Dental Panoramic Radiography in Age Estimation for Dental Care using Dark-Net 19

Ko, Jihyeong; Kim, Yu-Rin; Hwang, Han-Jeong; Chang, Won-Du; Han, Man-Seok; Nam, Seoul-Hee; Jung, Youngmee

doi:10.4283/jmag.2022.27.4.485

Cited by 3 publications

(5 citation statements)

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“…In expert-based forensic age estimation, teeth are crucial. Various dental factors, such as crown formation, mineralization, root growth, eruption sequence, pulp size, loss, calcification, mobility, and microstructure, are considered 8 , 9 . Wisdom teeth are reliable for estimating ages between 10 and 24 years 7 .…”

Section: Discussionmentioning

confidence: 99%

“…In recent literature, transfer learning approaches 6 , 8 – 11 have been primarily employed, with a relatively small number of OPGs used for training and testing the CNN. The overview in Table 3 comprises studies concerning age estimation through CNN and OPGs across a wide age range, but please note that the list is not exhaustive due to the diversity within the literature.…”

Section: Discussionmentioning

confidence: 99%

“…The age ranges investigated ranged from 2 to 90 years, with two studies 6 , 9 using almost exclusively OPGs of individuals under 30 years old. Ko et al 8 utilized the algorithm Darknet-19 for classifying dental age, achieving accuracy rates ranging from 59 to 84% and 49 to 96%-based on the acceptable deviation ranges of ± 5 and ± 10 years, respectively. Atas et al 10 tested transfer learning for six different CNNs.…”

Section: Discussionmentioning

confidence: 99%

“…All of these studies 6 , 8 – 11 have in common their use of a low resolution, typically up to 256 × 256 pixels, which has consistently yielded favorable results. As a result, this study also employed a 256 × 256 pixel resolution.…”

Section: Discussionmentioning

confidence: 99%

“…However, there is currently limited experience in CNN-based age estimation for postmortem individuals, and results for adults age groups often fall short of expectations. Furthermore, existing CNNs for age estimation with OPGs have typically been trained, validated, and tested on relatively small datasets, ranging from 304 to 10,400 OPGs for training, 76 to 2,600 OPGs for validation, and 95 to 2,000 OPGs for testing 5 , 6 , 8 – 11 . Therefore, age estimation using CNN and OPGs is frequently implemented based on transfer learning.…”

Section: Introductionmentioning

confidence: 99%

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Accelerating computer vision-based human identification through the integration of deep learning-based age estimation from 2 to 89 years

Heinrich

2024

Sci Rep

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Computer Vision (CV)-based human identification using orthopantomograms (OPGs) has the potential to identify unknown deceased individuals by comparing postmortem OPGs with a comprehensive antemortem CV database. However, the growing size of the CV database leads to longer processing times. This study aims to develop a standardized and reliable Convolutional Neural Network (CNN) for age estimation using OPGs and integrate it into the CV-based human identification process. The CNN was trained on 50,000 OPGs, each labeled with ages ranging from 2 to 89 years. Testing included three postmortem OPGs, 10,779 antemortem OPGs, and an additional set of 70 OPGs within the context of CV-based human identification. Integrating the CNN for age estimation into CV-based human identification process resulted in a substantial reduction of up to 96% in processing time for a CV database containing 105,251 entries. Age estimation accuracy varied between postmortem and antemortem OPGs, with a mean absolute error (MAE) of 2.76 ± 2.67 years and 3.26 ± 3.06 years across all ages, as well as 3.69 ± 3.14 years for an additional 70 OPGs. In conclusion, the incorporation of a CNN for age estimation in the CV-based human identification process significantly reduces processing time while delivering reliable results.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accelerating computer vision-based human identification through the integration of deep learning-based age estimation from 2 to 89 years

Heinrich

2024

Sci Rep

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Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs — a systematic review

Niño-Sandoval,

Doria-Martinez,

Escobar

et al. 2024

Int J Legal Med

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Age and Sex Estimation in Children and Young Adults Using Panoramic Radiographs with Convolutional Neural Networks

Şahin,

Kölüş

2024

Applied Sciences

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Image processing with artificial intelligence has shown significant promise in various medical imaging applications. The present study aims to evaluate the performance of 16 different convolutional neural networks (CNNs) in predicting age and gender from panoramic radiographs in children and young adults. The networks tested included DarkNet-19, DarkNet-53, Inception-ResNet-v2, VGG-19, DenseNet-201, ResNet-50, GoogLeNet, VGG-16, SqueezeNet, ResNet-101, ResNet-18, ShuffleNet, MobileNet-v2, NasNet-Mobile, AlexNet, and Xception. These networks were trained on a dataset of 7336 radiographs from individuals aged between 5 and 21. Age and gender estimation accuracy and mean absolute age prediction errors were evaluated on 340 radiographs. Statistical analyses were conducted using Shapiro–Wilk, one-way ANOVA, and Tukey tests (p < 0.05). The gender prediction accuracy and the mean absolute age prediction error were, respectively, 87.94% and 0.582 for DarkNet-53, 86.18% and 0.427 for DarkNet-19, 84.71% and 0.703 for GoogLeNet, 81.76% and 0.756 for DenseNet-201, 81.76% and 1.115 for ResNet-18, 80.88% and 0.650 for VGG-19, 79.41% and 0.988 for SqueezeNet, 79.12% and 0.682 for Inception-Resnet-v2, 78.24% and 0.747 for ResNet-50, 77.35% and 1.047 for VGG-16, 76.47% and 1.109 for Xception, 75.88% and 0.977 for ResNet-101, 73.24% and 0.894 for ShuffleNet, 72.35% and 1.206 for AlexNet, 71.18% and 1.094 for NasNet-Mobile, and 62.94% and 1.327 for MobileNet-v2. No statistical difference in age prediction performance was found between DarkNet-19 and DarkNet-53, which demonstrated the most successful age estimation results. Despite these promising results, all tested CNNs performed below 90% accuracy and were not deemed suitable for clinical use. Future studies should continue with more-advanced networks and larger datasets.

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Dental Panoramic Radiography in Age Estimation for Dental Care using Dark-Net 19

Cited by 3 publications

References 0 publications

Accelerating computer vision-based human identification through the integration of deep learning-based age estimation from 2 to 89 years

Accelerating computer vision-based human identification through the integration of deep learning-based age estimation from 2 to 89 years

Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs — a systematic review

Age and Sex Estimation in Children and Young Adults Using Panoramic Radiographs with Convolutional Neural Networks

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