European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2025
Predictive utility of the machine learning algorithms in predicting tendinopathy: a meta-analysis of diagnostic test studies.
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Muir Duncan, Elgebaly Ahmed, Kim Woo Jae, Althaher Ahmad, Narvani Ali, Imam Mohamed A
Аннотация
BACKGROUND: Tendinopathy, a degenerative condition of tendon collagen protein, is a common sports injury among elite athletes. Despite its prevalence, the manifestation and progression of tendinopathy remain unclear, and the efficiency of diagnosis and treatment modalities is uncertain. Artificial intelligence and machine learning (ML) have shown positive results in disease diagnosis and treatment evaluation. This systematic review examined many ML methods and their diagnostic yield in predicting tendinopathy.
METHODS: A comprehensive search of electronic databases, including Ovid Medline, EMBASE, PubMed and the Web of Science, was conducted. The quality of the studies was assessed using the Newcastle-Ottawa scale. The statistical analysis was performed using mada package on R software.
RESULTS: Four studies were considered eligible for this meta-analysis, constituting outcomes from 12,611 patients. The ML methods used in the selected studies included random forest, convolutional neural networks and linear support vector machines. The results showed that all selected studies demonstrated the relevance of ML in moderately (Reviewer 2, Comment 2) predicting tendinopathy. The pooled diagnostic yield of the ML algorithms estimated an overall sensitivity of 0.74 (95% CI: 0.64 to 0.82, p = < 0.001) and an overall specificity of 0.69 (95% CI: 0.49 to 0.85, p = 0.06). The diagnostic odds ratio was 6.01 (95% CI: 1.8 to 20.13), with substantial heterogeneity (I2 = 97.6%).
CONCLUSION: ML methods can predict tendinopathy accurately in elite and non-elite athletes. However, further research is needed to establish the specific clinical features associated with tendinopathy prevalence.
METHODS: A comprehensive search of electronic databases, including Ovid Medline, EMBASE, PubMed and the Web of Science, was conducted. The quality of the studies was assessed using the Newcastle-Ottawa scale. The statistical analysis was performed using mada package on R software.
RESULTS: Four studies were considered eligible for this meta-analysis, constituting outcomes from 12,611 patients. The ML methods used in the selected studies included random forest, convolutional neural networks and linear support vector machines. The results showed that all selected studies demonstrated the relevance of ML in moderately (Reviewer 2, Comment 2) predicting tendinopathy. The pooled diagnostic yield of the ML algorithms estimated an overall sensitivity of 0.74 (95% CI: 0.64 to 0.82, p = < 0.001) and an overall specificity of 0.69 (95% CI: 0.49 to 0.85, p = 0.06). The diagnostic odds ratio was 6.01 (95% CI: 1.8 to 20.13), with substantial heterogeneity (I2 = 97.6%).
CONCLUSION: ML methods can predict tendinopathy accurately in elite and non-elite athletes. However, further research is needed to establish the specific clinical features associated with tendinopathy prevalence.