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Deep learning of birth-related infant clavicle fractures: a potential virtual consultant for fracture dating

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Abstract

Background

In infant abuse investigations, dating of skeletal injuries from radiographs is desirable to reach a clear timeline of traumatic events. Prior studies have used infant birth-related clavicle fractures as a surrogate to develop a framework for dating of abuse-related fractures.

Objective

To develop and train a deep learning algorithm that can accurately date infant birth-related clavicle fractures.

Materials and methods

We modified a deep learning model initially designed for face-age estimation to date infant clavicle fractures. We conducted a computerized search of imaging reports and other medical records at a tertiary children’s hospital to identify radiographs of birth-related clavicle fracture in infants ≤ 3 months old (July 2003 to March 2021). We used the resultant database for model training, validation and testing. We evaluated the performance of the deep learning model via a four-fold cross-validation procedure, and calculated accuracy metrics: mean absolute error (MAE), root mean square error (RMSE), intraclass correlation coefficient (ICC) and cumulative score.

Results

The curated database consisted of 416 clavicle radiographs from 213 infants. Average chronological age (equivalent to fracture age) at time of imaging was 24 days. This model estimated the ages of the clavicle fractures with MAE of 4.2 days, RMSE of 6.3 days and ICC of 0.919. On average, 83.7% of the fracture age estimates were accurate to within 7 days of the ground truth.

Conclusion

Our deep learning study provides encouraging results for radiographic dating of infant clavicle fractures. With further development and validation, this model might serve as a virtual consultant to radiologists estimating fracture ages in cases of suspected infant abuse.

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Acknowledgments

This project was supported by the Radiological Society of North America (RSNA) Research & Education Foundation, through Research Seed Grant RSD2133 to Andy Tsai.

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Authors and Affiliations

  1. Department of Radiology, Boston Children’s Hospital, 300 Longwood Ave., Boston, MA, 02115, USA

    Andy Tsai, P. Ellen Grant, Simon K. Warfield, Yangming Ou & Paul K. Kleinman

  2. Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston, MA, USA

    P. Ellen Grant

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  1. Andy Tsai
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  2. P. Ellen Grant
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  3. Simon K. Warfield
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  4. Yangming Ou
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  5. Paul K. Kleinman
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Correspondence to Andy Tsai.

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Tsai, A., Grant, P.E., Warfield, S.K. et al. Deep learning of birth-related infant clavicle fractures: a potential virtual consultant for fracture dating. Pediatr Radiol 52, 2206–2214 (2022). https://doi.org/10.1007/s00247-022-05380-0

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  • DOI: https://doi.org/10.1007/s00247-022-05380-0

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