Detection of Jaundice From Ocular Images Via Deep Learning

Our study presents a detection model predicting a diagnosis of jaundice (clinical jaundice and occult jaundice) trained on prospective cohort data from slit-lamp photos and smartphone photos, demonstrating the model's validity and assisting clinical workers in identifying patient underlying hepatobiliary diseases.

Inclusion Criteria:The quality of slit-lamp images should be clinical acceptable. More than 90% of the slit-lamp image area, including three central 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