  Precis: A hybrid deep-learning model combines NFL reflectance and other OCT
parameters to improve glaucoma diagnosis. Objective: To investigate if a deep
learning model could be used to combine nerve fiber layer (NFL) reflectance and
other OCT parameters for glaucoma diagnosis. Patients and Methods: This is a
prospective observational study where of 106 normal subjects and 164 perimetric
glaucoma (PG) patients. Peripapillary NFL reflectance map, NFL thickness map,
optic head analysis of disc, and macular ganglion cell complex thickness were
obtained using spectral domain OCT. A hybrid deep learning model combined a
fully connected network (FCN) and a convolution neural network (CNN) to develop
and combine those OCT maps and parameters to distinguish normal and PG eyes.
Two deep learning models were compared based on whether the NFL reflectance map
was used as part of the input or not. Results: The hybrid deep learning model
with reflectance achieved 0.909 sensitivity at 99% specificity and 0.926 at
95%. The overall accuracy was 0.948 with 0.893 sensitivity and 1.000
specificity, and the AROC was 0.979, which is significantly better than the
logistic regression models (p < 0.001). The second best model is the hybrid
deep learning model w/o reflectance, which also had significantly higher AROC
than logistic regression models (p < 0.001). Logistic regression with
reflectance model had slightly higher AROC or sensitivity than the other
logistic regression model without reflectance (p = 0.024). Conclusions: Hybrid
deep learning model significantly improved the diagnostic accuracy, without or
without NFL reflectance. Hybrid deep learning model, combining reflectance/NFL
thickness/GCC thickness/ONH parameter, may be a practical model for glaucoma
screen purposes.
