Comparisons of corneal biomechanical and tomographic parameters among thin normal cornea, forme fruste keratoconus, and mild keratoconus
Abstract Background To compare the dynamic corneal response (DCR) and tomographic parameters of thin normal cornea (TNC) with thinnest corneal thickness (TCT) (≤ 500 µm), forme fruste keratoconus (FFKC) and mild keratoconus (MKC) had their central corneal thickness (CCT) matched by Scheimpflug imagi...
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| Autores principales: | , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
BMC
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/327f6c3f6ee44221be38a3b514ba8592 |
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| Sumario: | Abstract Background To compare the dynamic corneal response (DCR) and tomographic parameters of thin normal cornea (TNC) with thinnest corneal thickness (TCT) (≤ 500 µm), forme fruste keratoconus (FFKC) and mild keratoconus (MKC) had their central corneal thickness (CCT) matched by Scheimpflug imaging (Pentacam) and corneal visualization Scheimpflug technology (Corvis ST). Methods CCT were matched in 50 eyes with FFKC, 50 eyes with MKC, and 53 TNC eyes with TCT ≤ 500 µm. The differences in DCR and tomographic parameters among the three groups were compared. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic significance of these parameters. Back propagation (BP) neural network was used to establish the keratoconus diagnosis model. Results Fifty CCT-matched FFKC eyes, 50 MKC eyes and 50 TNC eyes were included. The age and biomechanically corrected intraocular pressure (bIOP) did not differ significantly among the three groups (all P > 0.05). The index of height asymmetry (IHA) and height decentration (IHD) differed significantly among the three groups (all P < 0.05). IHD also had sufficient strength (area under the ROC curves (AUC) > 0.80) to differentiate FFKC and MKC from TNC eyes. Partial DCR parameters showed significant differences between the MKC and TNC groups, and the deflection amplitude of the first applanation (A1DA) showed a good potential to differentiate (AUC > 0.70) FFKC and MKC from TNC eyes. Diagnosis model by BP neural network showed an accurate diagnostic efficiency of about 91%. Conclusions The majority of the tomographic and DCR parameters differed among the three groups. The IHD and partial DCR parameters assessed by Corvis ST distinguished FFKC and MKC from TNC when controlled for CCT. |
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