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Cornea. 2016 Mar;35(3):370-6. doi: 10.1097/ICO.0000000000000728.

Quantification of Astigmatism Induced by Pterygium Using Automated Image Analysis.

Author information

1
*Department of Ophthalmology, Kangwon National University Hospital, Chuncheon, Korea; †Department of Ophthalmology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea; ‡Biomedical Engineering Branch, Division of Convergence Technology, National Cancer Center, Goyang, Korea; and §Department of Ophthalmology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.

Abstract

PURPOSE:

To determine the factors influencing pterygium-induced astigmatism (PIA) and to develop a prediction model of PIA using these factors.

METHODS:

This cross-sectional study included 97 eyes of 97 patients who underwent a pterygium excision and a limbal conjunctival autograft. Anterior segment photographs were taken preoperatively, and corneal topography was done preoperatively and at 3 months postoperatively. PIA was defined as the vector difference between the topographic astigmatism preoperatively and at 3 months postoperatively. Image analysis was performed using anterior segment photographs to measure the relative length (RL) (horizontal length of pterygium invading the cornea/horizontal corneal diameter), relative width (width of pterygium invading the cornea/vertical corneal diameter), relative area (area of pterygium invading the cornea/total corneal area), and vascularity index (VI) (degree of vascularity). Association between these factors and PIA was evaluated with univariate and multivariate analyses. We also attempted to generate a model for prediction of PIA using these factors.

RESULTS:

Univariate analysis showed that the RL, relative width, relative area, and VI were significantly associated with PIA (P < 0.001 for all variables, Pearson coefficient (r) = 0.708, 0.555, 0.606, and 0.642, respectively). In multivariate analysis, only the RL (P < 0.001) and VI (P < 0.001) had significant correlation with PIA. A multiple regression model for PIA was generated as follows: PIA = 0.080 × RL (%) + 0.039 × VI - 0.823 (r = 0.502, F = 95.71, P < 0.001).

CONCLUSIONS:

Larger lengths and increased vascularity were associated with larger PIA. PIA can be predicted by evaluating the length and vascularity of pterygium involving the cornea.

PMID:
26684048
DOI:
10.1097/ICO.0000000000000728
[Indexed for MEDLINE]

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