A new AI-based model showed the ability to identify eyes with normal topographies at risk for developing post-LASIK ectasia.
“This method increases the number of cases correctly identified as at risk and reduces the number of eyes that had been inadequately considered at risk,” the authors wrote.
Six features, including percent tissue altered (PTA), residual stromal bed, corneal thickness, flap thickness, central ablation depth and age, were used to engineer through machine learning 14 additional features. The different interactions between these 20 variables were tested, sampling