Dento maxillo facial radiology最新文献

This technical report presents a novel CBCT-Based Grading System for Oropharyngeal Airway Narrowing, designed to provide clinicians with a standardised, objective method to assess oropharyngeal airway narrowing using Cone-Beam Computed Tomography (CBCT). The grading system is developed based on the least surface area on axial section measurements/minimal cross-sectional area (MCA) on CBCT. It classifies oropharyngeal narrowing into five distinct grades (Grade 0 to 4). Each grade also has subcategories that correspond to specific anatomical regions-distal to the soft palate (P), distal to the base of the tongue (T), or distal to both the soft palate and the tongue (B)-and includes precise surface area ranges, contributing to better understanding. Traditional methods have commonly relied upon lateral cephalometry or supine CT; however, CBCT offers 3D mapping in a natural upright position, ensuring functional relevance of the airway assessment. Owing to its high spatial resolution, adequate contrast between the soft tissue and empty space, relatively low radiation dose compared to multidetector row CT and visibility of the upper airway by utilising a large field of view (FOV) protocol, CBCT a useful diagnostic tool for evaluation of the airway. The fact that CBCT is taken in a sitting or standing position, where the head is in equilibrium and orofacial and neck musculature is in voluntary control, vis-à-vis the supine position, where this control is taken over by the autonomic nervous system, and the distal part of the soft palate compresses the already narrowed airway further adds to its usefulness. CBCT imaging, with its three-dimensional mapping capabilities, allows for precise visualisation of the airway from the level of the posterior nasal spine, where the hard palate ends, extending to the epiglottis-thus measuring the oropharyngeal airway. The system is particularly useful for early detection and evaluation of conditions such as obstructive sleep apnoea, hypertrophy of the nasopharyngeal tonsils (adenoids), predicting difficult airways for ease of intubation, guiding orthognathic surgical interventions, craniofacial anomalies, and complex orthognathic surgical planning. It holds promise for integration into AI-enabled diagnostic platforms and digital imaging software, offering consistency in research and practice. This report details the rationale, grading criteria, anatomical references, and potential applications of this classification. The system offers a streamlined approach for identifying airway compromise, ultimately aiding multidisciplinary use in optimising patient outcomes.

Objectives: This study aimed to analyze the correlation between disc position and condylar position and morphology through fused cone-beam computed tomography (CBCT) and magnetic resonance (MR) images.

Methods: Patients exhibiting temporomandibular disorder symptoms were included, and joints with poor osseous consistency were excluded. Angle of disc was measured in the fused image using the method proposed in this study. Joint spaces were measured, and condylar morphology was assessed in cone-beam computed tomography images. Statistical analysis was performed to examine the reliability of the measurement method and the correlation between disc position and condylar position/morphology. A logistic regression model was used for identifying factors associated with anterior disc displacement.

Results: Our results showed that inter- and intra-observer agreement for measurements of disc angle and joint space were excellent (intraclass correlation coefficient > 0.9). Superior joint space, posterior joint space, and natural logarithm of the posterior-to-anterior joint space ratio showed significant correlations with the angle (P < .01) and significant differences between groups (P < .01). The posterior-to-anterior joint space ratio was significantly smaller in the mild displacement group. The logistic regression model demonstrated that a beak-like shape in oblique sagittal view (OR = 5.235, P < .05) and reduced posterior-to-anterior ratio (OR = 0.301, P < .05) significantly increased the risk of anterior disc displacement.

Conclusions: Condylar position and morphology demonstrated statistically significant association with disc position. Multivariate logistic regression analysis revealed that condylar position and morphology in sagittal views in cone-beam computed tomography images can serve as indicators for disc displacement.

Aim: To quantify cross-regional generalization of dental age-estimation models, identify practical strategies to improve their performance, and report uncertainty in a transparent manner.

Methods: A total of 21,722 panoramic radiographs from two Brazilian regions were acquired using distinct equipment. A robust Inception-v4 model was evaluated under four scenarios: (1) training on Northeast data and testing on Southeast data; (2) fine-tuning using Southeast data only or both regions; (3) training from scratch on pooled data; (4) pooled training with augmentation. Model performance was assessed using mean absolute error (MAE), mean signed error (bias), R2, Bland-Altman analysis, and calibration metrics.

Results: A marked performance drop was observed when the model trained on Northeast data was applied to Southeast radiographs (MAE 4.97 years vs 3.10 years in-region), with negative bias and wider Bland-Altman limits at older ages. Training with pooled regions and modest fine-tuning improved accuracy and calibration across both cohorts (MAE 3.24-3.69; R2 0.93-0.95). Data augmentation yielded only small additional improvements and did not eliminate large residual errors. Heatmaps highlighted clinically relevant anatomical structures commonly used by dental experts for age estimation.

Conclusions: Cross-site and domain shifts significantly impact the performance of AI models for dental age estimation. Multi-regional training combined with light model adaptation provides robust, well-calibrated, and interpretable results across regions, whereas data augmentation alone has limited effectiveness. This study offers a two-region benchmark, code, and data-access protocols to support reproducible evaluation and guide clinical deployment.

Objectives: To compare the diagnostic accuracy of stationary intraoral tomosynthesis (s-IOT) with periapical radiography (PA) for misfit detection at the abutment-crown interface, across different misfit magnitudes and vertical angulations.

Methods: Twenty prototype sets of maxillae and mandibles with implants placed in the maxillary central incisor region were used, and ceramic copings were fabricated. Misfits of 50, 100, and 150 μm were simulated by interposing 50-μm-thick polyester strips at the abutment-crown interface. The group without +simulated misfit had no strips. PA and s-IOT images were acquired using 3 different vertical X-ray tube angulations: perpendicular to the implant (0°), positioned inferiorly (-10°), and superiorly (+10°). Five oral radiologists evaluated 480 images using a 5-point scale. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were analyzed using repeated measures ANOVA and Tukey's post-hoc test (α = 5%).

Results: No significant differences were observed between PA and s-IOT (p > 0.05). AUC at 50 µm was significantly lower than 100 µm for all vertical angulations, and than 150 µm for -10° and +10° (p < 0.05); 100 µm was also lower than 150 µm for +10° (p < 0.05). +10° showed lower AUC than 0° and -10° at 50 µm, than 0° at 100 µm, and than -10° at 150 µm (p < 0.05). Sensitivity increased with misfit magnitude; 100 µm and 150 µm were higher than 50 µm for -10° and +10° (p < 0.05). +10° showed lower sensitivity than 0° at 50 µm and -10° at 150 µm (p < 0.05). Specificity varied only by vertical angulation; 0° showed the lowest values (p < 0.05).

Conclusion: PA and s-IOT demonstrated comparable performance, +10° exhibited the lowest accuracy, and larger misfits were more easily detected.

Advances in knowledge: s-IOT provides additional bucco-lingual information; however, PA demonstrated comparable diagnostic accuracy in detecting misfits at the abutment-crown interface, while exposing patients to lower radiation doses. Additionally, when ideal parallelism cannot be achieved, directing the X-ray beam toward the implant apex, rather than the prosthetic crown, may optimize misfit detection.

Objective: The aim of this study is to investigate the effects of different rubber dam thicknesses on image quality in two exposure protocols.

Methods: For the study, three rubber dam thicknesses were used: thin (0.14 mm), medium (0.18 mm), and heavy (0.22 mm). To mimic clinical conditions, a rubber dam was used in two layers. Exposure was performed using two different protocols: Protocol 1: 0.080 s exposure time, 65 kV, 7 mA, and Protocol 2: 0.160 s exposure time, 65 kV, 7 mA. For each thickness and protocol, 47 measurements were taken (n = 47). Radiographic images were exported in TIFF and analyzed using ImageJ software. The region of interest was determined, and signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), Michelson, and Weber contrast values were obtained. One-way ANOVA, post hoc Tukey, and intra-class correlation were used for statistical analysis.

Results: No statistical difference was detected for protocol 1 between rubber dam thicknesses in SNR, CNR, Michelson, and Weber contrasts (p > 0.05). For protocol 2 (longer exposure time), heavy and medium thickness had lower SNR and CNR values than the thin one (p < 0.05). Michelson and Weber contrasts were statistically changed in different thicknesses of rubber dam (p < 0.05). ICC values were good and excellent.

Conclusions: The thick rubber dam reduced SNR and CNR values; likewise, Michelson and Weber contrasts were changed, which pointed a reduced image quality and negatively affected object visibility. Short exposure times are recommended to maintain image quality in clinical situations requiring the use of a thick rubber dam.

Objectives: The aim of this study is to show that the use of 0.55 T MRI combined with a "dental-dedicated" coil produces images of sufficient diagnostic value to assess lower third molars (LTMs), that are not inferior to currently utilized dental-oriented radiographic images.

Methods: Dental-dedicated MRI (ddMRI) scans were acquired using a Magnetom Free.Max 0.55 T scanner combined with a dedicated dental coil. Three observers assessed all images according to predefined criteria. 20% of images were assessed twice by all observers. Kappa statistics were performed to assess intra- and inter-observer agreement as well as inter-modality agreement.

Results: ddMRI was acquired on 67 patients (89 LTMs) in addition to initial radiographic exams (intraoral, panoramic and/or CBCT). Inter-observer agreement for each modality ranged from low to perfect (intraoral/panoramic 0.480-0.942 (average 0.74), CBCT 0.218-1.000 (average 0.69), ddMRI -0.038-0.889 (average 0.53)). Intra-observer agreement ranged from low to perfect (intraoral/panoramic -0.047-1.000 (average 0.76), CBCT 0.389-1.000 (average 0.83), ddMRI -0.025-1.000 (average 0.61)).Inter-modality agreement ranged from low to high (intraoral/panoramic vs. CBCT -0.078-0.743 (average 0.32), intraoral/panoramic vs. ddMRI -0.078-0.752 (average 0.30), CBCT vs. ddMRI 0.074-0.886 (average 0.49)).

Conclusion: ddMRI could be a feasible diagnostic modality for LTM imaging. The modality shows promise for radiation-free imaging in the future.

Advances in knowledge: This paper is the first to demonstrate the use of ddMRI in LTM imaging and to compare the modality to existing modalities. The added value of this radiation-free modality can be beneficial to dentists and patients in the future.

Objectives: External root resorption is a destructive process that usually develops without any symptoms and, when diagnosed, can lead to tooth extraction because it causes serious tooth tissue loss. Therefore, it is aimed to develop artificial intelligence algorithms that can assist in the diagnosis of external root resorption.

Methods: Totally, 110 extracted teeth were demineralized by applying 40% nitric acid solution for 8 hours, 8% sodium hypochlorite for 10 minutes, and then a distilled water washing procedure. The prepared teeth were placed on a radioconjugate phantom model and imaged. The data set obtained from the teeth used in the study consists of a total of 584 periapical radiographs. YOLOv5x-cls and YOLOv5x-seg models were used to detect external root resorption.

Results: The F1 score value of the YOLOv5x-cls model used for calcification of external root resorption was found to be 1.0, indicating that the model has a high success rate during the testing phase. In the YOLOv5x-seg model used for the segmentation of external root resorption, the F1 score values were found to be 0.8593. This value is an indication that the model is working effectively during the testing phase. It has also been determined that the classification is more successful than the segmentation model.

Conclusion: In this study, artificial intelligence algorithms were used in the radiological evaluation of teeth with chemical external root resorption using a phantom model compatible with jawbone radiopacity. High success rates have been achieved in the detection of external root resorption areas with artificial intelligence.

Advances in knowledge: This study presents an innovative approach to detecting external root resorption using artificial intelligence. In addition, the reliability of the study was increased by using the radioconjugate phantom model.

Objectives: To evaluate the diagnostic accuracy and generalizability of artificial-intelligence (AI) models for radiographic classification of jawbone cysts and tumours, and to propose a Clinical Interpretation Score (CIS) that rates the transparency and real-world readiness of published AI tools.

Methods: Eligible studies reporting sensitivity and specificity of AI classifiers on panoramic radiographs or cone-beam CT were retrieved. Two reviewers applied Joanna Briggs Institute (JBI) risk-of-bias criteria and extracted 2 × 2 tables and relevant metrics. Pooled estimates were calculated with random-effects meta-analysis; heterogeneity was quantified with I2.

Results: Nineteen studies were included, predominantly reporting convolutional neural networks. Pooled specificity was consistently high (≥0.90) across lesions, whereas sensitivity ranged widely (0.50-1.00). Stafne bone cavities achieved near-perfect metrics; ameloblastoma and odontogenic keratocyst showed moderate sensitivity (0.62-0.85) but retained high specificity. Cone-beam CT improved sensitivity relative to panoramic imaging. Substantial heterogeneity (I2 > 50% in most comparisons) reflected variable prevalence, imaging protocols and validation strategies.

Conclusions: Artificial-intelligence models demonstrate promising diagnostic performance in classifying several jawbone lesions, though their accuracy is influenced by imaging modality, lesion type, and prevalence. Despite encouraging technical results, many studies lack transparent reporting and external validation, limiting their clinical interpretability. The CIS provides a structured framework to evaluate the methodological transparency and clinical readiness of AI tools, helping to distinguish between technically sound models and those suitable for integration into diagnostic workflows.

In dental radiography, where fine details need to be recognizable, image quality and spatial resolution play an important role. This is particularly the case in 3D imaging (CBCT), because the radiation exposure is significantly higher compared with any 2D imaging method. The gold standard for measuring spatial resolution, and especially for relating it to contrast, is the measurement of the modulation transfer function (MTF). The usual procedure to obtain the MTF is to take a CBCT scan of a test phantom, which consists of different materials. The MTF is then measured at the interface of 2 materials. In this work, we propose an approach in which we determine the MTF in clinical CBCT scans at the boundary of physiological, implanted, or restored teeth, as well as surrounding tissue structures of different densities. It is assumed that all CBCTs inhibit some kind of interface between a radio-dense and radio-translucent area. Following the methodology used by the German standard DIN 6868-161, we developed our own numerical software for the computation of the MTF. The method enables a stable estimation of spatial resolution (MTF) in clinical CBCT images.

Objectives: This study aimed to develop and evaluate an artificial intelligence (AI) framework for detecting dental restorations and prosthesis devices on panoramic radiographs (PRs). Detecting these elements is essential for enhancing automated reporting, improving the accuracy of dental assessments, and reducing manual examination time.

Methods: A fast region-based convolutional neural network (Fast R-CNN) was trained using 186 PRs for the training set and 42 for validation. The model's performance was assessed on an external test dataset of 1133 PRs. Seven dental restorations and prosthesis devices were targeted: appliance, bridge, endodontic filling, crown filling, implant, retainer, and single crown. Precision, recall, and F1-score were calculated for each element to measure detection accuracy.

Results: The AI framework achieved high performance across all categories, with precision, recall, and F1-scores as follows: appliance (0.79, 0.96, 0.87), bridge (0.91, 0.86, 0.89), endodontic filling (0.98, 0.98, 0.98), crown filling (0.95, 0.95, 0.95), implant (0.99, 0.97, 0.98), retainer (0.98, 0.98, 0.98), and single crown (0.94, 0.96, 0.95). The system processes one panoramic image in under 30 seconds.

Conclusions: The AI framework demonstrated high recall and efficiency in detecting dental prosthesis and other dental restorations on PRs. Its application could significantly streamline dental diagnostics and automated reporting, enhancing both the speed and accuracy of dental assessments.

Advances in knowledge: This study highlights the potential of AI in automating the detection of multiple dental restorations and prosthesis on PRs, offering a valuable tool for dental professionals to improve diagnostic workflows.