Dento maxillo facial radiology最新文献

Objectives: The aim of this study is to propose and evaluate a novel method for measuring the central ray direction and detecting the rotation center of panoramic radiography using the panorama phantom.

Methods: To determine the central ray direction, two 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 analyzing the continuous changes in the central ray obtained in this manner, the movement of the rotation center was detected and visualized.

Results: 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 center 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.

Conclusions: By using the panorama phantom, we successfully obtained the central ray direction and detected the rotation center of the panoramic radiography.

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: 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: 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.

Objectives: This study aimed to compare the accuracy of cone-beam CT (CBCT) scanning and 3 different electronic apex locators (EALs) in the detection of simulated oblique root fractures (ORF) in different localizations.

Methods: The study utilised a total of 80 human maxillary incisors, which were categorised into two groups based on the location of the ORF (apical and middle third of the root) formed on the buccal side of the root surface. The measurement of the distance between the incisal edge and the intersection of the ORF with the root canal was conducted using a stereomicroscope. This measurement is referred to as the actual working length (AWL). Additionally, three EALs-Dentaport ZX, EndoRadar Pro, and Propex II-were utilised to determine the electronic working length (EWL). Furthermore, CBCT images were employed to assess the distance, known as the CBCT working length (CWL). The differences were determined by subtracting AWL from EWL and CWL.

Results: Based on the accuracy of the devices, there were no significant differences observed among Dentaport ZX, EndoRadar, Propex II, and CBCT measures in both the apical and middle third ORF groups, within the acceptable range of 0.5 and 1 mm.

Conclusions: The accuracy of Dentaport ZX, Propex II, and CBCT was higher in the middle third ORF group compared to the apical third ORF group, with a tolerance of 0.5 mm. However, there were no significant differences seen among the devices.