Correlation between corneal dynamic responses and keratoconus topographic parameters

Abstract

Objective To investigate the correlation between corneal biomechanical properties and topographic parameters using machine learning networks for automatic severity diagnosis and reference benchmark construction. Methods This was a retrospective study involving 31 eyes from 31 patients with keratonus. Two clustering approaches were used (i.e., shape-based and feature-based). The shape-based method used a keratoconus benchmark validated for indicating the severity of keratoconus. The feature-based method extracted imperative features for clustering analysis. Results There were strong correlations between the symmetric modes and the keratoconus severity and between the asymmetric modes and the location of the weak centroid. The Pearson product-moment correlation coefficient (PPMC) between the symmetric mode and normality was 0.92 and between the asymmetric mode and the weak centroid value was 0.75. Conclusion This study confirmed that there is a relationship between the keratoconus signs obtained from topography and the corneal dynamic behaviour captured by the Corvis ST device. Further studies are required to gather more patient data to establish a more extensive database for validation.

Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review

“…Sixteen original research articles identified in this review assessed severity or other clinical features of patients with keratoconus ( Supplementary Table 3 ) ( 104 , 113 – 127 ). Among studies evaluating keratoconus severity, the ground truth included staging based on topography findings ( 104 , 113 , 118 , 120 , 125 , 127 ), tomography findings ( 116 ), the Ectasia Screening Index ( 114 ), Keratoconus Severity Index ( 119 ), and Amsler-Krumeich criteria ( 120 , 121 , 126 ). Chen et al.…”