The Accuracy and Reliability of Orthodontic Cephalometry Analysis Using Web-Based Artificial Intelligence Program
Conditions: Cephalometry, Orthodontic, Cephalometric Analysis
Sponsor: International Islamic University Malaysia

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EVALUATION OF THE ACCURACY AND RELIABILITY OF 
ORTHODONTIC CEPHALOMETRY ANALYSIS USING THE WEB-
BASED ARTIFICIAL INTELLIGENCE (AI) PROGRAM 
Accuracy and Reliability of AI-Based Cephalometric Analysis 
 
Principal Investigator (PI):  
Asst. Prof. Dr Siti Hajjar Binti Nasir 
Department of Orthodontics   
Kulliyyah of Dentistry   
International Islamic University Malaysia 
 
Research Team:  
Co-Investigator 1: Nur Farisah Aliah Binti Jamaludin 
Co-Investigator 2: Nur Fatin Syahirah Binti Abdullah 
Statistical Advisor: Assoc. Prof. Dr Noraini Binti Abu Bakar 
 
Institution:   
Orthodontic Specialist Clinic   
Kulliyyah of Dentistry   
International Islamic University Malaysia   
Kuantan Campus, Pahang, Malaysia 
 
Study Duration: 
6 months (January 2023 – June 2023) 
 
Funding Source: 
IIUM Kulliyyah of Dentistry Postgraduate CHAIN Grant   
Grant Reference: CHAIN 22-001-0001 
 
Ethics Approval: 
IIUM Research Ethics Committee   
Reference Number: IREC 2023-045 
 
 
 

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Table of Contents 
 
1.0 Introduction ..................................................................................................................... 1 
1.1 Problem Statement ............................................................................................................. 2 
1.2 Research Questions ............................................................................................................ 2 
1.3 Research Hypothesis ........................................................................................................... 2 
1.4 Objective ............................................................................................................................ 3 
1.4.1 General Objective ........................................................................................................ 3 
1.4.2 Specific Objective ......................................................................................................... 3 
2.0 Literature Review ................................................................................................................... 3 
2.1 Orthodontics Cephalometric Analysis .................................................................................. 3 
2.2 Manual Tracing Vs Web-Based AI Program Cephalometric ................................................... 3 
2.3 Evaluation of The Accuracy and Reliability of Automated Cephalometry Analysis Software .. 5 
3.0 Materials and Methods .................................................................................................... 6 
3.1 Study Population ................................................................................................................ 6 
3.4 Methodology ...................................................................................................................... 6 
3.5 Data Analysis ...................................................................................................................... 8 
4.0 Expected Outcome ................................................................................................................. 9 
5.0 Gantt Chart  ........................................................................................................................... 9 
6.0 References ........................................................................................................................... 10 
 
 
 
 
 
 
 

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Abstract 
Cephalometric analysis aids in evaluating dentofacial proportions, identifying the 
anatomic basis of malocclusion and analysing growth and treatment-related changes. During 
orthodontic treatment planning, cephalometric analysis is considered an essential diagnostic 
method, particularly when a skeletal discrepancy exists. The introduction of Artificial 
Intelligence (AI) in orthodontics solved a variety of issues as it has been evidenced to be a 
time-saving and reliable tool to assist in developing correct diagnosis and successful treatment 
plans. Computerized software can automatically identify the landmarks and complete the 
measurements once the digital radiograph is imported using AI technology. In addition, 
superimposition of serial radiographs can be performed faster, and it also allows the user to 
obtain several analyses at a time, identify and analyse cephalometric landmarks, face analysis, 
tooth and mandible segmentation, bone age determination, prediction of orthognathic surgery, 
and temporomandibular bone segmentation. As a result, AI is increasingly being used in 
orthodontic treatment to improve speed, consistency, and accuracy because it is a promising 
tool for facilitating cephalometric tracing in routine clinical practice and analysing large 
databases for research purposes. Hence, it is imperative to investigate the reliability and 
usability of AI in cephalometric to ensure its accuracy before relying on the software for 
analysis.  
 
 
 

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1.0 Introduction 
Cephalometric analysis can be used to analyse the facial skeleton in a two-dimensional 
(2D) fashion and based on specialized lateral and anteroposterior (AP) skull radiographs in 
certain anatomical points (Lande et al., 2022). The invention of the cephalometer was 
introduced by B. Holly Broadbent in 1930, and since then, various complex analytical 
techniques have been introduced. Cephalometry is a fundamental diagnostic tool in 
orthodontics. It helps the diagnosis process by determining the aetiology of a malocclusion, 
making the orthodontic and orthognathic treatment planning, monitoring the changes due 
to growth or treatment, and able to predict the orthodontic and orthognathic treatment 
outcomes (Koloikitha et al, 2011).  
 
With the rapid advancement in technology, the manual tracing method is gradually 
being replaced by digital cephalometric analysis software. The software is being favoured 
as it offers various benefits, such as easy handling, allows several analyses to be performed 
at a time, takes up less storage space, allows superimposition of images, provides the option 
to manipulate the image for instance, the size and contrast of the image for better 
visualization of the image, reduced radiation dose, as well as the elimination of chemical 
and associated environmental hazards by reducing the usage of acetate tracing paper and 
the view box (Albarakati et al., 2012). Identifying cephalometric landmarks in lateral 
cephalograms  can be challenging because the skull is a highly complex 3D object which 
in a cephalogram is projected onto a single 2D plane, leading to overlapping structures and 
causing the left and right outlines to not be perfectly superimposed (Lindner et al., 2015) 
 
In the past, drawings of lateral cephalometric analysis were performed manually by 
orthodontists through direct measurement of cephalometric angles using a protractor on an 
overlay of a tracing paper secured over the radiograph. The landmarks and planes 
identification on the radiograph were done using pencils and rulers, with or without the aid 
of a view box. (Yassir, 2021). This technique is time consuming and is subject to landmark 
identification error, measurement error and magnification error (Zamrik et al., 2020).  
However, the development of computer and software technology have led to the 
introduction of computer-aided cephalometric analysis which bring about significant 
improvements in terms of speed, quality, and reliability (Coban G et al., 2022). 
Computerized cephalometric can automatically identify the landmarks and complete the 

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measurements once the digital radiograph is imported using artificial intelligence (AI) 
technology.  
 
Converting from a manual cephalometric analysis technique to a computer-assisted 
web-based cephalometric analysis provides many advantages, however, it requires 
professional supervision and may results in time wasted in front of a computer screen. In 
addition, a systemic review revealed little scientific evidence to support the use of 
automatic landmark identification when compared to manual tracing due to the greater 
number of errors (Yassir et al., 2021). Hence, studies on reliability and usability of AI still 
provide insufficient evidence, and more research should be done into that specific area.  
 
1.1 Problem Statement 
Manual cephalometric tracing and analysis can be time consuming and are 
frequently associated with measurement and calculation errors in addition to errors 
occurring due to human fatigue. Computerized cephalometric analysis software was 
developed to overcome these problems. This can be evident especially in research that is 
based on cephalometric tracing where hundreds of radiographs needed to be landmarked 
and measured. Furthermore, some of the cephalometric analyses involve identifying 
various complex anatomical landmarks that are difficult to be located. Therefore, the use 
of digital cephalometric analysis software is widely considered to be more convenient 
however the accuracy and the reliability need to be tested.  
1.2 Research Questions 
1. Is the accuracy of AI-based cephalometric analysis program comparable to the 
conventional manual cephalometric tracing method? 
2. Is the reliability of AI-based cephalometric analysis program better than the 
conventional manual cephalometric tracing method? 
1.3 Research Hypothesis 
1. The accuracy of AI-based cephalometric analysis is comparable to the 
conventional manual cephalometric tracing method. 
2. The reliability of AI-based cephalometric analysis is better than the 
conventional manual cephalometric tracing method. 

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1.4 Objective 
1.4.1 General Objective 
To evaluate the accuracy and reliability of AI-based cephalometric analysis 
programs when compared to the conventional tracing and analysis method. 
1.4.2 Specific Objective 
1. To investigate the accuracy of AI-based cephalometric analysis programs 
when compared to the conventional tracing and analysis method. 
2. To investigate the reliability of AI-based cephalometric analysis programs 
when compared to the conventional tracing and analysis method. 
 
2.0 Literature Review 
2.1 Orthodontics Cephalometric Analysis 
Cephalometric is an interpretation of measurements of the skull made on 
standardized radiographs of the living head. Since the introduction of cephalometric by 
Broadbent and Hofrath in the 1930s, the cephalometric technique has been regarded as an 
important tool for orthodontists and maxillo-facial surgeons engaged in studying dental 
malocclusions and 
the underlying skeletal 
discrepancies (P.Hlongwa, 2019). 
Cephalometric analysis aids in evaluating dentofacial proportions, diagnosis of 
anteroposterior and vertical discrepancies, identifying the anatomic basis of malocclusion 
and the evaluation of the relationship between soft tissue and dental structures (Yassir et 
al, 2021). It is an indispensable tool for prediction of growth, treatment planning and 
evaluating treatment results. It is also used to predict changes that will occur for 
orthodontics patients after their treatment in the future. A cephalometric tracing can be 
prepared and analysed manually or by a computer using a web-based artificial intelligence 
(AI) program. Therefore, the method used for cephalometric analysis must be accurate, 
safe, and highly reproducible.  
2.2 Manual Tracing Vs Web-Based AI Program Cephalometric 
Manual cephalometric analysis consumes valuable time due to the tedious 
procedures associated with it. The risks include misreading values and the measurements 
obtained may be susceptible to error (P Hlongwa, 2019). The reliability of the landmark 
identification is also questionable as it is hard to locate the tips of the caliper exactly on to 
the same point during every measurement to achieve consistency result (Umut Ozsoy et al, 

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2009). Based on the observer’s experience, manual tracing can take 15-20 minutes to 
complete depending on the quality of the radiographs and the number of parameters 
assessed (Aravind K.S et al, 2022). In addition, manual tracing technique requires the 
printing of radiographic films and are more prone to poor quality printing and are 
susceptible to magnification errors (H. Alqahtani, 2020). 
To overcome these problems, AI-based fully automated cephalometry was 
developed. Automated cephalometric analysis is one of the major applications of AI in the 
field of orthodontics. In recent years, various automated cephalometric software has been 
developed like CephX, WeDoCeph, CHEFBOT and WebCeph which utilizes artificial 
intelligence for automatic landmark identification instead of manual identification (Ravi 
K.M et al, 2022). This can significantly reduce the time and improve the efficiency of 
orthodontists in carrying out cephalometric analysis and diagnosis in routine clinical 
practice and research. Errors due to faulty identification of landmarks can result in 
inaccurate cephalometric interpretation which might lead to errors during orthodontic 
diagnosis and treatment planning.  
 The term AI is mostly associated with robotics which describes the development 
of software or machine by technology that can easily mimic human cognitive skills and 
perform specific activities (Sanjeev B.K, 2021). AI is now being used more frequently in 
orthodontics as it helps in data mining, automated diagnostics, and landmark detection of 
craniofacial morphometric studies. AI can identify landmarks as accurately as human 
examiners and it is a viable choice for a repeated recognition of numerous cephalometric 
landmarks. These AI-based fully automated cephalometry are gaining popularity among 
orthodontists because of their ability to obtain the information of patients quickly and has 
significantly influenced image analysis as it helps detect the anatomical landmark points 
consistently.  
WebCeph is a web-based fully automated AI driven platform that can perform nine 
different cephalometric analysis and two composite analyses along with interpretation. In 
addition, it can be used to store and maintain an archive of digital images of a patient's 
cephalogram, orthopantomogram and photographs. The major advantage of using this 
software is that multiple cephalometric analyses can be accomplished within seconds after 
digital cephalogram is uploaded. Apart from quick cephalometric analyses and 
interpretation, features like cloud-based storage of patient’s records, visual treatment 
simulation and superimposition can make WebCeph an efficient and promising tool for 
routine clinical orthodontic practice. AI-based fully automated cephalometry is more 

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precise because once the images are detected on-screen, measurements and data processing 
will occur automatically. 
WeDoCeph is a fully automated cephalometric online portal that can be used 
anytime and anywhere and the easiest way to get cephalometric stack analyse in minutes. 
It is an x-ray imaging software and diagnostic tool for cephalometric analysis that has 
features for data analysis. It is powered by AI and widely accepted AudaxCeph technology 
(Peter Kobal,2021). There is an incredible editing tools added in WeDoCeph which permits 
for converting visibility nation and move cephalometric landmarks. This AI-based fully 
automated cephalometry can used by simply uploading lateral or periapical x-ray of the 
patient for tracing and enter the analysis’ parameters such as analysis type, patient’s 
identifier, age, and sex. Then, algorithms of AI engine will automatically trace 
cephalometric landmarks, planes, arch and each soft and hard tissue silhouettes which form 
a strong base for cephalometric analysis. Then, the orthodontist will get the results within 
seconds in the forms of different reports, and they can view the results, save it, print, and 
analyse the results. There is no monthly or yearly subscription fee needed.  
 
2.3 Evaluation of The Accuracy and Reliability of Automated Cephalometry 
Analysis Software 
A study by Anuwangnukroh et al. (2018) evaluated the reliability of the 
cephalometric analysis using the dental imaging software (Carestream Dental). They found 
that the automated cephalometric analysis software has significant difference of more than 
2 degrees when compared to the manual hand-based analysis. They also found that  the 
differences come mainly from the angular parameters.  
On the other hand, another study by Katyal et al., (2022) investigated the automated 
tracing software using the WebCeph software. They found there is no significant difference 
in the skeletal, dental and the soft-tissue parameters between the two methods. However, it 
takes shorter time for the automated tracing to analyse the data when compared to the 
manual tracing where the time taken is 30.2 ± 6.4 s and 472 ± 40.4 s respectively (Katyal 
et al., 2022). Another study by  Tsolakis (2022) compared the digital automatic method and 
digital manual method of cephalometric analysis and found small difference between the 
two methods but it was not clinically significant.  

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3.0 Materials and Methods 
3.1 Study Population 
This prospective study includes 40 cephalometric radiographs of orthodontic 
patients seen in Orthodontic Specialist Clinic of Kulliyyah of Dentistry, IIUM 
Kuantan.  
The sample size calculation was done with a significance level of 0.05 and a power 
value of 95%. A sample of a minimum of 37 radiographs was needed. 
 
3.2 Inclusion criteria:  
• Pretreatment OR post-treatment lateral cephalometric radiographs 
• Orthodontic patients treated at the Orthodontic Specialist Clinic, IIUM 
• Radiographs obtained between January 2023 and June 2023 
• High-quality cephalograms without flaws affecting anatomical landmark 
positions 
3.3 Exclusion criteria:  
• Patients with surgical rigid fixations visible on radiographs 
• Presence of orthodontic appliances on radiographs 
• Presence of dental prostheses on radiographs 
• Patients with syndromes and craniofacial deformities. 
 
3.4 Methodology 
Ethical approval will be obtained before the commencement of the study. Forty 
cephalometric radiographs will be selected. Calibration will be done prior to landmark 
identification to ensure agreement between the examiners. 
For manual tracing, 18cm x 24cm of radiographic film images of lateral 
cephalograms will be obtained and traced manually on a view box using 
transilluminated light under dark room. Each cephalograms will be secured over the 
view box first, and then a sheet of matte acetate tracing paper sized 22cm x 28cm are 
taped over the radiographic film. Three orientation marks will be placed as a reference 
point over the film and the tracing paper. After that, hard and tissue landmarks will be 
traced onto the tracing paper manually using a HB pencil. A total of 11 anatomical 
landmarks will be plotted on each cephalograms which are the A point (subspinale, or 
A), B point (B), sella (S), nasion (N), orbitale (O), porion (Po), anterior nasal spine 
(ANS), posterior nasal spine (PNS), gonion (Go), menton (Me) and pogonion (Pog). 

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Measurements of the cephalometric parameters (8 angular and 8 linear) will be taken 
with the help of millimetre ruler and a protractor. All measurements will be performed 
twice by the same investigator with intervals of 4 weeks from the first measurements. 
Angular measurements (°) include the SNA, SNB, ANB, FMPA, MMPA, UIA, LIA, 
IIA. While the linear measurements (mm) include the NA, NPog, ANS-Me, SN, LFH, 
UFH, MxPI, MnPI. 
Table 1: Definition of the relevant cephalometric points 
Cephalometric points 
Definition 
Point A (A) 
The deepest point in the concavity of the anterior maxilla between 
the ANS and alveolar crest 
Point B (B) 
The deepest point in the concavity of the anterior mandible 
between the alveolar crest and Pog 
Sella (S) 
The midpoint of sella turcica 
Nasion (N) 
Junction of the frontal and nasal bones at the frontonasal suture 
Orbitale (O) 
The most inferior point on the infra- orbital margin 
Porion (Po) 
The most superior point of the external auditory meatus 
Anterior Nasal Spine 
(ANS) 
The anterior limit of the nasal floor 
Posterior Nasal Spine 
(PNS) 
The posterior limit of the nasal floor 
Gonion (Go) 
The most posterior and inferior point on the angle of the mandible 
Menton (Me) 
The most inferior point on the bony chin 
Pogonion (Pog) 
The most anterior point on the bony chin 
  
For AI-based fully automated cephalometric analysis, we will be utilizing the 
WeDoCeph (Audax d.o.o., software version 3.2.2). WeDoCeph software is a fully 
automated cephalometric portal that works on the web and can be used anytime and 
from anywhere. WeDoCeph software complies with essential landmark tracing and 
measurement. To operate the analyses, firstly lateral or periapical radiographs of the 
patients will be uploaded into the WeDoCeph software. Then, patient profile will be 
created in the system and digital images of cephalograms with high quality resolution 
of 300DPI will be uploaded. After the x-ray was uploaded, key-in the analysis’ 
parameters such as analysis type, patient’s identifier, age and sex. Each of the 
cephalometric landmarks will automatically be traced by the AI digitization of 

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WeDoCeph. Finally, the cephalometric measurements value will be obtained for the 
different parameters and the tracing analysis were saved as JPEG files. Then, it will be 
downloaded in portable document format (pdf) and entered into the same Microsoft 
Office Excel spreadsheet used for manual tracing values. The same process will be 
applied for all 40 digital cephalograms. Then, all of the radiographs measurements will 
be performed once again by the same investigator with intervals of 4 weeks from the 
first measurements to test for the reliability.  
 
 
Figure 1: Landmarks through AI-based fully automated tracing software.  
3.5 Data Analysis 
The mean, standard deviation, and standard error of the difference between the 
repeated measurements for each method and between the two methods will be 
calculated. The reliability of each method will be defined using the Pearson correlation 
coefficient (r2). The level of statistical significance will be set at P< 0.05. 
 
 
 
 
 
 
 

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4.0 Expected Outcome  
Table 2: Angular and Linear Measurement of Cephalometric Values 
 
AI-based 
(WeDoCeph) 
Manual 
 
Measurements 
Mean 
S.D 
r² 
Mean 
S.D 
r² 
P-value 
SNA (⁰) 
n 
n 
n 
n 
n 
n 
n 
SNB (⁰) 
n 
n 
n 
n 
n 
n 
n 
ANB (⁰) 
n 
n 
n 
n 
n 
n 
n 
FMPA (⁰) 
n 
n 
n 
n 
n 
n 
n 
MMPA (⁰) 
n 
n 
n 
n 
n 
n 
n 
UIA (⁰) 
n 
n 
n 
n 
n 
n 
n 
LIA (⁰) 
n 
n 
n 
n 
n 
n 
n 
IIA (⁰) 
n 
n 
n 
n 
n 
n 
n 
A-N perpendicular (mm) 
n 
n 
n 
n 
n 
n 
n 
POG-N perpendicular (mm) 
n 
n 
n 
n 
n 
n 
n 
ANS-Me perpendicular (mm) 
n 
n 
n 
n 
n 
n 
n 
SN (mm) 
n 
n 
n 
n 
n 
n 
n 
LFH (mm) 
n 
n 
n 
n 
n 
n 
n 
UFH (mm) 
n 
n 
n 
n 
n 
n 
n 
MxPI (mm) 
n 
n 
n 
n 
n 
n 
n 
MnPI (mm) 
n 
n 
n 
n 
n 
n 
n 
 
 
Table 3: Intraclass Correlation Coefficient 
 
Intraclass 
Correlation 
95% Confidence 
Interval 
F Test with True Value 0 
Lower 
Bound 
Upper 
Bound 
Value 
df1 df2 
sig 
Single 
Measures 
 
 
 
 
 
 
 
Average 
Measures 
 
 
 
 
 
 
 

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 5.0 Gantt Chart  
 
Project Activities 
2022 
2023 
2024 
Months 
NOV 
DEC 
JAN 
FEB 
MAR 
APR 
MAY 
JUN 
JUL 
AUG 
SEP 
OCT 
NOV 
DEC 
JAN 
FEB 
Distribution of 
research titles 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Literature review 
& proposal 
preparation 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Proposal 
presentation 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Hand-in written 
proposal  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Ethical Approval 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Data collection 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Data analysis 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Final research 
writing 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Complete report 
submission 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Draft for 
publication 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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Presentation at 
DSSC 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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6.0 References 
1. Subramanian A.K, Yong Chen, Abdullah Almaki, Dashrath K (2022). Cephalometric Analysis 
in Orthodontics Using Artificial Intelligence- A Comprehensive Review. BioMed Research 
International, 2-4. 
 
2. Katyal D, Balakrishnan N (2022). Evaluation of the accuracy and reliability of WebCeph- An 
artificial 
intelligence- based online software. APOS Trends in Orthodontics, 3-5. 
 
3. Mahto, R.K., Kafle, D., Giri, A. et al. (2022) Evaluation of fully automated cephalometric 
measurements obtained from web-based artificial intelligence driven platform. BMC Oral 
Health 22(132), 1-5. 
4. Yassir A.Y, Sarah A.N, Aya R.S. (021). The Accuracy and Reliability of WebCeph for 
Cephalometric Analysis. Journal of Taibah University Medical Sciences, 2-8. 
5. P Hlongwa. (2019). Cephalometric Analysis: Manual Tracing of a Lateral Cephalogram. South 
African Dental Journal, 74(6), 318.  
 
6. Lande L.S, Papaioannou A, Dunaway D.J, (2022). Anthrometrics. Aesthetic Surgery of the 
Facial Skeleton, 19-28. 
 
7. Kolokitha, OE., Topouzelis, N. (2011) Cephalometric Methods of Prediction in Orthognathic 
Surgery. J. Maxillofac. Oral Surg. 10, 236–245  
 
8. Meriç P, Naoumova J. Web-based Fully Automated Cephalometric Analysis: Comparisons 
between App-aided, Computerized, and Manual Tracings. Turk J Orthod 2020; 33(3): 142-9 
 
9. Albarakati, Kula K.S, Ghoneima A.A (2012) Dentomaxillofacial Radiology. The reliability 
and reproducibility of cephalometric measurements: a comparison of conventional and digital 
methods. 40(1)14-17. 
 
10. Zamrik O.M, İşeri H (2020) The Reliability and Reproducibility of an Android Cephalometric 
Smartphone Application in Comparison with the Conventional Method. The Angle 
Orthodontist. 91(2) 236-242. 
 
11. Çoban G, Öztürk T, Hashimli N, Yağci A. (2022). Comparison between cephalometric 
measurements using digital manual and web-based artificial intelligence cephalometric tracing 
software. Dental Press J Orthod. 27(4) 4-5. 
 
12. Leonardi R, Giordano D, Maiorana F, Spampinato C. (2008). Automatic cephalometric 
analysis. Angle Orthod. 78(1) 145-51 
 
13. Mahto, R.K., Kafle, D., Giri, A. (2022) Evaluation of fully automated cephalometric 
measurements obtained from web-based artificial intelligence driven platform. BMC Oral 
Health 132  
14. Mitra R, Chauhan A, Sardana S, et al., (2020) Determination of the comparative accuracy of 
amnual, semi- digital, and fully digital cephalometric tracing methods in orthodontics. Journal 
of Dentistry Defence Section. 14(2) 52-58 

Version 1.0 
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15. Mohan A, Sivakumar A, Nalabothu P. (2021) Evaluation of accuracy and reliability of
OneCeph digital cephalometric analysis in comparison with manual cephalometric analysis-a
cross-sectional study. BDJ Open. 7(1) 1-4
16. Anuwongnukroh N, Dechkunakorn S, Damrongsri S et al., (2017). Assessment of the reliability
of Automatic Cephalometric Analysis Software. International Journal of Mechanical
Engineering and Robotic Research 6(6) 61-65
17. Phulari, B. (2013). An atlas on cephalometric landmarks. JP Medical Ltd.
18. G. W. Arnett and R. T. Bergman (1993). “Facial keys to orthodontic diagnosis and treatment
planning. Part I.” American Journal of Orthodontics and Dentofacial Orthopedics. 103(4),
299–312
19. C. W. Wang, C. T. Huang, J. H. Lee et al. (2016). “A benchmark for comparison of dental
radiography analysis algorithms. Medical Image Analysis. 31, 63–76
20. V. Rajaraman, JohnMcCarthy (2014). Father of artificial intelligence. Resonance. 19(3), 198–
207
21. Y. M. Bichu, I. Hansa, A. Y. Bichu, P. Premjani, C. Flores-Mir, and N. R. Vaid (2021).
“Applications of artificial intelligence and machine learning in orthodontics: a scoping
review. Progress in Orthodontics. 22(1), 1–11
22. S. B. Khanagar, A. Al-Ehaideb, P. C. Maganur et al. (2021). “Developments, application, and
performance of artificial intelligence in dentistry - a systematic review,” Journal of Dental
Sciences. 16(1), 508–522
23. J.-H. Park, H.-W. Hwang, J.-H. Moon et al. (2019). “Automated identification of cephalometric
landmarks: part 1—comparisons between the latest deep-learning methods YOLOV3 and
SSD.” The Angle Orthodontist. 89(6), 903–909
24. Sanjeev B.K, Ali Al-ehaideb, Prabhadevi C.M et al. (2021). Developments, application, and
performance of artificial intelligence in dentistry – A systematic review. Journal of Dental
Sciences. 16(1), 508-522
25. H.-W. Hwang, J.-H. Moon et al. (2021). Evaluation of automated cephalometric analysis based
on the latest deep learning method. The Angle Orthodontist. 91 (3), 329–335
26. Mahto, R.K., Kafle, D., Giri, A. et al (2022). Evaluation of fully automated cephalometric
measurements obtained from web-based artificial intelligence driven platform. BMC Oral
Health 22. 132
27. H. Alqahtani (2020). Evaluation of an online website-based platform for cephalometric
analysis. Journal of Stomatology, Oral and Maxillofacial Surgery. 121(1), 53-57
28. Umut O.MSc, Bahadir M.D.MSc et al. (2009). Method selection in craniofacial measurements:
Advantages and disadvantages of 3D digitization method. Journal of Cranio-Maxillofacial
Surgery. 37(5), 285-290
29. Edgren BN (2013). The combined value of the frontal analysis and growth prediction.
Orthotown. 56-57Lydia Harris (2018). A Beginners Guide to Lateral Cephalometric
Radiographs. Dental protection.
30. Tsolakis I.A., Tsolakis A.I, Elshebiny T et al., (2022). Comparing a fully automated
cephalometric tracing method to a manual tracing method for orthodontic diagnosis. Journal of
Clinical Medicine. 6854(11). 2-8

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Confidential 
Page 1 
RESEARCH CONSENT FORM  
INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA 
PATIENT’S DETAILS: 
NAME:
DATE OF BIRTH: 
GENDER: 
IDENTITY CARD NUMBER: 
RESEARCH DETAILS: 
TITLE: The Accuracy and Reliability of Orthodontic Cephalometry Analysis Using The Web-
Based Artificial Intelligence (AI) Program 
Researchers: 
1. Asst Prof Dr Siti Hajjar Nasir
2. Assoc Prof. Dr Noraini Abu Bakar
3. Nur Farisah Aliah Binti Jamaludin
4. Nur Fatin Syahirah Binti Abdullah
INFORMED PATIENT CONSENT (To be completed by patient/ parent/ guardian) 
I fully understand of my/my child’s involvement in this research. The benefits and all likely risks, effects, 
discomforts or inconvenience arising from participation in the project have been explained to me by 
clinician. 
I know that I/ my child can withdraw from this research at any time without any effect on my ongoing 
orthodontic treatment. 
I understand that any information or material (blood, body fluids or other body material) I provide will 
be kept confidential and reported in an aggregated/ non attributed form. 
I am aware of the procedures and commitment required and allows my/ my child’s records to be used in 
this research and publication. 
I have been given the opportunity to ask questions and time to digest the information before proceeding 
with the decision. 
SIGNATURE: 
NAME:  
(Status: patient/ parent/ guardian) 
IDENTITY CARD NUMBER: 
DATE: 

Version 1.0 
20 Nov 2022 
Confidential 
Page 2 
Statement by the researcher/ person taking consent 
I have accurately read the information sheet to the potential participant, and to the best of my ability 
made sure that the participant understands that the following will be done: 
1. Interview
2. Clinical examination
I confirm that the participant was given the opportunity to ask questions about the study, and all the 
questions asked by the participant have been answered correctly and to the best of my ability. I confirm 
that the individual has not been coerced into giving consent, and the consent has been given freely and 
voluntarily. 
A copy of this Informed Consent Form has been provided to the patient. 
Signature of Researcher/ person taking the consent 
_____________________________________________ 
Print the Name of Researcher/ person taking the consent 
_____________________________________________ 
Identity card number: ___________________________ 
Date: ___________________ 
Witness: 
Signature of the Witness: _________________________ 
Print the Name of the Witness: ____________________ 
Identity card number: ____________________________ 
Date: ___________________