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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
Search for other papers by Guillermo Droppelmann in
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Search for other papers by Constanza Rodríguez in
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Search for other papers by Dali Smague in
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Search for other papers by Felipe Feijoo in
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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.