@inproceedings{f7ee73c500bd49d399a552401905fdb9,
title = "Automated Cobb Angle Measurement in Adolescent Idiopathic Scoliosis: Validation of a Previously-Published Deep Learning Method",
abstract = "The severity of scoliosis and surgical decisions are determined based on accurate measurement of the Cobb angle of the spine. There are several previously published deep learning models for automated measurement of Cobb angle, but none are externally validated in severe scoliosis patients. We evaluated the external performance of a previously published deep learning method for Cobb angle measurement in 2278 full-spine X- rays of 860 severe scoliosis patients. The model performed poorly and missed several vertebrae when labelling landmarks. Findings underscore the importance of external validation studies to assess model performance in patient subgroups with varying levels of scoliosis severity.",
keywords = "cobb angle, deep learning, scoliosis",
author = "Shi Yan and Caroline Constant and Taghi Ramazanian and {Maradit Kremers}, Hilal and Larson, {A. Noelle}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 10th IEEE International Conference on Healthcare Informatics, ICHI 2022 ; Conference date: 11-06-2022 Through 14-06-2022",
year = "2022",
doi = "10.1109/ICHI54592.2022.00085",
language = "English (US)",
series = "Proceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "495--496",
booktitle = "Proceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022",
}