Scoliosis screening and monitoring using self contained ultrasound and neural networks

Authors

Hastings Greer, Kitware, Inc.
Sam Gerber, Kitware, Inc.
Marc Niethammer, The University of North Carolina at Chapel Hill
Roland Kwitt, Universitat Salzburg
Matt McCormick, Kitware, Inc.
Deepak Chittajallu, Kitware, Inc.
Neal Siekierski, Kitware, Inc.
Matthew Oetgen, Childrens National Health System
Kevin Cleary, Childrens National Health System
Stephen Aylward, Kitware, Inc.

Document Type

Conference Proceeding

Publication Date

5-23-2018

Journal

Proceedings - International Symposium on Biomedical Imaging

Volume

2018-April

DOI

10.1109/ISBI.2018.8363857

Keywords

Computer Aided Diagnosis; Point-of-Care Ultrasound

Abstract

© 2018 IEEE. We aim to diagnose scoliosis using a self contained ultrasound device that does not require significant training to operate. The device knows its angle relative to vertical using an embedded inertial measurement unit, and it estimates its angle relative to a vertebrae using a neural network analysis of its ultrasound images. The composition of those angles defines the angle of a vertebrae from vertical. The maximum difference between vertebrae angles collected from a scan of a spine yields the Cobb angle measure that is used to quantify scoliosis severity.

APA Citation

Greer, H., Gerber, S., Niethammer, M., Kwitt, R., McCormick, M., Chittajallu, D., Siekierski, N., Oetgen, M., Cleary, K., & Aylward, S. (2018). Scoliosis screening and monitoring using self contained ultrasound and neural networks. Proceedings - International Symposium on Biomedical Imaging, 2018-April (). http://dx.doi.org/10.1109/ISBI.2018.8363857