Yu Sun1, Yaozhong Xing2, Zian Zhao1, Xianglong Meng3, Gang Xu2, Yong Hai2. 1. School of Cyber Science and Technology, Beihang University, Beijing, 100191, China. 2. Department of Orthopedic Surgery, Beijing Chaoyang Hospital, Capital Medical University, 8 Gong Ti Nan Road, Chaoyang District, Beijing, 100020, China. 3. Department of Orthopedic Surgery, Beijing Chaoyang Hospital, Capital Medical University, 8 Gong Ti Nan Road, Chaoyang District, Beijing, 100020, China. spinesurgeonmeng@ccmu.edu.cn.
Abstract
PURPOSE: The present study compared manual and automated measurement of Cobb angle in idiopathic scoliosis based on deep learning keypoint detection technology. METHODS: A total of 181 anterior-posterior spinal X-rays were included in this study, including 165 cases of idiopathic scoliosis and 16 normal adult cases without scoliosis. We labeled all images and randomly chose 145 as the training set and 36 as the test set. Two state-of-the-art deep learning object detection models based on convolutional neural networks were used in sequence to segment each vertebra and locate the vertebral corners. Cobb angles measured from the output of the models were compared to manual measurements performed by orthopedic experts. RESULTS: The mean Cobb angle in test cases was 27.4° ± 19.2° (range 0.00-91.00°) with manual measurements and 26.4° ± 18.9° (range 0.00-88.00°) with automated measurements. The automated method needed 4.45 s on average to measure each radiograph. The intra-class correlation coefficient (ICC) for the reliability of the automated measurement of the Cobb angle was 0.994. The Pearson correlation coefficient and mean absolute error between automated positioning and expert annotation were 0.990 and 2.2° ± 2.0°, respectively. The analytical result for the Spearman rank-order correlation was 0.984 (p < 0.001). CONCLUSION: The automated measurement results agreed with the experts' annotation and had a high degree of reliability when the Cobb angle did not exceed 90° and could locate multiple curves in the same scoliosis case simultaneously in a short period of time. Our results need to be verified in more cases in the future.
PURPOSE: The present study compared manual and automated measurement of Cobb angle in idiopathic scoliosis based on deep learning keypoint detection technology. METHODS: A total of 181 anterior-posterior spinal X-rays were included in this study, including 165 cases of idiopathic scoliosis and 16 normal adult cases without scoliosis. We labeled all images and randomly chose 145 as the training set and 36 as the test set. Two state-of-the-art deep learning object detection models based on convolutional neural networks were used in sequence to segment each vertebra and locate the vertebral corners. Cobb angles measured from the output of the models were compared to manual measurements performed by orthopedic experts. RESULTS: The mean Cobb angle in test cases was 27.4° ± 19.2° (range 0.00-91.00°) with manual measurements and 26.4° ± 18.9° (range 0.00-88.00°) with automated measurements. The automated method needed 4.45 s on average to measure each radiograph. The intra-class correlation coefficient (ICC) for the reliability of the automated measurement of the Cobb angle was 0.994. The Pearson correlation coefficient and mean absolute error between automated positioning and expert annotation were 0.990 and 2.2° ± 2.0°, respectively. The analytical result for the Spearman rank-order correlation was 0.984 (p < 0.001). CONCLUSION: The automated measurement results agreed with the experts' annotation and had a high degree of reliability when the Cobb angle did not exceed 90° and could locate multiple curves in the same scoliosis case simultaneously in a short period of time. Our results need to be verified in more cases in the future.
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Authors: Nicholas Bien; Pranav Rajpurkar; Robyn L Ball; Jeremy Irvin; Allison Park; Erik Jones; Michael Bereket; Bhavik N Patel; Kristen W Yeom; Katie Shpanskaya; Safwan Halabi; Evan Zucker; Gary Fanton; Derek F Amanatullah; Christopher F Beaulieu; Geoffrey M Riley; Russell J Stewart; Francis G Blankenberg; David B Larson; Ricky H Jones; Curtis P Langlotz; Andrew Y Ng; Matthew P Lungren Journal: PLoS Med Date: 2018-11-27 Impact factor: 11.069