Habibi Mohammad Reza Mazaheri, JafariMoghadam Azadeh, Norouzkhani Narges, Nazari Elham, Imani Bahareh, Kheirdoust Azam, Fatemi Aghda Seyed Ali
Department of Health Information Technology, Varastegan Institute for Medical Sciences, Mashhad, Iran.
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Int J Retina Vitreous. 2025 Mar 14;11(1):30. doi: 10.1186/s40942-025-00650-z.
Retinopathy of prematurity (ROP) is a leading cause of visual impairment and blindness in preterm infants. Early identification of key risk factors is essential for effective screening and timely intervention. This study utilizes an artificial neural network (ANN) to analyze and identify the most influential factors affecting the severity and extent of ROP in preterm neonates.
This descriptive-analytical study was conducted on 367 preterm infants in Bojnord, Iran, in 2021. The study examined multiple variables, including sex, history of multiple births, number of prior abortions, type of pregnancy and delivery, gestational age, oxygen therapy, severity of retinopathy, and disease extent within the retina. Statistical analyses were performed using one-way analysis of variance (ANOVA), Pearson's correlation coefficient, and an ANN to determine the relationships between independent variables and ROP progression.
The findings indicate that the severity of ROP was significantly associated with the type of pregnancy, gestational age, birth weight, and postnatal age (P < 0.05). Similarly, disease extent was significantly correlated with maternal parity, gestational age, birth weight, and postnatal age (P < 0.05). Among all factors examined, postnatal and gestational age exhibited the highest coefficient effects on ROP severity and disease extent. Additionally, follow-up evaluations revealed that infant age and birth weight were crucial in disease progression.
The results suggest that targeted interventions focusing on gestational age and neonatal weight may significantly reduce the incidence and severity of ROP in preterm infants. Integrating ANNs enhances predictive accuracy, enabling early diagnosis and improved clinical outcomes.
The findings of this study contribute to the advancement of ROP screening and treatment strategies in preterm neonates. Future research should focus on multi-center studies with larger sample sizes to refine predictive models and identify additional risk factors influencing ROP progression.
早产儿视网膜病变（ROP）是导致早产儿视力损害和失明的主要原因。早期识别关键风险因素对于有效筛查和及时干预至关重要。本研究利用人工神经网络（ANN）分析并识别影响早产儿ROP严重程度和范围的最具影响力的因素。
2021年，在伊朗博季努德对367名早产儿进行了这项描述性分析研究。该研究考察了多个变量，包括性别、多胎史、既往流产次数、妊娠和分娩类型、胎龄、氧疗、视网膜病变严重程度以及视网膜内疾病范围。使用单因素方差分析（ANOVA）、Pearson相关系数和人工神经网络进行统计分析，以确定自变量与ROP进展之间的关系。
研究结果表明，ROP的严重程度与妊娠类型、胎龄、出生体重和出生后年龄显著相关（P < 0.05）。同样，疾病范围与产妇产次、胎龄、出生体重和出生后年龄显著相关（P < 0.05）。在所有考察的因素中，出生后年龄和胎龄对ROP严重程度和疾病范围的系数影响最高。此外，随访评估显示婴儿年龄和出生体重对疾病进展至关重要。
结果表明，针对胎龄和新生儿体重的靶向干预可能会显著降低早产儿ROP的发生率和严重程度。整合人工神经网络可提高预测准确性，实现早期诊断并改善临床结果。
本研究结果有助于推进早产儿ROP的筛查和治疗策略。未来的研究应侧重于样本量更大的多中心研究，以完善预测模型并识别影响ROP进展的其他风险因素。
