Dento maxillo facial radiology最新文献

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.

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.

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 retrospective study aimed to investigate and evaluate the signal intensity ratio (SIR) on magnetic resonance imaging (MRI) and maximum standard uptake value (SUVmax) and Hounsfield unit (HU) values on single-photon emission computed tomography/computed tomography (SPECT/CT) in relation to the diagnosis of temporomandibular joint osteoarthritis (TMJ OA).

Methods: Ninety-six TMJs from 63 patients who took SPECT/CT and MRI between January 2017 and September 2023 were included. SUVmax and HUmedulla of TMJ were measured. SIR was measured and calculated based on the ratio of magnetic signal intensity between the condyle and cerebral cortex on proton density-weighted image (PDWI) and T2-weighted image (WI).

Results: The TMJ OA group showed high SUV max (7.98 ± 4.09; median: 6.5), compared to the normal (3.21 ± 0.76; median: 3.1), with a significant difference (P < .001). A significant difference was also observed in the HU, with the TMJ OA (457.14 ± 247.48) versus normal (296.91 ± 117.51) (P = .001). Both SIRs measured by PDWI and T2-WI were lower in the TMJ OA (0.89 ± 0.28; median: 0.9 and 1.19 ± 0.26; median: 1.2) compared to the normal (1.23 ± 0.23; median: 1.2 and 1.00 ± 0.23; median: 1.0) with a significant difference (P < .001).

Conclusions: This study can provide the basis that SIR can be helpful in diagnosis in patients clinically suspected of having OA.

Advances in knowledge: This study is the first to quantitatively evaluate condylar bone changes in TMJ OA by combining SUVmax from SPECT/CT, HU from CT, and SIR from MRI within the same cohort. This integrated imaging approach may contribute to a more objective and reliable diagnosis of TMJ osteoarthritis.

Objective: Sjögren disease (SD) is characterized by lymphocytic infiltration and fibrosis of the salivary glands. Shear wave elastography (SWE), an ultrasound-based modality that quantifies tissue stiffness, may assist in SD diagnosis. This study aimed to systematically review and meta-analyze the diagnostic performance of SWE in evaluating major salivary glands in individuals with SD, based on studies applying the 2016 ACR/EULAR classification criteria.

Methods: Six electronic databases and gray literature sources were searched. Cross-sectional and diagnostic accuracy studies were included. Risk of bias was appraised using the Joanna Briggs Institute tool. Quantitative synthesis was performed using random-effects meta-analyses.

Results: Eleven studies comprising 1029 participants (530 with SD; 499 controls; 90.67% female) were included. Meta-analyses revealed that SWE values were significantly higher in SD patients than in controls, with pooled mean differences of 0.78 m/s (95% CI: 0.54-1.02) and 12.37 kPa (95% CI: 8.65-16.10) in the parotid gland, and 0.48 m/s (95% CI: 0.33-0.63) and 9.09 kPa (95% CI: 4.88-13.31) in the submandibular gland. Parotid SWE values expressed in kPa showed the highest diagnostic accuracy (AUC = 82.9%), followed by values in m/s (AUC = 73.1%).

Conclusions: SWE effectively differentiates SD from healthy individuals, particularly when applied to the parotid gland. Standardization of SWE protocols may enhance diagnostic accuracy and foster clinical integration.

Advances in knowledge: This is the first meta-analysis focused exclusively on studies adopting the 2016 ACR/EULAR criteria for SD.

Objectives: To evaluate artefact-reducing filters as a means to optimize and ensure the accuracy of carious lesion diagnosis testing in 2 experimental models (presence and absence of adjacent metallic objects).

Methods: Fifty molar teeth were used, randomly divided into 5 groups (n = 10): G1-Sound teeth; G2-Carious teeth; G3-teeth with Class I cavity preparation restored with resin (Cl I + R); G4-Cl I + R with the use of a hyperdense lining material; and G5-Cl I + R with the use of a hypodense lining material. The Carestream CS 9600 tomograph was used, testing 2 experimental models (presence and absence of adjacent metallic objects), with tube voltages of 100 kV and 120 kV, voxel sizes of 75 and 150 µm, and applying the metal artefact reduction (MAR) filter. Three examiners scored according to the Likert scale. The Fleiss' kappa test was performed to analyse intra- and inter-examiner agreement, in addition to Cochran's Q test with a significance level of 5%, to compare the parameters of tube voltage, voxel size, and MAR filter.

Results: The Fleiss' kappa test showed excellent inter- and intraobserver agreement for all groups. All modalities of tube voltage, voxel size, and MAR filter showed very high accuracy, sensitivity, and specificity, providing diagnoses consistent with reality, achieving 99% accuracy when the model did not present adjacent metallic objects to the tooth, and 95% accuracy when such objects were present.

Conclusions: It is concluded that, although cone-beam computed tomography is not the exam of choice for diagnosing carious lesions, optimizing acquisition parameters and using MAR filters allows a reliable concomitant diagnosis in exams already indicated for other purposes.

Objectives: This study aimed to compare crestal facial bone measurements obtained from ultrasonography (USG) and cone-beam computed tomography (CBCT).

Methods: A total of 200 teeth from 15 systemically healthy individuals were included. Teeth were categorized as maxillary anterior (n = 50), maxillary posterior (n = 50), mandibular anterior (n = 50), and mandibular posterior (n = 50). Marginal bone level (MBL) and facial bone thickness at 1 mm (MBT-1), 2 mm (MBT-2), and 3 mm (MBT-3) apical to the bone crest were measured using both USG and CBCT. USG imaging utilized an 18 MHz transducer in B-mode, with standardized settings. Measurements were repeated twice by 2 independent examiners to assess intra- and inter-observer reliability. Interclass correlation coefficients (ICCs) and Bland-Altman plots were used for statistical comparisons.

Results: The ICCs between examiners ranged from 0.812 to 0.980. MBL, MBT-1, and MBT-2 measurements between ultrasound and CBCT readings showed excellent agreement (ICCs > 0.75). The agreement for MBT-3 in mandibular anterior was fair (ICC: 0.528). Overall, mean difference between the 2 methods for MBL was 0.06 mm and for MBT-1 was 0.018 mm, without systematic bias.

Conclusions: Ultrasound can be a valuable and reproducible tool for MBL and MBT-1 measurements, and it can serve as an alternative to CBCT. Despite reasonable agreement in MBT-2 and MBT-3, potential variability should be considered.

Advances in knowledge: While widely used for soft tissue measurements, USG has limited application in marginal alveolar bone assessment in living humans. This study demonstrates the potential use of USG in the evaluation of facial marginal alveolar bone in different regions of the oral cavity.

Objectives: To evaluate the impact of ambient exposure of photostimulable phosphor (PSP) plates and digital enhancement on detecting internal root resorption (IRR).

Methods: Thirty-five single-rooted teeth were selected, including 15 with artificially induced IRR (via 3-hour immersion in 37% hydrochloric acid) and 20 controls. Three repeated periapical radiographs were acquired of each tooth using the parallelling technique and PSP plates from the Express, VistaScan Mini, and CS 7600 digital radiographic imaging systems. For each set of 3 X-ray exposures, prior to scanning, one PSP plate was kept shielded from ambient light, another was exposed to ambient light for 5 seconds, while the third was exposed for 10 seconds. The presence of IRR in the total sample of 315 radiographs was assessed by 4 independent examiners using a 5-point scale. Initially, digital enhancement was not allowed, and these images were considered originals. A second round was conducted with adjustments permitted (enhanced radiographs). Sensitivity, specificity, and area under the receiver operating characteristic curve were calculated and compared using 2-way analysis of variance (α = 0.05).

Results: No significant differences were found among different light exposure times across all systems (P > .05). In the CS 7600, enhanced radiographs showed significantly higher sensitivity and lower specificity compared to originals (P < .05).

Conclusions: Ambient light exposure of PSP for up to 10 seconds does not compromise IRR diagnosis. Digital enhancement in CS 7600 may increase detection but reduce specificity, requiring cautious interpretation to avoid overdiagnosis.

Objectives: This study aimed to evaluate the diagnostic performance of machine learning (ML) algorithms based on radiomic features extracted from cone-beam CT (CBCT) images in differentiating the nasopalatine duct (NPD) from the nasopalatine duct cyst (NPDC), and to compare their performance with that of a dentomaxillofacial radiologist.

Methods: CBCT scans from 101 histopathologically confirmed NPDC cases and 101 age- and sex-matched controls with normal NPD were retrospectively analysed. Manual segmentation was performed to extract 1037 radiomic features (original, Laplacian of Gaussian, and wavelet-transformed). After dimensionality reduction, 5 ML models (support vector machine [SVM], random forest [RF], decision tree [DT], k-nearest neighbours [KNN], and logistic regression [LR]) were trained using 5-fold cross-validation. Performance was evaluated using the area under the ROC curve (AUC), sensitivity, specificity, precision, recall, and F1-score.

Results: Among the 11 optimal features identified through feature selection, large area high grey level emphasis and zone variance from the grey level size zone matrix (GLSZM) class were the most prominent. SVM achieved the highest performance in the test set (AUC and all other metrics = 1.00). The radiologist showed comparable but slightly lower overall performance than SVM (AUC = 0.94, with other metrics between 0.93 and 0.95).

Conclusions: ML algorithms based on radiomic features extracted from CBCT images can effectively differentiate NPD from NPDC. Unlike standard visual interpretation, this approach analyses quantitative image features via mathematical models, yielding objective and reproducible results. It may serve as a non-invasive, complementary decision-support tool, particularly in diagnostically challenging cases.