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Comparative Analysis of Third Molar Segmentation Performance Between Sexes Using Deep Learning Models.

Ayşe Bulut1, Melis Büşra Aşkın2, Gökalp Çınarer3

  • 1Department of Oral and Maxillofacial Radiology, Physiology, Faculty of Dentistry, Yozgat Bozok University, Yozgat 66100, Türkiye.

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Summary

Forensic identification can use artificial intelligence to determine sex from third molars in dental radiographs. This study shows that isolated third molars contain sex-related signals, offering a new method for forensic dentistry.

Keywords:
YOLOartificial intelligencegood health and well-beingoral physiologypanoramic radiographsegmentationsex estimationthird molar

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Area of Science:

  • Forensic Odontology
  • Artificial Intelligence
  • Radiographic Analysis

Background:

  • Sex determination is crucial in forensic identification, particularly with compromised biological evidence.
  • Current AI methods often require complete dentition or craniofacial data.
  • This study explores sex determination using only third molars in panoramic radiographs.

Purpose of the Study:

  • To investigate the feasibility of extracting sex-based information from segmented third molars in panoramic radiographs.
  • To evaluate the performance of different deep learning models for this task.
  • To assess the clinical relevance of segmentation-focused AI in forensic dentistry.

Main Methods:

  • A retrospective dataset of 2818 third molar annotations from 757 panoramic images was created.
  • Three segmentation-based deep learning models (YOLOv12n, YOLO26n, RT-DETR v2) were evaluated.
  • Detection metrics including sensitivity, recall, and mean Average Precision (mAP) were used for performance assessment.

Main Results:

  • YOLOv12n achieved the highest balanced performance with an mAP@0.50 of 0.810.
  • RT-DETR v2 showed higher sensitivity but longer training times and lower localization accuracy.
  • Class-based analysis revealed sex-specific third molar morphology, with better detection in female samples.

Conclusions:

  • Isolated third molars contain discernible sex-related information.
  • Segmentation-focused AI frameworks provide an interpretable and clinically relevant approach for forensic sex determination.
  • Future research should focus on larger datasets, multi-tooth integration, and explainable AI.