG can aid in diagnosing the potential causes of low back pain and monitoring the effectiveness of physical therapy, spinal manipulative therapy, and exercise protocols. It also discusses emerging technologies and advancements in sEMG techniques that aim to enhance the accuracy and reliability of paraspinal muscle assessment. In summary, the application of sEMG in paraspinal muscles provides valuable insights into muscle function, dysfunction, and therapeutic interventions. By examining the literature on sEMG in paraspinal muscles, this review offers a comprehensive understanding of the current state of research, identifies knowledge gaps, and suggests future directions for optimizing the use of sEMG in assessing paraspinal muscle activity.
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Affiliation(s)
- Moran Suo
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (M.S.); (J.W.); (H.H.); (J.Z.); (T.S.); (X.L.)
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Lina Zhou
- Department of Neurology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China;
| | - Jinzuo Wang
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (M.S.); (J.W.); (H.H.); (J.Z.); (T.S.); (X.L.)
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Huagui Huang
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (M.S.); (J.W.); (H.H.); (J.Z.); (T.S.); (X.L.)
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Jing Zhang
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (M.S.); (J.W.); (H.H.); (J.Z.); (T.S.); (X.L.)
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Tianze Sun
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (M.S.); (J.W.); (H.H.); (J.Z.); (T.S.); (X.L.)
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Xin Liu
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (M.S.); (J.W.); (H.H.); (J.Z.); (T.S.); (X.L.)
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Xin Chen
- Musculoskeletal Research Laboratory, Department of Orthopaedics & Traumatology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China;
| | - Chunli Song
- Department of Neurology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China;
| | - Zhonghai Li
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (M.S.); (J.W.); (H.H.); (J.Z.); (T.S.); (X.L.)
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
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Abd-Elsayed A, Robinson CL, Marshall Z, Diwan S, Peters T. Applications of Artificial Intelligence in Pain Medicine. Curr Pain Headache Rep 2024; 28:229-238. [PMID: 38345695 DOI: 10.1007/s11916-024-01224-8] [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] [Accepted: 01/30/2024] [Indexed: 03/03/2024]
Abstract
PURPOSE OF REVIEW This review explores the current applications of artificial intelligence (AI) in the field of pain medicine with a focus on machine learning. RECENT FINDINGS Utilizing a literature search conducted through the PubMed database, several current trends were identified, including the use of AI as a tool for diagnostics, predicting pain progression, predicting treatment response, and performance of therapy and pain management. Results of these studies show promise for the improvement of patient outcomes. Current gaps in the research and subsequent directions for future study involve AI in optimizing and improving nerve stimulation and more thoroughly predicting patients' responses to treatment.
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Affiliation(s)
- Alaa Abd-Elsayed
- Department of Anesthesiology, School of Medicine and Public Health, University of Wisconsin, 750 Highland Ave, Madison, WI, 53726, USA.
| | - Christopher L Robinson
- Department of Anesthesiology, Critical Care, and Pain Medicine Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Sudhir Diwan
- Albert Einstein College of Medicine, Lenox Hill Hospital, New York City, NY, USA
| | - Theodore Peters
- Department of Anesthesiology, School of Medicine and Public Health, University of Wisconsin, 750 Highland Ave, Madison, WI, 53726, USA
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Liew BXW, Pfisterer F, Rügamer D, Zhai X. Strategies to optimise machine learning classification performance when using biomechanical features. J Biomech 2024; 165:111998. [PMID: 38377743 DOI: 10.1016/j.jbiomech.2024.111998] [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: 08/25/2023] [Revised: 02/09/2024] [Accepted: 02/13/2024] [Indexed: 02/22/2024]
Abstract
Building prediction models using biomechanical features is challenging because such models may require large sample sizes. However, collecting biomechanical data on large sample sizes is logistically very challenging. This study aims to investigate if modern machine learning algorithms can help overcome the issue of limited sample sizes on developing prediction models. This was a secondary data analysis two biomechanical datasets - a walking dataset on 2295 participants, and a countermovement jump dataset on 31 participants. The input features were the three-dimensional ground reaction forces (GRFs) of the lower limbs. The outcome was the orthopaedic disease category (healthy, calcaneus, ankle, knee, hip) in the walking dataset, and healthy vs people with patellofemoral pain syndrome in the jump dataset. Different algorithms were compared: multinomial/LASSO regression, XGBoost, various deep learning time-series algorithms with augmented data, and with transfer learning. For the outcome of weighted multiclass area under the receiver operating curve (AUC) in the walking dataset, the three models with the best performance were InceptionTime with x12 augmented data (0.810), XGBoost (0.804), and multinomial logistic regression (0.800). For the jump dataset, the top three models with the highest AUC were the LASSO (1.00), InceptionTime with x8 augmentation (0.750), and transfer learning (0.653). Machine-learning based strategies for managing the challenging issue of limited sample size for biomechanical ML-based problems, could benefit the development of alternative prediction models in healthcare, especially when time-series data are involved.
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Affiliation(s)
- Bernard X W Liew
- School of Sport, Rehabilitation and Exercise Sciences, University of Essex, Colchester, Essex, United Kingdom.
| | - Florian Pfisterer
- Department of Statistics, LMU Munich, Munich Germany; Munich Center for Machine Learning, Munich, Germany
| | - David Rügamer
- Department of Statistics, LMU Munich, Munich Germany; Munich Center for Machine Learning, Munich, Germany
| | - Xiaojun Zhai
- School of Computer Science and Electrical Engineering, University of Essex, Colchester, Essex, United Kingdom
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7
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Yu X, Xu X, Huang Q, Zhu G, Xu F, Liu Z, Su L, Zheng H, Zhou C, Chen Q, Gao F, Lin M, Yang S, Chiang MH, Zhou Y. Binary classification of non-specific low back pain condition based on the combination of B-mode ultrasound and shear wave elastography at multiple sites. Front Physiol 2023; 14:1176299. [PMID: 37187960 PMCID: PMC10175639 DOI: 10.3389/fphys.2023.1176299] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 03/30/2023] [Indexed: 05/17/2023] Open
Abstract
Introduction: Low back pain (LBP) is a prevalent and complex condition that poses significant medical, social, and economic burdens worldwide. The accurate and timely assessment and diagnosis of LBP, particularly non-specific LBP (NSLBP), are crucial to developing effective interventions and treatments for LBP patients. In this study, we aimed to investigate the potential of combining B-mode ultrasound image features with shear wave elastography (SWE) features to improve the classification of NSLBP patients. Methods: We recruited 52 subjects with NSLBP from the University of Hong Kong-Shenzhen Hospital and collected B-mode ultrasound images and SWE data from multiple sites. The Visual Analogue Scale (VAS) was used as the ground truth to classify NSLBP patients. We extracted and selected features from the data and employed a support vector machine (SVM) model to classify NSLBP patients. The performance of the SVM model was evaluated using five-fold cross-validation and the accuracy, precision, and sensitivity were calculated. Results: We obtained an optimal feature set of 48 features, among which the SWE elasticity feature had the most significant contribution to the classification task. The SVM model achieved an accuracy, precision, and sensitivity of 0.85, 0.89, and 0.86, respectively, which were higher than the previously reported values of MRI. Discussion: In this study, we aimed to investigate the potential of combining B-mode ultrasound image features with shear wave elastography (SWE) features to improve the classification of non-specific low back pain (NSLBP) patients. Our results showed that combining B-mode ultrasound image features with SWE features and employing an SVM model can improve the automatic classification of NSLBP patients. Our findings also suggest that the SWE elasticity feature is a crucial factor in classifying NSLBP patients, and the proposed method can identify the important site and position of the muscle in the NSLBP classification task.
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Affiliation(s)
- Xiaocheng Yu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen, China
| | - Xiaohua Xu
- Department of Medical Imaging (DMI) - Ultrasound Division, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Qinghua Huang
- School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, China
| | - Guowen Zhu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen, China
| | - Faying Xu
- Department of Chinese Medicine (DCM), The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Zhenhua Liu
- Department of Chinese Medicine (DCM), The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Lin Su
- Department of Medical Imaging (DMI) - Ultrasound Division, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Haiping Zheng
- Department of Medical Imaging (DMI) - Ultrasound Division, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Chen Zhou
- Department of Medical Imaging (DMI) - Ultrasound Division, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Qiuming Chen
- Department of Chinese Medicine (DCM), The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Fen Gao
- Department of Medical Imaging (DMI) - Ultrasound Division, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Mengting Lin
- Department of Medical Imaging (DMI) - Ultrasound Division, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Shuai Yang
- Shenzhen Mindray Bio-Medical Electronics Co., Ltd, Shenzhen, China
| | - Mou-Hsun Chiang
- Department of Medical Imaging (DMI) - Ultrasound Division, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- *Correspondence: Mou-Hsun Chiang, ; Yongjin Zhou,
| | - Yongjin Zhou
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen, China
- *Correspondence: Mou-Hsun Chiang, ; Yongjin Zhou,
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Ren G, Yu K, Xie Z, Wang P, Zhang W, Huang Y, Wang Y, Wu X. Current Applications of Machine Learning in Spine: From Clinical View. Global Spine J 2022; 12:1827-1840. [PMID: 34628966 PMCID: PMC9609532 DOI: 10.1177/21925682211035363] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVES This review aims to present current applications of machine learning (ML) in spine domain to clinicians. METHODS We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. RESULTS Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. CONCLUSIONS ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work.
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Affiliation(s)
- GuanRui Ren
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Kun Yu
- Nanjing Jiangbei Hospital, Nanjing,
Jiangsu, China
| | - ZhiYang Xie
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - PeiYang Wang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Wei Zhang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Yong Huang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - YunTao Wang
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,YunTao Wang, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
| | - XiaoTao Wu
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,XiaoTao Wu, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
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Liew BXW, Kovacs FM, Rügamer D, Royuela A. Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2022; 31:2082-2091. [PMID: 35353221 DOI: 10.1007/s00586-022-07188-w] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/29/2022] [Accepted: 03/12/2022] [Indexed: 01/20/2023]
Abstract
PURPOSE Prognostic models play an important clinical role in the clinical management of neck pain disorders. No study has compared the performance of modern machine learning (ML) techniques, against more traditional regression techniques, when developing prognostic models in individuals with neck pain. METHODS A total of 3001 participants suffering from neck pain were included into a clinical registry database. Three dichotomous outcomes of a clinically meaningful improvement in neck pain, arm pain, and disability at 3 months follow-up were used. There were 26 predictors included, five numeric and 21 categorical. Seven modelling techniques were used (logistic regression, least absolute shrinkage and selection operator [LASSO], gradient boosting [Xgboost], K nearest neighbours [KNN], support vector machine [SVM], random forest [RF], and artificial neural networks [ANN]). The primary measure of model performance was the area under the receiver operator curve (AUC) of the validation set. RESULTS The ML algorithm with the greatest AUC for predicting arm pain (AUC = 0.765), neck pain (AUC = 0.726), and disability (AUC = 0.703) was Xgboost. The improvement in classification AUC from stepwise logistic regression to the best performing machine learning algorithms was 0.081, 0.103, and 0.077 for predicting arm pain, neck pain, and disability, respectively. CONCLUSION The improvement in prediction performance between ML and logistic regression methods in the present study, could be due to the potential greater nonlinearity between baseline predictors and clinical outcome. The benefit of machine learning in prognostic modelling may be dependent on factors like sample size, variable type, and disease investigated.
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Affiliation(s)
- Bernard X W Liew
- School of Sport, Rehabilitation and Exercise Sciences, University of Essex, Colchester, Essex, UK.
| | - Francisco M Kovacs
- Unidad de la Espalda Kovacs, Hospital Universitario HLA-Moncloa. University Hospital, Avenida de Menéndez Pelayo, 67, 28009, Madrid, Spain
| | - David Rügamer
- Department of Statistics, Ludwig-Maximilians-Universität München, München, Germany
| | - Ana Royuela
- Biostatistics Unit. Hospital Puerta de Hierro, IDIPHISA, CIBERESP, REIDE, Madrid, Spain
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Hornung AL, Hornung CM, Mallow GM, Barajas JN, Rush A, Sayari AJ, Galbusera F, Wilke HJ, Colman M, Phillips FM, An HS, Samartzis D. Artificial intelligence in spine care: current applications and future utility. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2022; 31:2057-2081. [PMID: 35347425 DOI: 10.1007/s00586-022-07176-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/18/2022] [Accepted: 03/08/2022] [Indexed: 01/20/2023]
Abstract
PURPOSE The field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given the advent of the intelligence-based spine care model, understanding the evolution of computation as it applies to diagnosis, treatment, and adverse event prediction is of great importance. Therefore, the current review sought to synthesize findings from the literature at the interface of artificial intelligence and spine research. METHODS A narrative review was performed based on the literature of three databases (MEDLINE, CINAHL, and Scopus) from January 2015 to March 2021 that examined historical and recent advancements in the understanding of artificial intelligence and machine learning in spine research. Studies were appraised for their role in, or description of, advancements within image recognition and predictive modeling for spinal research. Only English articles that fulfilled inclusion criteria were ultimately incorporated in this review. RESULTS This review briefly summarizes the history and applications of artificial intelligence and machine learning in spine. Three basic machine learning training paradigms: supervised learning, unsupervised learning, and reinforced learning are also discussed. Artificial intelligence and machine learning have been utilized in almost every facet of spine ranging from localization and segmentation techniques in spinal imaging to pathology specific algorithms which include but not limited to; preoperative risk assessment of postoperative complications, screening algorithms for patients at risk of osteoporosis and clustering analysis to identify subgroups within adolescent idiopathic scoliosis. The future of artificial intelligence and machine learning in spine surgery is also discussed with focusing on novel algorithms, data collection techniques and increased utilization of automated systems. CONCLUSION Improvements to modern-day computing and accessibility to various imaging modalities allow for innovative discoveries that may arise, for example, from management. Given the imminent future of AI in spine surgery, it is of great importance that practitioners continue to inform themselves regarding AI, its goals, use, and progression. In the future, it will be critical for the spine specialist to be able to discern the utility of novel AI research, particularly as it continues to pervade facets of everyday spine surgery.
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Affiliation(s)
- Alexander L Hornung
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | | | - G Michael Mallow
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - J Nicolás Barajas
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Augustus Rush
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Arash J Sayari
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | | | - Hans-Joachim Wilke
- Institute of Orthopaedic Research and Biomechanics, Trauma Research Center Ulm, Ulm University, Ulm, Germany
| | - Matthew Colman
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Frank M Phillips
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Howard S An
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Dino Samartzis
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA.
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D’Antoni F, Russo F, Ambrosio L, Bacco L, Vollero L, Vadalà G, Merone M, Papalia R, Denaro V. Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19105971. [PMID: 35627508 PMCID: PMC9141006 DOI: 10.3390/ijerph19105971] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/09/2022] [Accepted: 05/12/2022] [Indexed: 12/10/2022]
Abstract
Low Back Pain (LBP) is currently the first cause of disability in the world, with a significant socioeconomic burden. Diagnosis and treatment of LBP often involve a multidisciplinary, individualized approach consisting of several outcome measures and imaging data along with emerging technologies. The increased amount of data generated in this process has led to the development of methods related to artificial intelligence (AI), and to computer-aided diagnosis (CAD) in particular, which aim to assist and improve the diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of CAD in the diagnosis and treatment of chronic LBP. A systematic research of PubMed, Scopus, and Web of Science electronic databases was performed. The search strategy was set as the combinations of the following keywords: “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, “Neural Network”, “Computer Aided Diagnosis”, “Low Back Pain”, “Lumbar”, “Intervertebral Disc Degeneration”, “Spine Surgery”, etc. The search returned a total of 1536 articles. After duplication removal and evaluation of the abstracts, 1386 were excluded, whereas 93 papers were excluded after full-text examination, taking the number of eligible articles to 57. The main applications of CAD in LBP included classification and regression. Classification is used to identify or categorize a disease, whereas regression is used to produce a numerical output as a quantitative evaluation of some measure. The best performing systems were developed to diagnose degenerative changes of the spine from imaging data, with average accuracy rates >80%. However, notable outcomes were also reported for CAD tools executing different tasks including analysis of clinical, biomechanical, electrophysiological, and functional imaging data. Further studies are needed to better define the role of CAD in LBP care.
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Affiliation(s)
- Federico D’Antoni
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy; (F.D.); (L.B.); (L.V.)
| | - Fabrizio Russo
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
- Correspondence: (F.R.); (M.M.)
| | - Luca Ambrosio
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Luca Bacco
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy; (F.D.); (L.B.); (L.V.)
- ItaliaNLP Lab, Istituto di Linguistica Computazionale “Antonio Zampolli”, National Research Council, Via Giuseppe Moruzzi, 1, 56124 Pisa, Italy
- Webmonks S.r.l., Via del Triopio, 5, 00178 Rome, Italy
| | - Luca Vollero
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy; (F.D.); (L.B.); (L.V.)
| | - Gianluca Vadalà
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Mario Merone
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy; (F.D.); (L.B.); (L.V.)
- Correspondence: (F.R.); (M.M.)
| | - Rocco Papalia
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Vincenzo Denaro
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
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Hage R, Buisseret F, Houry M, Dierick F. Head Pitch Angular Velocity Discriminates (Sub-)Acute Neck Pain Patients and Controls Assessed with the DidRen Laser Test. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22072805. [PMID: 35408420 PMCID: PMC9002899 DOI: 10.3390/s22072805] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 03/31/2022] [Accepted: 04/03/2022] [Indexed: 06/01/2023]
Abstract
Understanding neck pain is an important societal issue. Kinematic data from sensors may help to gain insight into the pathophysiological mechanisms associated with neck pain through a quantitative sensorimotor assessment of one patient. The objective of this study was to evaluate the potential usefulness of artificial intelligence with several machine learning (ML) algorithms in assessing neck sensorimotor performance. Angular velocity and acceleration measured by an inertial sensor placed on the forehead during the DidRen laser test in thirty-eight acute and subacute non-specific neck pain (ANSP) patients were compared to forty-two healthy control participants (HCP). Seven supervised ML algorithms were chosen for the predictions. The most informative kinematic features were computed using Sequential Feature Selection methods. The best performing algorithm is the Linear Support Vector Machine with an accuracy of 82% and Area Under Curve of 84%. The best discriminative kinematic feature between ANSP patients and HCP is the first quartile of head pitch angular velocity. This study has shown that supervised ML algorithms could be used to classify ANSP patients and identify discriminatory kinematic features potentially useful for clinicians in the assessment and monitoring of the neck sensorimotor performance in ANSP patients.
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Affiliation(s)
- Renaud Hage
- CeREF Technique, Chaussée de Binche 159, 7000 Mons, Belgium; (F.B.); (F.D.)
- Traitement Formation Thérapie Manuelle (TFTM), Private Physiotherapy/Manual Therapy Center, Avenue des Cerisiers 211A, 1200 Brussels, Belgium
- Faculté des Sciences de la Motricité, UCLouvain, Place Pierre de Coubertin 1, 1348 Ottignies-Louvain-la-Neuve, Belgium
| | - Fabien Buisseret
- CeREF Technique, Chaussée de Binche 159, 7000 Mons, Belgium; (F.B.); (F.D.)
- Service de Physique Nucléaire et Subnucléaire, UMONS, Research Institute for Complex Systems, Place du Parc 20, 7000 Mons, Belgium
| | - Martin Houry
- Centre de Recherche FoRS, Haute-Ecole de Namur-Liège-Luxembourg (Henallux), Rue Victor Libert 36H, 6900 Marche-en-Famenne, Belgium;
| | - Frédéric Dierick
- CeREF Technique, Chaussée de Binche 159, 7000 Mons, Belgium; (F.B.); (F.D.)
- Faculté des Sciences de la Motricité, UCLouvain, Place Pierre de Coubertin 1, 1348 Ottignies-Louvain-la-Neuve, Belgium
- Laboratoire d’Analyse du Mouvement et de la Posture (LAMP), Centre National de Rééducation Fonctionnelle et de Réadaptation–Rehazenter, Rue André Vésale 1, 2674 Luxembourg, Luxembourg
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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: 20] [Impact Index Per Article: 6.7] [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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Girase H, Nyayapati P, Booker J, Lotz JC, Bailey JF, Matthew RP. Automated assessment and classification of spine, hip, and knee pathologies from sit-to-stand movements collected in clinical practice. J Biomech 2021; 128:110786. [PMID: 34656825 DOI: 10.1016/j.jbiomech.2021.110786] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 10/20/2022]
Abstract
Efficient, cost-effective methods for quantifying patient biomechanics at the point of care can facilitate faster and more accurate diagnoses. This work presents a new method to diagnose pre-surgical back, hip, and knee patients by analysing their sit-to-stand motion captured by a Kinect camera. Kinematic and dynamic time-series features were extracted from patient movements collected in clinic. These features were used to test a variety of machine learning methods for patient classification. The performance of models trained on time-series features were compared against models trained on domain-knowledge features, highlighting the importance of using time-series data for the classification of human movement. Additionally, the effectiveness of using semi-supervised learning is tested on partially labelled datasets, providing insight on how to boost classification performance in situations where labelled patient data is difficult to obtain. The best semi-supervised model achieves ∼73% accuracy in distinguishing individuals with low-back pain, and hip and knee degeneration from control subjects.
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Affiliation(s)
- Harshayu Girase
- Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, 94720, CA, USA
| | - Priya Nyayapati
- Department of Orthopaedic Surgery, University of California at San Francisco, San Francisco, 94158, CA, USA
| | - Jacqueline Booker
- School of Medicine, University of California at San Francisco, San Francisco, 94158, CA, USA
| | - Jeffrey C Lotz
- Department of Orthopaedic Surgery, University of California at San Francisco, San Francisco, 94158, CA, USA
| | - Jeannie F Bailey
- Department of Orthopaedic Surgery, University of California at San Francisco, San Francisco, 94158, CA, USA
| | - Robert P Matthew
- Department of Physical Therapy and Rehabilitation Science, University of California at San Francisco, San Francisco, 94158, CA, USA.
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Comparing shallow, deep, and transfer learning in predicting joint moments in running. J Biomech 2021; 129:110820. [PMID: 34717160 DOI: 10.1016/j.jbiomech.2021.110820] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/15/2021] [Accepted: 10/12/2021] [Indexed: 11/21/2022]
Abstract
Joint moments are commonly calculated in biomechanics research and provide an indirect measure of muscular behaviors and joint loads. However, joint moments cannot be easily quantified clinically or in the field, primarily due to challenges measuring ground reaction forces outside the laboratory. The present study aimed to compare the accuracy of three different machine learning (ML) techniques - functional regression [ MLfregress ], a deep neural network (DNN) built from scratch [ MLDNN ], and transfer learning [ MLTL ], in predicting joint moments during running. Data for this study came from an open-source dataset and two studies on running with and without external loads. Three-dimensional (3D) joint moments of the hip, knee, and ankle, were derived using inverse dynamics. 3D joint angle, velocity, and acceleration of the three joints served as predictors for each of the three ML techniques. Prediction performance was generally the best using MLDNN, and the worse using MLfregress. Absolute predictive performance was the best for sagittal plane moments, which ranged from a RMSE of 0.16 Nm/kg at the ankle using MLDNN, to a RMSE of 0.49Nm/kg at the knee using MLfregress. MLDNN resulted in the greatest improvement in relative prediction performance (relRMSE) by 20% compared to MLfregress for the ankle adduction-abduction moment. DNN with or without transfer learning was superior in predicting joint moments using kinematic inputs compared to functional regression. Synergizing ML with kinematic inputs has the potential to solve the constraints of obtaining high fidelity biomechanics data normally only possible during laboratory studies.
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Falla D, Devecchi V, Jiménez-Grande D, Rügamer D, Liew BXW. Machine learning approaches applied in spinal pain research. J Electromyogr Kinesiol 2021; 61:102599. [PMID: 34624604 DOI: 10.1016/j.jelekin.2021.102599] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/26/2021] [Accepted: 08/01/2021] [Indexed: 01/13/2023] Open
Abstract
The purpose of this narrative review is to provide a critical reflection of how analytical machine learning approaches could provide the platform to harness variability of patient presentation to enhance clinical prediction. The review includes a summary of current knowledge on the physiological adaptations present in people with spinal pain. We discuss how contemporary evidence highlights the importance of not relying on single features when characterizing patients given the variability of physiological adaptations present in people with spinal pain. The advantages and disadvantages of current analytical strategies in contemporary basic science and epidemiological research are reviewed and we consider how analytical machine learning approaches could provide the platform to harness the variability of patient presentations to enhance clinical prediction of pain persistence or recurrence. We propose that machine learning techniques can be leveraged to translate a potentially heterogeneous set of variables into clinically useful information with the potential to enhance patient management.
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Affiliation(s)
- Deborah Falla
- Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, UK.
| | - Valter Devecchi
- Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, UK
| | - David Jiménez-Grande
- Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, UK
| | - David Rügamer
- Department of Statistics, Ludwig-Maximilians-Universität München, Germany
| | - Bernard X W Liew
- School of Sport, Rehabilitation and Exercise Sciences, University of Essex, Colchester, Essex, UK
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