Dento maxillo facial radiology最新文献

Objectives: Differentiation among benign salivary gland tumours, Warthin tumours (WTs), and malignant salivary gland tumours is crucial to treatment planning and predicting patient prognosis. However, differentiation of those tumours using imaging findings remains difficult. This study evaluated the usefulness of elasticity determined from diffusion-weighted image (DWI)-based virtual MR elastography (MRE) compared with conventional magnetic resonance imaging (MRI) findings in differentiating the tumours.

Methods: This study included 17 benign salivary gland tumours, 6 WTs, and 11 malignant salivary gland tumours scanned on neck MRI. The long and short diameters, T1 and T2 signal intensities, tumour margins, apparent diffusion coefficient (ADC) values, and elasticity from DWI-based virtual MRE of the tumours were evaluated. The interobserver agreement in measuring tumour elasticity and the receiver operating characteristic (ROC) curves were also assessed.

Results: The long and short diameters and the T1 and T2 signal intensities showed no significant difference among the 3 tumour groups. Tumour margins and the mean ADC values showed significant differences among some tumour groups. The elasticity from virtual MRE showed significant differences among all 3 tumour groups and the interobserver agreement was excellent. The area under the ROC curves of the elasticity were higher than those of tumour margins and mean ADC values.

Conclusion: Elasticity values based on DWI-based virtual MRE of benign salivary gland tumours, WTs, and malignant salivary gland tumours were significantly different. The elasticity of WTs was the highest and that of benign tumours was the lowest. The elasticity from DWI-based virtual MRE may aid in the differential diagnosis of salivary gland tumours.

Objectives: Preoperative identification of different stromal subtypes of pleomorphic adenoma (PA) of the salivary gland is crucial for making treatment decisions. We aimed to develop and validate a model based on histogram analysis (HA) of ultrasound (US) images for predicting tumour stroma ratio (TSR) in salivary gland PA.

Methods: A total of 219 PA patients were divided into low-TSR (stroma-low) and high-TSR (stroma-high) groups and enrolled in a training cohort (n = 151) and a validation cohort (n = 68). The least absolute shrinkage and selection operator regression algorithm was used to screen the most optimal clinical, US, and HA features. The selected features were entered into multivariable logistic regression analyses for further selection of independent predictors. Different models, including the nomogram model, the clinic-US (Clin + US) model, and the HA model, were built based on independent predictors using logistic regression. The performance levels of the models were evaluated and validated on the training and validation cohorts.

Results: Lesion size, shape, cystic areas, vascularity, HA_mean, and HA_skewness were identified as independent predictors for constructing the nomogram model. The nomogram model incorporating the clinical, US, and HA features achieved areas under the curve of 0.839 and 0.852 in the training and validation cohorts, respectively, demonstrating good predictive performance and calibration. Decision curve analysis and clinical impact curves further confirmed its clinical usefulness.

Conclusions: The nomogram model we developed offers a practical tool for preoperative TSR prediction in PA, potentially enhancing clinical decision-making.

Objectives: Dental imaging plays a key role in the diagnosis and treatment of dental conditions, yet limitations regarding the quality and resolution of dental radiographs sometimes hinder precise analysis. Super-resolution with deep learning refers to a set of techniques used to enhance the resolution of images beyond their original size or quality using deep neural networks instead of traditional image interpolation methods which often result in blurred or pixelated images when attempting to increase resolution. Leveraging advancements in technology, this study aims to enhance the resolution of dental panoramic radiographs, thereby enabling more accurate diagnoses and treatment planning.

Methods: About 1714 panoramic radiographs from 3 different open datasets are used for training (n = 1364) and testing (n = 350). The state of the art 4 different models is explored, namely Super-Resolution Convolutional Neural Network (SRCNN), Efficient Sub-Pixel Convolutional Neural Network, Super-Resolution Generative Adversarial Network, and Autoencoder. Performances in reconstructing high-resolution dental images from low-resolution inputs with different scales (s = 2, 4, 8) are evaluated by 2 well-accepted metrics Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR).

Results: SSIM spans between 0.82 and 0.98 while PSNR are between 28.7 and 40.2 among all scales and models. SRCNN provides the best performance. Additionally, it is observed that performance decreased when images are scaled with higher values.

Conclusion: The findings highlight the potential of super-resolution concepts to significantly improve the quality and detail of dental panoramic radiographs, thereby contributing to enhanced interpretability.

Objectives: The study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in cone beam computed tomography (CBCT) volumes and to evaluate the performance of this model.

Methods: In 101 CBCT scans, MS were annotated using the CranioCatch labelling software (Eskisehir, Turkey) The dataset was divided into 3 parts: 80 CBCT scans for training the model, 11 CBCT scans for model validation, and 10 CBCT scans for testing the model. The model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.00001 for 1000 epochs. The performance of the model to automatically segment the MS on CBCT scans was assessed by several parameters, including F1-score, accuracy, sensitivity, precision, area under curve (AUC), Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) values.

Results: F1-score, accuracy, sensitivity, precision values were found to be 0.96, 0.99, 0.96, 0.96, respectively for the successful segmentation of maxillary sinus in CBCT images. AUC, DC, 95% HD, IoU values were 0.97, 0.96, 1.19, 0.93, respectively.

Conclusions: Models based on nnU-Net v2 demonstrate the ability to segment the MS autonomously and accurately in CBCT images.

Objectives: The aim of this systematic review was to verify the accuracy of linear measurements performed on low-dose CBCT protocols for implant planning, in comparison with those performed on standard and high-resolution CBCT protocols.

Methods: The literature search included four databases (Pubmed, Web of Science, Embase, and Scopus). Two reviewers independently screened titles/abstracts and full texts according to eligibility criteria, extracted the data, and examined the methodological quality. Risk of bias assessment was performed using the Quality Assessment Tool For In Vitro Studies. Random-effects meta-analysis was used for pooling measurement error data.

Results: The initial search yielded 4684 titles. In total, 13 studies were included in the systematic review, representing a total of 81 samples, while 9 studies were included in the meta-analysis. The risk of bias ranged from medium to low. The main results across the studies indicate a strong consistency in linear measurements performed on low-dose images in relation to the reference methods. The overall pooled planning measurement error from low-dose CBCT protocols was -0.24 mm (95% CI, -0.52 to 0.04) with a high level of heterogeneity, showing a tendency for underestimation of real values. Various studies found no significant differences in measurements across different protocols (eg, voxel sizes, mA settings, or dose levels), regions (incisor, premolar, molar) and types (height vs. width). Some studies, however, noted exceptions in measurements performed on the posterior mandible.

Conclusion: Low-dose CBCT protocols offer adequate precision and accuracy of linear measurements for implant planning. Nevertheless, diagnostic image quality needs must be taken into consideration when choosing a low-dose CBCT protocol.

Objectives: Automating the digital workflow for diagnosing impacted canines using panoramic radiographs (PRs) is challenging. This study explored feature extraction, automated cropping, and classification of impacted and nonimpacted canines as a first step.

Methods: A convolutional neural network with SqueezeNet architecture was first trained to classify two groups of PRs (91with and 91without impacted canines) on the MATLAB programming platform. Based on results, the need to crop the PRs was realized. Next, artificial intelligence (AI) detectors were trained to identify specific landmarks (maxillary central incisors, lateral incisors, canines, bicuspids, nasal area, and the mandibular ramus) on the PRs. Landmarks were then explored to guide cropping of the PRs. Finally, improvements in classification of automatically cropped PRs were studied.

Results: Without cropping, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve for classifying impacted and nonimpacted canine was 84%. Landmark training showed that detectors could correctly identify upper central incisors and the ramus in ∼98% of PRs. The combined use of the mandibular ramus and maxillary central incisors as guides for cropping yielded the best results (∼10% incorrect cropping). When automatically cropped PRs were used, the AUC-ROC improved to 96%.

Conclusions: AI algorithms can be automated to preprocess PRs and improve the identification of impacted canines.

Objectives: To investigate the management of imaging errors from panoramic radiography (PAN) datasets used in the development of machine learning (ML) models.

Methods: This systematic literature followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and used three databases. Keywords were selected from relevant literature.

Eligibility criteria: PAN studies that used ML models and mentioned image quality concerns.

Results: Out of 400 articles, 41 papers satisfied the inclusion criteria. All the studies used ML models, with 35 papers using deep learning (DL) models. PAN quality assessment was approached in 3 ways: acknowledgement and acceptance of imaging errors in the ML model, removal of low-quality radiographs from the dataset before building the model, and application of image enhancement methods prior to model development. The criteria for determining PAN image quality varied widely across studies and were prone to bias.

Conclusions: This study revealed significant inconsistencies in the management of PAN imaging errors in ML research. However, most studies agree that such errors are detrimental when building ML models. More research is needed to understand the impact of low-quality inputs on model performance. Prospective studies may streamline image quality assessment by leveraging DL models, which excel at pattern recognition tasks.

Objectives: To evaluate the feasibility of frozen soft tissues in simulating fresh soft tissues of pig mandibles using cone beam CT (CBCT).

Methods: Two fresh pig mandibles with soft tissues containing 2 tubes filled with a radiopaque homogeneous solution were scanned using 4 CBCT units and 2 field-of-view (FOV) sizes each. The pig mandibles were deep-frozen and scanned again. Three cross-sections were exported from each CBCT volume and grouped into pairs, with one cross-section representing a fresh and one a frozen mandible. Three radiologists compared the pairs and attributed a score to assess the relative image quality using a 5-point scale. Mean grey values and standard deviation were obtained from homogeneous areas in the tubes, compared using the Wilcoxon matched-pair signed-rank test and subjected to Pearson correlation analysis between fresh and frozen physical states (α = .05).

Results: Subjective evaluation revealed similarity of the CBCT image quality between fresh and frozen states. The distribution of mean grey values was similar between fresh and frozen states. Mean grey values of the frozen state in the small FOV were significantly greater than those of the fresh state (P = .037), and noise values of the frozen state in the large FOV were significantly greater than those of the fresh state (P = 0.007). Both mean grey values and noise exhibited significant and positive correlations between fresh and frozen states (P < 0.01).

Conclusions: The freezing of pig mandibles with soft tissues may serve as a method to prolong their usability and working time when CBCT imaging is planned.

Applications of cone-beam CT (CBCT) in orthodontics have been increasingly discussed and evaluated in science and practice over the last two decades. The present work provides a comprehensive summary of current consolidated practice guidelines, cutting-edge innovative applications, and future outlooks about potential use of CBCT in orthodontics with a special focus on upper airway analysis in patients with sleep-disordered breathing. The present scoping review reveals that clinical applications of CBCT in orthodontics are broadly supported by evidence for the diagnosis of dental anomalies, temporomandibular joint disorders, and craniofacial malformations. On the other hand, CBCT imaging for upper airway analysis-including soft tissue diagnosis and airway morphology-needs further validation in order to provide better understanding regarding which diagnostic questions it can be expected to answer. Internationally recognized guidelines for CBCT use in orthodontics are existent, and similar ones should be developed to provide clear indications about the appropriate use of CBCT for upper airway assessment, including a list of specific clinical questions justifying its prescription.

Objectives: The purpose of this study is to investigate the morphological changes that occur when mesiodens is located within the nasopalatine canal, as well as clinical characteristics.

Methods: Clinical records and CT images of patients who had mesiodens in the nasopalatine canal were retrospectively analysed. In addition to demographic information, clinical symptoms and complications associated with extraction of mesiodens were recorded. Using CT images, number, location, size, and tooth morphology were evaluated.

Results: This study included 32 patients and 38 mesiodens within the nasopalatine canal. Supernumerary teeth exhibited a characteristic feature of thin and elongated shape in the canal (narrow width and elongation were observed in 96.6% and 53.3% of the patients, respectively). Fusion was found in 4 patients and dilaceration in 12. A complication occurred in 2 patients, which was tooth remnant, not a neurologic complication. Only 5 mesiodens could be detected in the nasopalatine canal on panoramic images.

Conclusions: Morphological abnormalities in mesiodens within the nasopalatine canal were frequently detected, and these could be effectively diagnosed through 3D imaging analysis.