During cross-validation, the algorithm scored an average area under the receiver operating characteristic curve (AUC) of 0.95 (95% CI, 0.91–0.99), with a 91% (95% CI, 85–98%) sensitivity and an 85% (95% CI, 77–94%) specificity for identifying early FECD. Using 7380 unseen AS-OCT images from 41 eyes, a highly significant difference was found between the mean prediction outputs for each class (Table 1 ).
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Automated diagnosis and staging of Fuchs' endothelial cell corneal dystrophy using deep learning.
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