Automated Measurement of Lumbar Lordosis on Radiographs Using Machine Learning and Computer Vision

Global Spine Journal
Brian H ChoSamuel K Cho

Abstract

Cross sectional database study. To develop a fully automated artificial intelligence and computer vision pipeline for assisted evaluation of lumbar lordosis. 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). 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/radiograph. From the 151 test set radiographs, 50 were randomly chosen for surgeon measurement. When compared with those measurements, our algorithm achieved a mean absolute error of 8.055° and a median absolute error of 6.965° (not statistically significant, P > .05). This study is the first to use artificial intelligence and computer vision in a combined pipeline to...Continue Reading

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Citations

Apr 28, 2020·International Journal of Computer Assisted Radiology and Surgery·Claudio VergariLaurent Gajny
Aug 6, 2021·Skeletal Radiology·Paul H YiJan Fritz
Oct 12, 2021·Global Spine Journal·GuanRui RenXiaoTao Wu

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