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Automated Joint Space Detection Improves Bone Segmentation Accuracy
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Sex Prediction From the Clavicle Using Computerized Tomography Images via Traditional and Hybrid Deep Learning

Yusuf Secgin1, Muhammet Cakmak2, Deniz Senol3

  • 1Faculty of Medicine, Department of Anatomy, Karabük University, Karabük, Türkiye.

Clinical Anatomy (New York, N.Y.)
|May 1, 2026
PubMed
Summary

This study introduces hybrid deep learning models for accurate sex prediction from clavicle images, achieving up to 91% accuracy. This method shows promise for forensic medicine applications.

Keywords:
MobileNetV2claviclemultilayer perceptronsex predictiont‐distributed stochastic neighborhood embedding

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Area of Science:

  • Forensic Anthropology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate sex estimation is crucial in forensic investigations.
  • Traditional methods for sex estimation can be limited by skeletal material availability and condition.
  • Deep learning offers a novel approach for automated skeletal analysis.

Purpose of the Study:

  • To develop and evaluate hybrid deep learning models for high-accuracy sex prediction using clavicle images.
  • To compare the performance of proposed hybrid models against traditional deep learning models.
  • To identify key anatomical features contributing to accurate sex prediction.

Main Methods:

  • Computed Tomography (CT) scans of 1612 clavicles (807 female, 805 male) were used.
  • 3D clavicle images were segmented and saved as superior-inferior and right-left views.
  • MobileNetV2, DenseNet201, ResNet101, and hybrid MobileNetV2+MLP/t-SNE models were trained and evaluated.

Main Results:

  • The highest accuracy of 91% was achieved with hybrid MobileNetV2+MLP and MobileNetV2+MLP+t-SNE models without side discrimination.
  • Proposed hybrid models achieved a highest success rate of 88%.
  • ResNet101 yielded the lowest accuracy rates (81% with side discrimination, 83% without).

Conclusions:

  • Hybrid deep learning models, particularly MobileNetV2+MLP and MobileNetV2+MLP+t-SNE, demonstrate high accuracy in clavicle-based sex prediction.
  • This AI-driven approach offers a potential new method for sex estimation in forensic medicine.
  • The extremitas sternalis tip was identified as a significant feature for sex prediction via Grad-Cam analysis.