Development, comparison, and internal validation of prediction models to determine the visual prognosis of patients with open globe injuries using machine learning approaches.

INTRODUCTION: Open globe injuries (OGI) represent a main preventable reason for blindness and visual impairment, particularly in developing countries. The goal of this study is evaluating key variables affecting the prognosis of open globe injuries and validating internally and comparing different machine learning models to estimate final visual acuity. MATERIALS AND METHODS: We reviewed three hundred patients with open globe injuries receiving treatment at Khatam-Al-Anbia Hospital in Iran from 2020 to 2022. Age, sex, type of trauma, initial VA grade, relative afferent pupillary defect (RAPD), zone of trauma, traumatic cataract, traumatic optic neuropathy (TON), intraocular foreign body (IOFB), retinal detachment (RD), endophthalmitis, and ocular trauma score (OTS) grade were the input features. We calculated univariate and multivariate regression models to assess the association of different features with visual acuity (VA) outcomes. We predicted visual acuity using ten supervised machine learning algorithms including multinomial logistic regression (MLR), support vector machines (SVM), K-nearest neighbors (KNN), naïve bayes (NB), decision tree (DT), random forest (RF), bagging (BG), adaptive boosting (ADA), artificial neural networks (ANN), and extreme gradient boosting (XGB). Accuracy, positive predictive value (PPV), recall, F-score, brier score (BS), Matthew correlation coefficient (MCC), receiver operating characteristic (AUC-ROC), and calibration plot were used to assess how well machine learning algorithms performed in predicting the final VA. RESULTS: The artificial neural network (ANN) model had the best accuracy to predict the final VA. The sensitivity, F1 score, PPV, accuracy, and MCC of the ANN model were 0.81, 0.85, 0.89, 0.93, and 0.81, respectively. In addition, the estimated AUC-ROC and AUR-PRC of the ANN model for OGI patients were 0.96 and 0.91, respectively. The brier score and calibration log-loss for the ANN model was 0.201 and 0.232, respectively. CONCLUSION: As classic and ensemble ML models were compared, results shows that the ANN model was the best. As a result, the framework that has been presented may be regarded as a good substitute for predicting the final VA in OGI patients. Excellent predictive accuracy was shown by the open globe injury model developed in this study, which should be helpful to provide clinical advice to patients and making clinical decisions concerning the management of open globe injuries.

Retinal neurovascular assessment and choroidal vascularity index in patients with keratoconus.

This study aims to evaluate the macular and the optic nerve head thickness and vascular profiles in KC patients and compare them with two groups of healthy emmetrope and subjects with myopic-astigmatism. This cross-sectional study was conducted at Khatam-Al-Anbia Eye Hospital between 2022 and 2023. Subjects aged 18 to 40 were prone to be included in the study. The participants in this study were grouped into three categories: emmetrope (E), myopic-astigmatic (MA), and keratoconus (KC). All participants underwent a comprehensive ocular examination, as well as macular and optic nerve head (ONH) optical coherence tomography angiography (OCTA). In this study, 143 subjects, 50 cases in the KCN group, 46 cases in the E group, and 47 cases in the MA group, enrolled. There was no difference between the three groups regarding age (p = 0.123) and gender (p = 0.632). The superficial and deep capillary densities at the fovea, parafovea, and perifovea were significantly lower in KC patients than in the control groups (p < 0.01). The radial peripapillary capillary (RPC)- all vessels' density is significantly lower in the KC group (p < 0.001). Besides, the choroidal vascularity index (CVI) and choroidal luminal area (CLA) were considerably higher in KC patients (p < 0.001). The macular and ONH vascular profile in KC patients significantly differs from the vascular profile of healthy controls. Further scientific evidence regarding the systemic implications of keratoconus on the vascular system would be desirable to understand the connections between KC and vascular disease.