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Citation: Kim, Y.-R.; Choi, J.-H.; Ko,
J.; Jung, Y.-J.; Kim, B.; Nam, S.-H.;
Chang, W.-D. Age Group
Classification of Dental Radiography
without Precise Age Information
Using Convolutional Neural
Networks. Healthcare 2023,11, 1068.
https://doi.org/10.3390/
healthcare11081068
Academic Editors: Alessandro Nota
and Takahiro Kanno
Received: 16 February 2023
Revised: 29 March 2023
Accepted: 6 April 2023
Published: 8 April 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
healthcare
Article
Age Group Classification of Dental Radiography without
Precise Age Information Using Convolutional Neural Networks
Yu-Rin Kim 1,† , Jae-Hyeok Choi 2, †, Jihyeong Ko 3, Young-Jin Jung 3,4 , Byeongjun Kim 2, Seoul-Hee Nam 5, *
and Won-Du Chang 2,*
1Department of Dental Hygiene, Silla University, 140 Baegyang-daero 700 Beon-gil, Sasang-gu,
Busan 46958, Republic of Korea
2Department of Artificial Intelligence, Pukyong National University, Busan 48513, Republic of Korea
3Department of Biomedical Engineering, Chonnam National University, Yeosu 59626, Republic of Korea
4School of Healthcare and Biomedical Engineering, Chonnam National University,
Yeosu 59626, Republic of Korea
5Department of Dental Hygiene, Kangwon National University, Samcheok 25913, Republic of Korea
*Correspondence: nshee@kangwon.ac.kr (S.-H.N.); chang@pknu.ac.kr (W.-D.C.)
† These authors contributed equally to this work.
Abstract:
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.
Keywords: dental age estimation; forensic dentistry; deep learning; oral health; data augmentation
1. Introduction
In human civilization, faces and hands have their own individuality and have been
extensively studied sociobiologically. In particular, the teeth and bone skeleton of the
craniofacial region are generally the best-preserved parts in humans, and individual identi-
fication is possible because the size, shape, and proportion, as well as the results of surgery
and treatment, are different [
1
]. In addition, because the jawbone and teeth differ according
to the growth period, they have been used for age estimation. In particular, teeth are used
as reliable data when estimating age because they are the longest preserved among human
tissues and change according to age is relatively gradual [
2
]. The age estimation of teeth
plays an important role in clinical and forensic science. It is used for criminal responsibility
investigations of living individuals, such as in cases of large-scale disasters, fires, accidents,
and murders [3,4].
Demirjian’s “eight stages” is the most commonly used method for estimating age from
oral anatomical structures [
5
,
6
]. This is a very simple method for scoring bone calcification
and maturity, but it has limitations that apply to children and adolescents. A new scoring
method that corrects and supplements this has been proposed, but it is unsuitable because
of the high error in age estimation resulting from the subjective judgment of the observer;
Healthcare 2023,11, 1068. https://doi.org/10.3390/healthcare11081068 https://www.mdpi.com/journal/healthcare
Healthcare 2023,11, 1068 2 of 13
therefore, a study on a more accurate method is needed [
7
,
8
]. According to Caggiano
et al. [
6
], as a result of a study using third molar radiographs in a population in southern
Italy, the accuracy and reproducibility of the Demirjian method were confirmed over 90%,
but it was suggested that additional research on a larger study population is needed.
Recently, an age estimation method based on the methylation level of DNA extracted from
teeth using a real-time methylation-specific polymerase chain reaction was reported as
evidence that DNA methylation in the human genome isolated from bodily fluids changes
with age [
9
]. However, this method has a clear limitation in that age estimation is possible
only when the tooth is extracted posthumously. In contrast to other forensic age estimation
methods, the radiological method is accessible to clinicians, relatively simple, and non-
destructive [
10
]. It has the advantage that it can be applied to living people because it can be
estimated by the decrease and change in the size of the pulp cavity with increasing age [
11
].
According to a study by Kvaal et al. [
12
], the method of estimating age by measuring the
dimensions and length and width of teeth on apical radiographs has been suggested as a
representative method using radiographs for age estimation with relatively high accuracy.
In 2015, a method for estimating age using panoramic radiographs, commonly used
in dentistry, was proposed [
13
], and much research on age estimation based on artificial
intelligence (AI) has been conducted [
14
–
16
]. The reason for using the panorama is that it is
basic radiography for diagnostic and forensic medical treatment in dentistry, and the data
derived from the panorama are highly reliable [
17
]. Therefore, AI using panoramas has
been reported to be more accurate than traditional radiation methods because it can predict
tooth age more accurately and efficiently through machine learning [
14
–
16
]. Galibourg [
14
]
and Tao [
15
] applied machine learning to the existing scoring method for age estimation;
however, this method still had a large error owing to the subjective judgment of the observer.
Accordingly, attempts have recently been made to estimate age without human intervention
using convolutional neural networks (CNNs) [
18
–
20
]. CNNs have been used to diagnose
diseases such as breast cancer [
21
], skin cancer [
22
], diabetic retinopathy [
23
], dental
caries [
24
], and periodontal disease [
25
], as well as age estimations. However, research
on their clinical aspects is still lacking. Researchers investigating age estimation using
panoramas have reported the high usability of CNNs; however, the number of learned
panoramas was remarkably small and biased toward younger age groups, suggesting the
need for additional research. CNNs generally require huge datasets, but collecting images
with precise age information is challenging.
In this study, an attempt was made to estimate ages based on the median age of the
subjects by using a larger number of panoramic images from various age groups. Therefore,
this study was conducted to prove that precise estimation of tooth age is possible even
without precise age information. We designed a convolutional neural network for this
purpose and utilized it to estimate the approximate age of the teeth images.
The remainder of this paper is organized as follows. Section 2describes the research
method by explaining the data and network models. Section 3presents the experimental
results, and the discussions follow in Section 4. Finally, Section 5gives conclusions.
2. Materials and Methods
A total of 10,023 dental panoramic images were collected by the Institutional Review
Board of Kangwon National University, Republic of Korea. The images were grouped
into seven categories according to age (in decades from the 10s to the 70s). The original
panoramic images were 1504
×
2768 pixels in size and included other parts of the face,
such as jaws and noses. To facilitate learning, the tooth regions were cropped and resized
into 256
×
512. The images were augmented by rotating them through
−
0.2 to 0.2 rad, and
they were flipped horizontally to increase the stability of the model.
A network model was designed to estimate the age of the teeth in the cropped image,
as shown in Figure 1. The model starts with the preprocessing layer of the random flips and
rotations to augment a small number of teeth images. We used five sets of convolutional
and max-pooling layers, a flatten and dropout layer, and a dense and dropout layer, as
Healthcare 2023,11, 1068 3 of 13
shown in Figure 1. Convolutional layers were attached to the preprocessing layer to extract
spatial features. The five convolutional layers were utilized with different numbers of filters
(8, 16, 32, 64, and 128 in series), and the max-pooling layer with a pool size of (2,2) was
attached to the convolutional layer. The extracted features were then converted into the
one-dimensional form using the flatten layer, and dental age was estimated using the fully
connected layers. The dropout layers were attached to the flatten and fully connected layers.
Healthcare 2023, 11, x FOR PEER REVIEW 3 of 14
× 512. The images were augmented by rotating them through −0.2 to 0.2 rad, and they
were flipped horizontally to increase the stability of the model.
A network model was designed to estimate the age of the teeth in the cropped image,
as shown in Figure 1. The model starts with the preprocessing layer of the random flips
and rotations to augment a small number of teeth images. We used five sets of convolu-
tional and max-pooling layers, a flatten and dropout layer, and a dense and dropout layer,
as shown in Figure 1. Convolutional layers were attached to the preprocessing layer to
extract spatial features. The five convolutional layers were utilized with different numbers
of filters (8, 16, 32, 64, and 128 in series), and the max-pooling layer with a pool size of
(2,2) was attached to the convolutional layer. The extracted features were then converted
into the one-dimensional form using the flatten layer, and dental age was estimated using
the fully connected layers. The dropout layers were attached to the flatten and fully con-
nected layers.
Figure 1. Network architecture of the proposed AI model.
In this study, k-fold cross-validation was employed to verify the accuracy. Generally,
it is used with a small quantity of image data. As shown in Figure 2, k was set to 10. The
data were randomly divided into 10 folds (groups), and the training and testing were per-
formed 10 times by changing the test data. This means that the 10th fold was used for the
test data, and the rest were used for training. Then, the ninth to first folds were utilized
one by one for testing. The final errors or accuracies were calculated by averaging the
results of the ten folds.
Figure 1. Network architecture of the proposed AI model.
In this study, k-fold cross-validation was employed to verify the accuracy. Generally, it
is used with a small quantity of image data. As shown in Figure 2, k was set to 10. The data
were randomly divided into 10 folds (groups), and the training and testing were performed
10 times by changing the test data. This means that the 10th fold was used for the test data,
and the rest were used for training. Then, the ninth to first folds were utilized one by one
for testing. The final errors or accuracies were calculated by averaging the results of the
ten folds.
Healthcare 2023, 11, x FOR PEER REVIEW 4 of 14
Figure 2. Ten-fold cross-validation contributing to the accuracy and error in this study.
The target value for each age group was set to the median age, i.e., the target values
for ages in the 10s, 20s, 30s, 40s, 50s, 60s, and 70s were 15, 25, 35, 45, 55, 65, and 75, respec-
tively. This was because precise age information was not recorded during data acquisi-
tion.
Therefore, the concept of tolerance must be employed (see Figure 3). For a tolerance
age of 5 years, a dental image that is in the 30s group and estimated at 39.5 years is
considered to be estimated accurately because the median value of the 30s is 35. When the
tolerance range is 15 years, the confusion about whether an image should be assigned
to a particular age group or its neighboring group is accepted. In other words, a dental
image of an individual in their 30s and estimated to be 49.5 years old is considered an
accurate estimate.
Figure 3. Setting the median values and the range of the predicted dental age.
3. Results
The estimated ages and the corresponding actual ages are shown in the box plot in
Figure 4. The lines in the boxes represent the median values of the predicted ages. The
vertical lines connected to the boxes represent the range from the minimum to the maxi-
mum predicted age. Each colored box contains 25–75% of the predicted age values. Dia-
mond marks indicate abnormal values. The figure shows that the box of 25–50% of the
predicted ages was approximately within the target age group except for the 70s.
Figure 2. Ten-fold cross-validation contributing to the accuracy and error in this study.
The target value for each age group was set to the median age, i.e., the target values for
ages in the 10s, 20s, 30s, 40s, 50s, 60s, and 70s were 15, 25, 35, 45, 55, 65, and 75, respectively.
This was because precise age information was not recorded during data acquisition.
Therefore, the concept of tolerance must be employed (see Figure 3). For a tolerance
age of
±
5 years, a dental image that is in the 30s group and estimated at 39.5 years is
considered to be estimated accurately because the median value of the 30s is 35. When the
tolerance range is
±
15 years, the confusion about whether an image should be assigned
Healthcare 2023,11, 1068 4 of 13
to a particular age group or its neighboring group is accepted. In other words, a dental
image of an individual in their 30s and estimated to be 49.5 years old is considered an
accurate estimate.
Healthcare 2023, 11, x FOR PEER REVIEW 4 of 14
Figure 2. Ten-fold cross-validation contributing to the accuracy and error in this study.
The target value for each age group was set to the median age, i.e., the target values
for ages in the 10s, 20s, 30s, 40s, 50s, 60s, and 70s were 15, 25, 35, 45, 55, 65, and 75, respec-
tively. This was because precise age information was not recorded during data acquisi-
tion.
Therefore, the concept of tolerance must be employed (see Figure 3). For a tolerance
age of 5 years, a dental image that is in the 30s group and estimated at 39.5 years is
considered to be estimated accurately because the median value of the 30s is 35. When the
tolerance range is 15 years, the confusion about whether an image should be assigned
to a particular age group or its neighboring group is accepted. In other words, a dental
image of an individual in their 30s and estimated to be 49.5 years old is considered an
accurate estimate.
Figure 3. Setting the median values and the range of the predicted dental age.
3. Results
The estimated ages and the corresponding actual ages are shown in the box plot in
Figure 4. The lines in the boxes represent the median values of the predicted ages. The
vertical lines connected to the boxes represent the range from the minimum to the maxi-
mum predicted age. Each colored box contains 25–75% of the predicted age values. Dia-
mond marks indicate abnormal values. The figure shows that the box of 25–50% of the
predicted ages was approximately within the target age group except for the 70s.
Figure 3. Setting the median values and the range of the predicted dental age.
3. Results
The estimated ages and the corresponding actual ages are shown in the box plot
in Figure 4. The lines in the boxes represent the median values of the predicted ages.
The vertical lines connected to the boxes represent the range from the minimum to the
maximum predicted age. Each colored box contains 25–75% of the predicted age values.
Diamond marks indicate abnormal values. The figure shows that the box of 25–50% of the
predicted ages was approximately within the target age group except for the 70s.
Healthcare 2023, 11, x FOR PEER REVIEW 5 of 14
Figure 4. Boxplot of predicted ages according to their actual age group: the colored boxes indicate
25–75% of the age values predicted by the proposed model.
The numbers of images with errors are shown according to error size in Figure 5. The
numbers of error images were 4626 with a tolerance of ±5 years, 489 with a tolerance of 15
years, and 42 with a tolerance of 25 years.
Figure 5. Number of images with errors according to the absolute error in predicted ages.
Figure 6 shows the relationship between accuracy and tolerance in dental age predic-
tion. The accuracy increased as the tolerance age increased. The accuracy was 53.846%
with a tolerance of 5 and 95.121% with a tolerance of 15. The accuracy with a tolerance
of 25 was 99.581% (Table 1).
Figure 4.
Boxplot of predicted ages according to their actual age group: the colored boxes indicate
25–75% of the age values predicted by the proposed model.
The numbers of images with errors are shown according to error size in Figure 5. The
numbers of error images were 4626 with a tolerance of
±
5 years, 489 with a tolerance of
15 years, and 42 with a tolerance of 25 years.
Healthcare 2023,11, 1068 5 of 13
Healthcare 2023, 11, x FOR PEER REVIEW 5 of 14
Figure 4. Boxplot of predicted ages according to their actual age group: the colored boxes indicate
25–75% of the age values predicted by the proposed model.
The numbers of images with errors are shown according to error size in Figure 5. The
numbers of error images were 4626 with a tolerance of ±5 years, 489 with a tolerance of 15
years, and 42 with a tolerance of 25 years.
Figure 5. Number of images with errors according to the absolute error in predicted ages.
Figure 6 shows the relationship between accuracy and tolerance in dental age predic-
tion. The accuracy increased as the tolerance age increased. The accuracy was 53.846%
with a tolerance of 5 and 95.121% with a tolerance of 15. The accuracy with a tolerance
of 25 was 99.581% (Table 1).
Figure 5. Number of images with errors according to the absolute error in predicted ages.
Figure 6shows the relationship between accuracy and tolerance in dental age predic-
tion. The accuracy increased as the tolerance age increased. The accuracy was 53.846% with
a tolerance of
±
5 and 95.121% with a tolerance of
±
15. The accuracy with a tolerance of
±25 was 99.581% (Table 1).
Healthcare 2023, 11, x FOR PEER REVIEW 6 of 14
Figure 6. Graph showing the accuracy of the predicted dental age for each tolerance.
Figure 6. Graph showing the accuracy of the predicted dental age for each tolerance.
Table 1. Table of accuracies according to tolerances.
Tolerance (Years) Accuracy (%) Predicted Range (Years)
±5 53.846 Equal to the median value
±15 95.121 ±10
±25 99.581 ±20
Healthcare 2023,11, 1068 6 of 13
Figure 7shows a confusion matrix of the estimation results. Clearly, the most confusion
occurred between the adjusted groups (10s and 20s, 20s and 30s, and so on). The ages
were often overestimated for ages in the 40s and 50s: 49 and 94 images of the 40s and 50s
age groups were classified as being in the 60s and 70s, respectively, whereas images for
which age was underestimated were fewer (32 and 55 images for the age groups of the
40s and 50s, respectively). One reason was that the teeth of persons in their 40s and 50s
have frequently been treated extensively or removed. Confusions between the adjacent age
groups occurred frequently for the 60s, as 54% of teeth images were misclassified to 50s
or 70s. The confusions of 10s or 70s were relatively lower 13.11% and 25.14% of 10s or 70s
were misclassified to their adjacent age groups.
Healthcare 2023, 11, x FOR PEER REVIEW 7 of 14
Table 1. Table of accuracies according to tolerances.
Tolerance (Years) Accuracy (%) Predicted Range (Years)
±5 53.846 Equal to the median value
±15 95.121 ±10
±25 99.581 ±20
Figure 7 shows a confusion matrix of the estimation results. Clearly, the most confu-
sion occurred between the adjusted groups (10s and 20s, 20s and 30s, and so on). The ages
were often overestimated for ages in the 40s and 50s: 49 and 94 images of the 40s and 50s
age groups were classified as being in the 60s and 70s, respectively, whereas images for
which age was underestimated were fewer (32 and 55 images for the age groups of the
40s and 50s, respectively). One reason was that the teeth of persons in their 40s and 50s
have frequently been treated extensively or removed. Confusions between the adjacent
age groups occurred frequently for the 60s, as 54% of teeth images were misclassified to
50s or 70s. The confusions of 10s or 70s were relatively lower 13.11% and 25.14% of 10s or
70s were misclassified to their adjacent age groups.
Figure 7. Confusion matrix with actual and predicted ages.
Figure 8 shows examples of correctly classified images. In this study, 5397 of 10,023
dental panoramic radiographs were predicted successfully within a 5-year tolerance. Fig-
ure 8a–d show the radiographs of four persons in their 70s, 50s, 10s, and 70s, respectively,
and their ages were predicted to be 76.89, 57.89, 16.22, and 73.20 years, respectively.
Figure 7. Confusion matrix with actual and predicted ages.
Figure 8shows examples of correctly classified images. In this study, 5397 of 10,023 dental
panoramic radiographs were predicted successfully within a 5-year tolerance.
Figure 8a–d
show the radiographs of four persons in their 70s, 50s, 10s, and 70s, respectively, and their
ages were predicted to be 76.89, 57.89, 16.22, and 73.20 years, respectively.
The reasons for the misclassification of the images, especially when the age errors
were smaller than 15 years, could not be determined. No significant differences were found
between the misclassified images and the accurately classified images. Figure 9shows an
example. The actual age of the subject whose radiograph is shown in the figure was in the
20s; however, it was predicted to be 30.61 years.
The number of images with an estimated age error of more than
±
15 years was 489 out
of 10,023. A few examples are shown in Figure 10. Figure 10a–d show the radiographs of
subjects in their 50s, 30s, 70s, and 30s, respectively, whose ages were predicted to be 38.31,
55.07, 59.41, and 70.09 years, respectively. As shown, the underestimation or overestimation
of age seems to be highly related to implants or the number of treated teeth.
Healthcare 2023,11, 1068 7 of 13
Healthcare 2023, 11, x FOR PEER REVIEW 8 of 14
Figure 8. Dental radiographs of persons whose dental ages were successfully predicted using pan-
oramic radiography to within ±5 years: (a) actual age is in the 20s, predicted dental age is 24.41; (b)
actual age is in the 60s, predicted dental age is 60.88; (c) actual age is in the 30s, predicted dental age
is 32.67; and (d) actual age is in the 60s, predicted dental age is 61.85.
The reasons for the misclassification of the images, especially when the age errors
were smaller than 15 years, could not be determined. No significant differences were
found between the misclassified images and the accurately classified images. Figure 9
shows an example. The actual age of the subject whose radiograph is shown in the figure
was in the 20s; however, it was predicted to be 30.61 years.
Figure 9. Example of a misclassified image with an error of more than five years; the actual age was
in the 20s, but the predicted age was 30.61.
The number of images with an estimated age error of more than ±15 years was 489
out of 10,023. A few examples are shown in Figure 10. Figure 10a–d show the radiographs
of subjects in their 50s, 30s, 70s, and 30s, respectively, whose ages were predicted to be
38.31, 55.07, 59.41, and 70.09 years, respectively. As shown, the underestimation or over-
estimation of age seems to be highly related to implants or the number of treated teeth.
Figure 8.
Dental radiographs of persons whose dental ages were successfully predicted using
panoramic radiography to within
±
5 years: (
a
) actual age is in the 20s, predicted dental age is 24.41;
(b) actual age is in the 60s, predicted dental age is 60.88; (c) actual age is in the 30s, predicted dental
age is 32.67; and (d) actual age is in the 60s, predicted dental age is 61.85.
Healthcare 2023, 11, x FOR PEER REVIEW 8 of 14
Figure 8. Dental radiographs of persons whose dental ages were successfully predicted using pan-
oramic radiography to within ±5 years: (a) actual age is in the 20s, predicted dental age is 24.41; (b)
actual age is in the 60s, predicted dental age is 60.88; (c) actual age is in the 30s, predicted dental age
is 32.67; and (d) actual age is in the 60s, predicted dental age is 61.85.
The reasons for the misclassification of the images, especially when the age errors
were smaller than 15 years, could not be determined. No significant differences were
found between the misclassified images and the accurately classified images. Figure 9
shows an example. The actual age of the subject whose radiograph is shown in the figure
was in the 20s; however, it was predicted to be 30.61 years.
Figure 9. Example of a misclassified image with an error of more than five years; the actual age was
in the 20s, but the predicted age was 30.61.
The number of images with an estimated age error of more than ±15 years was 489
out of 10,023. A few examples are shown in Figure 10. Figure 10a–d show the radiographs
of subjects in their 50s, 30s, 70s, and 30s, respectively, whose ages were predicted to be
38.31, 55.07, 59.41, and 70.09 years, respectively. As shown, the underestimation or over-
estimation of age seems to be highly related to implants or the number of treated teeth.
Figure 9.
Example of a misclassified image with an error of more than five years; the actual age was
in the 20s, but the predicted age was 30.61.
The number of images with estimated age errors of more than
±
25 years was 42 out of
10,023. Figure 11 shows examples. Figure 11a,b,d show the radiographs of subjects in their
20s, whose ages were predicted to be 67.22, 69.52, and 51.78 years, respectively. Many of the
subjects’ teeth in these images had been removed (Figure 11b,d) or decayed (Figure 11a).
The image shown in Figure 11c is of a subject in the 20s group, whose age was predicted to
be 61.59 years. This case is slightly different from the others in that the subject had a single
implant but only a few treated teeth. The age classification may have been misclassified
because many teeth were lost and the alveolar bone was lowered in the image.
Healthcare 2023,11, 1068 8 of 13
Healthcare 2023, 11, x FOR PEER REVIEW 9 of 14
Figure 10. Dental radiographs of persons whose dental ages were misclassified by more than 15
years: (a) actual age is in the 50s, predicted dental age is 38.31; (b) actual age is in the 30s, predicted
dental age is 55.07; (c) actual age is in the 70s, predicted dental age is 59.41; and (d) actual age is in
the 30s, predicted dental age is 70.09.
The number of images with estimated age errors of more than ±25 years was 42 out
of 10,023. Figure 11 shows examples. Figure 11a,b,d show the radiographs of subjects in
their 20s, whose ages were predicted to be 67.22, 69.52, and 51.78 years, respectively. Many
of the subjects’ teeth in these images had been removed (Figure 11b,d) or decayed (Figure
11a). The image shown in Figure 11c is of a subject in the 20s group, whose age was pre-
dicted to be 61.59 years. This case is slightly different from the others in that the subject
had a single implant but only a few treated teeth. The age classification may have been
misclassified because many teeth were lost and the alveolar bone was lowered in the im-
age.
Figure 11. Dental radiographs of persons whose dental ages were misclassified by more than 25
years: (a) actual age is in the 20s, predicted dental age is 67.22; (b) actual age is in the 20s, predicted
Figure 10.
Dental radiographs of persons whose dental ages were misclassified by more than 15 years:
(
a
) actual age is in the 50s, predicted dental age is 38.31; (
b
) actual age is in the 30s, predicted dental
age is 55.07; (
c
) actual age is in the 70s, predicted dental age is 59.41; and (
d
) actual age is in the 30s,
predicted dental age is 70.09.
Healthcare 2023, 11, x FOR PEER REVIEW 9 of 14
Figure 10. Dental radiographs of persons whose dental ages were misclassified by more than 15
years: (a) actual age is in the 50s, predicted dental age is 38.31; (b) actual age is in the 30s, predicted
dental age is 55.07; (c) actual age is in the 70s, predicted dental age is 59.41; and (d) actual age is in
the 30s, predicted dental age is 70.09.
The number of images with estimated age errors of more than ±25 years was 42 out
of 10,023. Figure 11 shows examples. Figure 11a,b,d show the radiographs of subjects in
their 20s, whose ages were predicted to be 67.22, 69.52, and 51.78 years, respectively. Many
of the subjects’ teeth in these images had been removed (Figure 11b,d) or decayed (Figure
11a). The image shown in Figure 11c is of a subject in the 20s group, whose age was pre-
dicted to be 61.59 years. This case is slightly different from the others in that the subject
had a single implant but only a few treated teeth. The age classification may have been
misclassified because many teeth were lost and the alveolar bone was lowered in the im-
age.
Figure 11. Dental radiographs of persons whose dental ages were misclassified by more than 25
years: (a) actual age is in the 20s, predicted dental age is 67.22; (b) actual age is in the 20s, predicted
Figure 11.
Dental radiographs of persons whose dental ages were misclassified by more than 25 years:
(
a
) actual age is in the 20s, predicted dental age is 67.22; (
b
) actual age is in the 20s, predicted dental
age is 69.52; (
c
) actual age is in the 20s, predicted dental age is 61.59; and (
d
) actual age is in the 20s,
predicted dental age is 51.78.
Healthcare 2023,11, 1068 9 of 13
4. Discussion
Age is a predictor of physical, emotional, and social development and maturation.
Chronological age is an objective indicator of age and is simply the age at which elapsed
time is calculated according to a calendar. This is commonly considered the “actual age”.
However, biological age represents an individual’s level of biological and physiological
development, maturation, and physical health, and it can be lower or greater than the
actual age because it is determined by the individual’s current position in the lifespan [
26
].
Oral age is calculated as an objectively measurable oral health index and is a biological
age that reflects oral health status. Oral age reflects the development of modern medicine
and lifestyle changes and can be considered a concept related to biological age that can be
compared based on the average oral health status of an entire nation [
27
,
28
]. It is an age
estimation method that is widely used to estimate an accurate age. It is a widely used age
estimation method for estimating an accurate age, and the teeth are preserved for a long
time and can be observed directly. It is also useful for age estimation because it is known
that individual differences are the least due to relatively gradual changes according to
age [
29
]. Accordingly, the American Society of Forensic Odontology and the Study Group
on Forensic Age Diagnostics recommended radiographic dental age estimation to help
estimate chronological age [30,31].
Among intraoral radiographs, panoramic radiographs have the advantage of being
able to observe serious problems in the oral cavity at once because the imaging technique
is standardized and the upper and lower teeth can be seen at a glance, so they are useful in
estimating age [32].
Therefore, this study confirmed the potential possibility of age estimation using AI
in terms of clinical aspects of oral care, as well as forensic medicine, by determining the
difference between the actual age and predicted age using panoramic radiographic images,
which can be used to evaluate the overall oral condition.
Table 2lists the root mean square errors and accuracies according to the teeth status, as
the dataset used in the experiment has three types of teeth: healthy, treated (except implant),
and with implants. The root mean square errors (RMSE) of the different teeth status were
between 6.4 and 8.3. The performance with the healthy teeth was the best with an accuracy
of 96.453% and an RMSE age of 6.4563. This indicates the proposed model was not biased
toward the implants or treated teeth when estimating the ages of the teeth images.
Table 2. Root mean square errors and accuracies according to teeth status.
Teeth Status RMSE (Age) Accuracy (%)
±5±15 ±25
All 7.4598 53.846 95.121 99.581
Healthy teeth 6.4563 65.199 96.453 99.817
Treated (except implant) 7.3094 53.596 95.663 99.625
With implant 8.2653 47.684 93.250 99.354
Although the ages of the subjects could be predicted from their dental panoramic
images to within 15 years of their actual ages in approximately 95% of the cases, errors
greater than 25 years comprised 0.419% of the cases. From an analysis of images that
resulted in errors, dental prosthetic materials, such as implants, and periodontal diseases
requiring endodontic treatments were found to be the major causes of these errors in dental
age prediction from dental radiographs. The presence of several implants or endodontic
treatments in dental panoramic images can cause misclassification into older or younger
age groups. Furthermore, images with no teeth or panoramic images with almost all teeth
implanted were predicted to be of younger people in this study (Figure 12). This is thought
to be due to a lack of training data as a result of the significantly low data for edentulous
jaws, as well as misrecognizing implants as natural teeth.
Healthcare 2023,11, 1068 10 of 13
Healthcare 2023, 11, x FOR PEER REVIEW 11 of 14
all teeth implanted were predicted to be of younger people in this study (Figure 12). This
is thought to be due to a lack of training data as a result of the significantly low data for
edentulous jaws, as well as misrecognizing implants as natural teeth.
Figure 12. Dental radiographs of persons with dental implants whose dental ages were predicted to
be lower: (a) actual age is in the 70s, predicted dental age is 55.49; (b) actual age is in the 70s, pre-
dicted dental age is 52.46; and (c) actual age is in the 70s, predicted dental age is 57.99.
Table 3 lists the comparison of the proposed method to the conventional studies in
the aspect of the accuracies and datasets. The classification accuracy with a ±15 year tol-
erance is higher than the most conventional results [17, 33-37] except Mualla et al’s study
[38], but the accuracy of the proposed method with a ±5 tolerance is relatively lower. One
of the expected reasons for this result is the type of input data. Different from the previous
experiments, the utilized images in this study have approximate age information only.
For example, a tooth image of 29 years may be confused with the age of 30 easily, but it
was considered as a 6-year error (which is categorized as an error within ±15) in our ex-
periments because the dataset had no precise information.
Table 3. Accuracy comparison to the recent results for tooth age estimation.
Ref. No. Age Range
in Dataset Input Image Type Age Info. No. Age Group No. Images Performance
[33] 1–17 Whole image
Precise age
info.
5 456 81.83 (%)
[34] 15–23 Specific tooth image 5 1000 83.25%
[35] 5–24 Whole image 2 10,257 95.9 (%)
[20] 0–60+ Specific tooth image 3 1586 90.37 (%)
[38] 0–70 Whole image 8 1429 98.8 (%)
[36] 19–90 Whole image (regression) 4035 0.84 (𝑅)
[37] 0–93 Whole image (regression) 27,957 MAE: 1.64 years
[16] 4.5–89 Whole image (regression) 2289 0.90 (𝑅)
Proposed 10–80 Whole image Age group info.
only 7 10,023
±5: 53.846 (%)
±15: 95.121 (%)
±25: 99.581 (%)
Figure 12.
Dental radiographs of persons with dental implants whose dental ages were predicted
to be lower: (
a
) actual age is in the 70s, predicted dental age is 55.49; (
b
) actual age is in the 70s,
predicted dental age is 52.46; and (c) actual age is in the 70s, predicted dental age is 57.99.
Table 3lists the comparison of the proposed method to the conventional studies in the
aspect of the accuracies and datasets. The classification accuracy with a
±
15 year tolerance
is higher than the most conventional results [
17
,
33
–
37
] except Mualla et al’s study [
38
],
but the accuracy of the proposed method with a
±
5 tolerance is relatively lower. One of
the expected reasons for this result is the type of input data. Different from the previous
experiments, the utilized images in this study have approximate age information only.
For example, a tooth image of 29 years may be confused with the age of 30 easily, but
it was considered as a 6-year error (which is categorized as an error within
±
15) in our
experiments because the dataset had no precise information.
Table 3. Accuracy comparison to the recent results for tooth age estimation.
Ref. No. Age Range in
Dataset
Input Image
Type Age Info. No. Age
Group No. Images Performance
[33] 1–17 Whole image
Precise age info.
5 456 81.83 (%)
[34] 15–23 Specific tooth
image 5 1000 83.25%
[35] 5–24 Whole image 2 10,257 95.9 (%)
[20] 0–60+ Specific tooth
image 3 1586 90.37 (%)
[38] 0–70 Whole image 8 1429 98.8 (%)
[36] 19–90 Whole image (regression) 4035 0.84 R2)
[37] 0–93 Whole image (regression) 27,957 MAE: 1.64 years
[16] 4.5–89 Whole image (regression) 2289 0.90 R2)
Proposed 10–80 Whole image Age group info.
only 7 10,023
±5: 53.846 (%)
±15: 95.121 (%)
±25: 99.581 (%)
Nevertheless, in this study, it was confirmed that the accuracy of
±
15 was very high at
99.521%. This is a very high result compared to the accuracy of 56.5–69.8% when confirmed
with
±
10 years, which is the age error range commonly used in forensic dentistry [
39
].
Healthcare 2023,11, 1068 11 of 13
However, since there is a difference in the age error range from this study, care should be
taken in simple comparison.
This study has full potential because age estimation using CNN can obtain more accu-
rate results than traditional manual methods that are labor-intensive and time-consuming.
Age estimation serves a key function in forensic anthropology and evidence, particularly
in criminal investigations and disasters [
3
,
4
]. Despite rapid advances in DNA sequencing
technology, age estimation using DNA methods is not generally available [
40
]. Therefore,
age estimation using CNN through oral radiographs is highly likely to be used because
it is easy to access. The results of this study contribute to the field of dental care. If the
dental age of a person is estimated to be lower than the actual age, that person will be
more satisfied with the results and more interested in dental care; however, if the dental
age is estimated to be higher than the actual age, the person will be aware of dental care
and have regular oral examinations and treatments. Oral health education is essential for
maintaining healthy oral conditions and a dental age that matches or is lower than the
actual age [41].
Visual educational materials that are easily understood and delivered should be used
to promote oral health and healthy oral care habits. Therefore, age estimation through
panoramic images of oneself can not only identify one’s own oral health status but can
also be provided as educational material for improving oral health. When compared to
members of the same age group, one can easily identify which age group one’s oral health
status belongs to, which provides motivation to maintain oral health [
42
]. The main reason
for this low accuracy is believed to be the uncertain labeling of age in the data. The precise
differences between teeth of similar ages could not be determined because accurate age
labels for the training images were not known. Collecting additional data and information
by collaborating with dental clinics is planned for additional experiments. A model that
can recognize and distinguish normal and abnormal teeth, implants, periodontal diseases,
etc. can be developed by overcoming the limitations of this study. In addition, research
should be conducted based on the actual ages of the subjects and not the median age values
used in this study. Furthermore, such a model can be used in clinical practice to increase the
interest of patients in dental care and to compare actual ages with estimated dental ages.
5. Conclusions
This paper presented a work to estimate tooth age groups when the precise age
information of the tooth is not known. By comparing and analyzing the actual ages of the
subjects, the overall recognition accuracy was found to be acceptable. Therefore, this study
has the potential to be used as oral health education material using the difference between
the actual age and the predicted age through AI in dental clinics. This has a very high
clinical significance because it will play a positive role in motivating patients in terms of
oral health.
Author Contributions:
Conceptualization, Y.-R.K. and J.-H.C.; data curation, J.K., Y.-J.J. and B.K.;
investigation, Y.-R.K., Y.-J.J. and S.-H.N.; methodology, J.-H.C. and W.-D.C.; resources, S.-H.N.
and Y.-R.K.; supervision, S.-H.N. and W.-D.C.; validation, Y.-R.K., J.-H.C., J.K., Y.-J.J. and B.K.;
writing—original
draft, Y.-R.K. and S.-H.N.; writing—review and editing, S.-H.N. and W.-D.C. All
authors have read and agreed to the published version of the manuscript.
Funding:
This work was supported by a National Research Foundation of Korea (NRF) grant
funded by the Korean government (MSIT) (2020R1C1C1005306) and the Ministry of Education
(NRF-2021R1F1A1064249).
Institutional Review Board Statement:
This research was approved by the Kangwon National Uni-
versity (KNU) Institutional Review Board (KWNUIRB-2020-12-004-002, Chuncheon,
Republic of Korea
).
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
Healthcare 2023,11, 1068 12 of 13
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