ArticleBMC ophthalmology2024
Prediction models for retinopathy of prematurity occurrence based on artificial neural network.
Article in BMC ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Prediction of retinopathy of prematurity using machine learning models.Pediatric research · 2026Article
- Systematic review and meta-analysis of risk prediction models for retinopathy of prematurity in preterm infants.Biomedical reports · 2025Article
- Prediction of retinopathy of prematurity development and treatment need with machine learning models.BMC ophthalmology · 2025Article
- Structural Equation Modelling of Retinopathy of Prematurity Treatment Integrating Both Physical and Clinical Effects.Journal of clinical medicine · 2025Article
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6 authors.
Funding
Abstract
introductionEarly prediction and timely treatment are essential for minimizing the risk of visual loss or blindness of retinopathy of prematurity, emphasizing the importance of ROP screening in clinical routine.
objectiveTo establish predictive models for ROP occurrence based on the risk factors using artificial neural network.
methodsA cohort of 591 infants was recruited in this retrospective study. The association between ROP and perinatal factors was analyzed by univariate analysis and multivariable logistic regression. We developed predictive models for ROP screening using back propagation neural network, which was further optimized by applying genetic algorithm method. To assess the predictive performance of the models, the areas under the curve, sensitivity, specificity, negative predictive value, positive predictive value and accuracy were used to show the performances of the prediction models.
resultsROP of any stage was found in 193 (32.7%) infants. Twelve risk factors of ROP were selected. Based on these factors, predictive models were built using BP neural network and genetic algorithm-back propagation (GA-BP) neural network. The areas under the curve for prediction models were 0.857, and 0.908 in test, respectively.
conclusionsWe developed predictive models for ROP using artificial neural network. GA-BP neural network exhibited superior predictive ability for ROP when dealing with its non-linear clinical data.
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