Fig. 7From: A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumoniaUpper row: distribution of the 120 cases over the four clinical metrics: computed tomography severity score (a), lesion type (b), bilateral (c), and basal predominant (d) lesion distribution. On the x-axis, there is the quantitative LungQuant output corresponding to the qualitative indicator; on the y-axis, the visual assessment averaged over all radiologists. For the lesion distribution (bilateral and basal predominant), the grouping is according to the majority of radiologists sharing the same opinion. Youden’s cutoff is shown as a dotted vertical line. Lower row: scatterplot of the average clinical metric of the 120 cases (green dots) versus the respective LungQuant output. On the same plot, the linear (purple line), linear-constrained sigmoid (yellow line) and the unconstrained sigmoid fit (red line) are shownBack to article page