Using an adult retinal image analysis foundation model for retinopathy of prematurity staging: are there benefits?

Retinopathy of prematurity (ROP) is a disease of the developing retina that can potentially lead to blindness in prematurely born infants. Detection of ROP in its early stages is crucial for timely treatment and has motivated the development of deep learning models to detect ROP and/or to stage its severity from fundus images. However, as in most applications of pediatric medical image analysis, the availability of labeled training data for this task is usually highly limited. We, therefore, test whether fine-tuning a publicly available foundation model for adult retinal images (RETFound) for ROP staging results in accuracy benefits over using generic image classification models. We perform extensive experiments on the largest publicly available ROP dataset and surprisingly find that RETFound, despite having seen nearly one million adult fundus images during pre-training, does not outperform the generic models pre-trained on fully task-unspecific natural images from ImageNet.