Automatic Grading of Age-Related Cataract via Progressive Ordinal Attention and Topology-Constrained Prototype Learning.

Authors

Zexin Xu, Cheng Wan, Xinya Hu, Xiaoqing Wang, Yuting Hu, Zhe Zhang, Weihua Yang

Published in

Journal of imaging informatics in medicine. Jul 29, 2026. Epub Jul 29, 2026.

Abstract

Age-related cataract is a leading cause of visual impairment among older adults, significantly affecting quality of life and independence. Accurate severity grading is essential for determining optimal surgical timing and improving clinical outcomes; however, conventional slit-lamp examination relies heavily on subjective assessment, resulting in considerable inter-observer variability. In this study, we propose a progressive ordinal attention network (POA-Net) for automated four-level cataract classification. The proposed method is built on a convolutional neural network backbone and introduces a progressive ordinal cumulative attention mechanism to explicitly capture the continuous progression of disease severity. A spatial gating module is further incorporated to suppress background interference and enhance feature representation of the lens region. In addition, an ordinal prototype projection head with a topology-constrained learning strategy is designed to enforce structured feature distributions consistent with clinical knowledge. Experimental results demonstrate that POA-Net achieves an accuracy of 89.20% and a weighted kappa coefficient of 93.82%, outperforming several state-of-the-art convolutional and Transformer-based methods. The model shows improved capability in distinguishing adjacent severity levels and exhibits strong agreement with clinical annotations. These findings indicate that the proposed approach provides an accurate, consistent, and interpretable solution for automated cataract grading, with potential applications in large-scale screening and long-term monitoring, particularly in resource-limited settings.

PMID:
42527783
Bibliographic data and abstract were imported from PubMed on 30 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.