@inproceedings{e36a5fab118b483db763e71c70733f33,
title = "Automatic Extraction of Skeletal Maturity from Whole Body Pediatric Scoliosis X-rays Using Regional Proposal and Compound Scaling Convolutional Neural Networks",
abstract = "Skeletal maturity assessment plays an important role in the management of pediatric orthopedic conditions such as scoliosis, slipped capital femoral epiphysis (SCFE), and pectus. The most common methods to estimate bone age are the use of hand, shoulder, and pelvis x-rays; however, integrating multi-site data adds cost and ionizing radiation exposure. Whole body pediatric scoliosis x-rays, performed for measuring curvature of the spine, include in the field of view multiple development landmarks such as ossifications of the shoulder, pelvis, and proximal femurs in a single exam, potentially providing a comprehensive survey of skeletal maturity that can assist in surgical planning. Therefore, we propose a system to automatically extract multiple skeletal maturity classifications from a single whole body scoliosis x-ray exam. Since these anatomic regions of significance are as small as 2% of the image, we first apply a multi-class region proposal network to extract the humeral head and five pelvic regions based on the modified Oxford Bone Score. We then apply multiple compound scaling convolutional neural networks (EfficientNet) in parallel to clinically stage each region. Our regional detection achieved an F1-score of 0.99, and our staging models achieved an overall accuracy of 89% and intraclass correlation coefficient of 0.84. Our work holds promise for a skeletal maturity assessment system that uses a single image of the entire axial skeleton. This may enable more data points for surgical planning of orthopedic diseases in pediatric patients while minimizing exposure to harmful radiation.",
keywords = "convolutional neural networks, machine learning, modified Oxford Bone Score, scoliosis, skeletal maturity",
author = "Audrey Ha and John Vorhies and Andrew Campion and Charles Fang and Michael Fadell and Steve Dou and Safwan Halabi and David Larson and Emily Wang and Lee, {Yong Jin} and Joanna Langner and Japsimran Kaur and Bao Do",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 ; Conference date: 16-12-2020 Through 19-12-2020",
year = "2020",
month = dec,
day = "16",
doi = "10.1109/BIBM49941.2020.9313251",
language = "English (US)",
series = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "996--1000",
editor = "Taesung Park and Young-Rae Cho and Hu, {Xiaohua Tony} and Illhoi Yoo and Woo, {Hyun Goo} and Jianxin Wang and Julio Facelli and Seungyoon Nam and Mingon Kang",
booktitle = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",
address = "United States",
}