Role of surgeon seniority in predicting surgically induced astigmatism after phacoemulsification surgery: a machine...

To evaluate whether surgeon seniority influences postoperative astigmatic change after phacoemulsification cataract surgery and to assess the performance of machine-learning models in predicting postoperative astigmatic outcomes using preoperative clinical data.

This study included 169 eyes of 169 patients who underwent phacoemulsification cataract surgery. Surgeons were categorized as residents (n = 101) or specialists (n = 68). Astigmatism was quantified using both classical magnitude-based analysis and vector decomposition. Potential determinants of postoperative astigmatic change were evaluated using multivariable regression analyses. Machine-learning models, including Gradient Boosting, K-Nearest Neighbors, Decision Tree, and Support Vector Machine models, were used to predict classical and vector astigmatism outcomes. Model performance was assessed using mean absolute error, root mean square error, and the coefficient of determination.

Of the 169 eyes, 101 were operated on by residents and 68 by specialists. Postoperative astigmatic change did not differ significantly between resident- and specialist-performed procedures in either classical or vector analyses. Within the resident group, increasing surgical seniority was not associated with postoperative astigmatic change. In multivariable analysis, preoperative cylindrical error was the only significant predictor in the vector model, whereas no significant predictor was identified in the classical model. Overall, machine-learning models demonstrated limited predictive performance, with only modest utility for classical astigmatism and poor explanatory performance for vector outcomes.

Surgeon seniority was not a significant determinant of surgically induced astigmatism after phacoemulsification cataract surgery. Machine-learning models based on preoperative clinical data provided only limited predictive value, particularly for vector astigmatism outcomes. Incorporating intraoperative variables may improve future predictive performance.