Dento maxillo facial radiology最新文献

Objectives: This meta-research assessed methodologies used for evaluating peri-implant marginal bone levels on digital periapical radiographs in randomized clinical trials published between 2019 and 2023.

Methods: Articles were searched in four databases. Data on methods for assessing peri-implant marginal bone levels were extracted. Risk of bias assessment was performed.

Results: During full-text reading, 108 out of 162 articles were excluded. Methodological issues accounted for these exclusions, including the absence of radiograph-type information, the lack of radiographic positioners, the missing anatomical references, and the use of panoramic radiographs or tomography. Fifty-four articles were included, most from Europe (70%) and university-based (74%). Radiographic positioners were specified in 54% of articles. Examiner calibration was unreported in 54%, with 69% lacking details. In 59%, no statistical measure assessed examiner agreement. Blinding was unreported or unused in 50%. Marginal bone level changes were the primary outcome of 61%. Most articles (59.3%) raised "some concerns" regarding bias, while 37% showed a high risk of bias, and only two articles (3.7%) demonstrated a low risk of bias.

Conclusions: Several limitations and areas for improvement were identified. Future studies should prioritize protocol registration, standardize radiographic acquisitions, specify examiner details, implement calibration and statistical measures for agreement, introduce blinding protocols, and maintain geometric calibration standards.

Objectives: Due to the increasing use of cone-beam CT (CBCT) in dentistry and considering the effects of radiation on radiosensitive organs, the aim of this study was to investigate the effect of shielding on absorbed dose of eyes, thyroid, and breasts in scans conducted with different parameters using 2 different fields of view (FOV).

Methods: Dose measurements were calculated on a tissue-equivalent female phantom by repeating each scanning parameter 3 times and placing at least 2 thermoluminescent dosimeters (TLD) on each organ, with the averages then taken. The same CBCT scans were performed in 2 different FOV with shielding including thyroid collar, radiation safety glasses, and lead apron and without shielding. The differences between them were analysed statistically. Descriptive statistics and the Wilcoxon test were used for data analysis.

Results: The difference between measurements with and without shielding was statistically significant for all scans (P < .001). The dose reduction associated with the use of shielding ranged from 26.81% to 52.95%. The dose related to the FOV has shown a significant increase, ranging from 8.30% to 623.54%, due to both the variation in the area affected by the primary beam on the organs and changes in the amount of radiation.

Conclusion: There are significant differences in the absorbed dose depending on shielding and FOV usage. Therefore, the CBCT imaging protocol should be optimized by the operator, and special attention should be paid to the use of thyroid collars and radiation safety glasses, considering their effects on image quality.

Objectives: Cysts in jaws may have similar radiographic features. However, it is important to clarify the diagnosis prior to surgery. The aim of this study was to compare the radiomic features of radicular cysts (RCs), dentigerous cysts (DCs), and odontogenic keratocysts (OKCs) as a non-invasive diagnostic alternative to biopsy.

Methods: In total, 161 odontogenic cysts diagnosed histopathologically (55 RCs, 53 DCs, and 53 OKCs) were included in the present study. Each cyst was semi-automatically segmented on CBCT images, and radiomic features were extracted by an observer. A second observer repeated 20% of the evaluations and the radiomic features. Those achieving an inter-observer agreement level above 0.850 were included in the study. Consequently, five shape-based and 22 textural features were investigated in the study. Statistical analysis was performed comparing both three cyst features and making pairwise comparisons.

Results: All features included in the study showed statistical differences between cysts, with the exception of one textural feature (NGTDM coarseness) (P < .05). However, only one shape-based feature (shericity) and one textural feature (GLSZM large area emphasis) were statistically different in pairwise comparisons of all three cysts (P < .05).

Conclusion: Radiomics features of the RCs, DCs, and OKCs showed significant differences, and may have the potential to be used as a non-invasive method in the differential diagnosis of cysts.

Objectives: The purpose of this study was to compare the image quality of ultra-high-resolution CT (U-HRCT) with that of conventional multidetector row CT (convCT) and demonstrate its usefulness in the dentomaxillofacial region.

Methods: Phantoms were helically scanned with U-HRCT and convCT scanners using clinical protocols. In U-HRCT, phantoms were scanned in super-high-resolution (SHR) mode, and hybrid iterative reconstruction (HIR) and filtered-back projection (FBP) techniques were performed using a bone kernel (FC81). The FBP technique was performed using the same kernel as in convCT (reference). Two observers independently evaluated the 54 resulting images using a 5-point scale (5 = excellent diagnostic image quality; 4 = above average; 3 = average; 2 = subdiagnostic; and 1 = unacceptable). The system performance function (SPF) was calculated for a comprehensive evaluation of the image quality using the task transfer function and noise power spectrum. Statistical analysis using the Kruskal-Wallis test was performed to compare the image quality among the 3 protocols.

Results: The observers assigned higher scores to images acquired with the SHRHIR and SHRFBP protocols than to those acquired with the reference (P < 0.0001 and P < 0.0001, respectively). The relative SPF value at 1.0 cycles/mm in SHRHIR and SHRFBP compared to the reference protocol were 151.5% and 45.6%, respectively.

Conclusions: Through phantom experiments, this study demonstrated that U-HRCT can provide superior-quality images compared to conventional CT in the dentomaxillofacial region. The development of a better image reconstruction method is required to improve image quality and optimize the radiation dose.

The aim of this technical report was to assess whether the "Radiological Report" tool within the Artificial Intelligence (AI) software Diagnocat can achieve a satisfactory level of performance comparable to that of experienced dentomaxillofacial radiologists in interpreting cone-beam CT scans. Ten cone-beam CT scans were carefully selected and analysed using the AI tool, and they were also evaluated by two dentomaxillofacial radiologists. Observations related to tooth numeration, alterations in dental crowns, roots, and periodontal tissues were documented and subsequently compared to the AI findings. Kappa statistics, along with their corresponding 95% confidence intervals, were calculated to ascertain the degree of agreement. The agreement between the AI tool and the radiologists ranged from substantial to nearly perfect for identifying teeth, determining the number of roots and canals, assessing crown conditions, and detecting endodontic treatments. However, for tasks such as classifying bone loss, identifying posts, evaluating the quality of fillings, and appraising the situation of periodontal spaces, the agreement was deemed slight. In conclusion, the "radiological report" tool of the Diagnocat demonstrates satisfactory performance in reliably identifying teeth, roots, canals, assessing crown conditions, and detecting endodontic treatment. However, further investigations are needed to evaluate the tool's effectiveness in diagnosing posts, assessing the condition and quality of fillings, and determining the status of periodontal spaces.

Objective: To develop an accurate method for converting dose-area product (DAP) to patient dose for dental cone-beam computed tomography (CBCT) using deep learning.

Methods: A total of 24 384 CBCT exposures of an adult phantom were simulated with PCXMC 2.0, using permutations of tube voltage, filtration, source-isocenter distance, beam width/height, and isocenter position. Equivalent organ doses as well as DAP values were recorded. Next, using the aforementioned scan parameters as inputs, neural networks (NN) were trained using Keras for estimating the equivalent dose per DAP for each organ. Two methods were explored for positional input features: (1) "Coordinate" mode, which uses the (continuous) XYZ coordinates of the isocentre, and (2) "AP/JAW" mode, which uses the (categorical) anteroposterior and craniocaudal position. Each network was trained, validated, and tested using a 3/1/1 data split. Effective dose (ED) was calculated from the combination of NN outputs using ICRP 103 tissue weighting factors. The performance of the resulting NN models for estimating ED/DAP was compared with that of a multiple linear regression (MLR) model as well as direct conversion coefficients (CC).

Results: The mean absolute error (MAE) for organ dose/DAP on the test data ranged from 0.18% (bone surface) to 2.90% (oesophagus) in "Coordinate" mode and from 2.74% (red bone marrow) to 14.13% (brain) in "AP/JAW" mode. The MAE for ED was 0.23% and 4.30%, respectively, for the two modes, vs. 5.70% for the MLR model and 20.19%-32.67% for the CCs.

Conclusions: NNs allow for an accurate estimation of patient dose based on DAP in dental CBCT.

Objectives: To compare a novel photon-counting sensor, 2 CBCT protocols and 2 CMOS sensors on the detection of gaps between a gutta-percha cone and root canal walls.

Methods: Twenty-five mandibular incisors were prepared to 45/0.04 (size/taper) at working length. Teeth were placed in a partially dentate mandible and single gutta-percha cones of 7 sizes were placed at length, one at a time, for image acquisition with a photon-counting sensor, 2 CBCT protocols (90 µm3, 120 µm3) and 2 CMOS sensors. Three calibrated observers assessed images for gap presence. Sensitivity, specificity, accuracy, AUC, and agreement with gold standard were determined using ANOVA and Tukey test (P ≤ .05).

Results: Photon-counting sensor showed superior sensitivity and accuracy (88.47%, 81.57%), significantly higher than the CBCT protocols (50.70%-56.33%, 45.87%-53.17%). Contrarily, the photon-counting sensor showed the lowest specificity (40.27%), significantly lower than the CBCT protocols (90.27%, 97.23%). CMOS sensors showed sensitivity, specificity, and accuracy between 72.23%-74.53%, not differing from other modalities. All intraoral sensors showed AUC around 82.87%-84.03%, significantly higher than CBCT protocol 120 µm3 (74.07%). The file size was inversely related to gap size and percentual agreement with gold standard.

Conclusions: CMOS sensors showed consistent results, while the photon-counting sensor had the highest sensitivity but lacked specificity. CBCT protocols excelled in specificity but had lower sensitivity.

Advances in knowledge: Novel photon-counting sensors and CBCT imaging provided no significant advantage over conventional sensors in assessing gaps as an indicator of quality of root canal filling. Furthermore, smaller gaps were more difficult to detect, regardless of the imaging technique used.

Objectives: To identify landmarks in ultrasound periodontal images and automate the image-based measurements of gingival recession (iGR), gingival height (iGH), and alveolar bone level (iABL) using machine learning.

Methods: We imaged 184 teeth from 29 human subjects. The dataset included 1580 frames for training and validating the U-Net convolutional neural network machine learning model, and 250 frames from new teeth that were not used in training for testing the generalization performance. The predicted landmarks, including the tooth, gingiva, bone, gingival margin (GM), cementoenamel junction (CEJ), and alveolar bone crest (ABC), were compared to manual annotations. We further demonstrated automated measurements of the clinical metrics iGR, iGH, and iABL.

Results: Over 98% of predicted GM, CEJ, and ABC distances are within 200 µm of the manual annotation. Bland-Altman analysis revealed biases (bias of machine learning vs ground truth) of -0.1 µm, -37.6 µm, and -40.9 µm, with 95% limits of agreement of [-281.3, 281.0] µm, [-203.1, 127.9] µm, and [-297.6, 215.8] µm for iGR, iGH, and iABL, respectively, when compared to manual annotations. On the test dataset, the biases were 167.5 µm, 40.1 µm, and 78.7 µm with 95% CIs of [-1175 to 1510] µm, [-910.3 to 990.4] µm, and [-1954 to 1796] µm for iGR, iGH, and iABL, respectively.

Conclusions: The proposed machine learning model demonstrates robust prediction performance, with the potential to enhance the efficiency of clinical periodontal diagnosis by automating landmark identification and clinical metrics measurements.

Objective: To compare the accuracy, duration, and certainty of diagnosing approximal caries in bitewing radiographs displayed in 3 monitors under 2 luminance conditions.

Methods: A total of 39 teeth without evident caries were selected from 11 patients undergoing partial jaw resection. Before the operation, 13 bitewing radiographs were captured by a digital imaging system. Eight observers evaluated the images under dark (9 lux) and bright (200 lux) conditions, using 2 medical-grade monitors and a commercial monitor. Using histological results as the gold standard, the areas under the receiver operating characteristic curves under different conditions were compared using the Z-test. Multivariate analysis of variance was conducted to assess the impact of various factors on diagnostic duration, while ordinal logistic regression was used to examine factors influencing diagnostic certainty level. It was considered significant when P < .05.

Results: No significant difference was found in the diagnostic accuracy or duration for diagnosis of approximal caries under 2 luminance conditions with the 3 distinct monitors (P > .05). Ambient light, clinical experience, and the pathological grade of approximal caries have influence on the degree of diagnostic confidence (P < .05).

Conclusions: Different monitors and ambient luminance didn't influence the diagnostic accuracy or evaluation duration. Ambient luminance, clinical experience, and the depth of caries affect the degree of diagnostic confidence.

Advances in knowledge: The study employing bitewing radiographs from real patients indicates that ambient luminance, clinical experience, and the depth of caries affect the degree of diagnostic confidence.

Objectives: To compare the performance of the convolutional neural network (CNN) with the vision transformer (ViT), and the gated multilayer perceptron (gMLP) in the classification of radiographic images of dental structures.

Methods: Retrospectively collected two-dimensional images derived from cone beam computed tomographic volumes were used to train CNN, ViT, and gMLP architectures as classifiers for four different cases. Cases selected for training the architectures were the classification of the radiographic appearance of maxillary sinuses, maxillary and mandibular incisors, the presence or absence of the mental foramen, and the positional relationship of the mandibular third molar to the inferior alveolar nerve canal. The performance metrics (sensitivity, specificity, precision, accuracy, and f1-score) and area under the curve (AUC)-receiver operating characteristic and precision-recall curves were calculated.

Results: The ViT with an accuracy of 0.74-0.98, performed on par with the CNN model (accuracy 0.71-0.99) in all tasks. The gMLP displayed marginally lower performance (accuracy 0.65-0.98) as compared to the CNN and ViT. For certain tasks, the ViT outperformed the CNN. The AUCs ranged from 0.77 to 1.00 (CNN), 0.80 to 1.00 (ViT), and 0.73 to 1.00 (gMLP) for all of the four cases.

Conclusions: The ViT and gMLP exhibited comparable performance with the CNN (the current state-of-the-art). However, for certain tasks, there was a significant difference in the performance of the ViT and gMLP when compared to the CNN. This difference in model performance for various tasks proves that the capabilities of different architectures may be leveraged.