Associations between corneal biomechanics, axial elongation and myopia are important but previous results are conflicting. Our population-based study aimed to investigate factors associated with corneal biomechanics, and their relationships with myopia in children and adults. Data from 3643 children and 1994 parents showed that children had smaller deformation amplitudes (DA) than parents (p < 0.001). A larger DA was significantly associated with elongated axial length (AL; children: ß = 0.011; adults: ß = 0.0013), higher corneal curvature (children: ß = 0.0086; adults: ß = 0.0096), older age (children: ß = 0.010; adults: ß = 0.0013), and lower intraocular pressure (IOP; children: ß = −0.029; adults: ß = −0.031) in both cohorts. The coefficient of age for DA in children was larger than in adults (p < 0.001), indicating that the DA change with age in children is faster than in adults. DA was significantly associated with spherical equivalent (p < 0.001) resulting from its correlation with AL and corneal curvature. In conclusion, the cornea is more deformable in adults than in children, whereas corneal deformation amplitude increases faster with age in children than that in adults, along with AL elongation. Longer AL, steeper corneal curvature, older age and smaller IOP correspond to a more deformable cornea. The association between corneal deformation amplitude and refraction was mediated via AL and corneal curvature.

Small-incision lenticule extraction (SMILE) is a safe and effective surgical procedure for refractive correction. However, the nomogram from the VisuMax femtosecond laser system often overestimates the achieved lenticule thickness (LT), leading to inaccurate estimation of residual central corneal thickness in some patients. In order to improve the accuracy of predicting achieved LT, we used machine learning models to make predictions of LT and analyze the influencing factors of LT estimation in this study. We collected nine variables of 302 eyes and their LT results as input variables. The input variables included age, sex, mean K reading of anterior corneal surface, lenticule diameter, preoperative CCT, axial length, the eccentricity of the anterior corneal surface (E), diopter of spherical, and diopter of the cylinder. Multiple linear regression and several machine learning algorithms were employed in developing the models for predicting LT. According to the evaluation results, the Random Forest (RF) model achieved the highest performance in predicting the LT with an R² of 0.95 and found the importance of CCT and E in predicting LT. To validate the effectiveness of the RF model, we selected additional 50 eyes for testing. Results showed that the nomogram overestimated LT by 19.59% on average, while the RF model underestimated LT by −0.15%. In conclusion, this study can provide efficient technical support for the accurate estimation of LT in SMILE.