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RESULTS In the test dataset, this deep learning system achieved an AUC of 0.990 (95% CI, 0.975-1.00 with a sensitivity of 94.7% and a specificity of 100.0%, which was significantly larger than the AUCs with all of the OCT and SAP parameters 0.949 (95% CI, 0.921-0.976) with average GCIPL thickness (P=0.006), 0.938 (95% CI, 0.905-0.971) with average RNFL thickness (P=0.003), and 0.889 (0.844-0.934) with mean deviation of SAP (P less then 0.001; DeLong's test). CONCLUSIONS An SD-OCT-based deep learning system can detect glaucomatous struc
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