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

Ron J. M. A. Triepels, Maartje H. M. Segers, Paul Rosen, Rudy M. M. A. Nuijts, Frank J. H. M. van den Biggelaar, Ype P. Henry, Ulf Stenevi, Marie‐José Tassignon, David Young, Anders Behndig, Mats Lundström, Mor M. Dickman

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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.
Original languageEnglish
Pages (from-to)644-650
Number of pages7
JournalActa Ophthalmologica
Issue number6
Early online date15 Feb 2023
Publication statusPublished - Sept 2023


  • artificial intelligence
  • Bayesian network
  • cataract surgery
  • logistic regression
  • machine learning
  • multi‐layer perceptron
  • posterior capsule rupture


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