Development of machine learning models to predict posterior capsule rupture based on the EUREQUO registry

Abstract

Purpose To evaluate the performance of different probabilistic classifiers to predict posterior capsule rupture (PCR) prior to cataract surgery. Methods Three probabilistic classifiers were constructed to estimate the probability of PCR: a Bayesian network (BN), logistic regression (LR) model, and multi‐layer perceptron (MLP) network. The classifiers were trained on a sample of 2 853 376 surgeries reported to the European Registry of Quality Outcomes for Cataract and Refractive Surgery (EUREQUO) between 2008 and 2018. The performance of the classifiers was evaluated based on the area under the precision‐recall curve (AUPRC) and compared to existing scoring models in the literature. Furthermore, direct risk factors for PCR were identified by analysing the independence structure of the BN. Results The MLP network predicted PCR overall the best (AUPRC 13.1 ± 0.41%), followed by the BN (AUPRC 8.05 ± 0.39%) and the LR model (AUPRC 7.31 ± 0.15%). Direct risk factors for PCR include preoperative best‐corrected visual acuity (BCVA), year of surgery, operation type, anaesthesia, target refraction, other ocular comorbidities, white cataract, and corneal opacities. Conclusions Our results suggest that the MLP network performs better than existing scoring models in the literature, despite a relatively low precision at high recall. Consequently, implementing the MLP network in clinical practice can potentially decrease the PCR rate.

Deciphering the complexity of cataract surgery: A bibliometric and science mapping analysis of research patterns and surgical developments- a 40-year perspective

“…By meticulously delineating state-of-the-art methodologies, prevailing paradigms, and unresolved challenges, this study contributes an indispensable roadmap for guiding subsequent scholarly endeavors. It identifies thematic gaps and uncharted territories, thereby demarcating domains that remain ripe for exploration and further scholarly cultivation [25].…”

“…It is possible to say that Lundstrom M. [19], Mitchell P. [20], Stenevi U. [25], Klein R. [22], Klein BEK [23], Wang JJ.…”