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Performance of the deep convolutional neural network based magnetic resonance image scoring algorithm for differentiating between tuberculous and pyogenic spondylitis

Authors
 Kiwook Kim  ;  Sungwon Kim  ;  Young Han Lee  ;  Seung Hyun Lee  ;  Hye Sun Lee  ;  Sungjun Kim 
Citation
 Scientific Reports, Vol.8 : 13124, 2018 
Journal Title
 Scientific Reports 
Issue Date
2018
Abstract
The purpose of this study was to evaluate the performance of the deep convolutional neural network (DCNN) in differentiating between tuberculous and pyogenic spondylitis on magnetic resonance (MR) imaging, compared to the performance of three skilled radiologists. This clinical retrospective study used spine MR images of 80 patients with tuberculous spondylitis and 81 patients with pyogenic spondylitis that was bacteriologically and/or histologically confirmed from January 2007 to December 2016. Supervised training and validation of the DCNN classifier was performed with four-fold cross validation on a patient-level independent split. The object detection and classification model was implemented as a DCNN and was designed to calculate the deep-learning scores of individual patients to reach a conclusion. Three musculoskeletal radiologists blindly interpreted the images. The diagnostic performances of the DCNN classifier and of the three radiologists were expressed as receiver operating characteristic (ROC) curves, and the areas under the ROC curves (AUCs) were compared using a bootstrap resampling procedure. When comparing the AUC value of the DCNN classifier (0.802) with the pooled AUC value of the three readers (0.729), there was no significant difference (P = 0.079). In differentiating between tuberculous and pyogenic spondylitis using MR images, the performance of the DCNN classifier was comparable to that of three skilled radiologists.
Files in This Item:
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DOI
10.1038/s41598-018-31486-3
Appears in Collections:
1. Journal Papers (연구논문) > 1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실)
1. Journal Papers (연구논문) > 1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원)
Yonsei Authors
김기욱(Kim, Ki Wook)
김성원(Kim, Sungwon) ORCID logo https://orcid.org/0000-0001-5455-6926
김성준(Kim, Sungjun) ORCID logo https://orcid.org/0000-0002-7876-7901
이영한(Lee, Young Han) ORCID logo https://orcid.org/0000-0002-5602-391X
이혜선(Lee, Hye Sun) ORCID logo https://orcid.org/0000-0001-6328-6948
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URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/163457
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