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Review

Trends and Application of Artificial Intelligence Technology in Orthodontic Diagnosis and Treatment Planning—A Review

by
Farraj Albalawi
1,2,* and
Khalid A. Alamoud
1,2
1
Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
2
King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(22), 11864; https://doi.org/10.3390/app122211864
Submission received: 19 September 2022 / Revised: 18 November 2022 / Accepted: 19 November 2022 / Published: 21 November 2022
(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems)

Abstract

:
Artificial intelligence (AI) is a new breakthrough in technological advancements based on the concept of simulating human intelligence. These emerging technologies highly influence the diagnostic process in the field of medical sciences, with enhanced accuracy in diagnosis. This review article intends to report on the trends and application of AI models designed for diagnosis and treatment planning in orthodontics. A data search for the original research articles that were published over the last 22 years (from 1 January 2000 until 31 August 2022) was carried out in the most renowned electronic databases, which mainly included PubMed, Google Scholar, Web of Science, Scopus, and Saudi Digital Library. A total of 56 articles that met the eligibility criteria were included. The research trend shows a rapid increase in articles over the last two years. In total: 17 articles have reported on AI models designed for the automated identification of cephalometric landmarks; 12 articles on the estimation of bone age and maturity using cervical vertebra and hand-wrist radiographs; two articles on palatal shape analysis; seven articles for determining the need for orthodontic tooth extractions; two articles for automated skeletal classification; and 16 articles for the diagnosis and planning of orthognathic surgeries. AI is a significant development that has been successfully implemented in a wide range of image-based applications. These applications can facilitate clinicians in diagnosing, treatment planning, and decision-making. AI applications are beneficial as they are reliable, with enhanced speed, and have the potential to automatically complete the task with an efficiency equivalent to experienced clinicians. These models can prove as an excellent guide for less experienced orthodontists.

1. Introduction

Artificial intelligence (AI) is a new breakthrough in technological advancements based on the concept of simulating human intelligence. AI models are designed to assist humans in completing tasks more efficiently and are developed through training and programming, using a large number of datasets, and then executing these models to turn the data into actionable information in performing a specific task [1]. These emerging technologies highly influence the diagnostic process in medical sciences, with enhanced accuracy in diagnosis [2]. AI models have been applied in healthcare in order to assist healthcare professionals and enhance the accuracy in diagnosis, decision making, and predicting prognosis, with the ultimate goal of enhancing patient care and treatment outcomes. AI models have also demonstrated performances that are on par with trained and experienced healthcare professionals [3].
AI technology has been widely applied in dentistry. These models are based on Machine Learning (ML) systems, which are designed using algorithms that can be trained using a large number of data sets and later applied to new sets of data for testing and evaluation. ML based AI models have displayed excellent accuracies in making predictions based on the datasets without human interventions [4,5,6]. AI models based on the newly developed and advanced system known as Deep Learning (DL) are designed to mimic the functioning of the neural system of the human brain; hence they are termed artificial neural networks [7].
The AI system has been gaining high popularity in dentistry, as they have been widely applied for diagnosing and predicting oral diseases such as dental caries, periodontal diseases, and oral cancer [8,9,10,11]. AI models have demonstrated exceptional performances in diagnosis, determining the treatment needs, and predicting the prognosis of diseases [1,5,8,9,10,11,12,13]. Along with this, AI models have also been designed and applied in the field of forensic odontology, where the models have demonstrated excellent precision in performing tasks [14]. Considering these developments in AI, this review article intends to report on the trends and application of AI models designed for diagnosis and treatment planning in orthodontics.

2. Materials and Methods

2.1. Search Strategy

Data search for the articles that have reported on the application of AI models in orthodontics was carried out in the most renowned electronic databases. These databases mainly included PubMed, Google Scholar, Web of Science, Scopus, and Saudi Digital Library. This search was mainly carried out for original research articles published over the last 22 years, from 1 January 2000 until 31 August 2022. Several Medical Subheadings (MeSH terms) were used for searching the articles in the electronic databases. MeSH terms included: artificial intelligence, automated diagnosis, computer-assisted diagnosis, digital diagnosis, supervised learning, orthodontics, dentofacial orthopedics, unsupervised learning, diagnosis, prognosis, prediction, deep learning, machine learning, artificial neural networks, and convolutional neural networks. Boolean operators AND/OR were used to generate a combination of these key words in the advanced search. Language filters for the English language were also used. A manual search for articles was also performed simultaneously in the college central library after screening the references of the articles obtained from the electronic search.
At this stage, article selection was based on the title and abstract; 628 articles related to our research topic were extracted. Later, 436 articles were excluded due to duplication. The remaining 192 articles were applied for the eligibility criteria for being included in this review article.

2.2. Eligibility Criteria and Study Selection

Original research articles that reported on the application of AI models in orthodontics were included in this systematic review. Articles with no full text, narrative reviews, scoping reviews, letters to editors, opinion letters, case reports, short communications, conference proceedings, and articles other than English, were excluded (Figure 1).
A total of 56 articles that fulfilled the eligibility criteria were subjected to qualitative synthesis (Table 1).
Summary of AI models designed for different diagnostic tasks is presented in Table 2.

3. Results

3.1. Qualitative Synthesis of the Included Studies

The research trend shows a gradual increase in the number of research publications over the last two decades. However, in the last two years, the number of articles reported on the application of AI models in orthodontics has rapidly increased (Figure 2).

3.2. Study Characteristics

AI models developed for application in orthodontics have mainly focused on: the automated identification of cephalometric landmarks [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]; the estimation of bone age and maturity using cervical vertebra and hand-wrist radiographs [32,33,34,35,36,37,38,39,40,41,42,43]; palatal shape analysis [44,45]; determining the need for orthodontic tooth extractions [46,47,48,49,50,51,52]; automated skeletal classification [53,54]; and the diagnosis and planning of orthognathic surgeries [55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70] (Figure 3).

4. Discussion

Digitalization in medical diagnosis is a new breakthrough in the field of health sciences. In dentistry, digital technology has been widely applied in clinical practice, especially with the use of 3D intra oral scanners designed for scanning dental arches. These technical advancements have simplified the process of impression making and fabrication of prostheses [71,72]. The recent advancements in the field of medical sciences and computer technology have resulted in significant developments in AI based models that have been designed for diagnosing oral diseases, treatment planning, clinical decision making, and predicting treatment outcomes; these models have demonstrated excellent performances [1,9,10,11].

4.1. AI Models Designed for Automatically Identifying Cephalometric Landmarks

In orthodontics, cephalometric analysis is considered one of the most significant tools for evaluating the skeletal profile of the craniofacial region. The manual tracing of X-ray films and plotting the landmarks requires skill and expertise. Advancements in AI technology have resulted in the development of automated models that can predict landmarks without human assistance. Nishimoto S et al. [15] reported on an automated landmark predicting system based on a deep learning neural network, where the model demonstrated a performance equivalent to manual plotting. Park JH et al. [16] reported on the performance of two automatic deep learning models You-Only-Look-Once version 3 (YOLOv3) and Single Shot Multibox Detector (SSD) designed for identifying cephalometric landmarks. The YOLOv3 algorithm outperformed SSD in accuracy and displayed 5% higher accuracy than the benchmark methods reported in the literature. Kunz F et al. [18] reported on an automatic cephalometric identification model using an (AI) algorithm; this model demonstrated quality levels equivalent to experienced professional examiners. Hwang HW et al. [19] reported on the performance of an automated identification system, You-Only-Look-Once version 3 (YOLOv3), for the automatic identification of cephalometric landmarks. This model displaced excellent accuracies, which were similar to experienced human examiners. Zeng M et al. [20] reported on an AI based model designed for automatically predicting cephalometric landmarks; this model demonstrated competitive performance compared with other models. Bulatova G et al. [22] reported on an AI based model for automated cephalometric landmark identification; this model demonstrated a performance similar to a calibrated senior orthodontist. Hwang HW et al. [23] compared the performance of a deep learning based AI model with previously reported AI models in the literature designed to identify cephalometric landmarks automatically. This model demonstrated superior results in comparison with the previously reported AI models. Kim J et al. [24] reported that a CNN model designed to identify cephalometric landmarks displayed excellent accuracies. Kim YH et al. [25] reported on a deep learning based fully automatic AI model for identifying cephalometric landmarks; this model demonstrated better performance than two calibrated and experienced examiners. Kim MJ et al. [26] reported on a CNN model designed for the automated identification of cephalometric landmarks using cone-beam computed tomography (CBCT) scans; this model displayed better consistency in identifying the landmarks in comparison with the experienced human examiners. Another study conducted by Kim MJ et al. [27] reported on an automatic cephalometric landmark identification system using CBCT images; this model displayed a performance equivalent to the experienced examiners. Yao J et al. [28] reported on a CNN based AI based model for automatically identifying the cephalometric landmarks; this model demonstrated a higher accuracy. Le VNT et al. [29] reported on a human-AI collaboration for identifying cephalometric landmarks. The collaborative system was effective in identifying landmarks. Gil SM et al. [30] reported on a convolution neural network (CNN) based AI model for identifying cephalometric landmarks; this model demonstrated excellent performance, similar to a human examiner’s performance.

4.2. AI Models Designed for Bone Age and Maturity Estimation

The timing of orthodontic treatment is crucial in achieving the desired clinical outcomes. In treatment planning, quantifying the skeletal growth, mainly the mandible, impacts the diagnosis, treatment planning, and treatment outcomes [73]. Therefore, if orthodontic treatment is initiated during the optimum development phase, it will produce more favorable results. Otherwise, a much longer treatment time and surgical intervention may be needed to correct the deformities of the jaw [74,75]. The standard method for estimating bone maturity uses a hand-wrist radiograph and a lateral cephalogram to estimate cervical vertebral maturity (CVM). However, studies have reported that the reproducibility of the CVM varies among examiners [76,77]. In recent developments, AI has been widely used to estimate bone maturation using hand-wrist radiographs or CVM. Kok H et al. [32] reported on the efficiency of seven AI classifiers designed to determine growth; based on cervical vertebrae stages. These models demonstrated acceptable performance in determining the stages. Another study conducted by Kok H et al. [33] also reported the application of an artificial neural network (ANN) model for determining growth based on cervical vertebrae. This model demonstrated satisfactory results in determining the growth-development periods. Kok H et al. [34] also reported on the success rates of two AI models, artificial neural network models (NNMs) and naïve Bayes models (NBMs), designed for determining the growth and development based on the cervical vertebrae. NNMs displayed a higher success rate than the NBMs. Amasya H et al. [35] reported on the performance of five AI based Machine Learning (ML) models designed for CVM analysis. These AI models displayed more accuracy in comparison with the human observer. Amasya H et al. [36] also reported on the validation of the ANN model designed for CVM analysis; this model displayed better performance in comparison with four trained human observers. Seo H et al. [37] reported on the performance of six CNN based deep learning models designed for CVM analysis on cephalometric radiographs. These six models demonstrated more than 90% accuracy in performing the task. Zhou J et al. [38] reported on the performance of an AI model designed for automatically determining the CVM status using cephalometric radiographs. This model demonstrated good agreement with the human examiners. Kim DW et al. [39] reported on the AI model designed for predicting the hand-wrist skeletal maturation stages. The model demonstrated excellent accuracy in predicting the skeletal maturation stages. Kim EG et al. [40] reported on an AI based CNN model designed for estimating the CVM using lateral cephalograms. The model displayed 93% accuracy in performing the task. Mohammad-Rahimi H et al. [41] reported on an AI model for determining CVM and growth spurts using lateral cephalograms. The model demonstrated reasonable accuracy in determining the CVM stage and displayed high reliability in estimating the pubertal stage. Li H et al. [42] reported on AI based CNN model for CVM classification. This model demonstrated good accuracy in classifying the CVM. Atici SF et al. [43] reported on an AI based deep learning model designed for estimating the CVM stages; this model displayed higher accuracy in determining the CVM stages.

4.3. AI Models Designed for Palatal Shape Analysis

The palate has a complex structure, and its shape varies among individuals. The palate’s shape is related to the facial pattern and a wide range of factors such as breathing pattern and occlusion [78,79,80]. The shape of the palate is of great interest to orthodontists as it is a potential area for evaluation and assessing the outcome of orthodontic procedures like maxillary expansion [81,82].
Palatal measurements usually include palatal surface area, volume, and depth [78]. These measurements often require greater experience and are often subjected to observer errors. The recent technological advancements have resulted in the development of AI based models designed for palatal shape analysis. Croquet B et al. [44] reported on a deep learning model designed for analyzing the palatal shape. This automatic model demonstrated excellent repeatability with promising accuracy. Nauwelaers N et al. [45] reported on the application of an AI based deep learning model for palatal shape analysis, and this model achieved results similar to conventional approaches.

4.4. AI Models Designed for Determining the Need for Extractions

Determining the need for tooth extraction and deciding which teeth need to be extracted is a critical decision in orthodontic treatment planning as it is irreversible [83]. The orthodontists’ decision regarding extraction is based on their training, clinical experience, and treatment philosophies [84]. AI technology has been applied to designing models which can be used as an axillary tool for deciding on the need for orthodontic extractions. Xie X et al. [46] reported on the AI based ANN model for determining the need for extraction; this model was very efficient and displayed an accuracy of 80% in determining the need for extraction. Jung SK et al. [47] reported on the performance of an AI model designed to diagnose the need for orthodontic extraction. The model was very efficient in diagnosing extraction and non-extraction cases. Li P et al. [48] reported on the multilayer perceptron ANN model for predicting extraction and non-extraction cases. The model demonstrated an excellent accuracy of 94% for predicting the extraction and non-extraction cases. Choi HI et al. [49] reported on an AI based model designed to determine the need for extraction. This model displayed a success rate of 91% for determining extraction decisions. Suhail Y et al. [50] reported on a machine learning model to diagnose the need for extraction. The model demonstrated a performance that was in agreement with the trained examiners. Etemad L et al. [51] reported on a machine learning model for predicting the need for extraction and non-extraction. The model displayed acceptable results; however, the authors suggested a need for improvisation in the algorithms to improve generalizability.

4.5. AI Models Designed for Planning Orthognathic Surgeries

Individuals presenting dentofacial deformities, either due to congenital or acquired conditions, may require orthognathic surgeries in order to reposition the jaws into a functional relationship. In recent years, several articles have reported on the application of automated computerized methods designed for analyzing dentofacial deformities and elaborating treatment plans [85]. Patcas R et al. [57] reported on an AI model designed for predicting facial appearance post orthognathic surgery; this model displayed an acceptable performance in predicting facial attractiveness and appearance. Knoops PGM et al. [58] reported on a machine learning model designed for automated diagnosis and treatment planning. This model demonstrated an excellent sensitivity of 95.5% and specificity of 95.2% in diagnosing the patients. Stehrer R et al. [59] reported on an AI model for predicting perioperative blood loss following orthognathic surgery. The model demonstrated an excellent result and efficiently predicted the perioperative blood loss prior to surgery. Jeong SH et al. [60] reported on an AI model designed for judging the soft tissue profiles requiring orthognathic surgery. The model displayed an accuracy of 89.3% in judging the soft tissue profiles requiring surgery. Lee K-S et al. [61] reported on an AI based deep CNN model for differential diagnosis of orthognathic surgery. The model was successful and can be applied for differential diagnosis of orthognathic surgery. Tanikawa C et al. [62] reported on an AI model designed for predicting facial morphology post orthognathic surgery. The model demonstrated an excellent success rate in predicting facial morphology and can be applied for clinical purposes. Xiao D et al. [63] reported on an AI model designed for virtually simulating the surgical plan. The model showed higher accuracy in generating the shape models. Shin W et al. [68] reported on an AI model that automatically predicts the need for orthognathic surgery. The model was efficient with relative accuracy in predicting the need for surgery. Kim YH et al. [69] reported on an AI based deep learning model designed to diagnose orthognathic surgery. The model demonstrated excellent performance in predicting the diagnosis of orthognathic surgery.
A few of the limitations of this paper might be with the search strategy. Even though we have performed a comprehensive search for articles, some might have been missed. In general, these AI models’ limitations are mainly due to the limited amount of data sets that have been applied for training these models, and validating and testing. Another limitation of the data sets is the standardization since the data sets applied for assessing the performance of these AI models are obtained from one diagnostic center in most cases. Hence, the performance of these models may vary when exposed to different data sets from multiple centers. However, considering the performance of these AI models, there is an urgent need to develop and implement policies to accelerate the process of approval of these models for marketing and usage in clinical scenarios, which can help clinicians in the diagnosis and decision making process.

5. Conclusions

The past decade has witnessed tremendous advancements in digital diagnostic techniques. AI is a major development that has been successfully implemented in a wide range of image-based applications. These applications can facilitate clinicians in diagnosing, treatment planning, and decision making. These applications are extremely useful as they are reliable and fast methods that have the potential of automatically completing the task with an efficiency equivalent to experienced clinicians. These models can prove to be an excellent guide for less experienced orthodontists. However, there are a few limitations with most of these models, with respect to the limited number of datasets used for training and validating these models, and the reliability of the data, as they are obtained from a single hospital/institution or a single machine. Hence, greater improvisation needs to be conducted in this area for better reliability and generalizability.

Author Contributions

Conceptualization, F.A.; methodology, F.A. and K.A.A.; software, F.A.; validation, F.A.; formal analysis, F.A.; investigation, F.A.; resources, F.A.; data curation, F.A.; writing—original draft preparation, F.A. and K.A.A.; writing—review and editing, F.A. and K.A.A.; visualization, F.A.; supervision, F.A.; project administration, F.A. 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.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Flow chart for screening and selection of articles.
Figure 1. Flow chart for screening and selection of articles.
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Figure 2. Trends in research publications.
Figure 2. Trends in research publications.
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Figure 3. Application of AI in orthodontics.
Figure 3. Application of AI in orthodontics.
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Table 1. Details of the studies that reported in application of AI based models in orthodontics.
Table 1. Details of the studies that reported in application of AI based models in orthodontics.
Serial NoAuthors and ReferenceYear of PublicationType of Algorithm
Architecture
Objective of the StudyNo. of Patients/Images/Photographs for Training TestingStudy FactorModalityComparison If AnyEvaluation Accuracy/Average Accuracy/Statistical SignificanceResults
(+)Effective,
(−)Non Effective
(N) Neutral
Limitations of the StudyAuthors Suggestions/Conclusions
1Nishimoto s et al. [15]2019CNNsAutomatic cephalometric landmark detection153 samples for training, 66 for validatingLandmarksLateral cephalogramSpecialistNot clear (N) neutralCNNs based model predicted cephalometric analysis were not significantly different from those plotted manuallyThis model still in the state of development
2Park JH et al. [16]2019ANNsAutomatic identification of cephalometric landmarks1028 samples for training, 283 for validatingLandmarksLateral cephalogramBenchmarks in the literatureYOLOv3 algorithm outperformed SSD in accuracy(+)EffectiveThe accuracy was inferior to other methods when the size of objects is smallThis model can be of great use for use in clinical practice.
3Chen S et al. [17]2020MLAutomatic landmarks identification60 samplesLandmarksCone-Beam Computed Tomography
(CBCT) images
Not mentioned Not clear (+)Effective Not mentioned Fast and efficient CBCT image segmentation will be analyzed more efficiently
4Kunz F et al. [18]2020CNNsAutomated cephalometric X-ray analysis50 samplesLandmarksCephalometric X-rays12 experienced examinersNo statistically significant differences(+)Effective Not mentioned Results were of the same quality level as
experienced examiners
5Hwang HW et al. [19]2020CNNsAutomated identification of cephalometric landmarks1028 samples for training and 283 samples for testingLandmarksCephalogramsHuman examinersAccuracy similar to human examiners(+)EffectiveWhen the data set was less than 500 the AI model did not identify the landmarks correctlyLarger quantity of datasets will be required in the future.
6Zeng M et al. [20]2020CNNsAutomatic cephalometric landmark detection150 for training dataset and 250 test imagesLandmarksCephalogramsNot mentionedSignificant performance(+)EffectiveThe model needs improvement in the future work.This is a good model for detecting landmarks
7Lee JH et al. [21]2020 BCNNs Locating cephalometric landmarks150 images for training, 250 test imagesLandmarksCephalograms Two expert Examiners Significantly higher performance (+)EffectiveThe model was trained on regional geometrical
features only
Improves the accuracy and reliability of decisions of the specialists
8Bulatova G et al. [22]2021CNNsAutomatic cephalometric landmark identification110 samplesLandmarksCephalograms Senior orthodontic resident No statistical difference between the two(+)EffectiveThe operator is supposed to put a digital ruler which can be subjected to human errors.Can increase efficiency in routine clinical practice
9Hwang HW et al. [23]2021CNNsAutomated cephalometric analysis 1983 for training, 200 for testingLandmarksCephalograms Expert Examiner Superior than Human examiner(+)EffectiveThe performance of the model can be affected with the noise issues inherent in medical imaging.Superior performance than those reported in literature
10Kim J et al. [24]2021CNNsAutomated identification of cephalometric
landmarks
3150 for training, 100 for testing LandmarksCephalogramsTwo orthodontists Significant performance(+)EffectiveOnly hard tissue landmarks for trainingThis model can replace human task
11Kim YH et al. [25]2021CNNsAutomatic cephalometric landmark identification 800 for training, 100 for testingLandmarksCephalogramsTwo Calibrated examinersSignificant performance(+)EffectiveNot mentionedThis model achieved better results than examiners
12Kim MJ et al. [26]2021CNNsAutomatic cephalometric landmark identification860 samplesLandmarksCBCT imagesOne experienced orthodontistSignificant performance(+)EffectiveDid not compare the prediction accuracy of a model trained by a more experienced clinicianThis model showed better consistency than manual identification
13Kim MJ et al. [27]2021CNNsAutomatic cephalometric landmark identification860 samples 80% training, 20% validatingLandmarksCBCT imagesOne experienced orthodontistSignificant performance(+)EffectiveAmount of data required to achieve the expected accuracy could not be explainedThis model showed superior results compared to previous studies
14Yao J et al. [28]2022CNNsAutomatic cephalometric landmark location512 samples training, 200 for testing and validatingLandmarksCephalogramsTwo experienced orthodontistsHigher accuracy(+)EffectiveAmount of data volume was less and need to increased This model meets the requirements of different cephalometric analysis methods.
15Le VNT et al. [29] 2022CNNsHuman–AI collaboration for the identifying cephalometric landmarks1193 samples training, 100 for testingLandmarksCephalogramsTwenty dental studentsAccuracy was higher than dental students(+)EffectiveAmount of dataset was small and obtained for very young patients This beginner–AI collaboration model was effective in detecting the landmarks
16Gil SM et al. [30]2022CNNsAutomated identification of cephalometric
landmarks
2075 samples for training, 343 for validating LandmarksCephalogramsOne experienced examiner Demonstrated an high successful detection rate(+)EffectiveThe comparison was made with one single examinerThis model is an effective
alternative to manual identification
17Dot G et al. [31]2022CNNs Automatic localization ocephalometric landmarks 160 samples for training, 38 for validating Landmarks Computed tomography (CT) scans One experienced operator Excellent agreement with the human examiner (+)Effective This model still requires improvement as the data sets were limited This reliability of the model is par with that of the clinician
18Kök H et al. [32]2019 ANNs Determining
growth and development by cervical vertebrae stages
300 samplesReference pointsCephalometric radiographsOne trained orthodontist and Seven different AI algorithms This model displayed second-highest accuracy (+)EffectiveMore datasets needed for training and evaluation This model can be used as decision support to clinicians
19Kök H et al. [33]2020 ANNs Growth and development periods and gender
from the cervical vertebrae
419 samples
70% for training, 15% testing and,
15% for validating
Reference pointsCephalometric and hand-wrist radiographsResearcher Displayed high accuracy (+)EffectiveMore datasets needed for training and evaluationThe success of this model was satisfactory
20Kök H et al. [34]2020 ANNs Determining growth and development based on
cervical vertebra ratios
360 samplesReference pointsCephalometric radiographs Naïve Bayes models (NBMs)More successful than the reference model(+)EffectiveDatasets belonged to one population, need to study different and multi-racial This model was more successful than the previous models
21Amasya H et al. [35]2020ANNsDetermining cervical vertebral maturation (CVM) analysis647 samplesReference pointsCephalometric radiographsOne examiner Five different ML modelsBest results was achieved by ANN model(+)EffectiveAbsence of hand-wrist
radiographs
This model can be used for prediction of cervical vertebrae morphology
22Amasya H et al. [36]2020ANNsCervical vertebral maturation analysis647 samplesReference pointsCephalometric radiographsThree experienced dentomaxillofacial radiologists and one experienced orthodontistDisplayed better performance(+)EffectiveThe data obtained was from the wide age range (10–30 years)This model performed close to or even better than human observers
23Seo H et al. [37]2021CNNsCervical vertebral maturation analysis600 samplesReference pointsLateral cephalometric radiographsExperienced radiologist, Six deep learning modelsAll models demonstrated excellent accuracy Inception-ResNet-v2 performing the best (+)EffectiveSmall number of data set used This model will help practitioners in making accurate diagnoses and treatment plans
24Zhou J et al. [38]2021CNNsCervical vertebral maturation status1080 samples (980 for training and 100 for testing)Reference pointsCephalometric radiographsTwo experienced examinersThere was a good agreement between AI the
Human examiners
(+)EffectiveSmaller size of data set for testingThis model is a useful and reliable tool for assessing CVM.
25Kim DW et al. [39]2021MLPredicting the hand-wrist maturation stages based on the cervical vertebrae
(CV) images,
499 samplesReference pointsHand-wrist
radiographs and lateral
cephalograms
Three specialistsBetter prediction accuracy(+)EffectiveSmaller size of data set along with more pediatric patientsThis model can aid as a decision supporting tool
26Kim EG et al. [40]2022CNNsEstimating cervical vertebral maturation600 samples Reference pointsLateral cephalograms Four experienced specialists Demonstrated best accuracy(+)EffectiveDatasets were developed using radiographs from single institution This model displayed best accuracy and is of practical applicability
27Mohammad-Rahimi H et al. [41]2022CNNsCervical vertebral maturation (CVM) degree and growth spurts890 samplesReference pointsLateral cephalometric radiographsTwo orthodontistsSubstantial agreement between the experienced examiners and the AI model(+)EffectiveImprovements need to be done in data qualityThis model can provide practical
assistance to practicing dentists
28Li H et al. [42]2022CNNsEstimating cervical vertebral maturation6079 samples (70% for training, 15% testing and,
15% for validating)
Reference pointsCephalometric radiographsTwo experienced orthodontists ResNet152, DenseNet161, GoogLeNet,
VGG16
ResNet152 demonstrated best accuracy(+)EffectiveQuality and quantity of the datasets was severely affected This model can be used as an automatic auxiliary diagnostic tool
29Atici SF et al. [43]2022CNNsClassification of the Cervical Vertebrae Maturation1018 samplesReference pointsCephalometric radiographsExpert Orthodontist, CNN, MobileNetV2, ResNet101, and XceptionThis CNN model provide higher accuracy than the models(+)EffectiveNot mentioned This model can be used as effective tool for analyzing the skeletal maturity stage and timing of the treatment.
30Croquet B et al. [44]2021CNNsAutomated land –marking for palatal shape analysis1045 samples (732 for training, 209 for testing and 104 for validating)LandmarksDental castsSingle operatorThere was no difference between automatic and manual analysis with promising accuracy and
reliability,
(+)EffectiveThe data was of individuals with dentition till second molars may not reflect the true diversity of individuals of interest to orthodontistsThis model can be used for land-marking of digitized dental casts for clinical and research purpose.
31Nauwelaers N et al. [45]2021CNNsPalatal and dental shape estimation1324 samplesLandmarks3D Dental castsDifferent modelsSingular auto-encoder achieved competitive performance in
terms of accuracy, generalization, specificity, and variance
(+)EffectiveThe model was limited
to shapes that underwent an elaborate pre-processing
This model can a useful tool for shape analysis in the future
32Xie X et al. [46]2010ANNsDetermining the need for orthodontic tooth extraction 200 samples (180 for training, 20 for testing)IndicesCasts and cephalometrics Humans80% accuracy in determining the need for extraction(+)EffectiveLimited amount of samples This model can be considered a decision-making tool
33Jung SK et al. [47]2016ANNsDiagnosis of orthodontic tooth extractions156 samples (96 for training, 60 for testing)IndicesCasts and cephalometricsExperienced orthodontistHigh performance Excellent success rates(+)EffectiveDiagnosis of extractions was confined to nonsurgical proceduresCan be used as an new approach in orthodontics
34Li P et al. [48]2019ANNsDetermining the need of orthodontic tooth extraction302 samplesFeature variablesCasts and cephalometricsTwo experienced orthodontistsExcellent performance with 94.0% accuracy(+)EffectiveLimited amount of samplesThis model can provide a good guidance for less experienced orthodontists.
35Choi HI et al. [49]2019MLDetermining the need of orthodontic tooth extraction316 samplesDatasets Casts and cephalometricsOne experienced orthodontistHigh performance with 91% accuracy(+)EffectiveExclusion of skeletal asymmetry casesCan be applied for the diagnosis of cases
36Suhail Y et al. [50]2020MLDiagnosis of orthodontic tooth extraction287 samplesDatasetsCasts and cephalometricsFive experienced orthodontistIn agreement with the experienced orthodontists(+)EffectiveLimited feature set where the treatment outcomes were confined to only non-surgical orthodontic proceduresCan be considered a decision-making tool in clinical practice
37Etemad L et al. [51]2021MLDecision on orthodontic tooth extraction838 samplesDatasetsCasts and cephalometricsPrevious modelsPerformance was lesser than the previous models(+)EffectiveNot mentionedThis model lacks generalizability and in order to improve it needs advanced artificial
intelligence algorithms
38Real AD et al. [52]2022MLDetermining the need of orthodontic tooth extraction214 samplesDatasetsCasts and cephalometricsTwo experienced orthodontistsDemonstrated an accuracy of 93.9%(+)EffectiveDegree of over fitting that may have occurred in the modelsThis model achieved best performance when model and cephalometric
data are combined
39Yu HJ et al. [53]2020CNNsAutomated Skeletal Classification5890 samples (70% for training, 15% testing and,
15% for validating)
DatasetsClinical data and cephalometricsFive experienced orthodontistsDemonstrated an highest accuracy at 96.40%(+)EffectiveThe data were collected from a single organizationThis model has a potential for skeletal orthodontic diagnosis
40Wang H et al. [54]2021CNNsAutomated multiclass segmentation of the jaw and teeth30 samplesLandmarksCBCT scans4 experienced dentistsAccurate in its performance(+)EffectiveData of complicated dental status need to be consideredThis model can reduce the amount of time and effort spent in clinical settings and increase the efficiency and performance of dentists
41C.H Lu et al. [55]2009ANNs Image prediction post orthognathic surgery (OGS) 30 samplesLandmarksLateral Cephalogram
Facial images
Profile post- surgery profileVery less prediction errors (+)Effective Not mentioned Can be applied for predicting post-surgical facial profile
42H. H Lin et al. [56]2018CNNs Assessing facial asymmetry in patients undergone OGS 100 samplesLandmarks3D facial imagesSpecialistPredications were statistically significant p < 0.05(+)EffectiveSmall sample size was used for developing the modelHuman like efficient tool for decision making
43R. Patcas et al. [57]2019CNNs Assessing post OGS facial attractiveness 146 samples
Landmarks Facial photographs Profile post- surgery profileWas in comparison with the actual improvement(+)EffectiveDissimilarities between the subjective patient’s view and the computed score could existIs an efficient tool for assessing facial attractiveness
44P. G. M. Knoops et al. [58]2019CNNsDiagnosing of OGS4261 samplesLandmarksData sets
3D face scans
Not mentioned95.5% sensitivity, 95.2% specificity,(+)EffectiveLarger data sets needed for training the modelsAn efficient tool for diagnosing OGS
45R.Stehrer et al. [59]2019CNNsPredicting perioperative blood loss950 subjectsComparing with actual blood lossData setsData on actual blood lossStatistical significance (p< 0.001).(+)EffectiveData for the model was developed from records from one single clinicAn efficient tool for estimating perioperative blood loss
46S.H.Jeong et al. [60]2020CNNsPredicting soft tissue profiles that require OGS822 samplesLandmarksFacial photographs
2 orthodontist, 3 maxillofacial surgeons,
1 maxillofacial radiologist.
An Accuracy of 0.893(+)EffectiveCertain level of false positives and false negatives cases were revealed by the modelAn efficient tool predicting soft tissue profiles
47K.S. Lee et al. [61]2020DCNNsDifferential diagnosis of OGS220 samplesLandmarks Lateral Cephalogram Four different modelsModified-Alexnet displayed an
Accuracy of 0.919
(+)EffectiveComparison was done with a limited dataModified-Alexnet displayed the highest level performance
48C.Tanikawa et al. [62]2020ANNsPredicting facial morphology post OGS137 samplesLandmarksLateral
cephalogram and 3-D facial images
2 AI modelsExcellent success rates(+)EffectiveThe model was developed and tested with data from only two clinics.
An efficient tool predicting post OGS facial morphology
49D. Xiao et al. [63]2021CNNsFor planning of OGS47 samplesLandmarksCT Scans
Clinical data sets
Sparse representation methodSignificant (p <0.05).(+)EffectiveThe model trained on simulated pairs of deformed-normal bones and the number was limitedThis model outperformed an existing sparse representation method
50G. Lin et al. [64]2021CNNsAssessing the need for OGS in Unilateral Cleft Lip and Palate patients56 samplesLandmarks Lateral Cephalogram Boruta methodAn excellent accuracy of 87.4%.(+)EffectiveThe data used was limited and was from a single centerThis model is capable of predicting the need for surgery
51H.H.Lin et al. [65]2021CNNsAssessing pre and post OGS facial symmetry71 samplesLandmarksCBCT images4 orthodontists and 4 plastic surgeons and also with previously reported modelsAccuracy of 90%.(+)EffectiveThis model was trained with a limited data setsThis model exhibited high performance.
52L.J. Lo et al. [66]2021CNNsAssessing facial soft tissue symmetry before and after OGS158 samplesLandmarks3-D facial photographsPre and post- operativeStatistically Significant(+)Effective Dissimilarities might exist between the patient’s subjective view and the machine scoring The model can aid clinicians in assessing facial symmetry
53R.Horst et al. [67]2021CNNsPredicting the virtual soft tissue profile post-surgery133 samples (119 for training, 14 for testing)Landmarks3D photographs and CBCT imagesMass Tensor Model (MTM)Statistically significant (p = 0.02)(+)EffectiveIn asymmetric cases
and extreme cranial or caudal displacements, the model under predicted these displacements
This model can accurately predict the soft tissue profile post-surgery
54W.S.Shin et al. [68]2021CNNsPredicting the need for OGS413 samplesLandmarks Cephalogram 2 orthodontists,
3 maxillofacial surgeons, 1 maxillofacial
Radiologist.
An excellent accuracy of 0.954(+)EffectiveThis model involved only Korean patients from only one hospital
Displayed higher accuracy in predicting the need for OGS
55Y.H Kim et al. [69]2021CNNsDiagnosing orthodontic surgery960 samples (810 for training, 150 for testing)Landmarks Cephalogram ResNet-18, 34, 50, and 101Success rate was displayed by ResNet-18 = 93.80%,
ResNet-34 = 93.60%
(+)EffectiveThe data used was from a single centerThis model can diagnose whether to conduct orthognathic
surgery
56G. Dot et. al. [70]2022CNNsMulti-task segmentation of cranio-maxillofacial structures for OGS453 samples (300 for training, 153 for testing)LandmarksCT Scans2 OperatorsExcellent performance(+)EffectiveCannot assess the reliability of the results as the data was from one single centerThis model need to be trained from other databases for better reliability
Footnotes: ML = Machine Learning, ANNs = Artificial Neural Networks, CNNs = Convolutional Neural Networks, DCNNs = Deep Neural Networks, Bayesian Convolutional Neural Networks (BCNN), CT- scans-Computed Tomography, CBCT- Cone-Beam Computed Tomography, OCT-Optical Coherence Tomography.
Table 2. Summary of AI models designed for different diagnostic tasks.
Table 2. Summary of AI models designed for different diagnostic tasks.
AI Technique/Algorithm
Architecture
Diagnostic Tasks Functionality of the AI Model Input Features
Machine Learning (ML)Automated identification of landmarksLandmarksCone-Beam Computed Tomography
(CBCT) images [17]
Predicting the hand-wrist maturation stages based on the cervical vertebrae (CV) imagesReference pointsHand-wrist radiographs and lateral
cephalograms [39]
Determining the need of orthodontic tooth extractionDatasetsCasts and cephalometrics [49,50,51,52]
Artificial Neural Network (ANNs)Automated identification of landmarksLandmarks, Reference pointsLateral cephalogram [16], Cephalograms [18,35]
Cervical vertebral maturation analysisReference pointsCephalometric radiographs [36]
Determining growth and development by cervical vertebrae stages IndicesCephalometric radiographs [32], Cephalometric and hand-wrist radiographs [33,34]
Determining the need for orthodontic tooth extractionIndicesCasts and cephalometrics [46,47,48]
Predicting facial morphology post OGSLandmarksLateral cephalogram and 3-D facial images [62]
Deep Learning/Convolutional Neural Networks (CNNs)Automated identification of landmarksLandmarksCone-Beam Computed Tomography
(CBCT) images [15,18,26,27,31], Cephalograms [19,20,21,22,23,24,25,28,29,30]
Cervical vertebral maturation analysisReference pointsLateral cephalometric radiographs [37,38,40], Cephalometric radiographs [42]
Cervical vertebral maturation (CVM) degree and growth spurtsReference pointsLateral cephalometric radiographs [41]
Classification of the Cervical Vertebrae MaturationReference pointsCephalometric radiographs [43]
Automated land –marking for palatal shape analysisLandmarksDental casts [44,45]
Automated Skeletal ClassificationDatasetsClinical data and cephalometrics [53]
Automated multiclass segmentation of the jaw and teethLandmarksCBCT scans [54,70]
Image prediction post orthognathic surgery (OGS) LandmarksLateral Cephalogram
Facial images [55]
Assessing facial asymmetry in patients undergone OGS Landmarks3D facial images [56]
Assessing post OGS facial attractiveness LandmarksFacial photographs [57]
Diagnosing of OGS LandmarksData sets 3D face scans [58], Lateral Cephalogram [61,68,69], CT Scans and Clinical data sets [63]
Predicting perioperative blood lossData setsData on actual blood loss [59]
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Albalawi, F.; Alamoud, K.A. Trends and Application of Artificial Intelligence Technology in Orthodontic Diagnosis and Treatment Planning—A Review. Appl. Sci. 2022, 12, 11864. https://doi.org/10.3390/app122211864

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Albalawi F, Alamoud KA. Trends and Application of Artificial Intelligence Technology in Orthodontic Diagnosis and Treatment Planning—A Review. Applied Sciences. 2022; 12(22):11864. https://doi.org/10.3390/app122211864

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Albalawi, Farraj, and Khalid A. Alamoud. 2022. "Trends and Application of Artificial Intelligence Technology in Orthodontic Diagnosis and Treatment Planning—A Review" Applied Sciences 12, no. 22: 11864. https://doi.org/10.3390/app122211864

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