Heritability of pachymetric indices using Pentacam Scheimflug imaging

Heritability of Corneal Parameters in Nuclear Families With Keratoconus

Purpose

This study aimed to investigate the heritability of corneal parameters obtained by Pentacam in nuclear families with keratoconus (KC).

Methods

A total of 82 patients with KC and their biological parents (n = 164) were recruited in the current study. All subjects underwent corneal tomography with Pentacam. Family units were analyzed to calculate the heritability of corneal parameters by linear mixed effects model using the R statistical software.

Results

The pachymetry at apex, pupil, and thinnest point were all significantly heritable at 43.26%, 42.63%, and 43.09%, respectively. The heritability of flat meridian keratometry, steep meridian keratometry, and mean keratometry in the anterior surface were 10.36%, 9.05%, and 10.21%, respectively, and that of flat meridian keratometry, steep meridian keratometry, and mean keratometry in the posterior surface were 8.44%, 9.67%, and 9.06%, respectively. The posterior radius of curvature had higher heritability in comparison with anterior radius of curvature (19.16% vs. 14.37%). Moreover, among combined topometric indices, the heritability of index of vertical asymmetry was the highest (19.49%), and that of central keratoconus index was the lowest (6.64%).

Conclusions

The present study demonstrated a substantial heritability of corneal parameters in nuclear families with KC. The pachymetric indices are heritable and may be suitable as KC endophenotypes, suggesting a necessity to discover the genes associated with corneal thickness in KC.

Translational Relevance

The pachymetric indices are heritable and may be suitable as KC endophenotypes, indicating that the pachymetric indices might be a corneal characteristic to predict the occurrence of KC.

Artificial intelligence applications in different imaging modalities for corneal topography

Interpretation of topographical maps used to detect corneal ectasias requires a high level of expertise. Several artificial intelligence (AI) technologies have attempted to interpret topographic maps. The purpose of this study is to provide a review of AI algorithms in corneal topography from the perspectives of an eye care professional, a biomedical engineer, and a data scientist. A systematic literature review using Web of Science, Pubmed, and Google Scholar was performed from 2010 to 2020 on themes regarding imaging modalities, their parameters, purpose, and conclusions and their samples and performance related to AI in corneal topography. We provide a comprehensive summary of advances in corneal imaging and its applications in AI. Combined metrics from the Dual Scheimpflug and Placido device could be a good starting point to try AI models in corneal imaging systems. The range of area under the receiving operating curve for AI in keratoconus detection and classification was from 0.87 to 1, sensitivity was from 0.89 to 1, and specificity was from 0.82 to 1. A combination of different types of AI applications to corneal ectasia diagnosis is recommended.