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Predicting biological sex in pediatric skeleton X-rays using artificial intelligence.

Michael Janisch1, Mario Scherkl2, Nikolaus Stranger3

  • 1Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036, Graz, Styria, Austria.

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|December 12, 2025
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Summary
This summary is machine-generated.

Artificial intelligence (AI) can accurately predict biological sex from pediatric radiographs, outperforming human assessment. This AI-driven medical imaging approach shows promise for enhancing pediatric clinical practice.

Keywords:
Artificial intelligenceChildrenDigital radiographySex

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pediatric Radiology

Background:

  • The application of artificial intelligence (AI) in medical imaging is expanding.
  • The efficacy of AI in determining biological sex from pediatric radiographs is not well-established.

Purpose of the Study:

  • To evaluate the performance of convolutional neural network (CNN) models in classifying biological sex from pediatric radiographs.
  • To compare AI model performance against human raters.

Main Methods:

  • Utilized a large dataset of pediatric trauma radiographs (computed and digital radiography).
  • Processed images for grayscale normalization and contrast enhancement.
  • Trained EfficientNet CNN models (B0-B7), balancing the dataset by age, sex, and fracture visibility.
  • Compared AI performance with a subset of 1,000 images assessed by human raters.

Main Results:

  • AI models achieved a mean accuracy of 0.722 ± 0.032, with precision, recall, and F1-scores also indicating effective classification.
  • Performance demonstrated an increase with patient age, peaking in the 13-18 age group.
  • Pelvic X-rays yielded the highest classification metrics.
  • Human raters exhibited significantly lower agreement compared to AI models.

Conclusions:

  • AI models can accurately classify biological sex from pediatric radiographs, exceeding human performance.
  • The effectiveness of AI classification varies with patient age and anatomical region (e.g., pelvis).
  • AI-assisted medical imaging holds potential for improving pediatric clinical workflows.