Predicting Adverse Neurodevelopmental Outcomes in Premature Neonates with Intrauterine Growth Restriction Using a Three-Layered Neural Network
<b>Background/Objectives</b>: 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 networ...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2075-4418/15/1/111 |
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| author | Anca Bivoleanu Liliana Gheorghe Bogdan Doroftei Ioana-Sadiye Scripcariu Ingrid-Andrada Vasilache Valeriu Harabor Ana-Maria Adam Gigi Adam Iulian Valentin Munteanu Carolina Susanu Iustina Solomon-Condriuc Anamaria Harabor |
| author_facet | Anca Bivoleanu Liliana Gheorghe Bogdan Doroftei Ioana-Sadiye Scripcariu Ingrid-Andrada Vasilache Valeriu Harabor Ana-Maria Adam Gigi Adam Iulian Valentin Munteanu Carolina Susanu Iustina Solomon-Condriuc Anamaria Harabor |
| author_sort | Anca Bivoleanu |
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| description | <b>Background/Objectives</b>: 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). <b>Methods</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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. |
| format | Article |
| id | doaj-art-73a47f692b124604b678e2e5d29ecf39 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-73a47f692b124604b678e2e5d29ecf392025-01-10T13:16:45ZengMDPI AGDiagnostics2075-44182025-01-0115111110.3390/diagnostics15010111Predicting Adverse Neurodevelopmental Outcomes in Premature Neonates with Intrauterine Growth Restriction Using a Three-Layered Neural NetworkAnca Bivoleanu0Liliana Gheorghe1Bogdan Doroftei2Ioana-Sadiye Scripcariu3Ingrid-Andrada Vasilache4Valeriu Harabor5Ana-Maria Adam6Gigi Adam7Iulian Valentin Munteanu8Carolina Susanu9Iustina Solomon-Condriuc10Anamaria Harabor11Head of Neonatal Intensive Care Unit, “Cuza voda” Maternity Hospital, 700038 Iasi, RomaniaSurgical Department, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, RomaniaDepartment of Mother and Child Care “Grigore T. Popa”, University of Medicine and Pharmacy, 700115 Iasi, RomaniaDepartment of Mother and Child Care “Grigore T. Popa”, University of Medicine and Pharmacy, 700115 Iasi, RomaniaDepartment of Mother and Child Care “Grigore T. Popa”, University of Medicine and Pharmacy, 700115 Iasi, RomaniaClinical and Surgical Department, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, RomaniaClinical and Surgical Department, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, RomaniaDepartment of Pharmaceutical Sciences, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, RomaniaClinical and Surgical Department, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, RomaniaClinical and Surgical Department, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, RomaniaDepartment of Mother and Child Care “Grigore T. Popa”, University of Medicine and Pharmacy, 700115 Iasi, RomaniaClinical and Surgical Department, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, Romania<b>Background/Objectives</b>: 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). <b>Methods</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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.https://www.mdpi.com/2075-4418/15/1/111artificial neural networkIUGRpretermBailey-3 scaleneurodevelopmental delay |
| spellingShingle | Anca Bivoleanu Liliana Gheorghe Bogdan Doroftei Ioana-Sadiye Scripcariu Ingrid-Andrada Vasilache Valeriu Harabor Ana-Maria Adam Gigi Adam Iulian Valentin Munteanu Carolina Susanu Iustina Solomon-Condriuc Anamaria Harabor Predicting Adverse Neurodevelopmental Outcomes in Premature Neonates with Intrauterine Growth Restriction Using a Three-Layered Neural Network Diagnostics artificial neural network IUGR preterm Bailey-3 scale neurodevelopmental delay |
| title | Predicting Adverse Neurodevelopmental Outcomes in Premature Neonates with Intrauterine Growth Restriction Using a Three-Layered Neural Network |
| title_full | Predicting Adverse Neurodevelopmental Outcomes in Premature Neonates with Intrauterine Growth Restriction Using a Three-Layered Neural Network |
| title_fullStr | Predicting Adverse Neurodevelopmental Outcomes in Premature Neonates with Intrauterine Growth Restriction Using a Three-Layered Neural Network |
| title_full_unstemmed | Predicting Adverse Neurodevelopmental Outcomes in Premature Neonates with Intrauterine Growth Restriction Using a Three-Layered Neural Network |
| title_short | Predicting Adverse Neurodevelopmental Outcomes in Premature Neonates with Intrauterine Growth Restriction Using a Three-Layered Neural Network |
| title_sort | predicting adverse neurodevelopmental outcomes in premature neonates with intrauterine growth restriction using a three layered neural network |
| topic | artificial neural network IUGR preterm Bailey-3 scale neurodevelopmental delay |
| url | https://www.mdpi.com/2075-4418/15/1/111 |
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