Enhancing Post-Cataract Visual Acuity Prediction Using Machine Learning and Critical Preoperative Factors

The objective of this study is to analyze the potential preoperative factors affecting post-cataract visual acuity by machine learning, and establish a deep neural network to predict post-cataract visual acuity. 187 clinical data of patients were collected to investigate the relationship between preoperative factors and postoperative visual acuity. The impact of these preoperative factors on postoperative visual acuity was assessed through the coefficient of variation-based method, Pearson correlation coefficient-based method, random forest, and category boosting. Furthermore, we established a deep neural network to predict postoperative visual acuity, which validated the importance of the preoperative factors selected. It was found that axial length, cylinder, preoperative visual acuity, age, and keratometry reading two were critical preoperative factors associated with postoperative visual outcomes. Keratometry readings one, mean corneal radius, spherical equivalent, anterior chamber depth, intraocular lens, and reserved degree were relatively less critical preoperative factors. Experiments on postoperative visual acuity prediction show that machine learning methods based on preoperative factors achieve superior performance, with predictions based on critical preoperative factors outperforming those based on all preoperative factors. In addition, the proportion of the absolute error which is less than 0.225 between the predicted and the actual postoperative visual acuity based on the deep neural network is 78.95 percent. The study shows how to extract critical preoperative factors related to postoperative visual acuity through machine learning, and build a deep neural network to predict postoperative visual acuity using critical preoperative factors.