Accuracy of a system combining virtual reality and artificial intelligence for screening pediatric strabismus.

Background: To explore the accuracy of system combining virtual reality (VR) and artificial intelligence (AI) for screening pediatric strabismus. Methods: A total of 131 subjects aged 3 to 18 years were included in this study, out of which 110 were included in the final data analysis. Among them 28 were normal, 60 patients with external strabismus, 18 patients with internal strabismus, and 4 patients with vertical strabismus. After the patients were independently diagnosed and evaluated by two strabismus and pediatric ophthalmologists, the mean value was used as the gold standard. All patients completed the AI system within 2 minutes. All data were statistically analyzed using SPSS and MedCalc. The agreement between the two methods for diagnosis and classification of strabismus was compared by Kappa consistency test. The agreement between the two methods for measuring ocular strabismus results was assessed by Bland-Altman plots and interclass correlation efficiency (ICC), and linear regression plots were used to analyze the two methods correlations. Results: The system screened for strabismus with a sensitivity of 83.0%, a specificity of 79%, and moderate agreement with manual results (Kappa = 0.562, p < 0.001). The system performed well in the diagnosis and classification of strabismus (Kappa = 0.749 for external strabismus, Kappa = 0.898 for internal strabismus, and Kappa = 1 for vertical strabismus, p < 0.001). In terms of measuring the angle of ocular deviation, the two methods were strongly correlated (R = 0.7595) and highly consistent (p > 0.05) in the near mode of esotropia, and although the correlation was high (R = 0.7652) in the far mode and the ICC results showed high consistency (ICC = 0.689), the BA charts showed poorer consistency. The two methods showed strong correlation (R = 0.731 and 0.561) in the near and far looking modes in the exotropia group, but the agreement was low (ICC < 0.4, p < 0.05). In the vertical strabismus group in the near mode, the correlation between the two methods was weak (R = -0.2455), and the ICC was not statistically significant, although the agreement was good (p > 0.05 for the BA chart). There was no correlation or agreement between the two methods in the far mode in the vertical strabismus group (R = 0, p < 0.05, ICC not statistically significant). Conclusions: The system combines VR and AI can be used clinically to screen pediatric strabismus with high sensitivity and specificity. The system performs well in the diagnosis and classification of strabismus and can accurately calculate the ocular deviation angle in patients with esotropia. However, the calculation of ocular deviation angle in patients with exotropia and vertical strabismus is still deficient and needs further development.