Accuracy of automated forensic dental age estimation lab (F-DentEst Lab) on large Malaysian dataset

When a disaster occurs, the authority must prioritise two things. First, the search and rescue of lives, and second, the identification and management of deceased individuals. However, with thousands of dead bodies to be individually identified in mass disasters, forensic teams face challenges such...

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Published in:FORENSIC SCIENCE INTERNATIONAL
Main Authors: Mohammad, Norhasmira; Ahmad, Rohana; Kurniawan, Arofi; Yusof, Mohd Yusmiaidil Putera Mohd
Format: Article
Language:English
Published: ELSEVIER IRELAND LTD 2024
Subjects:
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001280965300001
author Mohammad
Norhasmira; Ahmad
Rohana; Kurniawan
Arofi; Yusof
Mohd Yusmiaidil Putera Mohd
spellingShingle Mohammad
Norhasmira; Ahmad
Rohana; Kurniawan
Arofi; Yusof
Mohd Yusmiaidil Putera Mohd
Accuracy of automated forensic dental age estimation lab (F-DentEst Lab) on large Malaysian dataset
Legal Medicine
author_facet Mohammad
Norhasmira; Ahmad
Rohana; Kurniawan
Arofi; Yusof
Mohd Yusmiaidil Putera Mohd
author_sort Mohammad
spelling Mohammad, Norhasmira; Ahmad, Rohana; Kurniawan, Arofi; Yusof, Mohd Yusmiaidil Putera Mohd
Accuracy of automated forensic dental age estimation lab (F-DentEst Lab) on large Malaysian dataset
FORENSIC SCIENCE INTERNATIONAL
English
Article
When a disaster occurs, the authority must prioritise two things. First, the search and rescue of lives, and second, the identification and management of deceased individuals. However, with thousands of dead bodies to be individually identified in mass disasters, forensic teams face challenges such as long working hours resulting in a delayed identification process and a public health concern caused by the decomposition of the body. Using dental panoramic imaging, teeth have been used in forensics as a physical marker to estimate the age of an individual. Traditionally, dental age estimation has been performed manually by experts. Although the procedure is fairly simple, the large number of victims and the limited amount of time available to complete the assessment during large-scale disasters make forensic work even more challenging. The emergence of artificial intelligence (AI) in the fields of medicine and dentistry has led to the suggestion of automating the current process as an alternative to the conventional method. This study aims to test the accuracy and performance of the developed deep convolutional neural network system for age estimation in large, out-of-sample Malaysian children dataset using digital dental panoramic imaging. Forensic Dental Estimation Lab (F-DentEst Lab) is a computer application developed to perform the dental age estimation digitally. The introduction of this system is to improve the conventional method of age estimation that significantly increase the efficiency of the age estimation process based on the AI approach. A total number of one-thousand-eight-hundred-and-ninety-two digital dental panoramic images were retrospectively collected to test the F-DentEst Lab. Data training, validation, and testing have been conducted in the early stage of the development of F-DentEst Lab, where the allocation involved 80 % training and the remaining 20 % for testing. The methodology was comprised of four major steps: image preprocessing, which adheres to the inclusion criteria for panoramic dental imaging, segmentation, and classification of mandibular premolars using the Dynamic Programming-Active Contour (DP-AC) method and Deep Convolutional Neural Network (DCNN), respectively, and statistical analysis. The suggested DCNN approach underestimated chronological age with a small ME of 0.03 and 0.05 for females and males, respectively.
ELSEVIER IRELAND LTD
0379-0738
1872-6283
2024
361

10.1016/j.forsciint.2024.112150
Legal Medicine

WOS:001280965300001
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001280965300001
title Accuracy of automated forensic dental age estimation lab (F-DentEst Lab) on large Malaysian dataset
title_short Accuracy of automated forensic dental age estimation lab (F-DentEst Lab) on large Malaysian dataset
title_full Accuracy of automated forensic dental age estimation lab (F-DentEst Lab) on large Malaysian dataset
title_fullStr Accuracy of automated forensic dental age estimation lab (F-DentEst Lab) on large Malaysian dataset
title_full_unstemmed Accuracy of automated forensic dental age estimation lab (F-DentEst Lab) on large Malaysian dataset
title_sort Accuracy of automated forensic dental age estimation lab (F-DentEst Lab) on large Malaysian dataset
container_title FORENSIC SCIENCE INTERNATIONAL
language English
format Article
description When a disaster occurs, the authority must prioritise two things. First, the search and rescue of lives, and second, the identification and management of deceased individuals. However, with thousands of dead bodies to be individually identified in mass disasters, forensic teams face challenges such as long working hours resulting in a delayed identification process and a public health concern caused by the decomposition of the body. Using dental panoramic imaging, teeth have been used in forensics as a physical marker to estimate the age of an individual. Traditionally, dental age estimation has been performed manually by experts. Although the procedure is fairly simple, the large number of victims and the limited amount of time available to complete the assessment during large-scale disasters make forensic work even more challenging. The emergence of artificial intelligence (AI) in the fields of medicine and dentistry has led to the suggestion of automating the current process as an alternative to the conventional method. This study aims to test the accuracy and performance of the developed deep convolutional neural network system for age estimation in large, out-of-sample Malaysian children dataset using digital dental panoramic imaging. Forensic Dental Estimation Lab (F-DentEst Lab) is a computer application developed to perform the dental age estimation digitally. The introduction of this system is to improve the conventional method of age estimation that significantly increase the efficiency of the age estimation process based on the AI approach. A total number of one-thousand-eight-hundred-and-ninety-two digital dental panoramic images were retrospectively collected to test the F-DentEst Lab. Data training, validation, and testing have been conducted in the early stage of the development of F-DentEst Lab, where the allocation involved 80 % training and the remaining 20 % for testing. The methodology was comprised of four major steps: image preprocessing, which adheres to the inclusion criteria for panoramic dental imaging, segmentation, and classification of mandibular premolars using the Dynamic Programming-Active Contour (DP-AC) method and Deep Convolutional Neural Network (DCNN), respectively, and statistical analysis. The suggested DCNN approach underestimated chronological age with a small ME of 0.03 and 0.05 for females and males, respectively.
publisher ELSEVIER IRELAND LTD
issn 0379-0738
1872-6283
publishDate 2024
container_volume 361
container_issue
doi_str_mv 10.1016/j.forsciint.2024.112150
topic Legal Medicine
topic_facet Legal Medicine
accesstype
id WOS:001280965300001
url https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001280965300001
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