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Correlation between corneal dynamic responses and keratoconus topographic parameters

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 keraton...

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Detalles Bibliográficos
Autores principales: Tai, Hsi-Yun, Lin, Jun-Ji, Huang, Yi-Hung, Shih, Po-Jen, Wang, I-Jong, Yen, Jia-Yush
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9247384/
https://www.ncbi.nlm.nih.gov/pubmed/35766023
http://dx.doi.org/10.1177/03000605221108100
Descripción
Sumario: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.