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Performance evaluation of a deep learning-based cascaded HRNet model for automatic measurement of X-ray imaging parameters of lumbar sagittal curvature

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Abstract

Purpose

To develop a deep learning-based cascaded HRNet model, in order to automatically measure X-ray imaging parameters of lumbar sagittal curvature and to evaluate its prediction performance.

Methods

A total of 3730 lumbar lateral digital radiography (DR) images were collected from picture archiving and communication system (PACS). Among them, 3150 images were randomly selected as the training dataset and validation dataset, and 580 images as the test dataset. The landmarks of the lumbar curve index (LCI), lumbar lordosis angle (LLA), sacral slope (SS), lumbar lordosis index (LLI), and the posterior edge tangent angle of the vertebral body (PTA) were identified and marked. The measured results of landmarks on the test dataset were compared with the mean values of manual measurement as the reference standard. Percentage of correct key-points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root-mean-square error (RMSE), and Bland–Altman plot were used to evaluate the performance of the cascade HRNet model.

Results

The PCK of the cascaded HRNet model was 97.9–100% in the 3 mm distance threshold. The mean differences between the reference standard and the predicted values for LCI, LLA, SS, LLI, and PTA were 0.43 mm, 0.99°, 1.11°, 0.01 mm, and 0.23°, respectively. There were strong correlation and consistency of the five parameters between the cascaded HRNet model and manual measurements (ICC = 0.989–0.999, R = 0.991–0.999, MAE = 0.63–1.65, MSE = 0.61–4.06, RMSE = 0.78–2.01).

Conclusion

The cascaded HRNet model based on deep learning algorithm could accurately identify the sagittal curvature-related landmarks on lateral lumbar DR images and automatically measure the relevant parameters, which is of great significance in clinical application.

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Data and material availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

Hangzhou Jianpei Technology Co., Ltd., employees are gratefully acknowledged for providing practical and technical resources.

Funding

This study was supported by the Talent Innovation and Entrepreneurship project of Lanzhou city (2020-RC-53, the Natural Science Foundation of Gansu Province, China (22JR5RA659, 21JR11RA204), and the Scientific Research Project of health industry in Gansu Province (GSWSKY2022-14).

Author information

Authors and Affiliations

  1. The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China

    Yuhua Wu, Yuwen Zheng & Chunyu Ma

  2. Department of Radiology, Gansu Provincial Hospital of Traditional Chinese Medicine (The first affiliated hospital of Gansu University of Traditional Chinese Medicine), Lanzhou, 730050, Gansu, China

    Xiaofei Chen & Fuwen Dong

  3. Hangzhou Jianpei Technology Company Ltd, Hangzhou, 311200, Zhejiang, China

    Linyang He & Guohua Cheng

  4. Department of Radiology, Gansu Provincial Hospital, No. 204, Donggang West Road, Lanzhou, 730000, Gansu, China

    Hongyan Yao & Sheng Zhou

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  1. Yuhua Wu
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Contributions

YW contributed to conceptualization and writing—original draft. YW, LH, and GC were involved in data curation. YW, XC, and FD contributed to formal analysis. SZ was involved in funding acquisition, project administration, and supervision. XC and FD contributed to investigation. YW and XC were involved in methodology. LH and GC contributed to resources and provided software. YZ, CM, and HY were involved in validation. XC contributed to visualization. YW, XC, and SZ were involved in writing—review and editing.

Corresponding author

Correspondence to Sheng Zhou.

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Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethics approval

This study was approved by the Ethics Committee of Gansu Provincial Hospital of Traditional Chinese Medicine (IRB no. 2020–112-01).

Informed consent

Because of the retrospective analysis of imaging data, patients’ informed consent was waived.

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Wu, Y., Chen, X., Dong, F. et al. Performance evaluation of a deep learning-based cascaded HRNet model for automatic measurement of X-ray imaging parameters of lumbar sagittal curvature. Eur Spine J (2023). https://doi.org/10.1007/s00586-023-07937-5

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