Table 1 The table presents the outcomes of the meta-regression analysis assessing the influence of various covariates on the performance of AI models in predicting and diagnosing different types of vertebral fractures. The covariates analysed include sample size, study type, study design, model type, validation method, imaging modality, image preprocessing, feature engineering, and year of publication. Regression coefficients with their corresponding standard errors (in round brackets) are provided for each covariate across four distinct model performance meta-analyses: Prediction, non-pathologic vertebral fracture diagnosis, osteoporotic vertebral fracture diagnosis, and Vertebral Compression Fracture diagnosis. P-values are shown next to the regression coefficients and standard errors, with the understanding that values greater than 0.05 indicate non-significance. The different explanatory variables were calculated singularly as sole covariates in separate meta-regression.

From: Artificial intelligence in risk prediction and diagnosis of vertebral fractures