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Advanced and interpretable corneal staining assessment through fine grained knowledge distillation

Year: 2025

Type: article

Abstract: Corneal staining is crucial for evaluating ocular surface diseases, yet existing AI models for CSS (Corneal Staining Score) assessments struggle with detailed lesion identification and lack applicability in real-world clinical settings. Moreover, the output of current AI-assist staining evaluation system only provides categories of grades, leading to potential “plateau” effect, which could misrepresent treatment response in clinical practices. Addressing these gaps, we developed the Fine-grained Knowledge Distillation Corneal Staining Score (FKD-CSS) model, which effectively distills fine-grained features into the CSS grading process and outputs continuous, nuanced scores for thorough assessments. Trained on 1471 images from 14 centers of heterogenous sources, FKD-CSS demonstrates robust accuracy with a Pearson's r of 0.898 against ground-truth and an area under the curve (AUC) of 0.881 in internal validation, rivaling senior ophthalmologists. Additionally, the model achieved expert performance with considerable Pearson's r (0.844–0.899) and AUCs (0.804–0.883) in external tests in six regions of China using 2376 corneal staining images of dry eye across 23 hospitals, and generalizes to multi-ocular-surface-disease test (Pearson's r: 0.816, AUC: 0.807), underscore its efficiency and explainability for CSS assessment. These results highlight FKD-CSS's potential as a precise, valuable tool for staging and outcome measurement of ocular surface diseases.

Source: npj Digital Medicine

DOI: 10.1038/s41746-025-01706-y