Small Sample Deep Learning for Newborn Gestational Age Estimation

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

Abstract

A baby’s gestational age determines whether or not they are preterm, which helps clinicians decide on suitable post-natal treatment. The most accurate dating methods use Ultrasound Scan (USS) machines, but these machines are expensive, require trained personnel and cannot always be deployed to remote areas. In the absence of USS, the Ballard Score can be used, which is a manual postnatal dating method. However, this method is highly subjective and results can vary widely depending on the experience of the rater. In this paper, we present an automatic system for postnatal gestational age estimation aimed to be deployed on mobile phones, using small sets of images of a newborn’s face, foot and ear. We present a novel two-stage approach that makes the most out of Convolutional Neural Networks trained on small sets of images to predict broad classes of gestational age, and then fuse the outputs of these discrete classes with a baby’s weight to make fine-grained predictions of gestational age. On a purpose- collected dataset of 88 babies, experiments show that our approach attains an expected error of 6 days and is three times more accurate than the manual postnatal method (Ballard). Making use of images improves predictions by 30% 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.

Citation

Torres Torres, M., Valstar, M. F., Henry, C., Ward, C., & Sharkey, D. (2017). Small Sample Deep Learning for Newborn Gestational Age Estimation. In Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017) (79-86). https://doi.org/10.1109/FG.2017.19

• Conference Name: 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017)
• Conference Location: Washington, DC, USA
• Start Date: May 30, 2017
• End Date: Jun 3, 2017
• Acceptance Date: Jan 23, 2017
• Online Publication Date: Jun 29, 2017
• Publication Date: 2017
• Deposit Date: Feb 27, 2017
• Publicly Available Date: Jun 29, 2017
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 79-86
• Book Title: Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017)
• ISBN: 978-1-5090-4024-7
• DOI: https://doi.org/10.1109/FG.2017.19
• Public URL: https://nottingham-repository.worktribe.com/output/862559
• Publisher URL: https://ieeexplore.ieee.org/document/7961726/
• Additional Information: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.