Bivoleanu Anca, Gheorghe Liliana, Doroftei Bogdan, Scripcariu Ioana-Sadiye, Vasilache Ingrid-Andrada, Harabor Valeriu, Adam Ana-Maria, Adam Gigi, Munteanu Iulian Valentin, Susanu Carolina, Solomon-Condriuc Iustina, Harabor Anamaria
Head of Neonatal Intensive Care Unit, "Cuza voda" Maternity Hospital, 700038 Iasi, Romania.
Surgical Department, Faculty of Medicine, University of Medicine and Pharmacy "Grigore T. Popa", 700115 Iasi, Romania.
Diagnostics (Basel). 2025 Jan 5;15(1):111. doi: 10.3390/diagnostics15010111.
: There is a constant need to improve the prediction of adverse neurodevelopmental outcomes in growth-restricted neonates who were born prematurely. The aim of this retrospective study was to evaluate the predictive performance of a three-layered neural network for the prediction of adverse neurodevelopmental outcomes determined at two years of age by the Bayley Scales of Infant and Toddler Development, 3rd edition (Bayley-III) scale in prematurely born infants by affected by intrauterine growth restriction (IUGR). : This observational retrospective study included premature newborns with or without IUGR admitted to a tertiary neonatal intensive care unit from Romania, between January 2018 and December 2022. The patients underwent assessment with the Amiel-Tison scale at discharge, and with the Bailey-3 scale at 3, 6, 12, 18, and 24 months of corrected age. Clinical and paraclinical data were used to construct a three-layered artificial neural network, and its predictive performance was assessed. : Our results indicated that this type of neural network exhibited moderate predictive performance in predicting mild forms of cognitive, motor, and language delays. However, the accuracy of predicting moderate and severe neurodevelopmental outcomes varied between moderate and low. : Artificial neural networks can be useful tools for the prediction of several neurodevelopmental outcomes, and their predictive performance can be improved by including a large number of clinical and paraclinical parameters.
对于早产的生长受限新生儿，一直需要改进对不良神经发育结局的预测。这项回顾性研究的目的是评估三层神经网络对受宫内生长受限（IUGR）影响的早产婴儿两岁时由贝利婴幼儿发展量表第三版（Bayley-III）确定的不良神经发育结局的预测性能。 ：这项观察性回顾性研究纳入了2018年1月至2022年12月期间罗马尼亚一家三级新生儿重症监护病房收治的有或无IUGR的早产新生儿。患者出院时接受阿米尔 - 蒂森量表评估，并在矫正年龄3、6、12、18和24个月时接受贝利-3量表评估。临床和辅助临床数据用于构建三层人工神经网络，并评估其预测性能。 ：我们的结果表明，这种类型的神经网络在预测轻度认知、运动和语言延迟方面表现出中等预测性能。然而，预测中度和重度神经发育结局的准确性在中等和低之间变化。 ：人工神经网络可以是预测多种神经发育结局的有用工具，并且通过纳入大量临床和辅助临床参数可以提高其预测性能。
