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Applying a Convolutional Neural Network Based Iterative Algorithm to Automatically Measure Spinal Curvature on Radiographs for Children with Scoliosis



Accurate measurement of spinal curvature for adolescent idiopathic scoliosis (AIS) is important because it affects treatment decisions. Currently, the Cobb angle measured on a radiograph is the gold standard for spinal curvature assessment. However, manual measurements introduce inter- and intra-observer reliability challenges, and while fully automatic methods have been developed, performance could be improved. This paper reported a new approach using convolutional neural networks (CNNs) and an iterative vertebra location algorithm to calculate the Cobb angle automatically by segmenting the spinal and vertebral boundaries on posteroanterior radiographs.


Two CNNs for spinal column and vertebra segmentation were trained using 110 and 272 images, respectively. An iterative vertebra location algorithm was developed to localize individual vertebrae in the spinal column for segmentation. To evaluate the accuracy of the automatic Cobb angle measurements calculated from the vertebra segmentations, 100 new radiographs were used. The mean absolute difference (MAD), standard deviation of absolute differences (SD), and percent within clinical acceptance (≤ 5°) between manual and automatic measurements were reported as evaluation metrics.


The MAD ± SD was 2.8° ± 2.8° and 88% of the measurements were within 5° of the manual measurements. The result was comparable to other literature, and this method worked for a wide range of curve severities. The average automatic measurement time per image was 90 s, which is clinically acceptable.


The automatic measurement method based on CNNs provided a comparable accuracy and speed on spinal curvature measurements on radiographs. It could be a valuable tool for reducing clinical workload and measurement variation.

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The authors would like to acknowledge the financial support of the Innovation grant from the Women and Children’s Health Research Institute and the Discovery Grant from the Natural Science and Engineering Research Council of Canada.

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by SS, JW and EL. The first draft of the manuscript was written by SS and JW and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Edmond Lou.

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The authors have no conflicts of interest to declare that are relevant to the content of this article.

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Ethics approval was granted from the University of Alberta Health Research Ethics Board (Pro00102044—Chart Review).

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Sigurdson, S., Wong, J., Reformat, M. et al. Applying a Convolutional Neural Network Based Iterative Algorithm to Automatically Measure Spinal Curvature on Radiographs for Children with Scoliosis. J. Med. Biol. Eng. (2022).

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  • Convolutional neural network
  • Machine learning
  • Radiograph
  • Scoliosis
  • Spinal curvature