Correlation between corneal dynamic responses and keratoconus topographic parameters. Issue 6 (June 2022)

• Record Type: Journal Article
• Authors: Tai, Hsi-Yun; Lin, Jun-Ji; Huang, Yi-Hung; Shih, Po-Jen; Wang, I-Jong; Yen, Jia-Yush
• Is Part Of: Journal of international medical research. Volume 50:Issue 6(2022)
• Publication Date: 2022-06
• Subjects: Corneal dynamic characteristics -- Corvis ST -- keratoconus -- machine learning; Medicine -- Periodicals; Pharmacology -- Periodicals; 610.5
• DOI: 10.1177/03000605221108100
• Languages: English
• ISSNs: 0300-0605

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.