Figure. 8
From: Artificial intelligence in risk prediction and diagnosis of vertebral fractures

This comprehensive forest plot aggregates the diagnostic accuracies of a multitude of studies, evaluating the AUROC (Area Under the Receiver Operating Characteristic curve) of various diagnostic models in the medical field tailored towards identifying vertebral compression fractures. Each entry details the study by author, publication year, and utilized model or technique, ranging from advanced algorithms like CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory networks) to ensemble methods and radiomic analyses. The size of the grey squares reflects the study’s sample size, directly influencing the visual weight of each study’s AUROC result on the plot. The black horizontal lines spanning from each square represent the 95% confidence intervals, providing a graphical representation of the estimate’s precision. At the plot’s base, the black diamond summarizes the combined AUROC across all studies, indicating the overall predictive strength of these models. Heterogeneity among the studies’ outcomes is quantified by an I² statistic, tau² (τ²), and p-value, signalling the extent of variability and its statistical significance. Studies with higher weights, denoted in percentages, suggest a greater impact on the pooled result due to their lower variance. This plot serves as a critical summary, enabling readers to visualize the efficacy of various predictive models in a specific medical domain.