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Classification of Bones01:18

Classification of Bones

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The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
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Classifying Sex from MSCT-Derived 3D Mandibular Models Using an Adapted PointNet++ Deep Learning Approach in a

Eva Shimkus1, Ivana Kružić2, Saša Mladenović3

  • 1Program in Biochemistry and Biophysics, Amherst College, Amherst, MA 01002, USA.

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|October 28, 2025
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Summary

Forensic anthropologists can now estimate sex from mandibles using deep learning on 3D models. This advanced AI tool achieves high accuracy, aiding in biological profile creation when other remains are missing.

Keywords:
Croatian populationGradio appPointNet++artificial intelligencedeep learningmandiblesex estimation

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Area of Science:

  • Forensic Anthropology
  • Computer Science
  • Medical Imaging

Background:

  • Accurate sex estimation is crucial for forensic anthropological biological profiles.
  • The mandible is a valuable skeletal element for sex estimation when crania or pelvic bones are absent.
  • Traditional methods for mandibular sex estimation have limitations.

Purpose of the Study:

  • To enhance sex estimation accuracy using deep learning on 3D mandibular models.
  • To develop an automated tool for real-time sex classification from 3D data.
  • To investigate sexually dimorphic regions of the mandible using AI.

Main Methods:

  • Utilized a dataset of 254 3D MSCT-derived mandibular models (127 male, 127 female).
  • Processed models into 4096-point clouds and analyzed with an adapted PointNet++ deep learning architecture.
  • Employed unsupervised (autoencoder, t-SNE) and supervised (logistic regression) analyses, with a 60/20/20 train/validation/test split.

Main Results:

  • Achieved 93% cross-validation accuracy and 92% test set accuracy for sex estimation.
  • Saliency maps identified key sexually dimorphic regions: chin, gonial, and condylar areas.
  • Developed a Gradio web application for real-time classification from STL files.

Conclusions:

  • Deep learning on 3D mandibles offers a robust and accurate method for sex estimation.
  • This automated approach surpasses traditional techniques in accuracy and efficiency.
  • The developed tool has significant potential for forensic practice, population studies, and integration with diverse 3D data.