Suspect glaucoma detection from corneal densitometry supported by machine learning

Abstract

Purpose: To discriminate suspect glaucomatous from control eyes using corneal densitometry based on the statistical modeling of the pixel intensity distribution of Scheimpflug images.

Methods: Twenty-four participants (10 suspect glaucomatous and 14 control eyes) were included in this retrospective study. Corneal biomechanics was assessed with the commercial Scheimpflug camera Corvis ST (Oculus). Sets of 140 images acquired per measurement were exported for further analysis. After corneal segmentation, pixel intensities corresponding to different corneal depths were statistically modeled for each image, from which corneal densitometry in the form of parameters α (brightness) and β (homogeneity) was derived. After data pre-processing, parameters α and β were input to six supervised machine learning algorithms that were trained, tested, and compared.

Results: There exists a statistically significant difference in α and β parameters between suspect glaucomatous and control eyes (both, P < 0.05/N, Bonferroni). From the implemented supervised machine learning algorithms, the K-nearest neighbors (K-NN) algorithm reached 83.93% accuracy to discriminate between groups only using corneal densitometry parameters (α and β).

Conclusion: Densitometry of the anterior cornea including epithelium on its own has the potential to serve as a clinical tool for early glaucoma risk assessment.

Keywords: Corneal densitometry; Image processing; Machine learning; Suspect glaucoma.