Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images
Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images
Artificial Intelligence may provide insight into exploring the potential covert association
 behind and reveal some early ocular architecture changes in individuals with hepatobiliary
 disorders. We conducted a pioneer work to explore the association between the eye and liver
 via deep learning, to develop and evaluate different deep learning models to predict the
 hepatobiliary disease by using ocular images.
Inclusion Criteria:

 -  The quality of fundus and slit-lamp images should clinical acceptable.

 -  More than 90% of the fundus image area including four main regions (optic disk,
 macular, upper and lower retinal vessel archs) are easy to read and discriminate.

 -  More than 90% of the slit-lamp image area including three main regions (sclera, pupil,
 and lens) are easy to read and discriminate.

 Exclusion Criteria:

 -  Images with light leakage (>10% of the area), spots from lens flares or stains, and
 overexposure were excluded from further analysis.