Intraocular Cytokine Level Prediction from Fundus Images and Optical Coherence Tomography.

ABSTRACT: The relationship between retinal images and intraocular cytokine profiles remains largely unexplored, and no prior work has systematically compared fundus- and OCT-based deep learning models for cytokine prediction. We aimed to predict intraocular cytokine concentrations using color fundus photographs (CFP) and retinal optical coherence tomography (OCT) with deep learning. Our pipeline consisted of image preprocessing, convolutional neural network-based feature extraction, and regression modeling for each cytokine. Deep learning was implemented using AutoGluon, which automatically explored multiple architectures and converged on ResNet18, reflecting the small dataset size. Four approaches were tested: (1) CFP alone, (2) CFP plus demographic/clinical features, (3) OCT alone, and (4) OCT plu

SUBMITTER: Takahashi H 

PROVIDER: S-EPMC12694473 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature