Artificial intelligence-based analysis of fundus images for zone classification in retinopathy of prematurity screening [Articol]

Abstract

Retinopathy of prematurity (ROP) is one of the notable causes of vision impairment among kids. The retinal zone, among the signs of ROP, is clinically considered a better predictor of severe forms than staging. This study explores five convolutional neural network (CNN) models used for computerized ROP zone classification based on fundus images of the retina. A total database of 1,365 images drawn from Al Amal Eye Center, located in Baghdad-Iraq, was trained into three classes mimicking three varied ROP zones. The images were used to finetune the models on top of pre-trained VGG16, VGG19, Xception, Inception-ResNetV2, and Inception-V3 models, whose network sizes and configurations were varied among the five models. Compared to minimal works done, the Inception-V3 model yielded the highest accuracy and reached 94.04% on zone detection. Computerized detection of the retinal lesions among pre-terms is fundamental to guiding the regimen plan that may incorporate laser therapy, intravitreal implantation, or close observation with intervention on demand. Merging computerized interpretation with the expertise of pediatric ophthalmologists may guide more consistent decisions on the management plan of the ROP. Future work will include external validation on independent multi-center datasets to assess generalizability.