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Automatic human identification from panoramic dental radiographs using the convolutional neural network.

Fei Fan1, Wenchi Ke2, Wei Wu1

  • 1West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China.

Forensic Science International
|July 30, 2020
PubMed
Summary

An automatic human identification system, DENT-net, uses customized convolutional neural networks (CNNs) for rapid and accurate identification from panoramic dental radiographs. This AI tool achieves high accuracy, aiding investigations in mass disasters and criminal cases.

Keywords:
Convolutional neural networkDeep learningForensic odontologyHuman identificationPanoramic dental radiographs

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Area of Science:

  • Forensic Science
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Accurate human identification is crucial for mass disaster and criminal investigations.
  • Existing automatic dental identification systems face challenges in speed and accuracy using panoramic dental radiographs (PDRs).

Purpose of the Study:

  • To develop an automatic human identification system (DENT-net) utilizing a customized convolutional neural network (CNN).
  • To evaluate the accuracy and speed of DENT-net for identifying individuals from PDRs.

Main Methods:

  • A customized CNN (DENT-net) was developed and trained on 15,369 PDRs from 6300 individuals.
  • PDRs underwent preprocessing including affine transformation and histogram equalization.
  • The DENT-net processed 128x128x7 patches, incorporating the whole PDR and extracted details.

Main Results:

  • Feature extraction took approximately 10 milliseconds per image, with retrieval time of 33.03 milliseconds in a 2000-individual database.
  • DENT-net achieved Rank-1 accuracy of 85.16% and Rank-5 accuracy of 97.74%.
  • CNN visualization indicated that teeth, maxilla, and mandible structures significantly contribute to identification.

Conclusions:

  • CNNs can achieve accurate and rapid human identification from PDRs.
  • The DENT-net system shows potential for aiding human identification in forensic investigations.
  • The system is user-friendly, though final decisions require human specialist review.