Open-source, machine and deep learning-based automated algorithm for gestational age estimation through smartphone lens imaging


Journal Article

© 2018 Optical Society of America. Gestational age estimation at time of birth is critical for determining the degree of prematurity of the infant and for administering appropriate postnatal treatment. We present a fully automated algorithm for estimating gestational age of premature infants through smartphone lens imaging of the anterior lens capsule vasculature (ALCV). Our algorithm uses a fully convolutional network and blind image quality analyzers to segment usable anterior capsule regions. Then, it extracts ALCV features using a residual neural network architecture and trains on these features using a support vector machine-based classifier. The classification algorithm is validated using leave-one-out cross-validation on videos captured from 124 neonates. The algorithm is expected to be an influential tool for remote and point-of-care gestational age estimation of premature neonates in low-income countries. To this end, we have made the software open source.

Full Text

Duke Authors

Cited Authors

  • Desai, AD; Peng, C; Fang, L; Mukherjee, D; Yeung, A; Jaffe, SJ; Griffin, JB; Farsiu, S

Published Date

  • December 1, 2018

Published In

Volume / Issue

  • 9 / 12

Start / End Page

  • 6038 - 6052

Electronic International Standard Serial Number (EISSN)

  • 2156-7085

Digital Object Identifier (DOI)

  • 10.1364/BOE.9.006038

Citation Source

  • Scopus