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Abstract #4102

Convolutional Neural Networks for Identifying Preterm Infants at High Risk of Developmental Disorders

Ahmed Serag1, Emma J Telford1, Scott Semple1, and James P Boardman1

1University of Edinburgh, Edinburgh, United Kingdom

Preterm birth is a major cause of neuropsychiatric impairment in childhood and leads to significant long-term clinical, educational and social problems. A major issue confronting clinicians who work with preterm infants and their families is the identification of infants who are most at risk for subsequent neurodevelopmental disability and who may benefit from early intervention services. We designed a system for the identification of preterm infants at high risk of developmental disorders using convolutional neural networks (CNN). The designed network yields an accuracy of 83.33%, and is applicable to the automated analysis of larger study cohorts.

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