Prediction of posterior elevation stability in keratoconus - Han, Xiaosong; Shen, Yang; Gu, Dantong; Zhang, Xiaoyu; Sun, Ling; Chen, Zhi; Zhou, Xingtao (Frontiers in Bioengineering and Biotechnology, 2023)
Purpose To investigate the features of progressive keratoconus by means of machine learning. Methods 163 eyes of 127 patients with at least 3 examination records were enrolled in this study. Pentacam HR was used to measure corneal topography. Steepest meridian keratometry (K1), flattest meridian keratometry (K2), steepest anterior keratometry (Kmax), central corneal thickness (CCT), thinnest corneal thickness (TCT), anterior radius of cornea (ARC), posterior elevation (PE), index of surface variation (ISV) and index of height deviation (IHD) were input for analysis. Support vector machine (SVM) and logistic regression analysis were applied to construct prediction models. Results Age, PE and IHD showed statistically significant difference as follow-up period prolongs. K2, PE and ARC were selected for model construction. Logistic regression analysis presented the mean area under the curve (AUC) score of 0.783, while SVM presented mean AUC of 0.659. The prediction sensitivity of SVM was 52.9%, and specificity 79.0%. Conclusion It is feasible to use machine learning to predict the progression and prognosis of keratoconus. Posterior elevation exhibits sensitive prediction effect.