Related Experiment Video
Updated: Mar 28, 2026
Subjective Refraction Test Using a Smartphone for Vision Screening
Published on: October 18, 2024
Quantification of Astigmatism Induced by Pterygium Using Automated Image Analysis
Sang Beom Han1, Hyun Sun Jeon, Moosang Kim
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.
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.
