Publications by authors named "Zi-Kai Ren"
Article Synopsis
- Corneal Fluorescence Staining (CFS) imaging is used to assess damage to the corneal epithelium, and automating the grading of these images can improve diagnostic accuracy by reducing subjectivity.
- Existing methods often overlook the spatial relationships between stained areas, which can hinder the assessment of corneal injuries.
- This study presents a three-stage automatic model that integrates topological features and multi-scale analysis to improve the grading process, resulting in better accuracy and insights into corneal epithelial damage.
Background: Corneal fluorescein staining is a key biomarker in evaluating dry eye disease. However, subjective scales of corneal fluorescein staining are lacking in consistency and increase the difficulties of an accurate diagnosis for clinicians. This study aimed to propose an automatic machine learning-based method for corneal fluorescein staining evaluation by utilizing prior information about the spatial connection and distribution of the staining region.
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