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Citation: Ossowska, A.; Kusiak, A.;
´
Swietlik, D. Artificial Intelligence in
Dentistry—Narrative Review. Int. J.
Environ. Res. Public Health 2022,19,
3449. https://doi.org/10.3390/
ijerph19063449
Academic Editor: Claudia Dellavia
Received: 24 January 2022
Accepted: 11 March 2022
Published: 15 March 2022
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International Journal of
Environmental Research
and Public Health
Review
Artificial Intelligence in Dentistry—Narrative Review
Agata Ossowska 1, Aida Kusiak 2and Dariusz ´
Swietlik 2, *
1Department of Periodontology and Oral Mucosa Diseases, Medical University of Gda´nsk,
80-204 Gda´nsk, Poland; agata.ossa@wp.pl
2Department of Biostatistics and Neural Networks, Medical University of Gda´nsk, 80-211 Gda´nsk, Poland;
akusiak@gumed.edu.pl
*Correspondence: dariusz.swietlik@gumed.edu.pl
Abstract:
Nowadays, artificial intelligence (AI) is becoming more important in medicine and in
dentistry. It can be helpful in many fields where the human may be assisted and helped by new
technologies. Neural networks are a part of artificial intelligence, and are similar to the human brain
in their work and can solve given problems and make fast decisions. This review shows that artificial
intelligence and the use of neural networks has developed very rapidly in recent years, and it may be
an ordinary tool in modern dentistry in the near future. The advantages of this process are better
efficiency, accuracy, and time saving during the diagnosis and treatment planning. More research and
improvements are needed in the use of neural networks in dentistry to put them into daily practice
and to facilitate the work of the dentist.
Keywords: artificial intelligence; neural networks; dentistry
1. Introduction
Nowadays, artificial intelligence (AI) is becoming more important in medicine and in
dentistry. It can be helpful in many fields where the human may be assisted and helped
by new technologies. The developments in AI started in 1943 but the term “artificial
intelligence” was created in 1956 at a conference in Dartmouth by John McCarthy. Machine
learning, neural networks, and deep learning are subsets of artificial intelligence. Machines
can learn through data to build algorithms and in this way, they can solve the prediction
problems without human help. Neural networks (NNs) use artificial neurons that are
similar to human neural networks and mimic the human brain in a mathematical non-
linear model. NNs are able to simulate human cognitive skills such as problem solving and
human thinking abilities, which includes learning and decision making. Neural networks
in a simple form have three layers: input layer (where the information enters the system),
the hidden layer (where the data are processed), and the output layer (where the system
decides what to do). Given a set of mathematical models, NNs are able to outline any input
to an output. If an adequately large amount of data are available, such NNs can be trained
to represent the intrinsic statistical figures of the provided data. The topology of the simple
artificial neural network is shown in Figure 3 in Swietlik D et al. (2004). There are also
more complex artificial neural networks where there are more hidden layers and these
are called multilayer perception (MLP) neural networks. The most commonly used types
of neural networks are artificial neural networks (ANN), convolutional neural networks
(CNN), and recurrent neural networks. Deep learning is a part of neural networks where
the computer learns on its own how to process the data. Deep learning neural networks
have between a few thousand and a few million neurons in the hidden layer [
1
–
5
]. Artificial
intelligence (AI) may be used in planning more effective therapies, prophylaxis, and the
reduction in treatment costs [
2
,
4
]. We can benefit from AI in medicine, mostly in the fields
such as radiology, pathomorphology, and oncology (by using “Thermalitics” technique
in breast cancer detection), in cardiology (to help in ECG analysis), in psychiatry (to
Int. J. Environ. Res. Public Health 2022,19, 3449. https://doi.org/10.3390/ijerph19063449 https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2022,19, 3449 2 of 10
diagnose, prevent and treat mental illnesses), nuclear medicine, and many others [
6
–
10
].
The computer models of neural networks are also one of the methods to understand the
functioning of the nervous system, which we cannot study in natural conditions due to the
limitations of modern research methods [11–15].
Artificial intelligence is also spreading in dentistry due to the technological advance-
ments and digitization of dentistry. Dental second opinions can now be made by computers
in many dental fields. NNs in dentistry can be used to make the process of diagnosis more
accurate, rapid, and efficient. Fast development and new studies related to neural networks
in dentistry were the reason to provide this narrative review. The aim of this study was to
outline the overall picture of the possibilities of using neural networks in modern dentistry.
2. Neural Networks in Restorative Dentistry
Dental caries is the most common dental disease and that is why its disclosure in
the early stage is crucial. For the screening and diagnosis of dental caries, dentists mostly
use dental probes, and through the observation of the texture and discoloration, they can
determine whether the tooth is sound or not. This method is very subjective and is based
on the dentist’s experience. In particular, the approximal surfaces may be problematic in
dental examination [
16
,
17
]. Additional tests such as radiographs are essential in modern
dentistry and can enhance the detection of caries. The most common types of radiological
images used in caries screening are bitewings, periapical X-rays, and panoramic X-rays.
CBCT is used less frequently in tooth decay detection [
18
,
19
]. Dental caries detection on
radiological images might be assisted by neural networks, which makes the examination
faster and more precise. Neural network use in conservative dentistry has developed
quickly, but is not very widespread yet [
20
]. Algorithms can be used to locate the edges
of anatomical and pathological structures, which might be very similar to each other due
to the image noise and low contrast [
21
]. In the work by Geetha et al., an artificial neural
network was used to determine whether there were caries or not in the 105 radiograph
images. They extracted sixteen feature vectors from the segmented image and these were
the input nodes. There were two output nodes that consisted of caries or sound tooth.
The accuracy of caries detection was 97.1%, and the false positive rate was 2.8%. This
study indicates that neural networks may be much more precise in tooth decay detection
than traditional dental examination [
22
]. Moreover, dental restorations may be revealed
by the use of artificial intelligence. In restorative dentistry, AI can be used to detect and
classify dental restorations such as in Abdalla-Aslan R et al.’s research from 2020. The
algorithms used in their work detected 93.6% of dental restorations on 83 panoramic
images. Additionally, restorations were classified into 11 categories by using the shape and
distribution of grey values [
23
]. Neural networks might be helpful in planning the selection
of the dental treatment and cavity preparation technique. Artificial neural networks were
used in Javed et al.’s study to predict the post-Streptococcus mutans prior to dental caries
excavation based on pre- Streptococcus mutans using an iOS App developed on an artificial
neural network (ANN). For the research, 45 primary molars with occlusal caries were
used. The colony forming units for pre- and post-Streptococcus mutans were recorded.
The study demonstrates that ANN can predict which excavation method is the best for
an individual patient. The accuracy of ANN was 99.03% and it was microbiologically
checked (Table 1). The prediction of post-Streptococcus mutans avoids the examination of
post-Streptococcus mutans, re-excavation, and re-examination, and pulpal trauma with the
excavated cavity [3].
Int. J. Environ. Res. Public Health 2022,19, 3449 3 of 10
Table 1.
Baseline characteristics of the studies included in the review by studying neural networks in
restorative dentistry.
Study [Ref.] Year of
Publication Type of Data Type of Neural Network Number of
Database
Accuracy of
Neural Network
Javed [3] 2019 Primary molars Artificial neural network (ANN) 45 teeth 99.03%
Geetha [22] 2020 Periapical
radiographs
Back -propagation neural network
105 images 97.7%
Abdalla-Aslan [23] 2020 Panoramic
radiographs
Cubic support vector machine
algorithm with
error-correcting output codes
83 images 93.6%
Neural networks in restorative dentistry may be used in a few clinical purposes. Fre-
quently performed diagnosis and the choice of treatment method can now be assisted by
artificial intelligence. The most common way to engage new technologies is the analysis of
dental X-rays and caries or restoration detection, but also in other fields such as microbiol-
ogy, which can be assisted by neural networks to make the best treatment decisions. More
studies need to be performed to introduce new technologies to daily practice but other
fields of restorative dentistry might also be helped by neural networks in decision-making.
3. Neural Networks in Endodontics
Artificial intelligence has an increasing relevance in endodontics. It can be useful in
detecting periapical lesions and root fractures, root canal system anatomy evaluation, pre-
dicting the viability of dental pulp stem cells, determining working length measurements,
and predicting the success of retreatment procedures [
24
]. Artificial neural networks may
be used as a decision-making system for locating the minor apical foramen on radiographs.
In Saghiri et al.’s research, endodontic files were used to determine the length of the canals
on the radiology images with the use of artificial neural networks and without. The mea-
surements were taken before the extraction of the teeth and after the extraction with the use
of stereomicroscopy. The correct assessment made by the endodontics was strict in 76% and
by the artificial neural network in 96% (Table 2). This shows that artificial neural networks
may be used to assess the localization of apical foramen more precisely than humans [
25
].
Apical periodontitis is an inflammatory process mainly caused by the bacterial infection of
the root canal system. It may be detected through radiographic diagnostics and manifest
as periapical translucencies that are also named periapical lesions. To reveal periapical
translucencies, most are taken as periapical or panoramic radiographs and cone-beam
computed tomographic images [
26
,
27
]. Setzer et al. in their research used deep learning
to detect periapical lesions on cone-beam computed tomographic (CBCT) images. The
accuracy of finding the lesions was 93% [
25
,
27
]. CNN was also used in Orhan et al.’s work
to detect periapical lesions on CBCT images. The convolutional neural network detected
142 of 153 periapical lesions (92.8% accuracy). The results obtained by CNN were similar to
those obtained by an experienced dental practitioner [
28
]. Convolutional neural network
(CNN) is a specialized kind of artificial neural network that is very useful when extracting
features from the image by engaging convolutional operations. These convolutional neural
networks were used in Pauwels et al.’s work. The periapical radiographs were evaluated
to find periapical lesions made in bovine ribs. The results were compared with three oral
radiologists and the CNN showed a perfect accuracy of 87% [
26
,
29
]. Ekert et al. assessed
panoramic images for the presence of periapical lesions with the help of CNN. They con-
cluded that different tooth types are difficult to assess on panoramic image in different
ways because of the radiographic image generation process. This is why the diagnostic may
be uncertain and the sensitivity needs to be improved, although the results of periapical
lesion detection by neural network have been satisfactory. In molars, the CNN’s sensitivity
was higher (87%) than on other teeth, whereas the specificity was lower [
26
]. Artificial
neural networks may not only be used in dental radiology, but also in genetics as it comes
to endodontics [
30
]. In the study of Poswar et al., artificial intelligence was used to analyze
the gene expression for radicular cysts (RCs) and periapical granulomas (PGs). The results
Int. J. Environ. Res. Public Health 2022,19, 3449 4 of 10
showed that not only the inflammation, but also other biological processes may individuate
the RCs and PGs because of their different gene expression [31].
Table 2.
Baseline characteristics of the studies included in the review by studying neural networks
in endodontics.
Study [Ref.] Year of
Publication Type of Data Type of Neural
Network
Number of
Database
Accuracy of
Neural Network
Saghiri [25] 2012 Teeth ANN 50 teeth 96%
Ekhert [26] 2019 Panoramic radiographs CNN - 87% (molars)
Setzer [27] 2020 CBCT images Deep Learning 20 images 93%
Orhan [28] 2020 CBCT images CNN 153 images 92.8%
Pauwels [29] 2021 Periapical radiographs CNN - 87%
Neural networks in endodontics may be useful in X-ray analysis and mostly in the
detection of periapical lesions. This detection process can still be improved to obtain good
accuracy for all teeth. Artificial intelligence might also be used in non-radiological areas
such as genetics or others to ease the diagnosis.
4. Neural Networks in Orthodontics
Artificial intelligence is spreading widely in the field of orthodontics. The most often
used types of algorithms in orthodontics are artificial neural networks (ANN), convolu-
tional neural networks (CNN), support vector machine, and regression algorithms [
32
].
Peilini et al. used an ANN in their study to predict whether patients need extractions or
not in their treatment plan. Moreover, they took the anchorage patterns into consideration.
The accuracy of the artificial neural network in the success of the treatment plan was 94.0%
for extractions and 92.8% in the prediction of the use of maximum anchorage. These results
indicate that ANN can be used by orthodontists to make more precise treatment plans [
33
].
Auconi et al. developed a system based on artificial neural networks with the purpose to
predict the treatment outcomes in class II and III patients. The analysis could anticipate the
co-occurrence of auxological anomalies during individual craniofacial growth and possibly
localize reactive sites for a therapeutic approach to malocclusion [
34
,
35
]. The research
indicates that the deep learning neural networks might be the best for TMJ osteoarthritis
detection. Temporomandibular joint (TMJ) disorders are the second most common mus-
culoskeletal condition affecting 5 to 12% of the population, and chronic disability in TMJ
osteoarthritis (OA) increases with age. The main goal is to diagnose the impairment of
the TMJ before morphological degeneration occurs. To achieve this goal in Bianchi et al.’s
research, TMJ CBCT scans, serum, and saliva tests were taken [
36
–
38
]. In the study by
Muraev et al., ANN was used to place the cephalometric points on cephalometric radiogra-
phy. The accuracy of CP placement was compared between the ANN and three groups of
doctors: expert, regular, and inexperienced. The results showed that ANN had a similar
accuracy in planning cephalometric points as an experienced dentist and in some cases,
they can be even more precise than new doctors [
39
]. In addition, ANN may help in the
determination of the growth and development periods. In the research by Kök et al., the
cephalometric and hand-wrist radiographs were obtained from patients aged between
eight and 17 years. The growth-development periods and gender were determined from
the cervical vertebrae by using ANN and the accuracy value of the results was found to be
94.27% [40].
To resume, the most common fields of orthodontics where neural networks may be
used are in diagnosis and treatment planning, automated anatomic analyses, assessment of
growth and development, and the evaluation of treatment outcomes (Table 3) [
32
]. It seems
that artificial intelligence in orthodontics may be widely used and its use for sure can be
extended even further.
Int. J. Environ. Res. Public Health 2022,19, 3449 5 of 10
Table 3.
Baseline characteristics of the studies included in the review by studying neural networks
in orthodontics.
Study [Ref.] Year of
Publication Type of Data Type of Neural Network Number of
Database
Accuracy of Neural
Network
Auconi [33] 2015 Cephalometric records Fuzzy clustering
repartition 54 cephalograms 83.3%
Peilini [34] 2019 Medical records ANN 302 patients
94.0% (extraction
pattens); 92.8 %
(anchorage patterns)
Bianchi [36] 2020
CBCT
blood serum saliva
clinical investigation
Logistic Regression,
Random Forest,
LightGBM, XGBoost
52 patients 82.3%
Muraev [39] 2020 Cephalometric records ANN 330 cephalograms 99.9%
Kök [40] 2021 Cephalometric and
hand-wrist radiographs ANN 419 patients 94.27%
5. Neural Networks in Dental Surgery
According to the found literature, neural networks may be widely used in dental
surgery. The purpose of Chien-Hsun Lu et al.’s study was to evaluate and improve post-
orthognathic surgery image predictions for the individual patient. Simulations made by
neural networks may be helpful for surgeons, orthodontists, and for the patients to improve
the treatment plans [
41
]. The research of Patcas et al. indicated that artificial intelligence
may characterize the impact of orthognathic surgery on facial attractiveness and age
appearance. Pre- and post-treatment photographs of orthognathic patients were collected
and convolutional neural networks were trained on >0.5 million images for age estimation
and with >17 million ratings for attractiveness. According to the algorithms, most patients’
appearance improved with treatment (66.4%), resulting in a younger appearance of nearly
one year. The same author used convolutional neural network to assess the attractiveness
of patients who had undergone cleft surgeries [
42
,
43
]. In Byung Su Kim et al.’s work,
convolutional neural networks were used to predict whether third molar extraction may
lead to paresthesia of the inferior alveolar nerve. Extraction of the lower third molar is
one of the most popular dental surgery procedure. The paresthesia of the nerve after
mandible wisdom tooth extraction is quite a common complication. The panoramic images
were used before the extraction and the anatomical relationship between the nerve canal
and dental roots was used by the CNN to predict the occurrence of nerve paresthesia.
However, the authors concluded that two dimensioned images as panoramic radiographs
may lead to more false positive and false negative results, therefore, future research is
needed [
44
]. Deep learning can be beneficial in odontogenic lesion detection. Two common
diseases that might occur in jaws, and especially in the posterior ramus and body of the
mandible, are ameloblastoma (AB) and odontogenic keratocyst (OK). In Liu et al.’s research,
panoramic radiographs were used to detect these two tumors due to the lower cost and
better accessibility than CT or MR images. Since it is difficult for human eyes to identify AB
and OK in panoramic radiographs, a convolutional neuron network based on the transfer
learning algorithm was used. The radiographs were especially prepared to obtain better
contrast in the region of interest. All of the lesions were confirmed by the histopathological
examination. The accuracy of the convolutional neural network was 90.36%, which was a
better result than the accuracy of three other neural networks used in the same research. The
above study indicates that neural networks may be useful to oral maxillofacial specialists
before surgery [45].
According to previous studies, neural networks may be used in implantology. Dental
implant treatment planning with the usage of three-dimensional cone-beam computed
tomography (CBCT) images can be facilitate by AI systems [
46
]. Moreover, convolutional
neural networks can be used to identify dental implant brands on panoramic radiographs
and to identify the stage of treatment [
47
]. The quality of the osteointegration can be as-
sessed by using convolutional neural networks (Table 4). The difficulties in osteointegration
might occur due to the presence of a soft tissue layer (non-mineralized bone tissue) around
Int. J. Environ. Res. Public Health 2022,19, 3449 6 of 10
the bone–implant interface, which can be exposed upon ultrasound examination [
48
]. Fi-
nally, artificial intelligence has been used in studies to measure the peri-implant bone
loss [49].
Table 4.
Baseline characteristics of the studies included in review by studying neural networks in
dental surgery.
Study [Ref.] Year of
Publication Type of Data Type of Neural
Network
Number of
Database
Accuracy of Neural
Network
Chien-Hsun Lu [41] 2009 Profile photographs ANN 30 patients 84.5%
Patcas [42] 2018 Photographs CNN 146 patients -
Patcas [43] 2019 Frontal and profile images CNN 20 patients -
Byung Su [44] 2021 Panoramic radiographs CNN 300 images 82.7%
Liu [45] 2021 Panoramic radiographs CNN 420 images 90.36%
Bayrakdar [46] 2021 CBCT CNN (Diagnocat) 75 images
72.2% for canals
detection; 66.4% for
sinuses/fossae and 95.3%
for missing tooth regions
Sukegawa [47] 2021 Panoramic radiographs CNN 9767 images 81.83%
Neural networks in dental surgery might be widely used in many areas starting
with the orthognathic surgeries, changes in the bones or post extraction complications,
and ending with implantology treatment. In particular, implantology is an area that is
developing very rapidly and the use of neural networks might be very helpful in daily
practice because of the need for high precision and meticulous planning. Neural networks
may also help to predict some complications that may occur during surgical treatment and
therefore avoid some of them.
6. Neural Networks in Periodontology
Periodontitis is a wide spread disease that concerns billions of people worldwide and
if untreated, leads to tooth mobility and in severe cases, to tooth loss. To prevent this from
happening, early disease detection and effective therapy needs to be carried out. To obtain
reliable diagnosis, a meticulous physical examination needs to be performed. For this
reason, dental probing to measure pocket depth and clinical attachment loss is performed.
Periodontal probing has limited accuracy because of the individual examiner’s assessment.
Commonly used additional examinations are dental radiographs, whose evaluation also
depends on the examiner’s experience. To minimize errors in diagnosis, some authors
have used neural networks. Krois et al. evaluated panoramic radiographs with the help
of convolutional neural networks to detect periodontal bone loss in percentage of the
tooth root length. The results were compared with the measures made by six experienced
dentists. The CNN had higher accuracy (83%) and reliability than the dentists (80%) in
detecting periodontal bone loss [
50
]. Peri-implant bone loss can be detected on dental
periapical radiographs, but the difficulty is that the margins of bone around the implants
are usually unclear or the margins can overlap. For this reason, convolutional neural
networks can assess the marginal bone level, top, and apex of implants on dental periapical
radiographs. In the study by Jun-Young Cha et al., the bone loss percentage was calculated
and classified by the automated system. This method can be used to assess the severity
of peri-implantitis [
51
]. In the research of Lee et al., a deep convolutional neural network
was used to analyze the radiographs and measure the radiographic bone loss (RBL) for
each tooth. RBL percentage, staging, and presumptive diagnosis according to the new
periodontitis classification made by CNN were compared with the measurements made
by independent examiners. The accuracy for the neural network was 85%. Thus, neural
networks may be useful tools to assess radiographic bone loss and to obtain image-based
periodontal diagnosis [
49
]. Other authors have also used neural networks to evaluate
radiographic bone loss, and in this way, developed an automatic method for staging
periodontitis according to the new criteria proposed at the 2017 World Workshop on
the Classification of Periodontal and Peri-implant Diseases and Conditions. Chang et al.
Int. J. Environ. Res. Public Health 2022,19, 3449 7 of 10
used panoramic images and convolutional neural networks to detect the periodontal
bone level (PBL), the cementoenamel junction level (CEJL), and the teeth, and in this way,
made a diagnosis of periodontitis stage [
52
]. Vadzyuk et al. took into consideration the
psychological features to predict the development of periodontal disease. They concluded
that patients’ level of anxiety and stress hormone levels had an impact on periodontitis
(Table 5). Assessment of the condition of teeth hard tissues, the level of oral hygiene,
and the evaluation of psychophysiological features with the use of neural networks can
effectively predict the risk of periodontal disease development in young people [53].
Table 5.
Baseline characteristics of the studies included in review by studying neural networks
in periodontology.
Study [Ref.] Year of
Publication Type of Data Type of Neural
Network
Number of
Database
Accuracy of Neural
Network
Lee [49] 2018 Periapical radiographs CNN 1044 images 81.0% for premolars
76.7% for molars
Krois [50] 2019 Panoramic radiographs CNN 353 images 81%
Chang [52] 2020 Panoramic radiographs CNN 340 images 93%
Vadzyuk [53] 2021
Survey (oral hygiene and
nutrition) dental examination,
psychological testing
ANN 156 students -
Jun-Young Cha [51] 2021 Periapical radiographs CNN 708 images 88.89%
The use of neural networks in periodontology can be a helpful tool for clinicians in
daily practice as well as for scientists. The precise assessment of bone loss can be crucial in
making periodontal diagnosis and treatment planning. More research and improvements
are needed to introduce this tool into everyday periodontal use.
7. Conclusions
Dentistry is a field of medicine where new technologies are developing very quickly.
Nowadays, artificial intelligence and neural networks are mostly used in dental radiology
to facilitate diagnosis, treatment planning, and prediction of the treatment results. Other
areas of dentistry where neural networks are used are genetics, psychology, microbiology,
and many others. The most frequently used types of neural networks are artificial neural
networks and convolutional neural networks. In restorative dentistry, neural networks
can detect tooth decay or restorations, moreover, they can facilitate the choice of caries
excavation method [
3
,
22
,
23
]. In endodontics, neural networks can be useful in detecting
periapical lesions and root fractures, root canal system anatomy evaluation, predicting
the viability of dental pulp stem cells, determining working length measurements, and
predicting the success of retreatment procedures [
25
–
29
]. In orthodontics, they can fa-
cilitate the diagnosis and treatment planning, cephalometric points marking, anatomic
analyses, assessment of growth and development, and the evaluation of treatment out-
comes [33,34,36,39,40]. In dental surgery, neural networks may be helpful in orthognathic
surgery planning, prediction of post-extraction complications, bone lesion detection, and
differentiation and implantological treatment planning [
41
–
47
]. Furthermore, artificial
intelligence is spreading into periodontology and in the above-mentioned studies, it was
used to evaluate the periodontal bone loss, peri-implant bone loss, and to predict the
development of periodontitis due to the psychological features [
49
–
54
]. This review shows
that artificial intelligence has developed very fast in recent years and it may become an
ordinary tool in modern dentistry in the near future. The advantages of this process are
better efficiency, accuracy and precision, better monitoring, and time saving [
55
]. More
research is needed with the use of neural networks in dentistry to put them into daily
practice and to facilitate the work of dentist.
Int. J. Environ. Res. Public Health 2022,19, 3449 8 of 10
Author Contributions:
Conceptualization, A.O., A.K. and D.´
S.; Data curation, A.O. and A.K.; Formal
analysis, A.K. and D.´
S.; Investigation, A.O. and D. ´
S.; Methodology, A.O., A.K. and D.´
S.; Project
administration, A.O. and A.K; Supervision, A.K. and D.´
S.; Writing—original draft, A.O., A.K. and
D.´
S.; Writing—review & editing, A.O., A.K. and D.´
S. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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