  In the United States, primary open-angle glaucoma (POAG) is the leading cause
of blindness, especially among African American and Hispanic individuals. Deep
learning has been widely used to detect POAG using fundus images as its
performance is comparable to or even surpasses diagnosis by clinicians.
However, human bias in clinical diagnosis may be reflected and amplified in the
widely-used deep learning models, thus impacting their performance. Biases may
cause (1) underdiagnosis, increasing the risks of delayed or inadequate
treatment, and (2) overdiagnosis, which may increase individuals' stress, fear,
well-being, and unnecessary/costly treatment. In this study, we examined the
underdiagnosis and overdiagnosis when applying deep learning in POAG detection
based on the Ocular Hypertension Treatment Study (OHTS) from 22 centers across
16 states in the United States. Our results show that the widely-used deep
learning model can underdiagnose or overdiagnose underserved populations. The
most underdiagnosed group is female younger (< 60 yrs) group, and the most
overdiagnosed group is Black older (>=60 yrs) group. Biased diagnosis through
traditional deep learning methods may delay disease detection, treatment and
create burdens among under-served populations, thereby, raising ethical
concerns about using deep learning models in ophthalmology clinics.
