Corneal segmentation and scoring method.

(A) Corneal segmentation grid and proportion (right eye). The horizontal and vertical ratios of each zone of the grid are 1:1.6:1. (B) Two examples of NEI scale evaluation. PEE of the five zones is assessed and scored using the NEI scale. (C) Corneal segmentation grid and proportion (left eye). NEI, National Eye Institute; PEE, punctate epithelial erosion.</p

Agreement between the entire model and ground truth data for the assessment of improvement or deterioration in 50 eyes (n = 100 images).

Agreement between the entire model and ground truth data for the assessment of improvement or deterioration in 50 eyes (n = 100 images).</p

S1 Dataset -

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</div

S3 Dataset -

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</div

Diagram of the proposed deep learning system.

In step 1, corneal region in fluorescein-stained slit lamp image is segmented using U-Net architecture with 1100 images and their corneal region labeled masks. In step 2, CNN-based classification model was trained with 200 images and their PEE and non-PEE labeled data to find the PEE candidate regions within the corneal region. In step 3, PEE quantification is performed using PEE density map and presented as MDV. PEE, punctate epithelial erosion; MDV, maximum density value.</p

Schematic diagram of dataset splitting for deep learning analysis.

For the PEE candidate regional model, 200 cases were divided into the training, development, and validation sets in a 7:2:1 ratio. For the corneal region segmentation model, PEE detection, and quantification model, 1100 cases were divided 5-folds. Also, data from 94 cases were used for external validation of the entire system, and data from another 100 cases were used for serial data analysis. PEE, punctate epithelial erosion.</p

S2 Dataset -

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</div

Fig 5 -

Ground truth NEI score of the development dataset (hospital 1 data) at each zone (A) and total NEI score (B), and ground truth NEI score of the external validation dataset (hospital 2 data) at each zone (C) and total NEI score (D). NEI, National Eye Institute.</p

Illustration of classification model and density map results.

(A) CAM of PEE candidate region classification. The red and yellow boxes represent true positive and true negative of the PEE classification model, respectively. (B) Density map results. Blue indicates low PEE density, and red indicates high PEE density. CAM, class activation map; PEE, punctate epithelial erosion.</p

Examples of PEE and non-PEE labeling generated using Microsoft paint software.

(A–C). Labeling of definite PEE (red color) and definite non-PEE (yellow color). (D). Extraction of patches sized 192 × 192 pixels for learning. PEE, punctate epithelial erosion.</p