Statistical analysis
REGRESSION
Based on the two preliminary experiments, we calculated that a sample size of 32 CT studies for each arm would be sufficient to detect a mean difference of 5% (alpha = 0.05, beta = 0.8) with regard to H2. To account for the risk of post-analysis exclusions, 20 extra patients were planned to be enrolled. We did not assume that the AI intervention would be noninferior in any setting; thus, two-tailed tests were used.A two-sided paired t test was used to analyse the mean differences between the two types of samples analysed by the radiologists in the first experiment. A two-sided two-sample t test was used to analyse the bias differences between the two arms of the trial. All differences were assessed for normality both visually (Q-Q plot) and numerically (Shapiro–Wilk test).Pearson correlation coefficient and significance value were calculated to validate the simple linear regression fitting. Despite the large residuals, the even spread of the outliers did not violate either the homoscedasticity or the normality assumptions of the regression analysis.Data management and analysis were conducted using SciPy 1.7.3