School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
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Department of Primary Care Medicine, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan
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Department of Physical Medicine and Rehabilitation, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
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Master’s Program in Long-Term Care, College of Nursing, Taipei Medical University, Taipei, Taiwan
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Department of Physical Medicine and Rehabilitation, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
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Society International 2019 guidelines ( 3 , 4 ), the combination of non-pharmacological and pharmacological therapies is strongly recommended as a non-surgical approach. Among the non-pharmacological treatments, exercise programs (aerobic or resistance
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therapy for the pathogenesis of OA are emerging trends in the field. Thus, to benefit patients, an effective, safe, and convenient strategy for preventing and treating OA must be identified. Exercise therapy is a specific type of physical activity
School of Health Sciences and Physiotherapy, The University of Notre Dame Australia, Fremantle, Western Australia, Australia
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Neurophysiology Research Laboratory, School of Medical and Health Sciences, Centre for Human Performance, Edith Cowan University, Joondalup, Western Australia, Australia
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Australian Ballet, Southbank, Victoria, Australia
Victorian Institute of Sport, Albert Park Victoria, Australia
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Neurophysiology Research Laboratory, School of Medical and Health Sciences, Centre for Human Performance, Edith Cowan University, Joondalup, Western Australia, Australia
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La Trobe Sport and Exercise Medicine Research Centre, La Trobe University, Bundoora, Victoria, Australia
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impaired function, causing people to leave the workforce early and frequently progress to pharmacological and surgical management ( 3 , 4 ). Best practice management for OA includes exercise targeted at peripheral impairments (e.g. muscle strength, range
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). Physical therapies are recommended for patients with LBP according to several guidelines ( 6 , 7 , 8 , 9 ). Motor control exercise (MCE) has been obtaining increasing attention in recent years ( 5 ). MCE is defined as an exercise to increase control and
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sequelae promote the need for an effective treatment ( 13 , 21 ). There is evidence on the effectiveness of therapeutic exercise, focused on balance training ( 22 , 23 ), proprioception in general ( 24 ) and force through proprioceptive neuromuscular
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( 19 , 20 ). Physiotherapy encompasses a set of passive (such as massage, dry needling, or electrotherapy, among others) and active procedures (such as therapeutic exercise) that are used to reduce the disability associated with CNP and CLBP. However
Health Sciences PhD Program, Universidad Católica de Murcia UCAM, Murcia, Spain
Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
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Purpose
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The integration of artificial intelligence (AI) in radiology has revolutionized diagnostics, optimizing precision and decision-making. Specifically in musculoskeletal imaging, AI tools can improve accuracy for upper extremity pathologies. This study aimed to assess the diagnostic performance of AI models in detecting musculoskeletal pathologies of the upper extremity using different imaging modalities.
Methods
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A meta-analysis was conducted, involving searches on MEDLINE/PubMed, SCOPUS, Cochrane Library, Lilacs, and SciELO. The quality of the studies was assessed using the QUADAS-2 tool. Diagnostic accuracy measures including sensitivity, specificity, diagnostic odds ratio (DOR), positive and negative likelihood ratios (PLR, NLR), area under the curve (AUC), and summary receiver operating characteristic were pooled using a random-effects model. Heterogeneity and subgroup analyses were also included. All statistical analyses and plots were performed using the R software package.
Results
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Thirteen models from ten articles were analyzed. The sensitivity and specificity of the AI models to detect musculoskeletal conditions in the upper extremity were 0.926 (95% CI: 0.900; 0.945) and 0.908 (95% CI: 0.810; 0.958). The PLR, NLR, lnDOR, and the AUC estimates were found to be 19.18 (95% CI: 8.90; 29.34), 0.11 (95% CI: 0.18; 0.46), 4.62 (95% CI: 4.02; 5.22) with a (P < 0.001), and 95%, respectively.
Conclusion
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The AI models exhibited strong univariate and bivariate performance in detecting both positive and negative cases within the analyzed dataset of musculoskeletal pathologies in the upper extremity.
Health Sciences PhD Program, Universidad Católica de Murcia UCAM, Murcia, Spain
Harvard T.H. Chan School of Public Health, Boston, USA
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Purpose
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Different deep-learning models have been employed to aid in the diagnosis of musculoskeletal pathologies. The diagnosis of tendon pathologies could particularly benefit from applying these technologies. The objective of this study is to assess the performance of deep learning models in diagnosing tendon pathologies using various imaging modalities.
Methods
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A meta-analysis was conducted, with searches performed on MEDLINE/PubMed, SCOPUS, Cochrane Library, Lilacs, and SciELO. The QUADAS-2 tool was employed to assess the quality of the studies. Diagnostic measures, such as sensitivity, specificity, diagnostic odds ratio, positive and negative likelihood ratios, area under the curve, and summary receiver operating characteristic, were included using a random-effects model. Heterogeneity and subgroup analyses were also conducted. All statistical analyses and plots were generated using the R software package. The PROSPERO ID is CRD42024506491.
Results
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Eleven deep-learning models from six articles were analyzed. In the random effects models, the sensitivity and specificity of the algorithms for detecting tendon conditions were 0.910 (95% CI: 0.865; 0.940) and 0.954 (0.909; 0.977). The PLR, NLR, lnDOR, and AUC estimates were found to be 37.075 (95%CI: 4.654; 69.496), 0.114 (95%CI: 0.056; 0.171), 5.160 (95% CI: 4.070; 6.250) with a (P < 0.001), and 96%, respectively.
Conclusion
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The deep-learning algorithms demonstrated a high level of accuracy level in detecting tendon anomalies. The overall robust performance suggests their potential application as a valuable complementary tool in diagnosing medical images.
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, alleviate residual symptoms and treat accompanying diseases ( 3 , 5 , 7 , 9 , 10 , 11 ). These programs can include physiotherapy (exercise therapy with stretching and strength training), cognitive-behavioral therapy and multidisciplinary protocols
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volume during exercise. 1 CECS of the lower limb is well reported; 2 whereas CECS of the forearm is a rare condition in the general population, but can be observed in motorcycling racers, climbers, and rowers. 3 Clinically, the
