Age Group Classification of Dental Radiography without Precise Age Information Using Convolutional Neural Networks Article Swipe
Yu‐Rin Kim
,
Jae-Hyeok Choi
,
Jihyeong Ko
,
Young–Jin Jung
,
Byeong-Jun Kim
,
Seoul‐Hee Nam
,
Won-Du Chang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/healthcare11081068
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/healthcare11081068
Automatic age estimation using panoramic dental radiographic images is an important procedure for forensics and personal oral healthcare. The accuracies of the age estimation have increased recently with the advances in deep neural networks (DNN), but DNN requires large sizes of the labeled dataset which is not always available. This study examined whether a deep neural network is able to estimate tooth ages when precise age information is not given. A deep neural network model was developed and applied to age estimation using an image augmentation technique. A total of 10,023 original images were classified according to age groups (in decades, from the 10s to the 70s). The proposed model was validated using a 10-fold cross-validation technique for precise evaluation, and the accuracies of the predicted tooth ages were calculated by varying the tolerance. The accuracies were 53.846% with a tolerance of ±5 years, 95.121% with ±15 years, and 99.581% with ±25 years, which means the probability for the estimation error to be larger than one age group is 0.419%. The results indicate that artificial intelligence has potential not only in the forensic aspect but also in the clinical aspect of oral care.
Related Topics
Concepts
Convolutional neural network
Artificial neural network
Artificial intelligence
Computer science
Estimation
Radiography
Deep neural networks
Age groups
Deep learning
Pattern recognition (psychology)
Dentistry
Statistics
Orthodontics
Medicine
Mathematics
Demography
Radiology
Sociology
Management
Economics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/healthcare11081068
- https://www.mdpi.com/2227-9032/11/8/1068/pdf?version=1680937060
- OA Status
- gold
- Cited By
- 11
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4362733257
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4362733257Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/healthcare11081068Digital Object Identifier
- Title
-
Age Group Classification of Dental Radiography without Precise Age Information Using Convolutional Neural NetworksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-08Full publication date if available
- Authors
-
Yu‐Rin Kim, Jae-Hyeok Choi, Jihyeong Ko, Young–Jin Jung, Byeong-Jun Kim, Seoul‐Hee Nam, Won-Du ChangList of authors in order
- Landing page
-
https://doi.org/10.3390/healthcare11081068Publisher landing page
- PDF URL
-
https://www.mdpi.com/2227-9032/11/8/1068/pdf?version=1680937060Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-9032/11/8/1068/pdf?version=1680937060Direct OA link when available
- Concepts
-
Convolutional neural network, Artificial neural network, Artificial intelligence, Computer science, Estimation, Radiography, Deep neural networks, Age groups, Deep learning, Pattern recognition (psychology), Dentistry, Statistics, Orthodontics, Medicine, Mathematics, Demography, Radiology, Sociology, Management, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 4, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
45Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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