# The use of neural networks to determine factors affecting the severity and extent of retinopathy in preterm infants

## TL;DR

This study uses neural networks to identify key factors influencing retinopathy severity in preterm infants, aiming to improve early detection and treatment.

## Contribution

The novel use of artificial neural networks to analyze risk factors for retinopathy of prematurity in preterm infants.

## Key findings

- ROP severity is significantly linked to gestational age, birth weight, and postnatal age.
- Disease extent correlates with maternal parity, gestational age, and birth weight.
- ANNs improve predictive accuracy for ROP progression compared to traditional statistical methods.

## Abstract

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.