Skip to main content

Research Repository

Advanced Search

Postnatal gestational age estimation of newborns using Small Sample Deep Learning

Torres Torres, Mercedes; Valstar, Michel; Henry, Caroline; Ward, Carole; Sharkey, Don


Mercedes Torres Torres

Michel Valstar

Caroline Henry

Carole Ward

Profile Image

Professor of Neonatal Medicine and Technologies


© 2018 A baby's gestational age determines whether or not they are premature, which helps clinicians decide on suitable post-natal treatment. The most accurate dating methods use Ultrasound Scan (USS) machines, but these are expensive, require trained personnel and cannot always be deployed to remote areas. In the absence of USS, the Ballard Score, a postnatal clinical examination, can be used. However, this method is highly subjective and results vary widely depending on the experience of the examiner. Our main contribution is a novel system for automatic postnatal gestational age estimation using small sets of images of a newborn's face, foot and ear. Our two-stage architecture makes the most out of Convolutional Neural Networks trained on small sets of images to predict broad classes of gestational age, and then fuses the outputs of these discrete classes with a baby's weight to make fine-grained predictions of gestational age using Support Vector Regression. On a purpose-collected dataset of 130 babies, experiments show that our approach surpasses current automatic state-of-the-art postnatal methods and attains an expected error of 6 days. It is three times more accurate than the Ballard method. Making use of images improves predictions by 33% compared to using weight only. This indicates that even with a very small set of data, our method is a viable candidate for postnatal gestational age estimation in areas were USS is not available.


Torres Torres, M., Valstar, M., Henry, C., Ward, C., & Sharkey, D. (2019). Postnatal gestational age estimation of newborns using Small Sample Deep Learning. Image and Vision Computing, 83-84, 87-99.

Journal Article Type Article
Acceptance Date Sep 12, 2018
Online Publication Date Dec 1, 2018
Publication Date 2019-03
Deposit Date Nov 12, 2019
Journal Image and Vision Computing
Print ISSN 0262-8856
Electronic ISSN 1872-8138
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 83-84
Pages 87-99
Keywords Computer Vision and Pattern Recognition
Public URL
Publisher URL


You might also like

Downloadable Citations