  The ultra-widefield (UWF) fundus image is an attractive 3D biomarker in
AI-aided myopia screening because it provides much richer myopia-related
information. Though axial length (AL) has been acknowledged to be highly
related to the two key targets of myopia screening, Spherical Equivalence (SE)
measuring and high myopia diagnosis, its prediction based on the UWF fundus
image is rarely considered. To save the high expense and time costs of
measuring SE and AL, we propose the Copula-enhanced Convolutional Neural
Network (CeCNN), a one-stop UWF-based ophthalmic AI framework to jointly
predict SE, AL, and myopia status. The CeCNN formulates a multiresponse
regression that relates multiple dependent discrete-continuous responses and
the image covariate, where the nonlinearity of the association is modeled by a
backbone CNN. To thoroughly describe the dependence structure among the
responses, we model and incorporate the conditional dependence among responses
in a CNN through a new copula-likelihood loss. We provide statistical
interpretations of the conditional dependence among responses, and reveal that
such dependence is beyond the dependence explained by the image covariate. We
heuristically justify that the proposed loss can enhance the estimation
efficiency of the CNN weights. We apply the CeCNN to the UWF dataset collected
by us and demonstrate that the CeCNN sharply enhances the predictive capability
of various backbone CNNs. Our study evidences the ophthalmology view that
besides SE, AL is also an important measure to myopia.
