## 3 Results
The ROC curve in Figure 9 shows an optimal cut-off point of 0.475 with a training AUC of 0.990, a sensitivity of 0.977 and a specificity of 0.919, corresponding to 59 true negative cases, 41 true positive cases, three false-positive cases and three false-negative cases. The bootstrapped validation values corresponding to the 95% CI are 0.843 \(\pm\) 0.058 for AUC, 0.844 \(\pm\) 0.095 for sensitivity and 0.838 \(\pm\) 0.081 for specificity. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variables** & **Mean** & **SD** & **Median** & **Range** & **Normality** & **Crtl vs.** & **Crtl-REITICS** \\
**Morpho-Geometric** & & & & & & & \\ \hline Total volume & 24.7 & 1.9 & 24.4 & 19.8–29.1 & 0.335 & \(<\)0.001 & \(<\)0.001 \\ Anterior surface & 43.35 & 0.59 & 43.24 & 42.49–47.44 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 \\ \multirow{2}{*}{Posterior surface area} & \multirow{2}{*}{44.70} & \multirow{2}{*}{0.93} & \multirow{2}{*}{44.53} & \multirow{2}{*}{43.53–51.14} & \multirow{2}{*}{\(<\)0.001} & \multirow{2}{*}{\(<\)0.001} & \multirow{2}{*}{\(<\)0.001} \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Std.** & **Z** & **p** & **95\% CI** \\ \hline
**Variables** & **Coefficient** & **Error** & **Value** & **Value** & **OR** & **Lower** & **Upper** \\ \hline
**Demographic** & & & & & & \\ \hline Age & 0.135 & 0.078 & 1.727 & 0.084 & 1.145 & 1.021 & 1.416 \\ Gender & 5.267 & 2.520 & 2.090 & 0.037 & 193.907 & 4.154 & \(2.14\times 10^{5}\) \\ \hline
**Clinical** & & & & & & \\ \hline CDVA & \(-\)6.345 & 4.613 & \(-\)1.376 & 0.169 & 0.002 & 0.001 & 1.821 \\ Coma-like & 4.072 & 1.917 & 2.124 & 0.034 & 58.687 & 3.615 & \(1.16\times 10^{4}\) \\ Q8 mm & 6.027 & 4.618 & 1.305 & 0.192 & 414.459 & 0.070 & \(3.02\times 10^{7}\) \\ Spherical-like & 1.673 & 1.259 & 1.329 & 0.119 & 5.329 & 0.671 & \(1.08\times 10^{2}\) \\ RMS total & 2.482 & 1.591 & 1.560 & 0.119 & 11.969 & 1.071 & \(8.65\times 10^{2}\) \\ High-order & 5.534 & 2.484 & 2.227 & 0.026 & 253.143 & 5.830 & \(3.12\times 10^{5}\) \\ Astigmatism & \(-\)1.054 & 1.218 & \(-\)0.866 & 0.387 & 0.348 & 0.014 & 2.963 \\ \hline
**Pachymetry** & & & & & & \\ \hline Central thickness & -0.242 & 0.116 & \(-\)2.083 & 0.037 & 0.785 & 0.577 & 0.941 \\ Temporal & 0.028 & 0.012 & 2.248 & 0.025 & 1.028 & 1.009 & 1.064 \\ Nasal & 0.007 & 0.011 & 0.641 & 0.522 & 1.007 & 0.987 & 1.034 \\ Superior & 0.092 & 0.058 & 1.587 & 0.113 & 1.096 & 0.995 & 1.263 \\ Inferior & \(-\)0.051 & 0.034 & \(-\)1.479 & 0.139 & 0.951 & 0.870 & 0.995 \\ \hline
**Morpho-Geometric** & & & & & & \\ \hline Total volume & 0.067 & 0.547 & 0.124 & 0.901 & 1.070 & 0.349 & 3.908 \\ Anterior surface area & \(-\)0.326 & 0.532 & \(-\)0.614 & 0.539 & 0.722 & 0.218 & 2.105 \\ Posterior surface area & \(-\)1.189 & 0.779 & \(-\)1.525 & 0.127 & 0.305 & 0.044 & 1.199 \\ \hline
**Constant** & 135.027 & 61.595 & 2.192 & 0.028 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the multivariate logistic regression model for the Ctrl vs. REITICS I patients. CDVA: Corrected Distance Visual Acuity; Q: asphericity; RMS, root mean square Figure 10 shows the distribution of the calculated scores in both groups, where the vast majority of the control cases obtained a score between 0 and 0.1, and between 0.9 and 1 for RETICS I. The parameters for the multivariate ordinal logistic regression model are summarized in Table 4. In this case, the variables that made a statistically significant contribution to the model were: CDVA, central thickness and temporal. The confusion matrix for the training dataset is shown in Table 5, with an overall accuracy of 0.698 at a 95% CI between 0.623 and 0.766. Sensitivity, specificity and balanced accuracy are also shown for each group. Figure 11 depicts power according to the simulated sample size for both models, including only the variables with maximum power over 0.9 at some point within the range, plus Age and Gender.