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Bioengineering
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Machine Learning and Computer Vision for Dental and Oral Surgical...
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Machine Learning and Computer Vision for Dental and Oral Surgical Applications

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (25 July 2026) | Viewed by 1817

Editors

Dr. Saverio Cosola

E-Mail Website
Guest Editor
San Rossore Dental Unit, Casa di cura San Rossore, 56122 Pisa, Italy
Interests: implant; bone reconstruction; oral health; oral surgery; implant prosthetics; probiotics; oral prevention
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giovanni Battista Menchini Fabris

E-Mail Website
Guest Editor
San Rossore Dental Unit, Casa di cura San Rossore, 56122 Pisa, Italy
Interests: oral implants; bone preservation; implant success; dental prosthetics; maxillary sinus; augmentation techniques; immediate implants; implant stability; bone quality in dentistry
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ugo Covani

E-Mail Website
Guest Editor
Department of Dentistry and Oral Surgery, Unicamillus International Medical University, Roma, Italy
Interests: oral surgery; immediate dental implant loading

Special Issue Information

Dear Colleagues,

While machine learning is currently in its early stages in the field of artificial intelligence (AI), it is already being applied in medicine and dentistry. These applications enable identification of potential radiolucent lesions, automatic measurement of their dimensions on radiographs, and even formulation of reasonably accurate diagnostic hypotheses. Similarly, software tools can analyze a simple clinical photograph to estimate the size of a mucosal lesion, suggest a possible etiology, and indicate the likelihood of progression. Of course, final diagnosis and treatment planning are always the responsibility of the clinician; however, such tools are already available to practitioners. They show high potential for development in early diagnosis, prevention, surgical planning with visual goals, and genetics, and future professionals will be able to embrace these opportunities just as they have successfully integrated many innovations over the past few decades. For example, several studies published in recent years have reported that the integration of AI into dental practice not only enhances diagnostic accuracy but also accelerates preventive workflows, enabling the early detection of lesions that might otherwise go unnoticed. Nowadays, researchers are increasingly exploring how machine learning and computer vision will transform clinical practice in dentistry and oral surgery, as well as their impact on patients’ perspectives.

Dr. Saverio Cosola
Prof. Dr. Giovanni Battista Menchini Fabris
Prof. Dr. Ugo Covani
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • neural networks
  • dental implants
  • metabolomic
  • dental planning
  • digital smile design
  • digital implantology
  • oral scanner
  • immediate prothesis
  • personalized dentistry
  • personalized medicine
  • multidisciplinary approach

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Published Papers (3 papers)

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Research

32 pages, 9783 KB  
Article
EGT-UNet: Evolutionary Game-Theoretic Adaptive Optimization for Pediatric Panoramic Tooth Segmentation
by Muhammet Emin Sahin, Hasan Ulutas, Halil I. Cosar, Tayyip Bicer, Recep B. Gunay and Süleyman K. Buyuk
Bioengineering 2026, 13(7), 840; https://doi.org/10.3390/bioengineering13070840 - 21 Jul 2026
Viewed by 225
Abstract
The present work proposes EGT-UNet, an innovative loss optimization mechanism based on Evolutionary Game Theory (EGT) aimed at pediatric panoramic tooth segmentations. In the absence of a large set of high-quality images in the current literature, we developed a novel pediatric panoramic image [...] Read more.
The present work proposes EGT-UNet, an innovative loss optimization mechanism based on Evolutionary Game Theory (EGT) aimed at pediatric panoramic tooth segmentations. In the absence of a large set of high-quality images in the current literature, we developed a novel pediatric panoramic image database, including 1269 images from Ordu University Faculty of Dentistry. The dataset includes subjects aged 3–14, where 67% are males and 33% are females. The annotation of all images was done at the pixel level by a professional orthodontist with a two-fold verification process. In order to prevent data leakage, the dataset was split at the subject level into training/validation (85%) and independent test (15%) sets. Data in the training set were divided into folds for five-fold cross-validation. From a structural point of view, EGT-UNet is an improved version of U-Net, equipped with the following modules: Squeeze-and-Excitation blocks, Attention Gates, and a dilated convolutional module mimicking Atrous Spatial Pyramid Pooling. The main novelty of the proposed approach involves the application of the dynamic change in loss weights. Specifically, in our work, the fusion of three losses—Dice, Focal Tversky and Boundary—was dynamically tuned via replicator dynamics using task-specific fitness functions. The difference with traditional loss-weighting methods is that in the latter case, fixed weights are applied. To evaluate the effect of each component of the EGT-UNet, we conducted an ablation study of six architectures varying in terms of hybrid loss function and dynamic/static weight tuning. Using the independent test dataset, we observed a similar performance level of all models with Dice scores ~0.93. Our best model, called “EGT Aggressive”, achieved Dice = 0.931 ± 0.044, IoU = 0.873 ± 0.066, and Boundary Dice = 0.631 ± 0.064. Importantly, this model demonstrated a statistically significant superiority over the baseline network according to region-related metrics and boundary metrics (Wilcoxon p < 0.05). In computational studies, we revealed an increased model robustness when dealing with highly complex mixed dentition images. Full article
(This article belongs to the Special Issue Machine Learning and Computer Vision for Dental and Oral Surgical Applications)
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19 pages, 1688 KB  
Article
Deep Learning-Based Evaluation of Maxillary Dental Midline Deviation on Orthodontic Frontal Photographs
by Sercan Taskin, Serra Aksoy, Mine Gecgelen Cesur, Pinar Demircioglu and Ismail Bogrekci
Bioengineering 2026, 13(6), 687; https://doi.org/10.3390/bioengineering13060687 - 15 Jun 2026
Viewed by 428
Abstract
Aim: This study aimed to detect the maxillary dental midline region on orthodontic frontal photographs using a YOLOv8-based deep learning approach and to evaluate how the detection outputs affect the classification performance of various machine learning algorithms in distinguishing symmetric from asymmetric midline [...] Read more.
Aim: This study aimed to detect the maxillary dental midline region on orthodontic frontal photographs using a YOLOv8-based deep learning approach and to evaluate how the detection outputs affect the classification performance of various machine learning algorithms in distinguishing symmetric from asymmetric midline conditions. Materials and Methods: A total of 146 standardized frontal photographs (72 with midline deviation ≥ 2 mm from the facial midline, defined by the soft-tissue nasion–subnasal line; 74 symmetric) were analyzed. YOLOv8 was used to obtain bounding-box and keypoint predictions, which were converted into a numerical feature vector and used to train 11 classifiers (including Naive Bayes, Logistic Regression with L1 and ElasticNet penalties, Support Vector Machine, AdaBoost, and others). Performance was assessed using accuracy (with 95% Wilson confidence intervals), precision, recall, F1-score, and ROC-AUC. Optimization of hyperparameters for the downstream classifiers employed five-fold cross-validation along with grid search inside the training data set (n = 126) while final classifier assessment was done using a reserved test data set (n = 20). As the YOLOv8 object detector was trained using the full image dataset before extracting features, the classification metrics presented here should be considered as exploratory results only. Results: YOLOv8 achieved mAP@0.5 = 0.995 for midline detection. Naive Bayes attained the highest classification accuracy of 75% (95% CI: 53–89%) with ROC-AUC = 0.75. AdaBoost achieved 65% (95% CI: 43–82%). Several models defaulted to majority-class prediction (accuracy = 40%), indicating insufficient feature discriminability. Conclusions: YOLOv8 detected the maxillary dental midline under the present internal experimental conditions. However, because leakage-free outer k-fold validation of the complete detection-plus-classification pipeline was not performed, the classification results should be considered preliminary. Future work should address information leakage, incorporate facial reference frame normalization, include inter-observer reliability assessment, and validate the approach on larger datasets. Full article
(This article belongs to the Special Issue Machine Learning and Computer Vision for Dental and Oral Surgical Applications)
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18 pages, 13506 KB  
Article
Development and External Validation of an Explainable AHP-ML Model for Orthodontic Tooth Extraction and Anchorage Decision Support
by Yang Yi, Xinhang Shen, Bin Wu, Yingyu Chen, Mao Liu and Bin Yan
Bioengineering 2026, 13(6), 671; https://doi.org/10.3390/bioengineering13060671 - 10 Jun 2026
Viewed by 498
Abstract
Tooth extraction and maximum anchorage assessment are key decision points in orthodontic treatment planning, yet existing machine learning models for orthodontic decision support often lack transparency, limiting their clinical interpretability and trustworthiness. In this study, we developed and externally validated an explainable orthodontic [...] Read more.
Tooth extraction and maximum anchorage assessment are key decision points in orthodontic treatment planning, yet existing machine learning models for orthodontic decision support often lack transparency, limiting their clinical interpretability and trustworthiness. In this study, we developed and externally validated an explainable orthodontic treatment decision-support model that integrates expert-derived Analytic Hierarchy Process (AHP) weighting with machine learning. A diagnostic indicator framework comprising 18 orthodontic variables was established through a literature review, clinical data analysis, and two rounds of expert surveys. A retrospective cohort of 485 patients receiving fixed-appliance orthodontic treatment was used for model development and internal validation. AHP-derived composite scores were incorporated into the machine learning models for two prediction tasks, namely tooth extraction and maximum anchorage requirement, and an expert-informed fuzzy-rule score was calculated from pretreatment indicators for the maximum anchorage task to capture clinically interpretable anchorage tendencies. Model performance was evaluated using ROC-AUC, F1 score, precision, recall, PR-AUC, calibration analysis, and decision curve analysis, while SHAP was applied to interpret feature contributions. The AHP-RF extraction model and AHP-enhanced LR maximum anchorage model achieved the highest AUCs among the compared models (0.864 and 0.822, respectively), although paired DeLong tests showed no significant differences from the closest competing models. SHAP analysis identified lower lip-to-E-line distance, U1-NA, and the AHP composite score as important predictors, indicating consistency between model outputs and clinical reasoning. In the external validation cohort, the extraction model correctly classified 57 of 74 cases, and the maximum anchorage model correctly classified 24 of 29 cases, supporting the preliminary transportability of the proposed framework. These results suggest that integrating AHP-derived expert knowledge with machine learning provides an explainable and clinically interpretable decision-support model for orthodontic treatment planning, with potential value in improving standardized, evidence-informed, and patient-specific orthodontic decision-making. Full article
(This article belongs to the Special Issue Machine Learning and Computer Vision for Dental and Oral Surgical Applications)
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