Extended Data Fig. 4: Performance of the detection model at the clip level.
From: Early detection of visual impairment in young children using a smartphone-based deep learning system
a, ROC curves of the detection model in the internal validation (NI, n = 6,735; mild, n = 8,310; severe, n = 6,685; VI versus NI, AUC = 0.925 (0.914–0.936); mild versus NI, AUC = 0.916 (0.904–0.928); severe versus NI, AUC = 0.935 (0.924–0.946)). b, ROC curves of the detection model in the external validation (NI, n = 7,392; mild, n = 2,580; severe, n = 1,569; VI versus NI, AUC = 0.814 (0.790–0.838); mild versus NI, AUC = 0.802 (0.770–0.831); severe versus NI, AUC = 0.834 (0.807–0.863)). c, ROC curves of the detection model in the at-home implementation by parents or caregivers (NI, n = 947; mild, n = 943; severe, n = 809; VI versus NI, AUC = 0.817 (0.756–0.881); mild versus NI, AUC = 0.809 (0.735–0.884); severe versus NI, AUC = 0.825 (0.764–0.886)). Parentheses show 95% bootstrap CIs. A cluster-bootstrap biased-corrected 95% CI was computed, with individual children as the bootstrap sampling clusters. NI, nonimpairment; VI, visual impairment; ROC curve, receiver operating characteristic curve; AUC, area under the curve; CI, confidence interval.
