Dento maxillo facial radiology最新文献

Objectives: This study evaluated the impact of variations in anteroposterior and lateral tilts of patients head on radiation-weighted doses to organs/tissues and effective doses using 3 different cone beam computed tomography (CBCT) machines.

Methods: An anthropomorphic phantom was used to estimate radiation doses in 3 CBCT machines (OP300, Eagle X 3D, and Eagle Edge). Thermoluminescent dosimeters were placed in regions corresponding to pre-stablished organ/tissues. CBCT examinations from the posterior mandible and anterior maxilla regions were acquired, with 3 different anteroposterior angulations (0°, 30°, and 45°), and from the posterior mandible in 3 different lateral angulations (0°, 20° to the left, and 20° to the right side). Radiation-weighted doses for each organ/tissue and effective doses were calculated for each machine and angulation.

Results: For the posterior mandible acquisitions, anteroposterior angulations of the head at 30° and 45° yielded a reduction in effective doses in all 3 devices. A 20° tilt to the right side resulted in lower doses than to the left [same side as the field of view (FOV)]. For the anterior maxilla, increased anteroposterior angulation was associated with reduction in effective dose in 2 devices.

Conclusions: Effective doses are lower when small FOV CBCT exams of the posterior mandible and anterior maxilla are acquired with increased anteroposterior head angulation at 30° and 45°. For FOV in the posterior mandible, a 20° lateral tilt towards the side opposite to the FOV also yields lower effective doses. The main contribution to these dose reductions is the decrease in dose to salivary glands.

Objectives: The aim of the present study was to assess the reproducibility of findings in cone beam CT (CBCT) scout images. Furthermore, the study aimed to assess whether a scout image shows pathology not seen within the CBCT volume (ie, added diagnostic information) and therefore must be assessed on the same terms as the full volume.

Methods: Using a retrospective design, 233 CBCT reports and scout images were assessed. Kappa statistics and percentage of accordance were used to evaluate intra- and inter-observer reproducibility as well as agreement between scout and CBCT report.

Results: Intra- and inter-observer reproducibility was overall low (kappa ranging from -0.008 to 1.000). Agreement between findings reported in the CBCT and scout was also low. One hundred fourteen impacted teeth, one apical periodontitis, and two sinus conditions seen in the scout image were not registered in the full volume report due to the extended size of the scout image.

Conclusions: Reproducibility of findings in scout images compared to CBCT volumes was low, and the scout showed very little additional diagnostic information.

Advances in knowledge: This study shows that although the reproducibility of viewing scout images is low, rare findings can go undetected if the scout is not assessed. Legislation regarding interpretation of scout images needs to be discussed.

Objectives: The purpose of this study was to generate radiographs including dentigerous cysts by applying the latest generative adversarial network (GAN; StyleGAN3) to panoramic radiography.

Methods: A total of 459 cystic lesions were selected, and 409 images were randomly assigned as training data and 50 images as test data. StyleGAN3 training was performed for 500 000 images. Fifty generated images were objectively evaluated by comparing them with 50 real images according to four metrics: Fréchet inception distance (FID), kernel inception distance (KID), precision and recall, and inception score (IS). A subjective evaluation of the generated images was performed by three specialists who compared them with the real images in a visual Turing test.

Results: The results of the metrics were as follows: FID, 199.28; KID, 0.14; precision, 0.0047; recall, 0.00; and IS, 2.48. The overall results of the visual Turing test were 82.3%. No significant difference was found in the human scoring of root resorption.

Conclusions: The images generated by StyleGAN3 were of such high quality that specialists could not distinguish them from the real images.

Objectives: Nasotracheal intubation is a standard blind procedure associated with various complications. The selection of the appropriate nostril is crucial to preventing most of these complications. The present study aimed to evaluate the predictive ability of cone-beam CT (CBCT) images to select the correct nostril for nasotracheal intubation.

Methods: The study encompassed 60 patients who underwent maxillofacial surgery with nasotracheal intubation under general anaesthesia. While the anaesthetist made the appropriate nostril selection clinically according to a simple occlusion test and spatula test, the radiologist made the selection after analysing various CBCT findings such as the angle and direction of nasal septum deviation (NSD), minimum bone distance along the intubation path, and the presence of inferior turbinate hypertrophy. The appropriateness of these choices made blindly at different times was evaluated using descriptive statistics, chi-squared test, and independent samples t-test.

Results: The study found that 83.3% of the suggested nostril intubations were successful. We also observed that intubation duration was longer when inferior turbinate hypertrophy was present (P = .031). However, there was no statistical relationship between the presence of epistaxis and septal deviation (P = .395). Nonetheless, in 64.3% of cases with epistaxis, the intubated nostril and the septum deviation direction were the same.

Conclusions: Pre-operative evaluations using CBCT can aid anaesthetists for septum deviation and turbinate hypertrophy, as both can impact intubation success rates and duration.

Objectives: This study aimed to assess the accuracy of ultra-low dose (ULD) cone-beam CT (CBCT) for detection of proximal caries.

Methods: This in vitro study evaluated 104 molar and premolar teeth. The teeth were mounted in dry skulls and underwent CBCT with 4 protocols of high-resolution (HR), normal (NORM), ULD-HR, and ULD-NORM; 78 CBCT images were scored by 3 observers for the presence and penetration depth of caries twice with a 2-week interval using a 5-point Likert scale. The teeth were then sectioned and observed under a stereomicroscope (gold standard). The 4 protocols were compared with each other and with the gold standard. The receiver operating characteristic curve was drawn, and the area under the curve (AUC) was calculated and compared by the Chi-square test (alpha = .05).

Results: The interobserver agreement ranged from 0.5233 to 0.6034 for ULD-NORM, 0.5380 to 0.6279 for NORM, 0.5856 to 0.6300 for ULD-HR, and 0.6614 to 0.7707 for HR images. The intra-observer agreement ranged from 0.6027 to 0.8812 for ULD-HR, 0.7083 to 0.7556 for HR, 0.6076 to 0.9452 for ULD-NORM, and 0.7012 to 0.9221 for NORM images. Comparison of AUC revealed no significant difference between NORM and ULD-NORM (P > .05), or HR and ULD-HR (P > .05). The highest AUC belonged to HR (0.8529) and the lowest to NORM (0.7774).

Conclusions: Considering the significant reduction in radiation dose in ULD CBCT and its acceptable diagnostic accuracy for detection of proximal caries, this protocol may be used for detection of proximal carious lesions and assessment of their depth.

Objectives: To develop and validate a modified deep learning (DL) model based on nnU-Net for classifying and segmenting five-class jaw lesions using cone-beam CT (CBCT).

Methods: A total of 368 CBCT scans (37 168 slices) were used to train a multi-class segmentation model. The data underwent manual annotation by two oral and maxillofacial surgeons (OMSs) to serve as ground truth. Sensitivity, specificity, precision, F1-score, and accuracy were used to evaluate the classification ability of the model and doctors, with or without artificial intelligence assistance. The dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and segmentation time were used to evaluate the segmentation effect of the model.

Results: The model achieved the dual task of classifying and segmenting jaw lesions in CBCT. For classification, the sensitivity, specificity, precision, and accuracy of the model were 0.871, 0.974, 0.874, and 0.891, respectively, surpassing oral and maxillofacial radiologists (OMFRs) and OMSs, approaching the specialist. With the model's assistance, the classification performance of OMFRs and OMSs improved, particularly for odontogenic keratocyst (OKC) and ameloblastoma (AM), with F1-score improvements ranging from 6.2% to 12.7%. For segmentation, the DSC was 87.2% and the ASSD was 1.359 mm. The model's average segmentation time was 40 ± 9.9 s, contrasting with 25 ± 7.2 min for OMSs.

Conclusions: The proposed DL model accurately and efficiently classified and segmented five classes of jaw lesions using CBCT. In addition, it could assist doctors in improving classification accuracy and segmentation efficiency, particularly in distinguishing confusing lesions (eg, AM and OKC).

Objectives: This study aimed to propose a new method for the automatic diagnosis of anterior disc displacement of the temporomandibular joint (TMJ) using MRI and deep learning. By using a multistage approach, the factors affecting the final result can be easily identified and improved.

Methods: This study introduces a multistage automatic diagnostic technique using deep learning. This process involves segmenting the target from MR images, extracting distance parameters, and classifying the diagnosis into 3 classes. MRI exams of 368 TMJs from 204 patients were evaluated for anterior disc displacement. In the first stage, 5 algorithms were used for the semantic segmentation of the disc and condyle. In the second stage, 54 distance parameters were extracted from the segments. In the third stage, a rule-based decision model was developed to link the parameters with the expert diagnosis results.

Results: In the first stage, DeepLabV3+ showed the best result (95% Hausdorff distance, Dice coefficient, and sensitivity of 6.47 ± 7.22, 0.84 ± 0.07, and 0.84 ± 0.09, respectively). This study used the original MRI exams as input without preprocessing and showed high segmentation performance compared with that of previous studies. In the third stage, the combination of SegNet and a random forest model yielded an accuracy of 0.89 ± 0.06.

Conclusions: An algorithm was developed to automatically diagnose TMJ-anterior disc displacement using MRI. Through a multistage approach, this algorithm facilitated the improvement of results and demonstrated high accuracy from more complex inputs. Furthermore, existing radiological knowledge was applied and validated.

Introduction: Medication-related osteonecrosis of the jaw (MRONJ) and jaw metastasis might share similar clinical and radiographic characteristics, with both demonstrating F-18 fluorodeoxyglucose (FDG) uptake on PET-CT. Prostate-specific membrane antigen (PSMA) PET-CT is used to demonstrate prostate cancer dissemination. Unlike FDG PET-CT, PSMA PET-CT is more specific to cancer than to inflammation. Therefore, we hypothesized that it might be a useful tool to differentiate between MRONJ and jaw metastasis.

Methods: All files of prostate cancer patients diagnosed with MRONJ and with available PSMA PET-CT studies were retrieved. A similar number of solid cancer patients with MRONJ and with available FDG PET-CT studies served as a second study group. All studies were reviewed by 2 blinded co-investigators (L.D. and M.F.).

Results: Seventeen patients who underwent PSMA PET-CT (24 studies) and 15 patients who underwent FDG PET-CT (29 studies) met the inclusion criteria. All patients with FDG PET-CT studies showed pathological uptake at the site of MRONJ in at least one of their studies versus only 23.5% of patients in the PSMA PET-CT group (P < .001). FDG PET-CT studies showed pathological uptake in 89.6% of the studies compared with only 20.8% in the PSMA PET-CT group (P < .001). The mean standardized uptake value (SUVmax) and the mean uptake volume in the FDG PET-CT group were significantly higher compared with the PSMA PET-CT group (P < .001 and P < .005, respectively). The interclass correlation coefficient for all parameters was higher than 0.95.

Conclusions: PSMA PET-CT is useful to differentiate between MRONJ and jaw metastasis.

Objectives: Verify whether hypervigilance to pain (HP) and sleep quality (SQ) are confounding variables in the infrared thermography (IT) examination of the temporomandibular joint and temporal and masseter muscles.

Methods: A cross-sectional and analytical study was conducted, collecting HP and SQ data from 80 participants without temporomandibular disorders (TMD), performing their IT and another 40 participants with TMD. For the selection of participants with and without TMD, the TMD Pain Screener questionnaire and axis I of the Diagnostic Criteria for Temporomandibular Disorders were applied. SQ was verified using the Pittsburgh Sleep Quality Index (PSQI) questionnaire. For the HP assessment the Pain Vigilance and Awareness Questionnaire (PVAQ) was applied. And the IT was performed through a FLIR infrared sensor camera, model T650 Infrared.

Results: No significant correlations were found between SQ and the temperatures of the areas of interest (P > .05), and regarding HP, a statistically significant positive correlation was found with the dimensionless (ρ = 0.289) and non-dimensionless (ρ = 0.223) asymmetries of temporal muscle temperatures. In the temperature comparisons between the participants without TMD and the participants with TMD, significant differences were found (P < .05), also when the group without TMD was controlled according to both HP and SQ (P < .05), with higher temperatures found in the TMD group.

Conclusions: HP and SQ can be considered confounding variables in IT examination of the temporomandibular region.

Objectives: This study aimed to evaluate the impact of 3D-printed mannequins on the training of predoctoral students.

Methods: Two 3D-printed training models were developed: a traditional model that simulates a sound adult patient and a customized model with pathological and physiological changes (impacted third molar and edentulous region). Students accomplished their pre-clinical training divided into a control group (CG, n = 23), which had access to the traditional model, and a test group (TG, n = 20), which had access to both models. Afterward, they performed a full mouth series on patients and filled out a perception questionnaire. Radiographs were evaluated for technical parameters. Descriptive statistics and the Mann-Whitney test were used to compare the groups.

Results: Students provided positive feedback regarding the use of 3D printing. The TG reported a more realistic training experience than the CG (P = .037). Both groups demonstrated good clinical performance (CG = 7.41; TG = 7.52), and no significant differences were observed between them.

Conclusions: 3D printing is an option for producing simulators for pre-clinical training in Oral Radiology, reducing student stress and increasing confidence during clinical care.