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 thousands of models with diverse predictive performance. Following fivefold cross-validation, the best performing model was selected.

This article was reviewed by Renato Ambrósio, Jr, MD, PhD Ectasia is an intriguing and mysterious complication of laser-vision-correction (LVC) procedures. The potentially devastating problem underscores the importance of determining the susceptibility of the cornea for developing progressive ectasia, and of going beyond detecting just mild or subclinical keratoconus. The corneal structure as well as the potential impact of LVC should be considered to predict ectasia risk in every patient. "The LVC procedure and eye rubbing are the primary environmental culprits in the development of ectasia in any cornea," said Renato Ambrósio, Jr, MD, PhD. "So, a basic factor for avoiding ectasia is educating the patient not to rub the eye."