Bias validation in the synthetic experiment
The first experiment involved 40 radiologists who answered a survey regarding 18, nine intuitive and nine unintuitive, synthetically generated images (Fig. Synthetically crafted lung CT slices. The same starting slice was predelineated differently to simulate real involvement rates (18 slices and 9 involvement rates in total). Samples shown on the bottom row (unintuitive) should be more susceptible to overestimation, according to area perception theory.We compared the radiologists’ mean errors over the nine intuitive cases against the mean errors over the nine unintuitive (bias-susceptible) cases. We found that radiologists, on average, could be objective in judging the geometric ratios in the basic samples (mean difference = 1.193%), but they showed an overestimation bias in the unintuitive cases (COVID-19 involvement overestimation across synthetic cases and radiological seniority. (Next, we studied the radiologists’ bias tendency (i.e., mean difference of overestimation) with respect to their seniority (years of experience in radiology). Correlation analysis showed a moderate Pearson coefficient of − 0.405 (