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Advancements in Artificial Intelligence for Dentomaxillofacial Radiology

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 13370

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Prof. Dr. Andre Luiz Ferreira Costa

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Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo, SP, Brazil
Interests: dentistry; dentomaxillofacial imaging; MRI/CBCT/CT; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Kaan Orhan

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Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
Interests: radiology; MRI/CBCT/CT/USG; dentistry; head and neck imaging; artificial intelligence
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Prof. Dr. Sérgio Lúcio Pereira de Castro Lopes

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Department of Diagnosis and Surgery, São José dos Campos School of Dentistry, São Paulo State University (UNESP), São José dos Campos 2245-000, SP, Brazil
Interests: dentomaxillofacial radiology; MRI; CBCT; computer-assisted diagnosis; TMJ
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has significantly transformed the field of dentomaxillofacial radiology, improving the diagnostic accuracy. AI algorithms have been developed to efficiently analyze and interpret complex medical and dental images, enabling faster and more precise detection of dental and maxillofacial pathologies. By leveraging deep learning and machine learning techniques, AI systems can detect abnormalities such as caries, fractures, and tumors with higher sensitivity and specificity, in addition to hidden aspects such as aspects of shape, intensity and texture in the images. Therefore, this Special Issue is focused on the latest developments of AI in dentomaxillofacial radiology, encompassing insights of computer-aided diagnosis, radiomics and machine learning tools for image interpretation.

Prof. Dr. Andre Luiz Ferreira Costa
Prof. Dr. Kaan Orhan
Prof. Dr. Sérgio Lúcio Pereira de Castro Lopes
Guest Editors

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Keywords

artificial intelligence
deep learning
dentomaxillofacial radiology
dentistry
diagnosis

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Published Papers (8 papers)

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18 pages, 3561 KiB

Open AccessArticle

Improving TMJ Diagnosis: A Deep Learning Approach for Detecting Mandibular Condyle Bone Changes

by Kader Azlağ Pekince, Adem Pekince and Buse Yaren Kazangirler

Diagnostics 2025, 15(8), 1022; https://doi.org/10.3390/diagnostics15081022 - 17 Apr 2025

Viewed by 263

Abstract

Objectives: This paper evaluates the potential of using deep learning approaches for the detection of degenerative bone changes in the mandibular condyle. The aim of this study is to enable the detection and diagnosis of mandibular condyle degenerations, which are difficult to observe and diagnose on panoramic radiographs, using deep learning methods. Methods: A total of 3875 condylar images were obtained from panoramic radiographs. Condylar bone changes were represented by flattening, osteophyte, and erosion, and images in which two or more of these changes were observed were labeled as “other”. Due to the limited number of images containing osteophytes and erosion, two approaches were used. In the first approach, images containing osteophytes and erosion were combined into the “other” group, resulting in three groups: normal, flattening, and deformation (“deformation” encompasses the “other” group, together with osteophyte and erosion). In the second approach, images containing osteophytes and erosion were completely excluded, resulting in three groups: normal, flattening, and other. The study utilizes a range of advanced deep learning algorithms, including Dense Networks, Residual Networks, VGG Networks, and Google Networks, which are pre-trained with transfer learning techniques. Model performance was evaluated using datasets with different distributions, specifically 70:30 and 80:20 training-test splits. Results: The GoogleNet architecture achieved the highest accuracy. Specifically, with the 80:20 split of the normal-flattening-deformation dataset and the Adamax optimizer, an accuracy of 95.23% was achieved. The results demonstrate that CNN-based methods are highly successful in determining mandibular condyle bone changes. Conclusions: This study demonstrates the potential of deep learning, particularly CNNs, for the accurate and efficient detection of TMJ-related condylar bone changes from panoramic radiographs. This approach could assist clinicians in identifying patients requiring further intervention. Future research may involve using cross-sectional imaging methods and training the right and left condyles together to potentially increase the success rate. This approach has the potential to improve the early detection of TMJ-related condylar bone changes, enabling timely referrals and potentially preventing disease progression. Full article

(This article belongs to the Special Issue Advancements in Artificial Intelligence for Dentomaxillofacial Radiology)

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22 pages, 32789 KiB

Open AccessArticle

Development of an AI-Supported Clinical Tool for Assessing Mandibular Third Molar Tooth Extraction Difficulty Using Panoramic Radiographs and YOLO11 Sub-Models

by Serap Akdoğan, Muhammet Üsame Öziç and Melek Tassoker

Diagnostics 2025, 15(4), 462; https://doi.org/10.3390/diagnostics15040462 - 13 Feb 2025

Viewed by 1330

Abstract

Background/Objective: This study aimed to develop an AI-supported clinical tool to evaluate the difficulty of mandibular third molar extractions based on panoramic radiographs. Methods: A dataset of 2000 panoramic radiographs collected between 2023 and 2024 was annotated by an oral radiologist using bounding boxes. YOLO11 sub-models were trained and tested for three basic scenarios according to the Pederson Index criteria, taking into account Winter (angulation) and Pell and Gregory (ramus relationship and depth). For each scenario, the YOLO11 sub-models were trained using 80% of the data for training, 10% for validation, and 10% for testing. Model performance was assessed using precision, recall, F1 score, and mean Average Precision (mAP) metrics, and different graphs. Results: YOLO11 sub-models (nano, small, medium, large, extra-large) showed high accuracy and similar behavior in all scenarios. For the calculation of the Pederson index, nano for Winter (average training mAP@0.50 = 0.963; testing mAP@0.50 = 0.975), nano for class (average training mAP@0.50 = 0.979; testing mAP@0.50 = 0.965), and medium for level (average training mAP@0.50 = 0.977; testing mAP@0.50 = 0.989) from the Pell and Gregory categories were selected as optimal sub-models. Three scenarios were run consecutively on panoramic images, and slightly difficult, moderately difficult, and very difficult Pederson indexes were obtained according to the scores. The results were evaluated by an oral radiologist, and the AI system performed successfully in terms of Pederson index determination with 97.00% precision, 94.55% recall, and 95.76% F1 score. Conclusions: The YOLO11-supported clinical tool demonstrated high accuracy and reliability in assessing mandibular third molar extraction difficulty on panoramic radiographs. These models were integrated into a GUI for clinical use, offering dentists a simple tool for estimating extraction difficulty, and improving decision-making and patient management. Full article

(This article belongs to the Special Issue Advancements in Artificial Intelligence for Dentomaxillofacial Radiology)

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27 pages, 2789 KiB

Open AccessArticle

Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic–Random Forest

by Gulfem Ozlu Ucan, Omar Abboosh Hussein Gwassi, Burak Kerem Apaydin and Bahadir Ucan

Diagnostics 2025, 15(3), 314; https://doi.org/10.3390/diagnostics15030314 - 29 Jan 2025

Viewed by 875

Abstract

Background/Objectives: Dental age estimation is a vital component of forensic science, helping to determine the identity and actual age of an individual. However, its effectiveness is challenged by methodological variability and biological differences between individuals. Therefore, to overcome the drawbacks such as the dependence on manual measurements, requiring a lot of time and effort, and the difficulty of routine clinical application due to large sample sizes, we aimed to automatically estimate tooth age from panoramic radiographs (OPGs) using artificial intelligence (AI) algorithms. Methods: Two-Dimensional Deep Convolutional Neural Network (2D-DCNN) and One-Dimensional Deep Convolutional Neural Network (1D-DCNN) techniques were used to extract features from panoramic radiographs and patient records. To perform age estimation using feature information, Genetic algorithm (GA) and Random Forest algorithm (RF) were modified, combined, and defined as Modified Genetic–Random Forest Algorithm (MG-RF). The performance of the system used in our study was analyzed based on the MSE, MAE, RMSE, and R² values calculated during the implementation of the code. Results: As a result of the applied algorithms, the MSE value was 0.00027, MAE value was 0.0079, RMSE was 0.0888, and R² score was 0.999. Conclusions: The findings of our study indicate that the AI-based system employed herein is an effective tool for age detection. Consequently, we propose that this technology could be utilized in forensic sciences in the future. Full article

(This article belongs to the Special Issue Advancements in Artificial Intelligence for Dentomaxillofacial Radiology)

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12 pages, 515 KiB

Open AccessArticle

Artificial Intelligence for Tooth Detection in Cleft Lip and Palate Patients

by Can Arslan, Nesli Ozum Yucel, Kaan Kahya, Ezgi Sunal Akturk and Derya Germec Cakan

Diagnostics 2024, 14(24), 2849; https://doi.org/10.3390/diagnostics14242849 - 18 Dec 2024

Cited by 1 | Viewed by 937

Abstract

Introduction: Cleft lip and palate patients often present with unique anatomical challenges, making dental anomaly detection and numbering particularly complex. The accurate identification of teeth in these patients is crucial for effective treatment planning and long-term management. Artificial intelligence (AI) has emerged as a promising tool for enhancing diagnostic precision, yet its application in this specific patient population remains underexplored. Objectives: This study aimed to evaluate the performance of an AI-based software in detecting and numbering teeth in cleft lip and palate patients. The research focused on assessing the system’s sensitivity, precision, and specificity, while identifying potential limitations in specific anatomical regions and demographic groups. Methods: A total of 100 panoramic radiographs (52 males, 48 females) from patients aged 6 to 15 years were analyzed using AI software. Sensitivity, precision, and specificity were calculated, with ground truth annotations provided by four experienced orthodontists. The AI system’s performance was compared across age and gender groups, with particular attention to areas prone to misidentification. Results: The AI system demonstrated high overall sensitivity (0.98 ± 0.03) and precision (0.96 ± 0.04). No statistically significant differences were found between age groups (p > 0.05), but challenges were observed in the maxillary left region, which exhibited higher false positive and false negative rates. These findings were consistent with the prevalence of unilateral left clefts in the study population. Conclusions: The AI system was effective in detecting and numbering teeth in cleft lip and palate patients, but further refinement is required for improved accuracy in the cleft region, particularly on the left side. Addressing these limitations could enhance the clinical utility of AI in managing complex craniofacial cases. Full article

(This article belongs to the Special Issue Advancements in Artificial Intelligence for Dentomaxillofacial Radiology)

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14 pages, 4246 KiB

Open AccessArticle

Evaluation of a Vendor-Agnostic Deep Learning Model for Noise Reduction and Image Quality Improvement in Dental CBCT

by Wojciech Kazimierczak, Róża Wajer, Oskar Komisarek, Marta Dyszkiewicz-Konwińska, Adrian Wajer, Natalia Kazimierczak, Joanna Janiszewska-Olszowska and Zbigniew Serafin

Diagnostics 2024, 14(21), 2410; https://doi.org/10.3390/diagnostics14212410 - 29 Oct 2024

Viewed by 830

Abstract

Background/Objectives: To assess the impact of a vendor-agnostic deep learning model (DLM) on image quality parameters and noise reduction in dental cone-beam computed tomography (CBCT) reconstructions. Methods: This retrospective study was conducted on CBCT scans of 93 patients (41 males and 52 females, mean age 41.2 years, SD 15.8 years) from a single center using the inclusion criteria of standard radiation dose protocol images. Objective and subjective image quality was assessed in three predefined landmarks through contrast-to-noise ratio (CNR) measurements and visual assessment using a 5-point scale by three experienced readers. The inter-reader reliability and repeatability were calculated. Results: Eighty patients (30 males and 50 females; mean age 41.5 years, SD 15.94 years) were included in this study. The CNR in DLM reconstructions was significantly greater than in native reconstructions, and the mean CNR in regions of interest 1-3 (ROI1-3) in DLM images was 11.12 ± 9.29, while in the case of native reconstructions, it was 7.64 ± 4.33 (p < 0.001). The noise level in native reconstructions was significantly higher than in the DLM reconstructions, and the mean noise level in ROI1-3 in native images was 45.83 ± 25.89, while in the case of DLM reconstructions, it was 35.61 ± 24.28 (p < 0.05). Subjective image quality assessment revealed no statistically significant differences between native and DLM reconstructions. Conclusions: The use of deep learning-based image reconstruction algorithms for CBCT imaging of the oral cavity can improve image quality by enhancing the CNR and lowering the noise. Full article

(This article belongs to the Special Issue Advancements in Artificial Intelligence for Dentomaxillofacial Radiology)

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15 pages, 3343 KiB

Open AccessArticle

Evaluation of a Decision Support System Developed with Deep Learning Approach for Detecting Dental Caries with Cone-Beam Computed Tomography Imaging

by Hakan Amasya, Mustafa Alkhader, Gözde Serindere, Karolina Futyma-Gąbka, Ceren Aktuna Belgin, Maxim Gusarev, Matvey Ezhov, Ingrid Różyło-Kalinowska, Merve Önder, Alex Sanders, Andre Luiz Ferreira Costa, Sérgio Lúcio Pereira de Castro Lopes and Kaan Orhan

Diagnostics 2023, 13(22), 3471; https://doi.org/10.3390/diagnostics13223471 - 18 Nov 2023

Cited by 11 | Viewed by 2621

Abstract

This study aims to investigate the effect of using an artificial intelligence (AI) system (Diagnocat, Inc., San Francisco, CA, USA) for caries detection by comparing cone-beam computed tomography (CBCT) evaluation results with and without the software. 500 CBCT volumes are scored by three dentomaxillofacial radiologists for the presence of caries separately on a five-point confidence scale without and with the aid of the AI system. After visual evaluation, the deep convolutional neural network (CNN) model generated a radiological report and observers scored again using AI interface. The ground truth was determined by a hybrid approach. Intra- and inter-observer agreements are evaluated with sensitivity, specificity, accuracy, and kappa statistics. A total of 6008 surfaces are determined as ‘presence of caries’ and 13,928 surfaces are determined as ‘absence of caries’ for ground truth. The area under the ROC curve of observer 1, 2, and 3 are found to be 0.855/0.920, 0.863/0.917, and 0.747/0.903, respectively (unaided/aided). Fleiss Kappa coefficients are changed from 0.325 to 0.468, and the best accuracy (0.939) is achieved with the aided results. The radiographic evaluations performed with aid of the AI system are found to be more compatible and accurate than unaided evaluations in the detection of dental caries with CBCT images. Full article

(This article belongs to the Special Issue Advancements in Artificial Intelligence for Dentomaxillofacial Radiology)

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21 pages, 1261 KiB

Open AccessSystematic Review

Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review

by Wael I. Ibraheem

Diagnostics 2024, 14(8), 806; https://doi.org/10.3390/diagnostics14080806 - 11 Apr 2024

Cited by 3 | Viewed by 3764

Abstract

Background and Objectives: The availability of multiple dental implant systems makes it difficult for the treating dentist to identify and classify the implant in case of inaccessibility or loss of previous records. Artificial intelligence (AI) is reported to have a high success rate in medical image classification and is effectively used in this area. Studies have reported improved implant classification and identification accuracy when AI is used with trained dental professionals. This systematic review aims to analyze various studies discussing the accuracy of AI tools in implant identification and classification. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and the study was registered with the International Prospective Register of Systematic Reviews (PROSPERO). The focused PICO question for the current study was “What is the accuracy (outcome) of artificial intelligence tools (Intervention) in detecting and/or classifying the type of dental implant (Participant/population) using X-ray images?” Web of Science, Scopus, MEDLINE-PubMed, and Cochrane were searched systematically to collect the relevant published literature. The search strings were based on the formulated PICO question. The article search was conducted in January 2024 using the Boolean operators and truncation. The search was limited to articles published in English in the last 15 years (January 2008 to December 2023). The quality of all the selected articles was critically analyzed using the Quality Assessment and Diagnostic Accuracy Tool (QUADAS-2). Results: Twenty-one articles were selected for qualitative analysis based on predetermined selection criteria. Study characteristics were tabulated in a self-designed table. Out of the 21 studies evaluated, 14 were found to be at risk of bias, with high or unclear risk in one or more domains. The remaining seven studies, however, had a low risk of bias. The overall accuracy of AI models in implant detection and identification ranged from a low of 67% to as high as 98.5%. Most included studies reported mean accuracy levels above 90%. Conclusions: The articles in the present review provide considerable evidence to validate that AI tools have high accuracy in identifying and classifying dental implant systems using 2-dimensional X-ray images. These outcomes are vital for clinical diagnosis and treatment planning by trained dental professionals to enhance patient treatment outcomes. Full article

(This article belongs to the Special Issue Advancements in Artificial Intelligence for Dentomaxillofacial Radiology)

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30 pages, 15168 KiB

Open AccessCase Report

The Stresses and Deformations in the Abfraction Lesions of the Lower Premolars Studied by the Finite Element Analyses: Case Report and Review of Literature

by Bogdan Constantin Costăchel, Anamaria Bechir, Mihail Târcolea, Lelia Laurența Mihai, Alexandru Burcea and Edwin Sever Bechir

Diagnostics 2024, 14(8), 788; https://doi.org/10.3390/diagnostics14080788 - 9 Apr 2024

Cited by 2 | Viewed by 1537

Abstract

Background: The purpose of the study was to investigate the behavior of hard dental structures of the teeth with abfraction lesions when experimental occlusal loads were applied. Methods: A 65-year-old patient came to the dentist because she had painful sensitivity in the temporomandibular joints and the lower right premolars. The patient was examined, and cone-beam computed tomography (CBCT) of the orofacial area was indicated. The data provided from the CBCT were processed with Mimics Innovation Suite 17 software to create the desired anatomical area in 3D format. Then, the structural calculation module was used in order to perform a finite element analysis of the lower right premolar teeth. A focused review of articles published between 2014 and 2023 from specialty literature regarding the FEA of premolars with abfraction lesions was also conducted. Results: The parcel area and the cervical third of the analyzed premolars proved to be the most vulnerable areas under the inclined direction of occlusal loads. The inclined application of experimental loads induced 3–4 times higher maximum shears, stresses, and deformations than the axial application of the same forces. Conclusions: FEA can be used to identify structural deficiencies in teeth with abfractions, a fact that is particularly important during dental treatments to correct occlusal imbalances. Full article

(This article belongs to the Special Issue Advancements in Artificial Intelligence for Dentomaxillofacial Radiology)

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