AI system is helpful for diagnosis of CLE (table1&2)

Table I. Our development set consists of 3007 patient cases with 924 clinical images labeled by dermatologists, of which 197 patients were included in our reader study. Table II. Evaluation metrics, including accuracy, specificity, sensitivity and the kappa coefficient, were utilized to evaluate the diagnostic performance of our model. Experimental results showed that our model achieved the best performance when compared with three widely used CNN models, including SE-ResNeXt101-32x4d, SE-ResNet101(3) and Inception-v3 (4). The AIDDA outperformed these existing models, obtaining the highest AUC (0.973). The AUCs of SE-ResNeXt101-32x4d, SE-ResNet101, and Inception-v3 were 0.968, 0.965, and 0.964, respectively.