Table 4.

Areas under the receiver operating characteristic curves to discriminate eyes with glaucoma from healthy eyes in the test sample for spectral domain optical coherence tomography minimum rim width relative to Bruch’s membrane opening and the corresponding predictions from the deep learning algorithm assessing fundus photographs.

	Deep Learning BMO-MRW Prediction	SDOCT BMO-MRW	P
Global	0.945 (0.874 – 0.980)	0.933 (0.856 – 0.975)	0.587
Temporal inferior	0.939 (0.885 – 0.973)	0.927 (0.843 – 0.969)	0.632
Temporal Superior	0.935 (0.879 – 0.970	0.904 (0.822 – 0.969)	0.334
Temporal	0.915 (0.829 – 0.962)	0.843 (0.734 – 0.927)	0.180
Nasal Superior	0.949 (0.886 – 0.980)	0.927 (0.848 – 0.974)	0.365
Nasal Inferior	0.942 (0.884 – 0.979)	0.952 (0.894 – 0.986)	0.689
Nasal	0.919 (0.837 – 0.973)	0.898 (0.794 – 0.958)	0.447

ROC = Receiver operating characteristic; SDOCT = Spectral Domain-Optical Coherence Tomography; BMO-MRW = minimum rim width relative to Bruch’s membrane opening