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- Title
Deep learning-based fully automated grading system for dry eye disease severity.
- Authors
Kim, Seonghwan; Park, Daseul; Shin, Youmin; Kim, Mee Kum; Jeon, Hyun Sun; Kim, Young-Gon; Yoon, Chang Ho
- Abstract
There is an increasing need for an objective grading system to evaluate the severity of dry eye disease (DED). In this study, a fully automated deep learning-based system for the assessment of DED severity was developed. Corneal fluorescein staining (CFS) images of DED patients from one hospital for system development (n = 1400) and from another hospital for external validation (n = 94) were collected. Three experts graded the CFS images using NEI scale, and the median value was used as ground truth. The system was developed in three steps: (1) corneal segmentation, (2) CFS candidate region classification, and (3) estimation of NEI grades by CFS density map generation. Also, two images taken on different days in 50 eyes (100 images) were compared to evaluate the probability of improvement or deterioration. The Dice coefficient of the segmentation model was 0.962. The correlation between the system and the ground truth data was 0.868 (p<0.001) and 0.863 (p<0.001) for the internal and external validation datasets, respectively. The agreement rate for improvement or deterioration was 88% (44/50). The fully automated deep learning-based grading system for DED severity can evaluate the CFS score with high accuracy and thus may have potential for clinical application.
- Subjects
DRY eye syndromes; MEDIAN (Mathematics); HOSPITALS; GRADING of students
- Publication
PLoS ONE, 2024, Vol 19, Issue 3, p1
- ISSN
1932-6203
- Publication type
Article
- DOI
10.1371/journal.pone.0299776