Toward automated assessment of conjunctival hyperemia: A semisupervised artificial intelligence approach.

ABSTRACT: This paper develops an automated approach for conjunctival hyperemia grading from slit-lamp images using semisupervised learning. We conducted a retrospective study including slit-lamp images from two study sites. Two independent graders assessed the severity of hyperemia according to the Efron Grading Scales. Segmentation of the conjunctiva and its vessels was performed using semisupervised segmentation with limited labeled data. Conjunctival vessel densities were estimated from the model outputs and compared against the manual clinical Efron gradings. Three hundred and seventeen slit-lamp images from the primary site and 164 from an external site were included. The semisupervised models with unlabeled data demonstrated significantly improved segmentation compared to a baseline fully supe

SUBMITTER: Wong D 

PROVIDER: S-EPMC12448267 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature