ROPRNet: Deep learning-assisted recurrence prediction for retinopathy of prematurity - Huang, Peijie; Xie, Yiying; Wu, Rong; Lin, Qiuxia; Cai, Nian; Chen, Haitao; Feng, Songfu (Biomedical signal processing and control, 2025)
•ROPRNet: a dual-modal model for recurrence prediction of retinopathy of prematurity.•Corrected gestational age at treatment is involved as a key factor for prediction.•ROPRNet uses dual-modal ROP data for prediction, i.e. fundus images and risk factors.•Enhanced ConvNeXt is designed with ATBA and RSG for fundus image feature extraction.•Multi-dimensional multi-scale feature fusion is designed for image feature aggregation. Retinopathy of Prematurity (ROP) recurrence is significant for the prognosis of ROP treatment. In this paper, corrected gestational age at treatment is involved as an important risk factor for the assessment of ROP recurrence. To reveal the complementary information from fundus images and risk factors, a dual-modal deep learning framework with two feature extraction streams, termed as ROPRNet, is designed to assist recurrence prediction of ROP after anti-vascular endothelial growth factor (Anti-VEGF) treatment, involving a stacked autoencoder (SAE) stream for risk factors and a cascaded deep network (CDN) stream for fundus images. Here, the specifically-designed CDN stream involves several novel modules to effectively capture subtle structural changes of retina in the fundus images, involving enhancement head (EH), enhanced ConvNeXt (EnConvNeXt) and multi-dimensional multi-scale feature fusion (MMFF). Specifically, EH is designed to suppress the variations of color and contrast in fundus images, which can highlight the informative features in the images. To comprehensively reveal the inherent medical hints submerged in the fundus images, an adaptive triple-branch attention (ATBA) and a special ConvNeXt with a rare-class sample generator (RSG) were designed to compose the EnConvNeXt for effectively extracting features from fundus images. The MMFF is designed for feature aggregation to mitigate redundant features from several fundus images from different shooting angles, involving a designed multi-dimensional and multi-sale attention (MD-MSA). The designed ROPRNet is validated on a real clinical dataset, which indicate that it is superior to several existing ROP diagnostic models, in terms of 0.894 AUC, 0.818 accuracy, 0.828 sensitivity and 0.800 specificity.