Automated detection and segmentation of Weiss ring in fundus photography images using deep learning.

We developed and evaluated a deep learning system integrating segmentation and classification for automated detection of Weiss rings on fundus photographs (FPs). A U-Net segmenter and two EfficientNet-B0 classifiers were trained, and their probability outputs were fused by a concatenation meta-classifier. Performance was assessed on an independent test set. Segmentation was evaluated with Dice similarity coefficient and intersection-over-union (IoU); classification with area under the receiver-operating-characteristic curve (AUC), accuracy, sensitivity, and specificity. We used Grad-CAM to provide interpretability and reported inter-observer agreement to contextualize segmentation performance. U-Net achieved Dice 0.578 and IoU 0.421 for Weiss-ring localization, consistent with variability observed in low-contrast/peripapillary cases. The integrated meta-classifier outperformed individual CNNs, yielding AUC 0.903, accuracy 0.812, sensitivity 0.692, and specificity 0.872. Attention maps highlighted peripapillary regions of Weiss-ring appearance, supporting model interpretability. Integrating segmentation with classification improved discrimination relative to classification alone. This FP-based tool is not intended to replace clinical examination for posterior vitreous detachment; rather, it may support archival review, education/quality assurance, and research phenotyping, and serve as an adjunct flag when wider-field imaging or OCT is unavailable. Given the limited field of view and variable visibility of Weiss rings on FPs, prospective validation against ultrawide-field and/or OCT reference standards is warranted.