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A multi-stage ensemble network system to diagnose adolescent idiopathic scoliosis

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To develop a deep learning algorithm to automatically evaluate and diagnose scoliosis on full spinal X-ray images.

Methods

This retrospective study collected full spinal X-ray images (anteroposterior) from four hospital databases from January 1, 2018, to March 31, 2021. The data were divided into training and validation sets. Full spinal X-ray images for external validation were independently collected at one hospital from April 1, 2021, to June 30, 2021. Model effectiveness was validated with a public dataset. Statistical software R was used to analyze the accuracy and sensitivity of the model curvature and anatomical balance parameters and assess interrater consistency.

Results

This study included 788 and 185 training and test datasets, respectively. The accuracy and recall of the algorithm model for the Cobb angle, apical vertebrae (AV), upper vertebrae, and lower vertebrae were 89.36%, 85.71%, 77.2%, and 80.24% and 97.35%, 93.38%, 84.11%, and 87.42%, respectively. The symmetric mean absolute percentage error at the Cobb angle was 5.99%, and the automatic measurement time was 1.7 s. The mean absolute error values of the Cobb angle and the distances between the center sacral vertical line and AV and C7 plumb line were 1.07° and 1.12 and 1.38 mm, respectively. Statistical analysis confirmed that the Cobb angle results were in good agreement with the gold standard (interclass coefficients of 0.996, 0.978, and 0.825; p < 0.001).

Conclusion

Our deep learning algorithm model had high sensitivity and accuracy for scoliosis, which could help radiologists improve their diagnostic efficiency.

Key Points

• Our deep learning algorithm model had high sensitivity and accuracy for scoliosis, which could help radiologists improve their diagnostic efficiency.

• Multi-center validation data were used in this study to guarantee the reliability of the research.

• Algorithmic model measures 200 times faster than radiologists.

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Abbreviations

AIS:

Adolescent idiopathic scoliosis

AV:

Apical vertebrae

AVT:

Apical vertebral translation

CAD:

Computer-aided diagnosis

CNN:

Convolutional neural network

CVA:

Coronal vertical axis

ICC:

Intraclass correlation coefficients

LV:

Lower vertebrae

MAE:

Mean absolute error

MSE-Net:

Multi-stage ensemble network

SMAPE:

Symmetric mean absolute percentage error

UV:

Upper vertebrae

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Acknowledgements

We thank Baotou Central Hospital, The First Affiliated Hospital of Baotou Medical College, The Affiliated Hospital of Inner Mongolia Medical University, and The Second Affiliated Hospital of Inner Mongolia Medical University for their assistance. Thank you all doctors for your hard work, thank you engineers of AI Lab, Deepwise & League of PhD Technology for your full support.

Funding

Science and technology project in Inner Mongolia, China (2019GG115). 2021 Zhiyuan Talent Project of Inner Mongolia Medical University; Innovation Team Development Plan of Inner Mongolia Education Department(NMGIRT2227); Inner Mongolia Natural Science Foundation (2020MS08124); Inner Mongolia “Grassland Talents” Youth Innovation and Entrepreneurship Talents Project (2020).

Author information

Authors and Affiliations

  1. Department of Anatomy The Basic Medicine College, Inner Mongolia Medical University, Hohhot, 010000, Inner Mongolia, China

    Chao Wu, Mingjie Gao, Haiyan Wang, Yujie He, Xiaohe Li & Zhijun Li

  2. Department of Radiology, Baotou Central Hospital, Baotou, 014040, Inner Mongolia, China

    Chao Wu

  3. Department of Spine Surgery, The Second Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010010, Inner Mongolia, China

    Gedong Meng

  4. AI Lab, Deepwise & League of PhD Technology Co. Ltd, 21st Floor, Beijing, 100080, China

    Jie Lian, Jingxu Xu, Chencui Huang, Shu Zhang & Yizhou Yu

  5. Department of Radiology, The Second Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010010, Inner Mongolia, China

    Yunfeng Zhang

Authors
  1. Chao Wu
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Corresponding author

Correspondence to Xiaohe Li.

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Guarantor

The scientific guarantor of this publication is Xiaohe Li, Chao Wu.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Cobb angles were evaluated by accuracy, symmetric mean absolute percentage error (SMAPE), mean absolute error (MAE), and consistency. Consistency was calculated with intraclass correlation coefficients (ICCs). AVT and CVA were evaluated using ICC and MAE. The Kendall coefficient was used to evaluate vertebral location consistency.

Informed consent

Written informed consent was not required for this study because this study is a retrospective study.

Ethics approval

This study was approved by the Ethics Committee of Inner Mongolia Medical University (YKD2019GG115).

Methodology

• multicentre study

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Chao Wu and Gedong Meng are co-first authors.

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Cite this article

Wu, C., Meng, G., Lian, J. et al. A multi-stage ensemble network system to diagnose adolescent idiopathic scoliosis. Eur Radiol 32, 5880–5889 (2022). https://doi.org/10.1007/s00330-022-08692-9

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  • DOI: https://doi.org/10.1007/s00330-022-08692-9

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