Detection of subclinical keratoconus using a novel combined tomographic and biomechanical model based on an automated decision tree.

Early detection of keratoconus is a crucial factor in monitoring its progression and making the decision to perform refractive surgery. The aim of this study was to use the decision tree technique in the classification and prediction of subclinical keratoconus (SKC). A total of 194 eyes (including 105 normal eyes and 89 with SKC) were included in the double-center retrospective study. Data were separately used for training and validation databases. The baseline variables were derived from tomography and biomechanical imaging. The decision tree models were generated using Chi-square automatic interaction detection (CHAID) and classification and regression tree (CART) algorithms based on the training database. The discriminating rules of the CART model selected metrics of the Belin/Ambrósio deviation (BAD-D), stiffness parameter at first applanation (SPA1), back eccentricity (Becc), and maximum pachymetric progression index in that order; On the other hand, the CHAID model selected BAD-D, deformation amplitude ratio, SPA1, and Becc. Further, the CART model allowed for discrimination between normal and SKC eyes with 92.2% accuracy, which was higher than that of the CHAID model (88.3%), BAD-D (82.0%), Corvis biomechanical index (CBI, 77.3%), and tomographic and biomechanical index (TBI, 78.1%). The discriminating performance of the CART model was validated with 92.4% accuracy, while the CHAID model was validated with 86.4% accuracy in the validation database. Thus, the CART model using tomography and biomechanical imaging was an excellent model for SKC screening and provided easy-to-understand discriminating rules.

Assessment of Visual Quality in Eyes with Forme Fruste Keratoconus and Mild and Moderate Keratoconus Based on Optical Quality Analysis System II Parameters.

PURPOSE: The study aimed to evaluate the visual quality of forme fruste keratoconus (FFK) and mild and moderate keratoconus by using an optical quality analysis system II (OQAS-II) and to explore the correlation between optical quality parameters and the disease progression. METHODS: Twenty-one normal eyes, twenty-one FFK eyes, twenty-one mild keratoconus eyes, and twenty-one moderate keratoconus eyes were included in this prospective study. The optical quality parameters, such as object scatter index (OSI), modulation transfer function cutoff (MTF cutoff), strehl ratio (SR), and OQAS-II values at contrasts of 100% (OV-100), 20% (OV-20), and 9% (OV-9), were measured by OQAS-II. The repeatability of these parameters was analyzed by intraclass correlation coefficient (ICC), repeatability coefficient (RC), and coefficient of variation (CVw). Correlations between optical quality parameters and mean central keratometry readings (K mean) were evaluated. The sensitivity and specificity of the parameters were analyzed using the receiver operating characteristic (ROC). RESULTS: All the optical quality parameters among four groups showed good repeatability (all ICC≥0.75). The MTF cutoff, SR, OV-100, OV-20, OV-9 in FFK, mild and moderate keratoconus eyes were significantly lower than those in the normal group (all P < 0.05). The ROC analyses of the MTF cutoff, SR, OV-100, OV-20, and OV-9 showed significant area under the curve (AUC) in discriminating FFK form normal, mild keratoconus from FFK, and moderate keratoconus from mild keratoconus (all P < 0.05). The ROC analyses of the MTF cutoff, SR, OV-100, OV-20, and OV-9 showed significant area under the curve (AUC) in discriminating FFK form normal, mild keratoconus from FFK, and moderate keratoconus from mild keratoconus (all P < 0.05). The ROC analyses of the MTF cutoff, SR, OV-100, OV-20, and OV-9 showed significant area under the curve (AUC) in discriminating FFK form normal, mild keratoconus from FFK, and moderate keratoconus from mild keratoconus (all P < 0.05). The ROC analyses of the MTF cutoff, SR, OV-100, OV-20, and OV-9 showed significant area under the curve (AUC) in discriminating FFK form normal, mild keratoconus from FFK, and moderate keratoconus from mild keratoconus (all P < 0.05). The ROC analyses of the MTF cutoff, SR, OV-100, OV-20, and OV-9 showed significant area under the curve (AUC) in discriminating FFK form normal, mild keratoconus from FFK, and moderate keratoconus from mild keratoconus (all K mean) were evaluated. The sensitivity and specificity of the parameters were analyzed using the receiver operating characteristic (ROC). r = -0.710, P < 0.05). The ROC analyses of the MTF cutoff, SR, OV-100, OV-20, and OV-9 showed significant area under the curve (AUC) in discriminating FFK form normal, mild keratoconus from FFK, and moderate keratoconus from mild keratoconus (all. CONCLUSION: The repeatability of OQAS-II is good in measuring visual quality of normal as well as FFK, mild, and moderate keratoconus. The visual quality of the FFK, mild, and moderate keratoconus is worse than that in normal eyes. The OQAS-II has the potential value in screening FFK from normal eyes and might be a useful tool for evaluating the progression of keratoconus.

Assessment of Corneal Pachymetry Distribution and Morphologic Changes in Subclinical Keratoconus with Normal Biomechanics.

PURPOSE: To investigate the pachymetry distribution of central cornea and morphologic changes in subclinical keratoconus with normal biomechanics and determine their potential benefit for the screening of very early keratoconus. METHODS: This retrospective comparative study was performed in 33 clinically unaffected eyes with normal topography and biomechanics from 33 keratoconus patients with very asymmetric ectasia (VAE-NTB; Corvis Biomechanical Index defined) and 70 truly normal eyes from 70 age-matched subjects. Corneal topographic, tomographic, and biomechanical metrics were measured using Pentacam and Corvis ST. The distance and pachymetry difference between the corneal thinnest point and the apex were defined as DTCP-Apex and DPTCP-Apex, respectively, to evaluate the pachymetry distribution within the central cornea. The discriminatory power of metrics was analysed via the receiver operating characteristic curve. A logistic regression analysis was used to establish predictive models. RESULTS: The parameters, DTCP-Apex and DPTCP-Apex, were significantly higher in VAE-NTB than those in normal eyes. For differentiating normal and VAE-NTB eyes, the Belin-Ambrósio deviation (BAD-D) showed the largest area under the curve (AUC; 0.799), followed by ARTmax (0.798), DTCP-Apex (0.771), tomography and biomechanical index (0.760), maximum pachymetry progression index (PPImax, 0.756), DPTCP-Apex (0.753), and back eccentricity (B_Ecc, 0.707) with no statistically significant differences among these AUCs. In the VAE-NTB group, the parameter B_Ecc was significantly and positively correlated with DTCP-Apex (P=0.011) and DPTCP-Apex (P=0.035), whereas the posterior elevation difference had a significant positive association with DPTCP-Apex (P=0.042). A model using the indices DTCP-Apex, B_Ecc, PPImax, and index of height asymmetry demonstrated the highest AUC of 0.846 with 91.43% specificity. CONCLUSIONS: Abnormal pachymetry distribution within the central cornea and subtle morphologic changes are detectable in subclinical keratoconus with normal biomechanics. This may improve VAE-NTB eyes detection.