In:
Forensic Sciences Research, Oxford University Press (OUP), Vol. 7, No. 3 ( 2022-07-03), p. 456-466
Abstract:
Disaster victim identification issues are especially critical and urgent after a large-scale disaster. The aim of this study was to suggest an automatic detection of natural teeth and dental treatment patterns based on dental panoramic radiographs (DPRs) using deep learning to promote its applicability as human identifiers. A total of 1 638 DPRs, of which the chronological age ranged from 20 to 49 years old, were collected from January 2000 to November 2020. This dataset consisted of natural teeth, prostheses, teeth with root canal treatment, and implants. The detection of natural teeth and dental treatment patterns including the identification of teeth number was done with a pre-trained object detection network which was a convolutional neural network modified by EfficientDet-D3. The objective metrics for the average precision were 99.1% for natural teeth, 80.6% for prostheses, 81.2% for treated root canals, and 96.8% for implants, respectively. The values for the average recall were 99.6%, 84.3%, 89.2%, and 98.1%, in the same order, respectively. This study showed outstanding performance of convolutional neural network using dental panoramic radiographs in automatically identifying teeth number and detecting natural teeth, prostheses, treated root canals, and implants. Key PointsIt is useful to use dental panoramic radiographs to perform the disaster victim identification (DVI).Individual dental treatment patterns could be unique and powerful characteristics as human identifier.Deep learning can automatically detect teeth and dental treatment patterns with favourable accuracy.Constructing a big database including detected teeth information would make it possible to fully automate entire process of DVI.
Type of Medium:
Online Resource
ISSN:
2096-1790
,
2471-1411
DOI:
10.1080/20961790.2022.2034714
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2022
detail.hit.zdb_id:
2885963-7
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