作者
Brian H Cho, Deepak Kaji, Zoe B Cheung, Ivan B Ye, Ray Tang, Amy Ahn, Oscar Carrillo, John T Schwartz, Aly A Valliani, Eric K Oermann, Varun Arvind, Daniel Ranti, Li Sun, Jun S Kim, Samuel K Cho
发表日期
2020/8
期刊
Global spine journal
卷号
10
期号
5
页码范围
611-618
出版商
SAGE Publications
简介
Study Design
Cross sectional database study.
Objective
To develop a fully automated artificial intelligence and computer vision pipeline for assisted evaluation of lumbar lordosis.
Methods
Lateral lumbar radiographs were used to develop a segmentation neural network (n = 629). After synthetic augmentation, 70% of these radiographs were used for network training, while the remaining 30% were used for hyperparameter optimization. A computer vision algorithm was deployed on the segmented radiographs to calculate lumbar lordosis angles. A test set of radiographs was used to evaluate the validity of the entire pipeline (n = 151).
Results
The U-Net segmentation achieved a test dataset dice score of 0.821, an area under the receiver operating curve of 0.914, and an accuracy of 0.862. The computer vision algorithm identified the L1 and S1 vertebrae on 84.1% of the test set with an average speed of 0.14 seconds …
学术搜索中的文章
BH Cho, D Kaji, ZB Cheung, IB Ye, R Tang, A Ahn… - Global spine journal, 2020