Prof. Renato Ambrósio from Brazil developed an index that combines tomographic and biomechanical parameters in order to reach maximal accuracy. Based on a so-called random forest algorithm – a modern machine learning approach – an overall risk score is provided to evaluate ectasia susceptibility.
A random forest algorithm consists of 500 different uncorrelated decision trees – each using tomographic and biomechanical parameters. Each tree makes a classification being either normal or ectasia based on the input variables. The TBI finally represents the percentage, how many trees the cornea classified as normal and how many as abnormal.
In a validation study this index had the highest accuracy for the detection of subclinical keratoconus compared to all other tested methods including corneal topography and tomography. Current cross-validation from Brazil, India, Iran, Hong Kong and Germany indicate similar results.
