Dento maxillo facial radiology最新文献

Objectives: To develop and evaluate a deep learning (DL) model to reduce metal artefacts originating from the exomass in cone-beam CT (CBCT) of the jaws.

Methods: Five porcine mandibles, each featuring six tubes filled with a radiopaque solution, were scanned using four CBCT units before and after the incremental insertion of up to three titanium, titanium-zirconium, and zirconia dental implants in the exomass of a small field of view. A conditional denoising diffusion probabilistic model, using DL techniques, was employed to correct axial images from exomass-related metal artefacts across the CBCT units and implant scenarios. Three examiners independently scored the image quality of all datasets, including those without an implant (ground truth), with implants in the exomass (original), and DL-generated ones. Quantitative analysis compared contrast-to-noise ratio (CNR) to validate artefact reduction using repeated measures analysis of variance in a factorial design followed by Tukey test (α = .05).

Results: The visualisation of the hard tissues and overall image quality was reduced in the original and increased in the DL-generated images. The score variation observed in the original images was not observed in the DL-generated images, which generally scored higher than the original images. DL-generated images revealed significantly greater CNR than both the ground truth and their corresponding original images, regardless of the material and quantity of dental implants and the CBCT unit (P < .05). Original images revealed significantly lower CNR than the ground truth (P < .05).

Conclusions: The developed DL model using porcine mandibles demonstrated promising performance in correcting exomass-related metal artefacts in CBCT, serving as a proof-of-principle for future applications of this approach.

Objectives: The main objective was to develop and evaluate an artificial intelligence model for tooth segmentation in magnetic resonance (MR) scans.

Methods: MR scans of 20 patients performed with a commercial 64-channel head coil with a T1-weighted 3D-SPACE (Sampling Perfection with Application Optimized Contrasts using different flip angle Evolution) sequence were included. Sixteen datasets were used for model training and 4 for accuracy evaluation. Two clinicians segmented and annotated the teeth in each dataset. A segmentation model was trained using the nnU-Net framework. The manual reference tooth segmentation and the inferred tooth segmentation were superimposed and compared by computing precision, sensitivity, and Dice-Sørensen coefficient. Surface meshes were extracted from the segmentations, and the distances between points on each mesh and their closest counterparts on the other mesh were computed, of which the mean (average symmetric surface distance) and 95th percentile (Hausdorff distance 95%, HD95) were reported.

Results: The model achieved an overall precision of 0.867, a sensitivity of 0.926, a Dice-Sørensen coefficient of 0.895, and a 95% Hausdorff distance of 0.91 mm. The model predictions were less accurate for datasets containing dental restorations due to image artefacts.

Conclusions: The current study developed an automated method for tooth segmentation in MR scans with moderate to high effectiveness for scans with respectively without artefacts.

Objectives: To identify if supplemental preoperative cone beam CT (CBCT) imaging could improve outcomes related to endodontic access cavity preparation, using 3D-printed maxillary first molars (M1Ms) in a rigorously simulated, controlled human analogue study.

Methods: Eighteen operators with 3 experience-levels took part in 2 simulated clinical sessions, 1 with and 1 without the availability of CBCT imaging, in a randomized order and with an intervening 8-week washout period. Operators attempted the location of all 4 root canals in each of 3 custom-made M1Ms (2 non-complex and 1 complex mesiobuccal [MB] canal anatomy). The primary outcome was tooth volume removed. Secondary outcomes were linear cavity dimensions, canals located, and procedural time. Operator confidence and "helpfulness" of available imaging were recorded. Statistical analysis of data included: paired t-tests, Fisher's exact test, linear mixed-effect modelling, and Mann-Whitney U test, with an alpha level of .05 for all.

Results: When supplemental preoperative CBCT was available, there were significant reductions in volume of the access cavity and procedural times, with significantly increased MB2 canal location, but only for teeth with non-complex anatomies and for more experienced operators. Linear mixed-effect modelling identified image type and operator experience as significant predictors of tooth volume removed and procedural time. There was significantly lower confidence in canal location and perceived "helpfulness" (all Experience Groups) when conventional imaging only was used compared with when CBCT was available.

Conclusions: Supplemental preoperative CBCT had several beneficial impacts on access cavity preparation, although this only applied to teeth with non-complex anatomy and for more experienced operators.

Objectives: Carotid artery calcification (CAC) is occasionally detected in panoramic radiographs (PRs). Bilateral vessel-outlining (BVO) CACs are independent risk markers for future vascular events and have been associated with large plaque area. If accounting for plaque area, BVO CACs may no longer be an independent risk marker for vascular events. The aim of this study was to explore the association between BVO CACs and vascular events and its relationship with carotid ultrasound plaque area.

Methods: In this cohort study we prospectively included 212 consecutive participants with CACs detected in PR that were performed to plan and evaluate odontologic treatment. Of these 212, 43 (20%) had BVO CACs. Plaque area was assessed with ultrasound at baseline. Primary outcome was major adverse cardiovascular events (MACEs) during follow-up.

Results: Vessel-outlining CAC was associated with larger plaque area on the same side (P = .03) and BVO CACs were associated with larger total plaque area (both sides summed) than other CAC features (P = .004). Mean follow-up was 7.0 years and 72 (34%) participants had more than 1 MACE. In bivariable analyses, both BVO CACs (HR 2.5, P < .001) and total plaque area (HR 1.8 per cm2, P = .008) were associated with MACE. When entering BVO CACs, plaque area and other relevant co-variates in a multivariable model, BVO CACs were virtually unchanged (HR 2.4, P = .001), but total plaque area was no longer significant (HR 1.0, P = .92).

Conclusion: Present results support the contention that BVO CACs are a stronger predictor for future vascular events than carotid ultrasound plaque area.

Objectives: To summarize the current evidence on the performance of artificial intelligence (AI) algorithms for the temporomandibular joint (TMJ) disc assessment and TMJ internal derangement diagnosis in magnetic resonance imaging (MRI) images.

Methods: Studies were gathered by searching 5 electronic databases and partial grey literature up to May 27, 2024. Studies in humans using AI algorithms to detect or diagnose internal derangements in MRI images were included. The methodological quality of the studies was evaluated using the Quality Assessment Tool for Diagnostic of Accuracy Studies-2 (QUADAS-2) and a proposed checklist for dental AI studies.

Results: Thirteen studies were included in this systematic review. Most of the studies assessed disc position. One study assessed disc perforation. A high heterogeneity related to the patient selection domain was found between the studies. The studies used a variety of AI approaches and performance metrics with CNN-based models being the most used. A high performance of AI models compared to humans was reported with accuracy ranging from 70% to 99%.

Conclusions: The integration of AI, particularly deep learning, in TMJ MRI, shows promising results as a diagnostic-assistance tool to segment TMJ structures and classify disc position. Further studies exploring more diverse and multicentre data will improve the validity and generalizability of the models before being implemented in clinical practice.

Objectives: To investigate the ultrasound (US) characteristics of metastatic malignancies (MM) in the major salivary glands and to assess the diagnostic value of the close relationship with the glandular capsule in identifying MM.

Methods: From January 2016 and April 2022, 122 patients with major salivary gland malignancies, including 20 patients with MM and 102 patients with primary malignancies (PM) confirmed by histopathological examination, were enrolled in this study. Their clinicopathologic and US data were recorded and analysed. The diagnostic performance of the close relationship with the glandular capsule for differentiating MM from PM was analysed.

Results: The mean age of MM were older than that of PM (59.50 ± 14.57 vs. 49.96 ± 15.73, P = .013). Compared with PM patients, MM were associated with a higher prevalence of local pain symptoms (P = .007) and abnormal facial nerve function (P < .001). MM were also more frequently characterized by unclear borders, rough margins, irregular shapes, heterogeneous internal echos, absence of cystic areas, presence of calcifications, close relationship with the glandular capsule, and US-reported positive cervical lymph nodes (all P < .05). The close relationship with the glandular capsule showed to be a good indicator in distinguishing between MM and PM, with an area under the receiver operating characteristic curve of 0.863, a sensitivity of 100%, a specificity of 72.5%, and an accuracy of 92.2%. Positive and negative predictive were calculated at 41.7% and 100%, respectively.

Conclusions: The US finding of a close relationship with the glandular capsule is a highly sensitive diagnostic indicator for MM. Following this finding, US-guided needle biopsy should be recommended to further confirm the diagnosis.

Background: Sinusitis is a commonly encountered clinical condition that imposes a considerable burden on the healthcare systems. A significant number of maxillary sinus opacifications are diagnosed as sinusitis, often overlooking the precise differentiation between cystic formations and inflammatory sinusitis, resulting in inappropriate clinical treatment. This study aims to improve diagnostic accuracy by investigating the feasibility of differentiating maxillary sinusitis, retention cysts, and normal sinuses.

Methods: We developed a deep learning-based automatic detection model to diagnose maxillary sinusitis using ostiomeatal unit CT images. Of the 1080 randomly selected coronal-view CT images, including 2158 maxillary sinuses, datasets of maxillary sinus lesions comprised 1138 normal sinuses, 366 cysts, and 654 sinusitis based on radiographic findings, and were divided into training (n = 648 CT images), validation (n = 216), and test (n = 216) sets. We utilized a You Only Look Once based model for object detection, enhanced by the transfer learning method. To address the insufficiency of training data, various data augmentation techniques were adopted, thereby improving the model's robustness.

Results: The trained You Only Look Once version 8 nano model achieved an overall precision of 97.1%, with the following class precisions on the test set: normal = 96.9%, cyst = 95.2%, and sinusitis = 99.2%. With an average F1-score of 95.4%, the F1-score was the highest for normal, then sinusitis, and finally, cysts. Upon evaluating a performance on difficulty level, the precision decreased to 92.4% on challenging test dataset.

Conclusions: The developed model is feasible for assisting clinicians in screening maxillary sinusitis lesions.

The aim of this study is to propose and evaluate a novel method for measuring the central ray direction and detecting the rotation centre of panoramic radiography using the panorama phantom. To determine the central ray direction, 2 points passing through the same x-coordinate in a panoramic radiograph were identified and connected. The angles formed by the central ray with the midline and the angle to the arch form were measured using mathematical calculations. Further, by analysing the continuous changes in the central ray obtained in this manner, the movement of the rotation centre was detected and visualized. The angle between the central ray and the midline exhibited a progressive decrease from the anterior to the posterior direction. With regards to the arch form, the angle of the central ray exhibited an increasing pattern as it moved from the anterior to the posterior direction, culminating in its peak value at the lower second premolar cusp region, followed by a consistent decrease. The rotation centre approximately started from the distolateral aspect of the coronoid process and then anteromedially moved to the midline in a curved line passing between the mandibular notch and coronoid process. By using the panorama phantom, we successfully obtained the central ray direction and detected the rotation centre of the panoramic radiography.