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Automatic feature
segmentation in dental
panoramic radiographs
Rohan Jagtap
1
, Yalamanchili Samata
2
,
Amisha Parekh
3
, Pedro Tretto
4
,
Tamara Vujanovic
5
, Purnachandrarao Naik
2
,
Jason Griggs
3
, Alan Friedel
6
,
Maxine Feinberg
6
, Prashant Jaju
7
,
Michael D. Roach
3
, Mini Suri
6
and Michelle
Briner Garrido
8
1
Division of Oral & Maxillofacial Radiology, Department of Care Planning
& Restorative Sciences, University of Mississippi Medical Center School
of Dentistry, Jackson, MS, USA
2
Department of Oral Medicine and Radiology, SIBAR Institute of Dental
Sciences, Guntur, AP, India
3
Department of Biomedical Materials Science, School of Dentistry,
University of Mississippi Medical Center, Jackson, MS, USA
4
Department of Oral Surgery, Regional Integrated University of Alto
Uruguai and Missions, Erechim, Brazil
5
Southeast A Regional Representative, American Association for Dental,
Oral and Craniofacial Research National Student Research Group
President, Local Chapter of Student Research Group. Dental Student,
UMMC School of Dentistry Class of 2025, University of Mississippi
Medical Center, Jackson, MS, USA
6
VELMENI Inc., Sunnyvale, CA, USA
7
Department of Oral Medicine and Radiology, Rishiraj College of Dental
Sciences & Research Centre, Bhopal, MP, India
8
Department of Oral Pathology, Radiology and Medicine, Kansas City
School of Dentistry, University of Missouri, Kansas City, MO, USA
Corresponding author:
Rohan Jagtap, DDS, MHA, OMFR, FPFA, FADI, FACD, Director, Division of Oral & Maxillofacial Radiology,
Assistant Professor, Department of Care Planning & Restorative Sciences, Assistant Professor, Department of
Radiology, School of Medicine, Past-Chair, American Dental Education Association Radiology Section,
President-Elect, International Association for Dental Research Diagnostic Group, University of Mississippi
Medical Center, 2500 North State Street, Jackson, MS 39216, USA.
Email: drrohanjagtap@gmail.com
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which permits non-commercial use, reproduction and distribution of the work without further permission provided the original
work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Original Research Article SCIENCE PROGRESS
Science Progress
2024, Vol. 107(4) 1–13
© The Author(s) 2024
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/00368504241286659
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Abstract
Objective The purpose of the present study was to verify the diagnostic performance of an AI
system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on
panoramic radiography.
Methods This is a cross-sectional study. A dataset comprising 1000 panoramic radiographs col-
lected from 500 adult patients was analyzed by an AI system and compared with annotations pro-
vided by two oral and maxillofacial radiologists.
Results A strong correlation (R > 0.5) was observed between AI perception and observers 1 and
2 in carious teeth (0.691–0.878), implants (0.770–0.952), restored teeth (0.773–0.834), teeth with
fixed prostheses (0.972–0.980), and missing teeth (0.956–0.988).
Discussion Panoramic radiographs are commonly used for diagnosis and treatment planning.
However, they often suffer from artifacts, distortions, and superimpositions, leading to potential mis-
interpretations. Thus, an automated detection system is required to tackle these challenges. Artificial
intelligence (AI) has revolutionized various fields, including dentistry, by enabling the development of
intelligent systems that can assist in complex tasks such as diagnosis and treatment planning.
Conclusion The automatic detection by the AI system was comparable to oral radiologists and
may be useful for automatic identifications in panoramic radiographs. These findings signify the
potential for AI systems to enhance diagnostic accuracy and efficiency in dental practices, poten-
tially reducing the likelihood of diagnostic errors caused by unexperienced professionals.
Keywords
Artificial intelligence, panoramic radiographs, dentistry, diagnosis, caries, implants, fixed prosthesis,
dental restoration, teeth numbering
Introduction
Artificial Intelligence (AI) is the branch of computer science that deals with designing
computer system that can imitate intelligent human behavior to perform complex
tasks, such as problem-solving, decision-making, human behavior understanding, and
reasoning, among others.
1,2
Ever since the conception of the term by John McCarthy
in 1956, AI has been widely utilized in numerous fields including agriculture, automo-
tive, industry, as well as medicine.
3–5
AI has tremendous potential in the field of medi-
cine, ranging from automatic disease diagnosis to the use of intelligent systems for
assisted surgery.
6–9
Machine learning (ML) is a branch of AI in which a computer
model identifies patterns from a dataset, learns, and makes predictions without human
instructions aiming to design a system with automated learning ability.
10–13
Traditional
machine learning techniques consisted of some features involving human intervention,
making it more error-prone and time-consuming.
3,11
To overcome this drawback, a more
autonomous multilayered neural network system called ‘deep learning’(DL) was devel-
oped.
3,11,13–15
DL is multilayered system can detect hierarchical features such as lines,
edges, textures, complex shapes, or even lesions and whole organs within a structure.
16
It attempts to predict outcomes by restructuring unlabeled and unstructured multilevel
data.
13
DL is composed of numerous interconnected and sequentially stacked processing
units called neurons, which collectively form artificial neural networks (ANN).
3,11
It comprises
an input layer, an output layer, and multiple hidden layers in between.
11,17
Such ANNs possess
remarkable information processing, learning, and generalization capabilities inspired by the
analytical processes of the human brain.
13,16
The involvement of numerous neurons in the
2Science Progress 107(4)
network makes an ANN capable of solving complex real-world problems compared to con-
ventional ML techniques.
18
Convolutional neural network (CNN), the most used subclass of
ANN, is a special network architecture that uses a mathematical operation called convolution
to process digital signals such as sound, images, and videos.
11
CNNs are primarily used for
processing large and complex signals due to their ability to recognize and classify broader
digital signals.
11
To process such wider signals, they use a sliding window to scan and
analyze from left to right and top to bottom.
11
CNN can be employed for automated
feature detection from two-dimensional (2D) and three-dimensional (3D) images.
19,20
It
involves the automated detection, segmentation, and classification of complex patterns in
an image.
20
Radiographic examination is an integral part of the diagnosis, management, and treat-
ment planning of most dental diseases.
15,21,22
A panoramic radiograph, a low-dose and
cost-effective imaging modality, is routinely used in dental practices due to its ability
to portray all dentoalveolar structures together.
15
It can assist dentists in diagnosing
dental pathologies, lesions, anomalies, and fractures of the maxillofacial structures, as
well as planning restorative and prosthetic rehabilitation treatment.
21
However, pano-
ramic radiograph images may sometimes be affected by enlargement, geometric dis-
tortions, unequal magnification, and multiple superimpositions, which could lead to
misinterpretation and misdiagnosis.
21,23
Hence, an automatic detection system for
evaluating panoramic radiographs is needed to overcome these challenges.
Applications of AI in dentistry span across various specialties, including radiology,
endodontics, periodontics, oral and maxillofacial surgery, and orthodontics. AI has
demonstrated significant potential in dental disease diagnosis, treatment planning, and
reducing errors in dental practice.
13,14,16,22,24,25
Previously, it has shown promising
results in numerous areas, including the detection of dental caries, identification of ver-
tical root fractures (VRFs), diagnosis and classification of periodontal disease types, clas-
sification of malocclusion, automatic identification of cephalometric landmarks, as well
as assistance in treatment planning.
The purpose of the current study is to assess the diagnostic performance of VELMENI
Inc., an AI system that uses a convolutional neural network (CNN)-based architecture, for
automatically detecting teeth, caries, implants, restorations, and fixed prostheses on pano-
ramic radiographs.
Materials and methods
Radiographic dataset
Panoramic radiographs were randomly selected from the EPIC (an electronic medical
record system used mostly in hospitals) and MiPacs systems of the Department of Oral
and Maxillofacial Radiology at the University of Mississippi Medical Center, from
June 2022 to May 2023. 1000 anonymized dental panoramic radiographs of 500 indivi-
duals 18 years or older were used to identify teeth, caries, implants, restorations, and fixed
prostheses. Most patients identified themselves as Caucasians. This dataset compromised
only panoramic radiographs with exposure parameters as low as reasonably achievable and
diagnostically acceptable. Panoramic radiographs with artifacts caused by patient position,
Jagtap et al. 3
motion, or superposition of foreign subjects were not included in this study. The research
protocol was approved by the IRB (2023–177). Panoramic radiographs were obtained
using the Planmeca ProMax (Helsinki, Finland) with parameters of 66 kVp, 8 mA, and
15.8 s The reporting of this cross-sectional study conforms to STROBE guidelines.
28
Image annotation
The identification and detection of teeth, caries, implants, restorations (including amalgam
and composites), and fixed prostheses were independently determined by two oral and
maxillofacial radiologists, each with a minimum of 5 years of experience, to minimize
personal bias. Each of the one thousand anonymized dental panoramic radiographs were
analyzed and the findings were added to an Excel
®
spreadsheet including the number of
teeth with caries (including all types such as enamel, dentin, secondary, radicular, etc.),
number of implants, number of teeth with fillings (including amalgam, composite, etc.),
number of teeth with fixed dental prostheses (FDPs), and the number of missing teeth.
Furthermore, the convolutional neural network (CNN)-based architecture was ana-
lyzed for the detection of the number of teeth with caries, fillings, FDPs, and the
number of implants on the same panoramic radiographs. The artificial intelligence (AI)
system used for analysis was VELMENI Inc., based in CA, USA. A dark filled circle
was used to indicate agreement on the labeling of the above dental findings between
the AI system and observer 1, while a red empty circle was used for agreement on label-
ing between the AI system and observer 2.
Statistical analysis. Pearson’s product moment correlation co-efficient was used to
compare the observations between AI detected dental findings and observers 1 and 2.
Results
The Pearson Product Moment showed a strong correlation (R > 0.5) between the percep-
tion of the AI and perceptions of Observer 1 and Observer 2 for all structures that were
identified in the panoramic radiograph. For the number of teeth with caries, the AI cor-
relation was found to be 0.691–0.878 (Table 1 and Figure 1). For the number of implants,
the AI correlation was found to be 0.770–0.952 (Table 2 and Figure 2). For the number of
teeth with fillings, the AI correlation was found to be 0.773–0.834 (Table 3 and Figure 3).
For the number of teeth with fixed prostheses, the AI correlation was found to be 0.972–
0.980 (Table 4 and Figure 4), while that for the number of missing teeth was found to be
0.956–0.988 (Table 5 and Figure 5).
Discussion
Performing accurate diagnosis is one of the crucial steps in the dental office. Therefore,
dental radiography, especially panoramic radiography, becomes a common and essential
tool for assessing and planning patient treatment. Its widespread use among professionals
and acceptance by patients stem from the ability to visualize all orofacial structures in a
single image, as well as its simplicity of technique, low radiation dose absorbed by the
4Science Progress 107(4)
patient, low cost, and painlessness.
26,27
Despite its benefits, panoramic radiography has
some limitations, such as a lack of three-dimensionality, the potential presence of arti-
facts, and a lack of homogeneity in regions of interest. These limitations may lead to
incorrect interpretations by clinicians.
28
For these reasons, the utilization of automatic
detection methods to assist dentists and their teams in interpreting panoramic radiographs
will be highly significant for accurate diagnosis and planning.
The purpose of the present study was to verify the diagnostic performance of
VELMENI Inc., for the automatic detection of teeth, caries, implants, restorations, and
fixed prosthesis on panoramic radiography. The results of the study, as revealed by
Table 1. Interobserver agreement (Pearson product moment) for number of teeth with caries.
Observer 1 Observer 2 AI
Observer 1 0.730
P < 0.001
0.691
P < 0.001
Observer 2 0.730
P < 0.001
0.878
P < 0.001
AI 0.691
P < 0.001
0.878
P < 0.001
Figure 1. Interobserver agreement (Pearson product moment) for the number of teeth with
caries showing strong correlation between the perception of the AI and perceptions of observer
1 and observer 2.
Jagtap et al. 5
Table 2. Interobserver agreement (Pearson product moment) for number of implants.
Observer 1 Observer 2 AI
Observer 1 0.797
P < 0.001
0.952
P < 0.001
Observer 2 0.797
P < 0.001
0.770
P < 0.001
AI 0.952
P < 0.001
0.770
P < 0.001
Figure 2. Interobserver agreement (Pearson product moment) for the number of implants
showing strong correlation between the perception of the AI and perceptions of observer
1 and observer 2.
Table 3. Interobserver agreement (Pearson product moment) for number of teeth with fillings.
Observer 1 Observer 2 AI
Observer 1 0.917
P < 0.001
0.834
P < 0.001
Observer 2 0.917
P < 0.001
0.773
P < 0.001
AI 0.834
P < 0.001
0.773
P < 0.001
6Science Progress 107(4)
Pearson Product Moment correlation analysis, highlight key points regarding the agree-
ment between the VELMENI artificial intelligence system, based on a CNN architecture,
and two human observers in interpreting panoramic radiographs.
A study used an automated system for tooth detection and numbering and found that
the CNN’s performance was comparable to that of experts, potentially aiding in docu-
ment completion processes and saving time for professionals.
29
For accurate tooth iden-
tification and numbering, it is important that the AI model is trained on a large dataset.
30
In the present study, the identification of the number of missing teeth yielded one of the
best results, with a strong positive correlation of AI with both the human observers.
Table 4. Interobserver agreement (Pearson product moment) for number of teeth with FDPs.
Observer 1 Observer 2 AI
Observer 1 0.991
P < 0.001
0.972
P < 0.001
Observer 2 0.991
P < 0.001
0.980
P < 0.001
AI 0.972
P < 0.001
0.980
P < 0.001
Figure 3. Interobserver agreement (Pearson product moment) for the number of teeth with
fillings showing strong correlation between the perception of the AI and perceptions of observer
1 and observer 2.
Jagtap et al. 7
The use of Convolutional Neural Networks (CNNs) for identifying dental implants is a
highly researched topic in the field of AI applied to implantology. Numerous studies have
presented promising outcomes in the identification of dental implants in panoramic and
periapical radiographs.
31–35
These findings align with the results of our study, wherein a
strong correlation was observed between AI and the two human observers. Given the vast
array of brands and types of dental implants found worldwide, these results offer valuable
assistance to dentists, addressing the need for identifying these points for the continuation
of prosthetic treatment or for the replacement of past treatments where there is no access
to the history of the prior treatment.
31
Convolutional Neural Networks (CNNs) are AI models particularly well-suited for
image classification and, consequently, are widely used in cavity identification.
36
Early
Table 5. Interobserver agreement (Pearson product moment) for number of missing teeth.
Observer 1 Observer 2 AI
Observer 1 0.946
P < 0.001
0.956
P < 0.001
Observer 2 0.946
P < 0.001
0.988
P < 0.001
AI 0.956
P < 0.001
0.988
P < 0.001
Figure 4. Interobserver agreement (Pearson product moment) for the number of teeth with
FDPs showing strong correlation between the perception of the AI and perceptions of observer 1
and observer 2.
8Science Progress 107(4)
diagnosis of carious lesions pose a challenge due to low sensitivity and potential exam-
iner disagreement.
36–38
However, studies demonstrate that CNNs offer good accuracy in
caries diagnosis.
38–40
A literature review
36
highlighted that the accuracy of AI models in
cavity detection ranges between 83.6% and 97.1%. Furthermore, AI assistance for den-
tists in exclusively enamel caries detection resulted in a significant increase in detection,
rising from 44.3% to 75.8%. The present study also found a strong correlation in cavity
detection between AI and the two human observers, with Pearson correlation coefficients
of R =0.691 with Observer 1 and R =0.878 with Observer 2.
Previously, a study employed the U-Net architecture for automatic segmentation of
amalgam and composite resin restorations in panoramic images, achieving highly accurate
detection results.
41
Conversely, another study utilized a CNN and obtained good results in
detecting metallic restorations, with a sensitivity of 85.48%, while the sensitivity in detect-
ing composite resin restorations was 41.11%.
27
The present study found a strong correl-
ation between CNN and the two human observers, with correlation coefficients ranging
from R =0.773 to R =0.834. These findings emphasize the significant contribution of
AI in dental restoration detection and enhance the existing knowledge in the field.
The detection of the number of teeth with fixed dental prostheses (FDPs) yielded the
best results along with the identification of the number of missing teeth mentioned earlier.
In our study, a strong correlation was found between observers 1 and 2 and the AI, being
R=0.972 and R =0.980, respectively. Previous studies have utilized CNNs for the detec-
tion of fixed dental prostheses and full crowns, achieving remarkable precision and
Figure 5. Interobserver agreement (Pearson product moment) for the number of missing teeth
showing strong correlation between the perception of the AI and perceptions of observer 1 and
observer 2.
Jagtap et al. 9
efficiency results.
42,43
The findings from these studies further emphasize the potential of
CNNs in dental prosthesis detection, with accuracy and efficiency comparable to or even
surpassing those of dentists with 3 to 10 years of experience.
Although the current study showed promising results in identifying common condi-
tions in panoramic radiographs, it’s essential to recognize its limitations. The dataset uti-
lized was restricted and lacked external data, potentially impacting the generalization of
results. To address these limitations, future studies should consider employing larger and
more diverse datasets. Our patient population consisted exclusively of the state of
Mississippi, meaning that our findings may not be generalizable to other regions.
Variations in demographics, socio-economic status, and healthcare access could affect
the applicability of the results to broader populations. This approach could offer
further insights and enhance the validity of the findings presented in this study.
Conclusion
The findings of the present study emphasize that AI systems based on deep learning
methods may be useful for the automatic detection of teeth, caries, implants, restorations,
and fixed prosthesis on panoramic images for clinical applications, thereby helping to
improve efficiency. Furthermore, machine learning in dentistry might utilize fundamental
dental radiography to complement further clinical examinations and the training of future
dental practitioners.
Lastly, the strong correlation between the VELMENI Inc. AI system’s detections and
radiologists’annotations highlights the clinical relevance of AI in dental diagnostics. This
reliability indicates that AI may effectively assist in identifying dental conditions, poten-
tially increasing diagnostic accuracy, reducing human error, and enhancing treatment
planning. Integrating AI into clinical practice might improve efficiency, support dental
professionals, and lead to better patient outcomes.
Abbreviations
AI Artificial intelligence;
ANN Artificial neural network;
CNN Convolutional neural network;
DL Deep learning;
ML Machine learning
2D two-dimensional
3D three-dimensional
VRFs vertical root fractures
FDPs fixed dental prostheses.
Acknowledgements
We thank the University of Mississippi Medical Center School of Dentistry Division of Oral and
Maxillofacial Radiology Clinic.
10 Science Progress 107(4)
Authors’contributions
R.J., A.S., P.N., designed a study, data analysis, annotation, and coordination. T.V. collected data.
A.P., P.T., and P.J., performed literature review. J.G. performed the statistical analysis. A.P., P.T.,
and M.R. wrote the manuscript. A.S., M.S., and R.J editing the draft of the manuscript. R.J., M.F.,
and A.F. did final approval of the manuscript. MBG editing the draft of the manuscript. All authors
read and approved the final manuscript.
Data availability statement
Data may be available upon request.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/
or publication of this article.
Ethics approval
University of Mississippi Medical Center Institutional Review Committee gave ethical approval.
IRB Approval number - (IRB-2023–177).
Funding
The authors received no financial support for the research, authorship, and/or publication of this
article.
ORCID iDs
Rohan Jagtap https://orcid.org/0000-0002-9115-7235
Avula Samatha https://orcid.org/0000-0003-1603-2253
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