Open AccessOpen-source, machine and deep learning-based automated algorithm for gestational age estimation through smartphone lens imaging
Author(s) -
Arjun Desai,
Chunlei Peng,
Leyuan Fang,
Dibyendu Mukherjee,
Andrew Ho-Fai Yeung,
Stephanie J Jaffe,
Jennifer B. Griffin,
Sina Farsiu
Publication year - 2018
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.9.006038
Subject(s) - computer science , artificial intelligence , convolutional neural network , machine learning , usable , artificial neural network , gestational age , algorithm , retinopathy of prematurity , support vector machine , computer vision , pattern recognition (psychology) , pregnancy , multimedia , biology , genetics
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.