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AI / informaticsJournal of imaging informatics in medicine · 2026

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

Zexin Xu, Cheng Wan, Xinya Hu … Weihua Yang

OphthalmologyAI / informatics

AI model grades cataract severity into four levels with high accuracy.

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

Zexin Xu … Weihua Yang

Journal of imaging informatics in medicine · 2026

Background

Age-related cataract is a leading cause of visual impairment among older adults, significantly affecting quality of life and independence.

Results

correctly matched the expert-assigned severity level in most patients

89.2%

Accuracy

93.82%

Weighted Kappa

More results

In this study, we propose a progressive ordinal attention network (progressive ordinal attention network) for automated four-level cataract classification.

A spatial gating module is further incorporated to suppress background interference and enhance feature representation of the lens region.

More results

The model shows improved capability in distinguishing adjacent severity levels and exhibits strong agreement with clinical annotations.

“

Conclusion

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.

Post

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

AI model grades cataract severity into four levels with high accuracy.

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