Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From...

The aim of this study was to evaluate the efficacy of artificial intelligence-derived morphometric parameters in characterizing Fuchs corneal endothelial dystrophy (FECD) from specular microscopy images.

This cross-sectional study recruited patients diagnosed with FECD, who underwent ophthalmologic evaluations, including slit-lamp examinations and corneal endothelial assessments using specular microscopy. The modified Krachmer grading scale was used for clinical FECD classification. The images were processed using a convolutional neural network for segmentation and morphometric parameter estimation, including effective endothelial cell density, guttae area ratio, coefficient of variation of size, and hexagonality. A mixed-effects model was used to assess relationships between the FECD clinical classification and measured parameters.

Of 52 patients (104 eyes) recruited, 76 eyes were analyzed because of the exclusion of 26 eyes for poor quality retroillumination photographs. The study revealed significant discrepancies between artificial intelligence-based and built-in microscope software cell density measurements (1322 ± 489 cells/mm 2 vs. 2216 ± 509 cells/mm 2 , P < 0.001). In the central region, guttae area ratio showed the strongest correlation with modified Krachmer grades (0.60, P < 0.001). In peripheral areas, only guttae area ratio in the inferior region exhibited a marginally significant positive correlation (0.29, P < 0.05).

This study confirms the utility of CNNs for precise FECD evaluation through specular microscopy. Guttae area ratio emerges as a compelling morphometric parameter aligning closely with modified Krachmer clinical grading. These findings set the stage for future large-scale studies, with potential applications in the assessment of irreversible corneal edema risk after phacoemulsification in FECD patients, as well as in monitoring novel FECD therapies.