Journal of Experimental and Clinical Medicine

https://dergipark.org.tr/omujecm

Review Article J Exp Clin Med

2021; 38(S2): 157-162
doi: 10.52142/omujecm.38.si.dent.13

A review of the use of artificial intelligence in orthodontics

Berat Serdar AKDENİZ* , Muhammet Emir TOSUN

Department of Orthodontics, Faculty of Dentistry, Kırıkkale University, Kırıkkale, Turkey

Received: 26.05.2020 • Accepted/Published Online: 31.12.2020 • Final Version: 19.05.2021

Abstract
The clinical use of artificial intelligence technology in orthodontics has increased significantly in recent years. Artificial intelligence can be utilized
in almost every part of orthodontic workflow. It is an important decision-making aid as well as being a tool for building more efficient treatment
methods. The use of artificial intelligence reduces costs, accelerates the diagnosis and treatment process and reduces or even eliminates the need
for manpower. This review article evaluates the current literature on artificial intelligence and machine learning in the field of orthodontics. The
areas that the artificial intelligence is still absent have also been discussed in detail. Despite its shortcomings, artificial intelligence is considered to
be a fundamental part of orthodontic practice in the near future.
Keywords: artificial intelligence, digital orthodontics, machine learning, orthodontics

1. Introduction
Digital data processing technologies in medical and dental can be integrated into dental imaging systems to identify even
fields have gained attention in the last two decades. Utilization the smallest deviations which human eye cannot recognize.
of digital technology, especially artificial intelligence (AI) With this outstanding ability, it can easily be used to make
technology, can help to reduce the cost and duration of accurate diagnosis of cephalometric landmarks (Tong et al.,
treatment, the need for human expertise and the number of 1989).
medical error cases. This approach also has a revolutionary
Artificial intelligence-based software systems have
potential in public health scenarios in developing countries.
significant and modificative role in the field of orthodontics
Artificial intelligence, which was brought forward by and they are considered as the future of dental applications. For
McCarthy in 1956, can be described as the behavior of the non- this reason, we aimed to review the literature on the use of AI
biologic beings which has the capacity to perceive complex technology in the orthodontic field (Table 1). Artificial
environments, learn and react accordingly (Nilsson and intelligence is used in every area of orthodontics from patient
Nilsson, 1998). Artificial intelligence does not necessarily communication and diagnosis to treatment processes.
mimic the human brain, it is rather a problem-solving tool Orthodontic software programs which use AI technologies are
which has its own set of rules. Studies have been conducted to based on “machine learning” technology. “The machine” uses
achieve human-like behaviors with AI and it has been found raw data to collect information from a database in machine
that computers exceed human results in many parameters learning technology. These software programs can analyze
(Faber et al., 2019). Artificial intelligence technology has been diagnostic dental radiographs and photos, also they can give
used in a wide spectrum from differential diagnosis and guidance to the dentists, during 3D intraoral scanning, to reach
radiographic interpretation to restorative treatment in dental an ideal model easily (Kattadiyil et al., 2014). The use of AI
field (Khanna, 2010). Dental management software, which can be divided into two main application areas in orthodontics
uses AI to gather and store the patient data, is available in the in particular: diagnosis and treatment (Fig. 1).
market. In this point, artificial intelligence can be used to
2. Artificial intelligence and orthodontic diagnosis
generate complete detailed virtual databases which are easily
Patient data, carefully obtained from an adequate database
accessible. Interactive and voice recognizing interfaces help
containing a detailed list of the patient's problems, form the
dental clinicians to easily complete some complex tasks.
basis of correct and accurate orthodontic diagnosis. The
Software with AI technology can document the necessary data
orthodontic diagnostic database can be obtained from written
and transfer them to the clinician faster and more efficiently
or verbal interview data; clinical examination and examination
than its human counterparts (Kannan, 2017). With its unique
of patient records including dental impressions, radiographs,
learning ability, AI can be trained to perform different tasks. It
and diagnostic photographs (Proffit et al., 2018).

* Correspondence: bsakdeniz@hotmail.com
 Akdeniz and Tosun / J Exp Clin Med
Table 1. Current literature on the use of artificial intelligence in orthodontic

Year Author Article

2002 Akçam et al. Fuzzy modelling for selecting headgear types
2006 Noroozi et al. Orthodontic treatment planning software
2006 Zarei et al. An intelligent system for prediction of orthodontic treatment outcome
2009 Kim et al. Prognosis prediction for class III malocclusion treatment by feature wrapping method
2009 Tanikawa et al. Automated cephalometry: system performance reliability using landmark-dependent criteria
2010 Khanna et al. Artificial intelligence: contemporary applications and future compass
2010 Mario et al. Paraconsistent artificial neural network as auxiliary in cephalometric diagnosis
2010 Tanikawa et al. Automatic recognition of anatomic features on cephalograms of preadolescent children
Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic
2010 Xie et al.
treatment
Decision-making system for orthodontic treatment planning based on direct implementation of expertise
2010 Yagi et al.
knowledge
2011 Auconi et al. A network approach to orthodontic diagnosis
2011 Banumathi et al. Diagnosis of dental deformities in cephalometry images using support vector machine
2014 Buschang et al. Predicted and actual end-of-treatment occlusion produced with aligner therapy
Evaluation of facial attractiveness for patients with malocclusion: a machine-learning technique
2014 Yu et al.
employing Procrustes
2015 Auconi et al. Prediction of Class III treatment outcomes through orthodontic data mining.
2015 Gupta et al. A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images
2016 Jung et al. New approach for the diagnosis of extractions with neural network machine learning
An automatic method for skeletal patterns classification using craniomaxillary variables on a Colombian
2016 Nino-Sandoval et al.
population
2016 Wang et al. Objective method for evaluating orthodontic treatment from the lay perspective: An eye-tracking study
2017
Grünheid et al. How accurate is Invisalign in nonextraction cases? Are predicted tooth positions achieved?
2017 Kannan et al. Artificial Intelligence-Applications in Healthcare
2017 Lee et al. Fully automated deep learning system for bone age assessment
2017 Murata et al. Towards a fully automated diagnostic system for orthodontic treatment in dentistry
Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and
2017 Nino-Sandoval et al.
III.
2017 Spampinato et al. Deep learning for automated skeletal bone age assessment in X-ray images
2018 Iglovikov et al. Paediatric bone age assessment using deep convolutional neural networks
Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand
2018 Larson et al.
radiographs
Automatic 3-dimensional cephalometric landmarking based on active shape models in related
2018 Montúfar et al.
projections
Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed
2018 Montúfar et al.
tomography volumes
2019 Faber et al. Artificial intelligence in orthodontics
Usage and comparison of artificial intelligence algorithms for determination of growth and development
2019 Kök et al.
by cervical vertebrae stages in orthodontics.
Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness
2019 Patcas et al.
and estimated age
Artificial intelligence in orthodontics: Evaluation of a fully automated cephalometric analysis using a
2020 Kunz et al.
customized convolutional neural network
Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential
2020 Lee et al.
Diagnosis of Orthognathic Surgery Indications
Clinicians experience some time and accuracy constraints
ARTIFICIAL INTELLIGENCE in patient evaluation process. For the reason that patient
IN ORTHODONTICS
evaluation and getting patient records are time-consuming
Clinical Assistance Orthodontic Diagnosis Orthodontic Treatment
steps, automation of diagnosis and imaging is essential to
increase the speed and accuracy of the evaluation (Murata et
Patient Management Patient Data Collecting Treatment Planning al., 2017).
Voice Recognition
Cephalometric and
Photographic
Measurements
Treatment Simulation The need of a thorough simultaneous evaluation of
different parts of facial structures from different aspects makes
Database Building Growth Estimation Appliance Production
orthodontic diagnosis a challenging task. Digital dentistry tools
Facial Analysis
have enabled the collection of patient data on a digital platform
and the creation of a digital database that can be used for
Fig. 1. The areas of orthodontics that artificial intelligence was used diagnosis and treatment. Although digital data acquisition
accelerated the speed of diagnosis and treatment phases, it still

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does not eliminate the need for an expert clinician for analysis asserted that the developed software had a high success rate
and decision-making steps (Yagi et al., 2010). The automation (over 90%) in differential diagnosis of cephalometric
systems which use AI and machine learning technologies landmarks. The automated tracing module was integrated into
remarkably have decreased the evaluation workload and a recent web-based software. The web-based software can also
prevented the diagnostic variations (Murata et al., 2017). detect soft tissue profile in profile photographs and with its
orthognathic surgery planning module, it can simulate possible
Different algorithms of AI systems were tested in several
soft tissue changes after planned orthognathic treatment.
studies in the orthodontic field. All these algorithms needed a
big database of patient examination records as input. The 4. Estimation of growth and development
results showed that the use of AI during diagnosis reduced the Timing is one of the main components of orthodontic
need for an expert clinician and the number of diagnostic treatment. Growth and development can be estimated by
errors. The researchers concluded that the AI applications were anthropometric indicators like chronologic age, menarche,
promising in orthodontic field (Kim et al., 2009; Yagi et al., vocal changes, height increase and skeletal maturation (skeletal
2010; Auconi et al., 2011;; Niño-Sandoval et al., 2016; Wang age) (Hägg and Taranger, 1982). Radiographs are widely used
et al., 2016; Murata et al., 2017). for detection of skeletal maturation indicators (Hägg and
Taranger, 1980). Deep learning (a machine learning algorithm
Noroozi et al. (2006) described a software which used
that uses multiple layers to progressively extract higher level
“fuzzy logic” concept. The software took graphical and
features from the raw input) and AI technologies were used by
numeric patient data as input and could recommend treatment
several authors to automate the age estimation by examining
plan for non-surgical orthodontic patients. Fuzzy logic enables
hand and wrist radiographs. With deep learning ability, AI
the software work with the nominal parameters. Human brain
systems can evaluate the radiographs after the input of a vast
is naturally accustomed to these “fuzzy” parameters. The
database consists of race, age, and gender. Results show that
authors asserted that the software program could suggest
the AI systems can evaluate the skeletal maturity with a
treatment options even for the specific situations like missing
performance like a radiologist (Lee et al., 2017; Spampinato et
teeth.
al., 2017; Iglovikov et al., 2018; Larson et al., 2018).
3. Automated cephalometric tracing
Maturation levels of cervical vertebrae are also used for
Tracing of cephalometric radiographs can either be done
assessment of skeletal maturity. Kök et al. (2019) compared
manually or digitally with computer aid. Although the use of
seven different, widely used AI algorithms to estimate cervical
computers for cephalometric tracing aims to save time by
vertebrae maturation levels. Artificial Neural Networks (ANN)
reducing tracking errors and increasing the diagnostic value of
algorithm, which is a mathematical model of human nervous
cephalometric analysis, the inconsistency in identifying
system formed by artificial nerve cells, showed better results.
anatomical landmarks is still a major source of random error
The authors concluded that ANN could be used in the future
(Miller et al., 1971).
applications for determining cervical vertebrae stage.
In order to overcome this problem, efforts have been made
5. Facial proportions
to automate cephalometric analysis with the aim of reducing
Evaluation of facial proportions includes measurement of
errors and the time required for analysis (Hutton et al., 2000).
ratios and linear lengths between facial structures. Although
Levy-Mandel et al. (1985) conducted the first study on lateral cephalometric radiographs and profile photographs are
automatic extraction of anatomical landmarks on lateral widely used for linear assessments, it is difficult to perform
cephalometric radiographs. They preprocessed the image with sensitive measurements because of the magnification
an edge-detector and knowledge-based line-following differences. Ratios and angular measurements are independent
algorithm, involving a production system with organized sets of dimensions and generally used for photographic assessment.
of rules and a simple interpreter, was subsequently applied.
Measurements of “ideal” facial proportions are currently
Automated cephalometric tracing was subsequently studied by
used by surgeons and orthodontists to comprehend the ideals
several other researchers and proved to perform as successfully
of beauty and reproduce aesthetically “beautiful” proportions
as expert dentists and could be used to accelerate the
(Harrar et al., 2018). However, the classical rules of ideal facial
cephalometric diagnostic phase (Tanikawa et al., 2009, 2010;
aesthetics have some deficiencies in reflecting the beauty
Mario et al., 2010; Banumathi et al., 2011; Gupta et al., 2015;
perception of the population because facial beauty is a very
Montúfar et al., 2018a, 2018b; Kunz et al., 2020). Although AI
subjective concept and there is not widely used and validated
systems have not been utilized for fully automated
set of rules for facial aesthetics, which is approved by the
cephalometric tracing yet, they have reached the maturity to be
population. (Knight and Keith, 2005; Yin et al., 2014).
used in some existing cephalometric software programs to
suggest possible locations of anatomical structures. Today, AI applications do not only perform basic tasks
such as optical facial recognition, but they are also matured
Lee et al. (2020) used deep convolutional neural network-
enough to simulate much complex cognitive tasks including
based analysis for automated cephalometric tracing. Authors

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analysis and interpretation of facial data. Studies in this field variables. On the other hand, it has some shortcomings, such
showed that AI systems seemed to be promising tools to build as limitations in identifying all possible outcomes and
a validated formula for the human perception of facial establishing a linear relationship between variables and their
attractiveness (Patcas et al., 2019; Yu et al., 2014). outcomes (Zarei et al., 2006).
6. Artificial intelligence and orthodontic treatment planning Artificial neural networks were cited as good candidates to
Extraction decision develop a predictive model for orthodontic therapy, thanks to
Planning phase is the most significant and critical part of their ability to detect complex non-linear relationships between
orthodontic treatment. Extractions should be carefully planned inputs and outputs. Artificial neural networks were shown to
due to their irreversible nature. Clinicians come to the stage of have the ability to learn and generalize beyond the situations
deciding to extractions after combining the patient data derived they were faced with (Zarei et al., 2006).
from clinical evaluations, diagnostic photographs, dental
There are studies in the literature which showed that the
models and radiographs with their clinical expertise. Although
treatment results of Class II and Class III patients could be
practitioners with less experience can learn from the decisions
simulated by utilizing artificial neural networks technique. The
of their more experienced colleagues, the lack of a standard
researchers conclude that the neural networks technique is a
assessment method for the decision-making process requires a
promising tool which can be used for simulation of different
different approach. Neural networks were used to mimic
malocclusion models (Zarei et al., 2006; Auconi et al., 2015).
human decision-making process for orthodontic extractions.
Simulation of orthodontic treatment has gained
Sagittal, vertical and molar relationships, tooth inclinations,
popularity by clear aligner systems produced by a digital
overjet, overbite, protrusion index, soft tissue characteristics
process.
and patient complaints were given as input. Artificial
intelligence system can then guide the clinician to decide the Moving the teeth with “tooth positioning appliances”
extraction, based on the analysis fed from the mentioned through sequential stages which are formed by “set-ups” on
inputs. Studies showed that artificial intelligence systems can plaster models was a concept introduced by Kesling (1945).
assist clinicians by preventing errors in decision step and can The major drawback of this technique was that there was a need
provide 80 to 90% accuracy when making an orthodontic to manually subdivide the movement into small increments by
extraction decision (Jung and Kim, 2016; Xie et al., 2010). different plaster set-ups for each increment (Faltin et al., 2003).

Appliance selection The introduction of the Invisalign system in 1997, which

Headgears are widely used as an extraoral anchorage device was the first treatment technique in the field of orthodontics
for growth modification, and they also provide force for molar using digital 3D technology, made Kesling's idea much more
distalization. Although they are typically used for the Class II practical. Instead of requiring a new model for each step of the
patients with increased overbite and overjet and decreased treatment, Invisalign used a set of algorithms to generate
mandibular plane angle, case selection is still challenging for altered digital 3D models to produce a set of aligners. The
inexperienced clinicians especially when planning the system digitally simulated incremental movements of the teeth.
“borderline” or “marginal” cases because the decision-making Based on input data and statistical analysis, AI software helps
process to choose an appropriate headgear type is considered to estimate tooth movement and the outcome of orthodontic
more appropriate to be treated not separately, but rather in a treatment. Similar software programs are used for production
continuous manner, that is, fuzzy logic. of different orthodontic appliances (Vecsei et al., 2017). To
have a valid and effective aligner treatment, it is essential to
Akçam and Tanaka (2002) developed a professional system
have comparable predicted and actual outcomes (Buschang et
based on fuzzy logic, which could infer an optimum selection
al., 2014). The tooth control capability and outcome prediction
of headgear type for orthodontic patients. The model in their
of this AI-based digital system have been discussed extensively
study used overjet, overbite, and mandibular plane angle as
in the previous literature.
input parameters. System used three different fuzzy logic
clusters to choose from low, medium, or high pull headgear A case report by Faltin et al. (2003) compared the estimated
types. Eight expert orthodontists evaluated the headgear end results provided by the ClinCheck software, the software
recommendation for 85 patients. Average satisfaction rate of for planning Invisalign treatments, to actual clinical results and
the examiners was as high as 95,6%. Therefore, the usefulness concluded that the similarities between virtual and clinical
of the proposed inference logic system was confirmed. results seemed to be satisfying. As a result, treatment and the
treatment plan with the system were proved to have a reliable
Estimation of treatment results and appliance production
estimation capability.
Multi-regression models are used in the dental and medical
field to assess the relationship between a range of features and In two more recent papers Buschang et al. (2014) and
the outcomes. This technique has the potential to identify the Grünheid et al. (2017) again compared ClinCheck treatment
best predictors and it also offers a model that expresses the results to clinical results with the aim of testing the simulation
dependent variables in terms of correlated independent capacity of the software. They found that although the software

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was successful in simulating simpler treatment plans, there analysis towards an informed clinical aesthetic practice. Aesthetic
were significant differences between the simulation and Plast. Surg. 42, 137-146.
clinical results in more complex treatments. The ClinCheck 11. Hutton, T.J., Cunningham, S., Hammond, P., 2000. An evaluation
software showed extremely limited reliability when it came to of active shape models for the automatic identification of
cephalometric landmarks. Eur. J. Orthod. 22, 499-508.
simulation of extraction therapy. ClinCheck models failed to
accurately reflect patients' final occlusion in complex 12. Iglovikov, V.I., Rakhlin, A., Kalinin, A.A., Shvets, A.A., 2018.
Paediatric bone age assessment using deep convolutional neural
treatments. networks. In deep learning in medical image analysis and
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7. Conclusion
Quebec, pp. 300-308.
It is quite clear that AI technology has a significant impact on
13. Jung, S.K., Kim, T.W., 2016. New approach for the diagnosis of
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14. Kannan, P.V., 2017. Artificial intelligence applications in
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15. Kattadiyil, M.T., Mursic, Z., AlRumaih, H., Goodacre, C.J., 2014.
One of the most common criticisms against AI technology Intraoral scanning of hard and soft tissues for partial removable
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