Development of machine learning models to predict posterior capsule rupture based on the EUREQUO registry
(2023) In Acta Ophthalmologica 101(6). p.644-650- 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... (More)
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.
(Less)
- author
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- artificial intelligence, Bayesian network, cataract surgery, logistic regression, machine learning, multi-layer perceptron, posterior capsule rupture
- in
- Acta Ophthalmologica
- volume
- 101
- issue
- 6
- pages
- 644 - 650
- publisher
- Wiley-Blackwell
- external identifiers
-
- pmid:36789777
- scopus:85148295978
- ISSN
- 1755-375X
- DOI
- 10.1111/aos.15648
- language
- English
- LU publication?
- yes
- id
- 01eae2b1-c24e-47a6-9f8f-3371ab94cf48
- date added to LUP
- 2023-03-08 09:25:21
- date last changed
- 2024-06-13 13:16:07
@article{01eae2b1-c24e-47a6-9f8f-3371ab94cf48,
abstract = {{<p>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.</p>}},
author = {{Triepels, Ron J.M.A. and Segers, Maartje H.M. and Rosen, Paul and Nuijts, Rudy M.M.A. and van den Biggelaar, Frank J.H.M. and Henry, Ype P. and Stenevi, Ulf and Tassignon, Marie José and Young, David and Behndig, Anders and Lundström, Mats and Dickman, Mor M.}},
issn = {{1755-375X}},
keywords = {{artificial intelligence; Bayesian network; cataract surgery; logistic regression; machine learning; multi-layer perceptron; posterior capsule rupture}},
language = {{eng}},
number = {{6}},
pages = {{644--650}},
publisher = {{Wiley-Blackwell}},
series = {{Acta Ophthalmologica}},
title = {{Development of machine learning models to predict posterior capsule rupture based on the EUREQUO registry}},
url = {{http://dx.doi.org/10.1111/aos.15648}},
doi = {{10.1111/aos.15648}},
volume = {{101}},
year = {{2023}},
}