Dento maxillo facial radiology最新文献

Objectives: The present study examined the potential use of CT panoramic mandibular indices on cone beam CT (CBCT) for the assessment of bone density in patients with primary and secondary causes of low bone mass.

Study design: The study enrolled 104 postmenopausal women and 66 patients with endocrine-related low bone mass (diabetes mellitus, acromegaly, Cushing syndrome), who underwent dual-energy X-ray absorptiometry (DXA) and CBCT scanning. The study assessed the correlation between DXA parameters (lumbar spine, femoral neck, total hip T-score, bone mineral density [BMD], and trabecular bone score [TBS]) and CBCT-derived indices (CT mandibular index superior [CTI(S)], CT mandibular index inferior [CTI(I)], and CT mental index [CTMI]).

Results: Significant correlations were found between the CBCT indices and both quantitative (BMD, T-score) and qualitative (TBS) measures of bone mass. In postmenopausal women, all 3 CBCT indices showed strong correlations with DXA parameters. In secondary endocrine causes, CTMI and CTI(S) were significantly correlated with TBS scores, and CTMI also showed a significant correlation with lumbar BMD.

Conclusion: The study concludes that CTI(S), CTI(I), and CTMI are valuable for assessing bone density and quality in patients with low bone mass, both in primary and secondary osteoporosis related to diabetes mellitus, acromegaly, and Cushing syndrome.

Advances in knowledge: These findings support the use of CBCT as a useful tool for evaluating bone health in the clinical setting and optimizing dental implant result. It is among the first studies to evaluate bone mass quality and quantity in secondary endocrine causes of low bone mass.

Objectives: To conduct a prospective cohort study evaluating the influence of different teaching methodologies on the radiographic diagnosis of root resorptions by undergraduate dental students.

Methods: Forty-eight undergraduate students were randomly divided into 4 groups (n = 12) according to the methodology applied to teach about root resorption: traditional face-to-face teaching (control), remote teaching, gamification, and case study. The first stage was to perform a pre-methodology index test to assess prior knowledge about root resorption. Then, all groups received study material on a virtual platform and 1 week later, the teaching methodologies were applied. Twenty-four hours after each methodology application, the students performed a diagnostic test by analysing 28 digital periapical radiographs, classifying them according to the absence or type of root resorption present (external superficial, internal inflammatory, or external cervical). After 10 days, 3 students in each group (25%) were randomly selected and re-evaluated the 28 images to calculate intra-rater agreement. All students repeated the index test 30 days after the interventions. Statistical analysis used linear regression models, Pearson's correlation, and chi-square test (P < .05).

Results: Gamification resulted in better student performance in the index and radiographic diagnostic tests (P < .001). Superficial external resorption was the most challenging to diagnose, regardless of the method, while inflammatory internal obtained a higher percentage of correct responses (P < .001) in the diagnostic test.

Conclusions: All methods involving student interaction demonstrated better outcomes compared to the traditional model in the diagnosis of root resorptions. Gamification resulted in the best performance and may be an effective resource in the learning process.

Advancements in knowledge: Adopting gamification enhanced student performance and may be a valuable learning strategy to contribute to a more accurate diagnosis and safer clinical practice.

Objectives: To evaluate patients' perceptions of the use of artificial intelligence (AI) in dental imaging diagnostics across 6 centres worldwide, hereby named according to their respective cities: Ribeirão Preto (Brazil), Aarhus (Denmark), Lyon (France), Tromsø (Norway), Porto (Portugal), Louisville (USA).

Methods: A survey was administered at each centre, focusing on patient attitudes and beliefs regarding AI in dental imaging diagnostics. The survey comprised 16 statements rated on a Likert scale, patient characteristics, and an optional comment section. Inter-centre differences were analysed using chi-square and Fisher's exact tests, and correlation analyses were performed between participant characteristics and their perceptions of AI.

Results: A total of 2,581 responses were collected. Most participants expressed positive perceptions of AI as a complementary diagnostic tool, rather than a replacement for human dentists. Key concerns included the need for human oversight, data privacy, and potential cost increases. Differences were observed between centres, with participants from Ribeirão Preto being more likely to accept AI replacing dentists, whereas those from Aarhus and Tromsø expressed greater scepticism about AI's diagnostic capabilities. Although higher levels of education and knowledge about AI correlated with more optimistic perspectives about AI's diagnostic capabilities, they were also associated with an increased preference for human supervision.

Conclusions: Overall, patients favour the use of AI in dental imaging as an auxiliary diagnostic tool, with human supervision remaining essential. Cultural and demographic factors significantly influence perceptions.

Advances in knowledge: The findings highlight the need for tailored communication strategies to address patient concerns if AI is integrated into dental care.

Objectives: This study explores the application of artificial intelligence (AI), specifically deep learning, in the detection and classification of mandibular fractures using CT scans.

Methods: Data from 459 patients were retrospectively obtained from West China Hospital of Stomatology, Sichuan University, spanning from 2020 to 2023. The CT scans were divided into training, testing, and independent validation sets. This research focuses on training and validating a deep learning model using the nnU-Net segmentation framework for pixel-level accuracy in identifying fracture locations. Additionally, a 3D-ResNet with pre-trained weights was employed to classify fractures into 3 types based on severity. Performance metrics included sensitivity, precision, specificity, and area under the receiver operating characteristic curve (AUC).

Results: The study achieved high diagnostic accuracy in mandibule fracture detection, with sensitivity >0.93, precision >0.79, and specificity >0.80. For mandibular fracture classification, accuracies were all above 0.718, with a mean AUC of 0.86.

Conclusions: Detection and classification of mandibular fractures in CT images can be significantly enhanced using the nnU-Net segmentation framework, aiding in clinical diagnosis.

Objectives: To evaluate deep learning (DL)-based models for detecting periapical bone rarefaction (PBRs) in panoramic radiographs (PRs), analysing their feasibility and performance in dental practice.

Methods: A search was conducted across seven databases and partial grey literature up to November 15, 2024, using Medical Subject Headings and entry terms related to DL, PBRs, and PRs. Studies assessing DL-based models for detecting and classifying PBRs in conventional PRs were included, while those using non-PR imaging or focusing solely on non-PBR lesions were excluded. Two independent reviewers performed screening, data extraction, and quality assessment using the Quality Assessment of Diagnostic Accuracy Studies-2 tool, with conflicts resolved by a third reviewer.

Results: Twelve studies met the inclusion criteria, mostly from Asia (58.3%). The risk of bias was moderate in 10 studies (83.3%) and high in 2 (16.7%). DL models showed moderate to high performance in PBR detection (sensitivity: 26%-100%; specificity: 51%-100%), with U-NET and YOLO being the most used algorithms. Only one study (8.3%) distinguished Periapical Granuloma from Periapical Cysts, revealing a classification gap. Key challenges included limited generalization due to small datasets, anatomical superimpositions in PRs, and variability in reported metrics, compromising models comparison.

Conclusion: This review underscores that DL-based has the potential to become a valuable tool in dental image diagnostics, but it cannot yet be considered a definitive practice. Multicentre collaboration is needed to diversify data and democratize those tools. Standardized performance reporting is critical for fair comparability between different models.

Advances in knowledge: This study represents the first critical synthesis on this theme, examining a group of lesions with complex manifestations that have been neglected in comparable technological development studies, where research focus has usually been limited to radicular cysts. We identified gaps in classification tasks, insufficient use of ethnically diverse and heterogeneous datasets, and the need for multicentric studies. The variability in data reporting prevents transparent comparisons, even precluding our planned meta-analysis. Consequently, we emphasize the necessity for standardized reporting protocols similar to PRISMA for systematic reviews or STARD for diagnostic or prognostic studies, particularly since accuracy metrics remain inadequately documented while critically important.

Digital X-ray sensors have significantly changed dental radiography, enabling faster image acquisition and reducing radiation doses for patients. Despite the advancements in technology, noise in X-ray imaging remains a challenge. In this study, noise was examined using the Noise-Power Spectrum (NPS) and a non-parametric method. Blank images were taken under different exposure times and voltage settings. The analyses show that noise decreases with longer exposure times. Among the examined sensors, 2 showed distinct NPS peaks, and 1 exhibited no relationship between exposure time and noise levels. These results are discussed on terms of specific sensor structures, artefacts and/or unaccessible post-processing algorithms.

Objective: Surgery is the standard treatment for medication-related osteonecrosis of the jaw (MRONJ). However, there are few reports on the appropriate extent of the bone resection. This pilot study explores the feasibility of a new method for estimating the extent of resection using bone single-photon emission CT (SPECT)-standardized uptake value (SUV).

Methods: We retrospectively analysed 8 MRONJ patients who underwent curettage (n = 2), curettage with removal of the separated sequestrum (n = 2), or marginal resection (n = 4) as part of extensive surgery. The resected regions were compared with the regions estimated using SPECT-SUV. The agreement between the SPECT cold region and the resected region was evaluated using the Dice coefficient (defined as the ratio of 2× overlap volume to resected volume plus SPECT cold region volume), overlap ratio, and volume ratio. The inclusion of CT findings (osteolytic, gap- and irregular-type periosteal reactions, and mixed-type osteosclerosis) in the estimated region was also evaluated. Additionally, histopathological findings from 3 marginal resection cases were used to validate the estimated region.

Results: In all cases, the resected region included the cold regions observed on bone SPECT, with radiotracer accumulation confirmed around the resected region. CT-osteolytic regions were included within the estimated region. The Dice coefficient was 0.53 ± 0.10, the overlap ratio was 86.7 ± 7.2%, and the volume ratio was 235.0 ± 74.7%. Histopathological analysis revealed significant osteocyte necrosis in cold regions, whereas areas with an SUV of 9 displayed normal osteocytes, newly formed bone, and mild inflammatory cell infiltration.

Conclusion: This study suggests that the setting of the SPECT cold region using bone SPECT-SUV may allow for the estimation of the extent of resection in early-to-intermediate-stage MRONJ.

Objectives: This study aimed to assess the efficacy of deep learning applications for the detection of nasal bone fracture on X-ray nasal bone lateral view.

Methods: In this retrospective observational study, 2968 X-ray nasal bone lateral views of trauma patients were collected from a radiology centre, and randomly divided into training, validation, and test sets. Preprocessing included noise reduction by using the Gaussian filter and image resizing. Edge detection was performed using the Canny edge detector. Feature extraction was conducted using the gray-level co-occurrence matrix (GLCM), histogram of oriented gradients (HOG), and local binary pattern (LBP) techniques. Several machine learning algorithms namely CNN, VGG16, VGG19, MobileNet, Xception, ResNet50V2, and InceptionV3 were employed for the classification of images into 2 classes of normal and fracture.

Results: The accuracy was the highest for VGG16 and Swin Transformer (79%) followed by ResNet50V2 and InceptionV3 (0.74), Xception (0.72), and MobileNet (0.71). The AUC was the highest for VGG16 (0.86) followed by VGG19 (0.84), MobileNet and Xception (0.83), and Swin Transformer (0.79).

Conclusions: The tested deep learning models were capable of detecting nasal bone fractures on X-ray nasal bone lateral views with high accuracy. VGG16 was the best model with successful results.

Objective: To evaluate the clinical and radiologic features of acute calcific tendinitis of the longus colli (ACTLC).

Methods: This retrospective, cross-sectional study analysed 30 patients diagnosed with ACTLC from January 2013 to December 2022. Two experienced radiologists independently reviewed CT and MR images to confirm the ACTLC diagnosis and document radiologic findings. Clinical data, including symptoms and laboratory results, were also assessed. The study received approval from the institutional ethics committee, with patient consent waived.

Results: The cohort had a mean age of 49 years and included 19 females (63%). All patients presented with acute cervicalgia, and 29 (97%) exhibited calcifications at the C1-C2 level. A novel imaging feature, termed the "beak sign," was observed in 24 of these 29 patients (83%), defined by an acute angle at the margin of calcification pointing towards the C1-C2 intervertebral space. Prevertebral soft-tissue oedema was present in all patients, with 25 (83%) also showing retropharyngeal fluid accumulation. Among the 14 patients who underwent MRI, 11 (79%) exhibited atlantoaxial joint effusion, a feature rarely reported in ACTLC. Follow-up imaging revealed inferior migration of calcifications in 2 patients, with 1 developing a cyst-like appearance in the post-calcific phase-an unreported finding in ACTLC.

Conclusions: This study represents the largest ACTLC cohort confirmed by cross-sectional imaging. Prevertebral calcifications and soft-tissue oedema were consistently observed in all patients, with the majority also exhibiting retropharyngeal fluid accumulation. This article introduces the "beak sign," a novel imaging finding observed in most cases, and identifies atlantoaxial joint effusion as a newly recognized, common imaging manifestation in ACTLC.

Advances in knowledge: This review of 30 patients with acute calcific tendinitis of the longus colli introduces the "beak sign"-an acute angle at the calcification margin pointing towards the C1-C2 intervertebral space-as a novel imaging feature observed in most cases. Additionally, it identifies atlantoaxial joint effusion as a newly recognized, common imaging manifestation of this condition.

Objectives: This study aimed to establish updated diagnostic reference levels (DRLs) for dental imaging modalities in South Korea.

Methods: In cooperation with the Korea Disease Control and Prevention Agency, various types of institutions (dental clinics, dental hospitals, and dental university hospitals) were selected to investigate the status of diagnostic radiation equipment use. Subsequently, over 300 units were randomly selected for each imaging device type (intraoral, panoramic, and cone-beam CT [CBCT]) as measurement samples. DRLs were defined as the 75th percentile of the dose area product distribution. The differences in dose were analysed based on the type of institution, age of use, country of manufacture, and presence of a multifunction device.

Results: The national DRLs for dental imaging established in this survey were as follows: intraoral imaging at 48 mGy·cm2 for adults and 31 mGy·cm2 for children; panoramic imaging at 354 mGy·cm2 for adults and 224 mGy·cm2 for children; and CBCT at 1856 mGy·cm2 for adults and 1350 mGy·cm2 for children. Private dental clinics and hospitals recorded approximately twice the dose levels of university dental hospitals. CBCT devices in dental hospitals and those that have been in used for 5-10 years showed significantly high radiation doses.

Conclusions: The DRLs established through this study were found to be significantly increased, especially in adult and paediatric panoramic radiographs and paediatric CBCT images, compared with those in previous surveys; moreover, they were higher than those in other countries. The findings of this study can serve as a basis for national dose reduction efforts.