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Purpose: 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: 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: 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: 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.

Background: Goutallier's fatty infiltration of the supraspinatus muscle is a critical condition in degenerative shoulder disorders. Deep learning research primarily uses manual segmentation and labeling to detect this condition. Employing unsupervised training with a hybrid framework of segmentation and classification could offer an efficient solution., Aim: To develop and assess a two-step deep learning model for detecting the region of interest and categorizing the magnetic resonance image (MRI) supraspinatus muscle fatty infiltration according to Goutallier's scale., Materials and Methods: A retrospective study was performed from January 1, 2019 to September 20, 2020, using 900 MRI T2-weighted images with supraspinatus muscle fatty infiltration diagnoses. A model with two sequential neural networks was implemented and trained. The first sub-model automatically detects the region of interest using a U-Net model. The second sub-model performs a binary classification using the VGG-19 architecture. The model's performance was computed as the average of five-fold cross-validation processes. Loss, accuracy, Dice coefficient (CI. 95%), AU-ROC, sensitivity, and specificity (CI. 95%) were reported., Results: Six hundred and six shoulders MRIs were analyzed. The Goutallier distribution was presented as follows: 0 (66.50%); 1 (18.81%); 2 (8.42%); 3 (3.96%); 4 (2.31%). Segmentation results demonstrate high levels of accuracy (0.9977 ± 0.0002) and Dice score (0.9441 ± 0.0031), while the classification model also results in high levels of accuracy (0.9731 ± 0.0230); sensitivity (0.9000 ± 0.0980); specificity (0.9788 ± 0.0257); and AUROC (0.9903 ± 0.0092)., Conclusion: The two-step training method proposed using a deep learning model demonstrated strong performance in segmentation and classification tasks., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Saavedra, Droppelmann, Jorquera and Feijoo.)

Background: Low back pain (LBP) is one of the most prevalent musculoskeletal disorders in adults worldwide. Alterations in postural and lumbopelvic control and functionality appear to be determining factors in its resolution. Methods: A cross-sectional study was performed. Patients with LBP were enrolled. Lumbar pain; postural control (PC), total area of the center of pressure (TACOP), and the velocity of the center of pressure (VCOP); lumbopelvic control (LPC); and functionality were evaluated. Statistical tests were implemented to determine differences between sex and age and correlation models among the variables. Results: Thirty adult patients with LBP were analyzed. A strong relationship was found between pain and functionality [r = 0.64; p < 0.001]. A moderate relationship was found between pain and TACOP [r = 0.395; p = 0.031]. A moderate relationship was observed between TACOP and functionality [0.413; p = 0.023] and between LPC and TACOP [r = 0.416; p = 0.001]. Conclusions: This study demonstrates the significant impact of LBP on postural control, lumbopelvic control, and functionality. These results highlight the importance of addressing postural and lumbopelvic control in LBP treatment. No significant differences based on gender and age were found, but all clinical variables differed significantly between the LBP and control groups, underscoring the unique impairments associated with LBP.

Purpose: 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: 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: 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: 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.

Introduction: there is a lack of studies concerning hydration status before training in professional soccer player., Purpose: to describe hydration status before regular training practices in professional soccer players., Methods: a total of 156 male soccer players (age 25.4 ± 5.2 y) from six professional Chilean clubs were included. No hydration or food intake recommendations were made before experiment, with the aim to assess hydration status under athlete's regular "real" conditions. Body mass, height and urine specific gravity (USG) measurements were performed before training practices., Results: 98% of athletes showed dehydration (between moderate and severe) before regular training practices., Discussion: dehydration is the most prevalent hydration status in professional Chilean soccer players before training, which may negatively affect athlete's performance and may increase their risk of heat-related injuries., (Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.)