Automated Clinical Assessment of Corneal Guttae in Fuchs Endothelial Corneal Dystrophy.

Purpose: To describe the validation and implementation of an automated system for the detection and quantification of guttae in Fuchs endothelial corneal dystrophy (FECD).
Design: Observational reliability study.
Methods: Patients with FECD underwent retroillumination corneal photography, followed by determination of the distributions and sizes of corneal guttae by an automated image analysis algorithm. Performance of the automated system was assessed via (1) validation against manual guttae segmentation, (2) reproducibility studies to ensure consistency, and (3) evaluation for agreement with the Krachmer scale. It was then deployed to perform large-scale guttae assessment with anatomic subregion analysis in a batch of 40 eyes.
Results: Compared to manual segmentation, the automated system was reasonably accurate in identifying the correct number of guttae (mean count of 78 guttae per 1 × 1 mm test frame, overestimation: +10 per frame), but had a tendency to significantly overestimate guttae size (mean guttae size 1073 μm ² , overestimation: +255 μm ² ). Automated measurements of guttae counts and sizes were reproducible within a 1% discrepancy range across repeat intra-eye assessments. Automated guttae counts, interguttae distances, and density of interguttae gaps lesser than 40 μm (ie, D40 density) were highly correlated with the Krachmer scale (P < .001 for all). Large-scale guttae assessment demonstrated the automated system's potential to selectively identify a region of the corneal endothelium most affected by densely packed guttae.
Conclusions: Automated guttae assessment facilitates the precise identification and quantification of guttae characteristics in FECD patients. This can be used clinically as a personalized descemetorrhexis zone for Descemet stripping only and/or Descemet membrane transplantation.
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