Dento maxillo facial radiology最新文献

Objectives: The current study aimed to automatically detect tooth presence, tooth numbering, and types of periodontal bone defects from cone-beam CT (CBCT) images using a segmentation method with an advanced artificial intelligence (AI) algorithm.

Methods: This study utilized a dataset of CBCT volumes collected from 502 individual subjects. Initially, 250 CBCT volumes were used for automatic tooth segmentation and numbering. Subsequently, CBCT volumes from 251 patients diagnosed with periodontal disease were employed to train an AI system to identify various periodontal bone defects using a segmentation method in web-based labelling software. In the third stage, CBCT images from 251 periodontally healthy subjects were combined with images from 251 periodontally diseased subjects to develop an AI model capable of automatically classifying patients as either periodontally healthy or periodontally diseased. Statistical evaluation included receiver operating characteristic curve analysis and confusion matrix model.

Results: The area under the receiver operating characteristic curve (AUC) values for the models developed to segment teeth, total alveolar bone loss, supra-bony defects, infra-bony defects, perio-endo lesions, buccal defects, and furcation defects were 0.9594, 0.8499, 0.5052, 0.5613 (with cropping, AUC: 0.7488), 0.8893, 0.6780 (with cropping, AUC: 0.7592), and 0.6332 (with cropping, AUC: 0.8087), respectively. Additionally, the classification CNN model achieved an accuracy of 80% for healthy individuals and 76% for unhealthy individuals.

Conclusions: This study employed AI models on CBCT images to automatically detect tooth presence, numbering, and various periodontal bone defects, achieving high accuracy and demonstrating potential for enhancing dental diagnostics and patient care.

Objectives: This study was undertaken to generate high-quality radiographic annotations of initial proximal carious lesions based on micro-CT scans. Specifically, we projected manually and automatically acquired annotations of micro-CT scans onto corresponding traditional dental radiographs.

Methods: We utilized the Diagnostic Insights for Radiographic Early-caries with micro-CT (ACTA-DIRECT) dataset of manually annotated initial proximal carious lesions in micro-CT scans and radiographs, the former serving as reference-standard. Production of high-quality radiographic annotations entailed the following: (1) acquiring a reference-standard (for a semi-automated approach) or generating a fully automated micro-CT-based annotation (for a fully automated approach); (2) simulating the corresponding radiograph by projecting the micro-CT scan to find the suitable projection parameters; and (3) superimposing micro-CT-based caries annotations onto radiographs, using identical projection parameters. To evaluate the subsequent accuracy of the annotations on radiograph, we assessed the sensitivity, specificity, and International Caries Classification and Management System (ICCMS) staging of micro-CT-based automated annotations. Projection accuracy was qualitatively gauged.

Results: Micro-CT-based automated annotations outperformed conventional annotations achieving a sensitivity of 50% (95% CI: 42%-59%) compared to 42% (95% CI: 34%-51%) and specificity of 99% (95% CI: 96%-100%) compared to 92% (95% CI: 87%-94%). Among correctly identified micro-CT-based automated annotations, 94% (61/65) were also accurately classified; and 80% of micro-CT projections were ranked as suitably similar to corresponding radiographs.

Conclusions: Micro-CT imaging offers resource-rich depictions, enabling more accurate annotations than those achievable through conventional means. By projecting micro-CT-based annotations of initial proximal caries onto radiographs, some limitations of the conventional radiograph annotation process may be overcome.

Objectives: The lack of consensus regarding the association between airway narrowing and the severity of obstructive sleep apnea (OSA) presents a significant challenge in understanding and diagnosing this sleep disorder. The study aimed to systematically review the literature to investigate the relationship between upper airway measurements and the severity of OSA defined by the apnea-hypopnea index (AHI).

Methods: PubMed, Scopus, and Web of Science were systematically searched on 21 March 2023 for articles on OSA patients as diagnosed by polysomnography, investigating the correlation between upper airway measurements and AHI using cone-beam CT (CBCT) or multidetector CT (MDCT). Quality assessment was done using the Newcastle-Ottawa Scale. The results were subsequently synthesized descriptively.

Results: The database search identified 1253 results. Fourteen studies, encompassing 720 patients, met the eligibility criteria. Upper airway length showed moderate to weak positive correlation with AHI. Minimal cross-sectional area had varying correlations with AHI, ranging from strong negative to no correlation. Nasopharyngeal volumes showed moderate negative to weak correlations with AHI. Total upper airway volume ranged from strong negative to weak correlation with AHI. Other measurements exhibited weak or very weak correlations with AHI.

Conclusions: Among the variables investigated, the minimal cross-sectional area and, to a lesser extent, the volume of the upper airway in OSA patients demonstrated the most promising correlation with the AHI. However, the preponderance of evidence suggests that upper airway length, cross-sectional area and volume as measured by CBCT or MDCT are weak predictors of OSA.

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.