May 2026· Dento maxillo facial radiology· Vol 55, pp. 602 - 613· 0 citations· 49 references
Medicine
TL;DR
The use of a fluoride tray for soft tissue separation during CBCT scans facilitates gingival visualization while maintaining patient comfort, enabling accurate and time-efficient AI-driven segmentation of maxillary marginal and supracrestal gingiva.
Abstract
Abstract Objectives To clinically validate an artificial intelligence (AI)-based tool for automated maxillary gingival segmentation of marginal and supracrestal gingiva on cone beam CT (CBCT) scans using a novel soft tissue separation technique with a fluoride tray. Methods A validation set of 35 CBCT scans, covering maxilla, acquired with fluoride trays was processed using a cloud-based AI platform (Relu, Leuven, Belgium) for gingival segmentation. The resulting models were refined by an expert using 3-dimensional (3D) mesh-processing software and compared with the original AI outputs to assess accuracy. Additionally, 6 CBCT scans were manually segmented, using intraoral scans as reference, and compared with the AI model. Surface-based and voxel-wise analyses, color-coded maps, consistency, and time-efficiency were evaluated. Wilcoxon signed-rank test was used to assess time differences among methods. Results Artificial intelligence vs expert refinement showed strong agreement with high overlap (medianDSC ≥ 96%) and minor surface deviations (medianMSD∼0.00 mm). Minor differences were found between anterior and posterior regions (medianΔDSC = 1%, ΔMSD∼0.00 mm). Artificial intelligence vs manual segmentations showed median dice similarity coefficient (DSC) of 82% and small median MSD of 0.15 mm. Labial/buccal area showed the highest surface deviations from color-coded maps. Bland-Altman plots showed low intra- and inter-operator consistency in time, while AI showed excellent consistency. Artificial intelligence demonstrated significantly faster segmentation, achieving 4x faster with expert refinement and 20x faster with manual approach. Conclusion The use of fluoride tray facilitates separation of oral soft tissues on CBCT scans, enabling accurate and time-efficient AI-driven segmentation of maxillary marginal and supracrestal gingiva. This supports integration into digital workflows and more efficient treatment planning. Advances in knowledge The use of a fluoride tray for soft tissue separation during CBCT scans facilitates gingival visualization while maintaining patient comfort. The resulting 3D gingival models from AI-based segmentation can be integrated with automatically segmented dentomaxillofacial structures, enhancing clinical visualization of oral soft and hard tissues and facilitating diagnosis and treatment planning, including periodontal evaluation, implant planning, prosthodontics, and orthodontics.
The reduced segmentation time and consistent performance across CBCT systems support the potential integration of cloud-based AI segmentation into digital dental workflows, especially for anterior teeth, where accurate morphology is clinically relevant.
Gabriel Cunha Adiverci, Erielma Lomba Dias Julião, A. Leite et al.· E -journal of dentistry· 0 citations
Artificial intelligence (AI)-based tooth segmentation has the potential to improve the efficiency and consistency of digital clinical workflows; however, the generalizability of commercially available algorithms on clinical datasets and their interpretability remain limited.
This study proposed a two-level evaluation method. It evaluated the performance of a black-box commercial algorithm using an expert-annotated clinical reference dataset of 126 intraoral scans representing diverse clinical presentations, including severe crowding, spacing, missing teeth, and dental restorations. Two specialists independently generated reference annotations. In the next level, a transparent two-stage segmentation method combining YOLOv8-based tooth localization and numbering with a 3D U-Net segmentation model was developed using 80 scans from an open-source dataset and evaluated alongside the commercial algorithm. Performance was evaluated using the Dice Similarity Coefficient (DSC) and tooth classification accuracy. Tooth detection and numbering were assessed descriptively using confusion matrices and accuracy. End-to-end segmentation performance was compared at the scan level using paired parametric or nonparametric tests, depending on the distribution of paired differences. Regional performance differences between anterior and posterior regions and between maxillary and mandibular jaws were assessed using independent parametric or nonparametric tests, as appropriate.
The commercial algorithm achieved significantly higher segmentation performance than the transparent model, with a median DSC of 0.90 (IQR 0.86–0.95) versus 0.76 (IQR 0.66–0.84), respectively (
p
< 0.001). In contrast, the transparent model showed tooth-numbering accuracy of 0.92 versus 0.83. No significant differences in DSC were observed between anterior and posterior regions for either algorithm. Maxillary and mandibular performance did not differ significantly for the commercial algorithm, whereas the transparent model performed significantly better in the mandible.
The commercial algorithm demonstrated strong segmentation performance on an independent, expert-annotated clinical dataset. Although the transparent approach achieved lower segmentation accuracy, it offered greater methodological transparency and insight into decision-making.
Unknown authors· Frontiers in Oral Health· 0 citations
INTRODUCTION
Accurate localization of the mandibular canal in Cone-Beam Computed Tomography (CBCT) images is critical for preventing iatrogenic nerve injury during maxillofacial surgery and dental implant procedures. This systematic review and meta-analysis aimed to evaluate the diagnostic performance, anatomical localization accuracy, and time efficiency of deep learning-based artificial intelligence (AI) systems in automated mandibular canal segmentation compared to traditional manual expert annotations.
MATERIALS AND METHODS
A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, IEEE Xplore, and Embase databases in accordance with PRISMA guidelines. Studies evaluating the performance of AI models for mandibular canal detection on CBCT scans using expert annotations as the reference standard were included. The primary outcome measure was the Dice Similarity Coefficient (DSC), while secondary outcomes included Average Symmetric Surface Distance (ASSD) and processing time. Statistical analyses were performed using a random-effects model.
RESULTS
A total of 38 unique studies comprising over 8,420 CBCT volumes were included in the quantitative synthesis. The pooled DSC for AI-driven segmentation was calculated as 0.82 (95% CI: 0.79-0.85). Subgroup analyses revealed that transformer-based architectures (DSC: 0.89) demonstrated significantly superior performance compared to traditional convolutional neural networks (CNNs). The pooled ASSD exhibited a high anatomical accuracy of 0.42 mm (95% CI: 0.38-0.47), which is close to voxel dimensions. Furthermore, the autonomous segmentation process was completed in an average of 32 seconds, whereas manual expert annotation took 600 seconds (p < 0.001), confirming an 18.7-fold timesaving in the clinical workflow.
DISCUSSION
Deep learning algorithms provide highly accurate, reproducible, and time-efficient results at a human-expert level in the automated segmentation of the mandibular canal on CBCT images. The integration of these AI systems into clinical protocols has the potential to enhance surgical safety and standardize preoperative planning processes in dental implantology.
Ramazan Ağırağaç· Journal of Stomatology Oral...· 0 citations
This study aimed to evaluate the diagnostic accuracy of an artificial intelligence (AI)-assisted cone-beam computed tomography (CBCT) analysis system for predicting the spatial proximity of the inferior alveolar nerve (IAN) to impacted mandibular third molars (M3M), using expert radiologist assessment as the reference standard. A retrospective diagnostic accuracy study was conducted on an internal institutional cohort of 312 patients (mean age 28.21 ± 6.62 years; January 2021–December 2024). A deep learning system employing a modified U-Net architecture automatically segmented the IAN canal and M3M on CBCT and classified the IAN–M3M spatial relationship into three categories: no contact (> 2 mm), proximity (0–2 mm), and contact/overlap. Diagnostic accuracy was evaluated on an independent hold-out test set of 486 M3 sites (283 patients). Two senior oral and maxillofacial radiologists provided the reference standard. Sensitivity, specificity, PPV, NPV, AUC, and Cohen’s kappa were calculated; 95% CIs were derived by Wilson score method (proportions) and bootstrap resampling (AUC, kappa). The AI system achieved an overall accuracy of 90.1% (438/486; 95% CI: 87.1–92.5%), weighted AUC of 0.925 (95% CI: 0.904–0.944), and Cohen’s κ of 0.851 (95% CI: 0.821–0.912), indicating almost perfect agreement with the expert reference standard. Per-category sensitivity ranged from 88.2% to 91.2% and specificity from 92.8% to 97.4%. Bland–Altman analysis revealed a mean difference of 0.06 mm (95% LoA: −0.63 to 0.75 mm). Mean DSC was 0.90 ± 0.04 for IAN canal and 0.93 ± 0.03 for M3M segmentation. AI processing time was 4.75 ± 1.12 s versus 189.12 ± 41.99 s for expert assessment (39.79-fold reduction; P < 0.001). Subgroup analysis showed highest accuracy for mesioangular (93.1%) and horizontal (91.6%) impaction. The AI-assisted CBCT analysis system demonstrated high diagnostic accuracy and excellent agreement with expert radiological assessment in predicting IAN proximity to impacted mandibular third molars, while substantially reducing processing time. These results support the potential of AI-driven automated CBCT analysis as a preoperative decision-support tool; however, the reference standard was radiographic rather than intraoperative, and prospective multicenter validation incorporating surgical outcome data will be required before definitive clinical deployment recommendations can be made.
Huan Hu, Jiahang Wu, Hongji Pu et al.· BMC Oral Health· 0 citations
STATEMENT OF PROBLEM
Assessment of anterior dental and gingival esthetics has been commonly based on visual judgment and manual measurements. These approaches are time-consuming and show considerable examiner-dependent variation.
PURPOSE
The purpose of this study was to develop and validate a computer vision-based approach for automated extraction of anterior tooth morphologic parameters and the Pink Esthetic Score (PES) and the White Esthetic Score (WES) from intraoral scan data.
MATERIAL AND METHODS
Intraoral scans from 490 maxillary anterior teeth (245 pairs) of orthodontic patients were analyzed. An automated approach extracted dental and gingival contours, length-to-width ratios, and geometric and relative color differences (ΔE00, ΔL, Δa) compared with corresponding contralateral homologous teeth. Agreement between automated and manual measurements was evaluated using Bland-Altman analysis. The 245 pairs were randomly divided into exploration (n=175 pairs) and validation (n=70 pairs) sets. In the exploration set, logistic regression models combined with descriptive statistics established quantitative grading thresholds based on expert scores. In the validation set, agreement and reliability between automated and expert consensus scores were assessed using weighted Cohen kappa (κ) and intraclass correlation coefficients (ICC). Bland-Altman analysis and the Wilcoxon signed-rank test evaluated systematic bias. Furthermore, a Z test was performed to compare inter-examiner agreement with and without visual contour assistance across 245 pairs (α=.05).
RESULTS
Expert evaluations demonstrated moderate to almost perfect intra-examiner and inter-examiner agreement. The Bland-Altman analysis demonstrated excellent agreement between automated and manual measurements for tooth length-to-width ratios, indicating negligible systematic bias (mean difference=-0.001, 95% LoA: -0.061 to 0.058). Reliability between automated PES and WES scores and expert consensus scores was excellent for total PES (ICC=0.926, 95% CI: 0.881-0.954) and total WES (ICC=0.960, 95% CI: 0.936-0.975). Across all esthetic subcategories (95% CIs ranging from 0.639 to 1.000), agreement was almost perfect for 7 parameters (κ>0.890) and substantial for soft tissue contour (κ=0.790). No significant differences were found between automated and expert consensus scores (Wilcoxon P>.05). Furthermore, visual contour assistance improved inter-examiner agreement.
CONCLUSIONS
The developed approach demonstrated excellent agreement with expert consensus and provided stable, reproducible esthetic measurements from intraoral scan data. By integrating objective metrics with visual annotations, it offered a practical tool for standardized esthetic assessment and may support clinical decision-making in digitally assisted esthetic dentistry. Further validation in broader clinical settings is warranted.
Wenjia Chen, Tian Zhou, Mengyu Liang et al.· The Journal of prosthetic de...· 0 citations
Background/Objectives: To evaluate the impact of dental laboratory material overlays on metal artifact reduction in multidetector computed tomography (MDCT) of the oral cavity across different CT protocols and reconstruction algorithms. Methods: An anthropomorphic mandible phantom was constructed using extracted human teeth, metal-ceramic restorations, and ballistic gelatin as a soft-tissue analog. Six dental materials (Elite HD+, Vonflex S Putty, Tropicalgin, MB Wax 1, Zetaplus, and COE-PAK) were tested as overlays. MDCT imaging was performed following low-dose and standard protocols with primary and O-MAR reconstructions, yielding 28 datasets. Two radiologists independently assessed artifact indices (AIs), overall image quality (OIQ), and spherical marker visibility (SMV), with excellent inter-observer agreement (ICC = 0.985). Results: Vonflex S Putty model consistently demonstrated the lowest AIs and highest OIQ scores, while maintaining stable performance across both protocols and reconstructions. Elite HD+ and Zetaplus also showed favorable results, while COE-PAK and MB Wax 1 frequently underperformed. Tropicalgin exhibited high AIs with primary reconstruction but improved markedly with O-MAR. SMV analysis confirmed Vonflex S Putty as the only material to consistently outperform the control under low-dose conditions. Conclusions: Silicone-based overlays, particularly Vonflex S Putty, are effective in reducing metal artifacts and improving image quality in MDCT. Overlay selection, combined with MAR algorithms such as O-MAR, represents a complementary strategy for optimizing diagnostic accuracy in patients with metallic restorations.
Unknown authors· Diagnostics· 0 citations
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