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\begin{document}

\title{Domain Adaptation-Based Deep Learning Models for Forecasting and
Diagnosis of Glaucoma Disease}



\author[1,1]{Yeganeh Madadi }%
\author[2]{Hashem Abu-Serhan }%
\author[2]{Siamak Yousefi }%
\affil[1]{University of Tennessee Health Science Center}%
\affil[2]{Affiliation not available}%


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  \date{June 15, 2024}


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\begin{abstract}
The main factor causing irreversible blindness is glaucoma. Early
detection greatly reduces the risk of further vision loss. To address
this problem, we developed a domain adaptation-based deep learning model
called Glaucoma Domain Adaptation (GDA) based on 66,742 fundus
photographs collected from 3272 eyes of 1636 subjects. GDA learns
domain-invariant and domain-specific representations to extract both
general and specific features. We also developed a progressive weighting
mechanism to accurately transfer the source domain knowledge while
mitigating the transfer of negative knowledge from the source to the
target domain. We employed low-rank coding for aligning the source and
target distributions. We trained GDA based on three different scenarios
including eyes annotated as glaucoma due to 1) optic disc abnormalities
regardless of visual field abnormalities, 2) optic disc or visual field
abnormalities except ones that are glaucoma due to both optic disc and
visual field abnormalities at the same time, and 3) visual field
abnormalities regardless of optic disc abnormalities. We then evaluate
the generalizability of GDA based on two independent datasets. The AUCs
of GDA in forecasting glaucoma based on the first, second, and third
scenarios were 0.90, 0.88, and 0.80 and the Accuracies were 0.82, 0.78,
and 0.72, respectively. The AUCs of GDA in diagnosing glaucoma based on
the first, second, and third scenarios were 0.98, 0.96, and 0.93 and the
Accuracies were 0.93, 0.91, and 0.88, respectively. The proposed GDA
model achieved high performance and generalizability for forecasting and
diagnosis of glaucoma disease from fundus photographs. GDA may augment
glaucoma research and clinical practice in identifying patients with
glaucoma and forecasting those who may develop glaucoma thus preventing
future vision loss.%
\end{abstract}%



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