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Abstract：
BACKGROUND: Early 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.
OBJECTIVE: To establish predictive models for ROP occurrence based on the risk factors using artificial neural network.
METHODS: A 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.
RESULTS: ROP 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.
CONCLUSIONS: We 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.
OBJECTIVE: To establish predictive models for ROP occurrence based on the risk factors using artificial neural network.
METHODS: A 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.
RESULTS: ROP 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.
CONCLUSIONS: We 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.
摘要：
背景：早期预测和及时治疗对于最大程度地减少早产儿视网膜病变的视力丧失或失明的风险至关重要，强调ROP筛查在临床常规中的重要性。
目的：利用人工神经网络建立基于危险因素的ROP发生预测模型。
方法：这项回顾性研究招募了591名婴儿。通过单因素分析和多因素logistic回归分析ROP与围产期因素之间的关系。我们使用反向传播神经网络开发了ROP筛查的预测模型，并应用遗传算法对其进行了进一步优化。为了评估模型的预测性能，曲线下的面积,灵敏度,特异性，负预测值，阳性预测值和准确性用于显示预测模型的性能。
结果：在193名（32.7%）婴儿中发现了任何阶段的ROP。选择12个ROP的危险因素。基于这些因素,利用BP神经网络和遗传算法反向传播(GA-BP)神经网络建立预测模型。预测模型的曲线下面积分别为0.857和0.908。分别。
结论：我们使用人工神经网络开发了ROP的预测模型。GA-BP神经网络在处理ROP的非线性临床数据时表现出较好的预测能力。
目的：利用人工神经网络建立基于危险因素的ROP发生预测模型。
方法：这项回顾性研究招募了591名婴儿。通过单因素分析和多因素logistic回归分析ROP与围产期因素之间的关系。我们使用反向传播神经网络开发了ROP筛查的预测模型，并应用遗传算法对其进行了进一步优化。为了评估模型的预测性能，曲线下的面积,灵敏度,特异性，负预测值，阳性预测值和准确性用于显示预测模型的性能。
结果：在193名（32.7%）婴儿中发现了任何阶段的ROP。选择12个ROP的危险因素。基于这些因素,利用BP神经网络和遗传算法反向传播(GA-BP)神经网络建立预测模型。预测模型的曲线下面积分别为0.857和0.908。分别。
结论：我们使用人工神经网络开发了ROP的预测模型。GA-BP神经网络在处理ROP的非线性临床数据时表现出较好的预测能力。
