Table 2.

Mean global and sectoral minimum rim width relative to Bruch’s membrane opening values obtained from spectral-domain optical coherence tomography and corresponding mean values for the predictions from the deep learning algorithm in the test sample. The table also shows the Pearson’s correlation coefficient and mean absolute error between predictions and observations from the test sample.

	Deep Learning Prediction from Fundus Photos Mean ± SD, μm	SDOCT BMO-MRW Mean ± SD, μm	P for difference in means	r (R2)	P for correlation	MAE, μm
Global	228.8 ± 63.1	226.0 ± 73.8	0.415	0.88 (77%)	<0.001	27.8
Temporal inferior	221.8 ± 78.9	228.4 ± 91.3	0.141	0.81 (66%)	<0.001	41.7
Temporal Superior	206.8 ± 60.9	208.6 ± 75.7	0.667	0.78 (62%)	<0.001	36.8
Temporal	165.9 ± 34.8	163.1 ± 54.7	0.487	0.68 (46%)	<0.001	32.2
Nasal Superior	253.3 ± 72.9	254.6 ± 90.4	0.784	0.82 (67%)	<0.001	38.8
Nasal Inferior	273.6 ± 77.8	277.6 ± 104.3	0.522	0.78 (60%)	<0.001	51.9
Nasal	257.7 ± 71.2	253.9 ± 90.1	0.459	0.82 (68%)	<0.001	40.4

SD = Standard Deviation; MAE = Mean Absolute Error; SDOCT = Spectral Domain-Optical Coherence Tomography; BMO-MRW = Minimum rim width relative to Bruch’s membrane opening; μm = microns