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Abstract 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.

Abstract Purpose: To evaluate the effect of cataract surgery and visual impairment and the associated risk factors on the frequency of falls among older adults in northeast Iran. Methods: This cross-sectional study, conducted between 2019 and 2020, analyzed the potential risk factors of falling in older adults over 50 years of age. To this end, 380 patients were randomly selected by convenience sampling and classified into two groups: those who had undergone cataract surgery in the last 12 months (surgery group) and those who had not (cataract group). The data were collected from the medical records and face-to-face interviews, and logistic regression was used to identify potential risk factors for falling. Results: The frequency of falls in the cataract and surgery groups was 18.9% and 11.6%, respectively. The mean decimal visual acuity of the dominant eye was significantly lower in the cataract group than in the surgery group (P < 0.001). There were no significant differences in the mean number of medications used, Charlson Comorbidity Index score, Instrumental Activities of Daily Living score, and 10-Meter Walk Test speed between the two groups. According to the results of backward logistic regression, taking more than four medications per day and slow gait speed were the most important factors influencing the frequency of falls in older adults. Conclusion: Logistic regression analysis indicated that undergoing cataract surgery is not a significant protective factor against falls. However, older adults in the surgery group experienced fewer falls than in the other group. Besides, the results suggest that taking more than four medications daily and having a slow gait speed are significant fall risk factors.