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A novel approach for screening standard anteroposterior pelvic radiographs in children

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Abstract

Anteroposterior pelvic radiography is the first‐line imaging modality for diagnosing developmental dysplasia of the hip (DDH). Nonstandard radiographs with pelvic malposition make the correct diagnosis of DDH challenging. However, as the only method available for screening standard pelvic radiographs, traditional manual assessment is relatively laborious and potentially erroneous. We retrospectively collected 3,247 pelvic radiographs. There were 2,887 radiographs randomly selected to train and optimize the AI model. Then 362 radiographs were used to test the model’s diagnostic performance. Its diagnostic accuracy was assessed using receiver operating characteristic (ROC) curves and measurement consistency using Bland–Altman plots. In 362 radiographs, the AI model’s area under ROC curves, accuracy, sensitivity, and specificity for quality assessment was 0.993, 99.4% (360/362), 98.6% (138/140), and 100.0% (222/222), respectively. Compared with clinicians, the 95% limits of agreement (Bland–Altman analysis) for pelvic tilt index (PTI) and pelvic rotation index (PRI), as determined by the model, were -0.052–0.072 and -0.088–0.055, respectively.

Conclusions: The artificial intelligence-assisted method was more efficient and highly consistent with clinical experts. This method can be used for real-time validation of the quality of pelvic radiographs in current picture archiving and communications systems (PACS).

What is Known:

• Nonstandard pediatric radiographs with pelvic malposition make the correct diagnosis of developmental dysplasia of the hip (DDH) challenging.

• Traditional manual assessment remains the only method available for screening standard pediatric pelvic radiographs, which is relatively laborious and potentially erroneous.

What is New:

• This study proposed an artificial intelligence-assisted model to assess the quality of pediatric pelvic radiographs accurately and efficiently.

• We recommend the integration of the model into current picture archiving and communications systems (PACS) for real-time screening of standard pediatric pelvic radiographs.

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Data availability

The data are available from the corresponding author upon reasonable request and with permission from the Xijing Hospital, Air Force Military Medical University.

Abbreviations

AI:

Artificial intelligence

COVL:

Center obturator vertical line

DDH:

Developmental dysplasia of the hip

Mask R-CNN:

Mask region-convolutional neural network

PTI:

Pelvic tilt index

PRI:

Pelvic rotation index

ROC:

Receiver operator characteristic

SIA:

Symphyseal-ischial angle

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Acknowledgements

The authors gratefully acknowledge Ting Xie, Department of Radiation Medicine, Xijing Hospital, for her help in the data collection.

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Authors and Affiliations

  1. Department of Orthopedics, Xijing Hospital, Air Force Medical University, No.15 Changle Xi Road, Xi’an, 710032, China

    Jia Sha, Luyu Huang, Jincong Lin, Zongzhi Fan & Yabo Yan

  2. School of Telecommunications Engineering, Xidian University, Xi’an, China

    Yaopeng Chen & Yi Li

Authors
  1. Jia Sha
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  2. Luyu Huang
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  4. Jincong Lin
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  7. Yabo Yan
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jia Sha, Luyu Huang and Yabo Yan. The first draft of the manuscript was written by Jia Sha and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yabo Yan.

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Ethics approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Air Force Medical University (Date: March 4, 2022, Decision No.: KY20223254-1).

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All authors agreed with the content and gave explicit consent to submit.

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The authors declare no competing interests.

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Sha, J., Huang, L., Chen, Y. et al. A novel approach for screening standard anteroposterior pelvic radiographs in children. Eur J Pediatr 182, 4983–4991 (2023). https://doi.org/10.1007/s00431-023-05164-0

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