## 4 Discussion  
The range of techniques that allow the characterization and evaluation of the degree of KC severity vary, but the ultimate trends use machine-learning [44] and neural networks [45]. This study defines two automated learning models based on a set of independent variables (demographic, optical, pachymetric, morpho-geometric) to characterize the optical-geometrical cornea structure in different KC phases. More specifically, it considered a probability model of the risk associated with suffering early-stage KC, as well as another model to classify the degree of KC severity depending on patients' visual limitation levels. This approach offers some very useful advantages. First, it summarizes information that derives from many parameters of different natures (qualitative or quantitative, measured in distinct units) in a single number that can be easily read and understood, which minimizes the risk of some key pieces of information going unnoticed. Otherwise, this risk is fairly high, as common analytical reports usually contain many printed pages of different parameters, which often include no associated normality range and must be read in a matter of seconds. Second, it evaluates the combined action of these different parameters, which could imply a high value in a particular key parameter, clearly indicative of disease being present, but might be less evident when the increase in many different key parameters is slight. In this situation, a score can assist health professionals in their decision-making process as it provides an objective and quantitative scale that takes into account the joint action of a set of diverse parameters [3]. A multivariate logistic regression model was fitted using healthy (Ctrl) and RETICS grade I individuals, which always made predictions ranging between 0 and 1 when applied to new data. This prediction came closer to 0 when input data were similar to those that characterized healthy controls, and close to 1 if they were similar to grade I KC patients. All those individuals with an intermediate  
Figure 12: Screenshots showing: (**a**) a correctly classified healthy individual (true negative); (**b**) an incorrectly classified healthy individual (false-positive); (**c**) a correctly classified grade I KC individual (true positive); (**d**) an incorrectly classified grade I KC individual (false-negative). score were considered to be more or less halfway between both groups, i.e., they showed some kind of abnormality vs. healthy controls, which indicates KC-developing patients, but had not yet reached the cut-off point to be diagnosed as diseased. Therefore, this multivariate logistic regression output could be considered to be proportionally associated with the probability of suffering KC. A higher model output indicated a stronger probability. Even though this relationship is not linear, such scores are frequently used as predicting tools to assess patient status and prognosis in different fields [3; 46; 47; 48]. For initial KC development, the analyzed model-based studies generally showed a good discrimination between normal and early KC eyes. However, one of the main challenges faced by ophthalmologists today is that no consensus has been reached by experts about the characterization patterns of early corneal ectasia [7; 49]. This is mainly the result of the diversity of designations used to refer to subtler KC manifestations. It is also due to the fact that the wide variety of indices used for detection are technology-specific, which does not make them easily interchangeable [19; 49]. As it is difficult to directly compare these studies, we resorted to using authors' own writings to compare them to our work. Several studies have reported similar results to ours about models based on Scheimpflug metrics [5; 6; 7; 20]. [PERSON] et al. [7] proposed a first model based on the combination of five metrics (AUC: 0.86, Sensitivity: 83%, Specificity: 83%).