Figure. 5

A forest plot displaying the predictive performance of various statistical models is presented, pooling the results from several studies conducted between 2020 and 2023. Each study is listed with the author, the year of publication, and the specific model used, such as neural networks, decision trees, or convolutional neural networks (CNNs). The predictive accuracy of each model is quantified by the AUROC (Area Under the Receiver Operating Characteristic curve), with the size of the grey square indicating the model’s performance and correlating to the sample size of the study. The horizontal lines represent the 95% confidence intervals (CI) for the AUROC, and the overall pooled predictive accuracy across all studies is illustrated by the diamond at the bottom of the plot. This summary measure combines the strength of evidence from the individual studies. Heterogeneity in study outcomes is expressed through the I² statistic and its associated tau² (τ²) and p-value, providing insight into the variability among the different predictive models. A p-value less than 0.05 indicates statistically significant predictive accuracy. The weighting of each study, displayed as a percentage, is based on the inverse of the variance, granting more influence to studies with more precise effect estimates.