Dento maxillo facial radiology最新文献

Objectives: To investigate the predictability of late cervical lymph node metastasis using radiomics analysis of ultrasonographic images of tongue cancer.

Methods: We selected 128 patients with tongue cancer who underwent intraoral ultrasonography at the pre-treatment, 35 of whom had late cervical lymph node metastasis. Radiomics analysis was used to extract and quantify the image features. Segmentations were performed on three regions: the hypoechoic region with a 3-mm margin (0 + 3-mm margin), the hypoechoic region alone (0-mm margin), and the 3-mm region surrounding the hypoechoic area (3-mm margin). Support vector machine (SVM) and neural network (NNT) were used as the machine learning models, and sensitivity, specificity, and area under the curve (AUC) from the receiver operating characteristic curves were determined for diagnostic performances.

Results: The AUC values in the test group were 0.893, 0.929, and 0.679 for the SVM models with 0 + 3-, 0-, and 3-mm margins, respectively. The AUC values in the test group were 0.905, 0.952, and 0.821 for the NNT models with 0 + 3-, 0-, and 3-mm margins, respectively.

Conclusions: Radiomics analysis and machine learning models using ultrasonographic images of pre-treated tongue cancer with a hypoechoic area (0-mm margin) could be the best models to predict late cervical lymph node metastasis.

Advances in knowledge: This study makes a significant contribution to the tongue cancer treatment because radiomics analysis and machine learning models using ultrasonographic images of before the primary treatment for the tongue cancer could predict late cervical lymph node metastasis with high accuracy.

Objectives: This systematic review aimed to assess the performance of artificial intelligence (AI) in the evaluation of maxillary sinus mucosal alterations in imaging examinations compared to human analysis.

Methods: Studies that presented radiographic images for the diagnosis of paranasal sinus diseases, as well as control groups for AI, were included. Articles that performed tests on animals, presented other conditions, surgical methods, did not present data on the diagnosis of MS or on the outcomes of interest (area under the curve, sensitivity, specificity, and accuracy), compared the outcome only among different AIs were excluded. Searches were conducted in 5 electronic databases and a gray literature. The risk of bias (RB) was assessed using the QUADAS-2 and the certainty of evidence by GRADE.

Results: Six studies were included. The type of study considered was retrospective observational; with serious RB, and a considerable heterogeneity in methodologies. The IA presents similar results to humans, however, imprecision was assessed as serious for the outcomes and the certainty of evidence was classified as very low according to the GRADE approach. Furthermore, a dose-response effect was determined, as specialists demonstrate greater mastery of the diagnosis of MS when compared to resident professionals or general clinicians.

Conclusions: Considering the outcomes, the AI represents a complementary tool for assessing maxillary mucosal alterations, especially considering professionals with less experience. Finally, performance analysis and definition of comparison parameters should be encouraged considering future research perspectives.

Advances in knowledge: AI is a potential complementary tool for assessing maxillary sinus mucosal alterations, however studies are still lacking methodological standardization.

To develop and test a prototype for interproximal radiography positioning intended for paediatric dentistry and compare the technical quality of image receptor-holding devices (IRHDs) commonly used in clinical practice. Six prototypes, with 3 wedge dimensions (0.5, 1, and 2 mm, respectively) in upper and lower positions on the bitewing surface were compared regarding their capacity to acquire interproximal radiographs without overlapping surfaces with other IRHDs: RinnXCP, Hawe Kerr, and Cone Indicator. Fifteen graduate students obtained images of deciduous molars in a child's skull and mandible. The chi-square and Fisher's exact tests were employed to analyse significant differences in the number of repetitions and failures in obtaining correct images. A one-way analysis of variance (ANOVA) assessed the difference between the mean times required for image acquisition according to each IRHD, adopting a significance level of 5%. The effectiveness of the tested devices, evaluated by the number of correct and incorrect acquisitions using Fisher's exact test, revealed a P-value of .057. The ANOVA demonstrated statistically significant differences in the mean acquisition times (P < .0000). The upper and lower wedge prototypes 2 mm performed better in acquiring only 1 radiograph (76.7%) and took less time to execute the technique (1.17 and 1.12 min, respectively). The prototypes showed comparable performance to alternative IRHDs but provided advantages in time efficiency and radiation exposure.

Objectives: To screen the existing scientific literature and to evaluate the reliability of skull joints as biological markers for age estimation when analysed via CT.

Methods: The study followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and was registered in Open Science Framework (DOI: 10.17605/OSF.IO/PCVEF). Eligible studies included observational cross-sectional research that assessed skull joints for age estimation through CT. Data from 5 databases were screened: Medline/PubMed, Scopus, LILACS, SciELO, and Open Grey. The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Tools for Systematic Reviews.

Results: A total of 15 studies out of 4633 initially screened were eligible. The studies tested age estimation based on the spheno-occipital joint (53.33%) and cranial sutures, namely coronal, sagittal, and lambdoid (46.66%). Multi-slice CT was the most commonly used imaging modality (66.66%), with a slice thickness of <1 mm in 93.33% of studies. All the studies that assessed the spheno-occipital joint endorsed its application for age estimation, but some (37.5%) stressed limitations. Four (57.14%) out of the 7 studies that assessed the cranial sutures raised concerns about their use for age estimation (3 advised against it). Most of the studies had a low risk of bias (86.66%).

Conclusion: Findings supported the forensic application of the spheno-occipital joint for age estimation in adolescents and young adults, despite variability in fusion age. Cranial sutures were largely discouraged as sole markers because of unsatisfactory accuracy and high error risks.

Objective: The aim of this study was to develop a radiomics model based on cone beam CT (CBCT) to differentiate odontogenic cysts (OCs), odontogenic keratocysts (OKCs), and ameloblastomas (ABs).

Methods: In this retrospective study, CBCT images were collected from 300 patients diagnosed with OC, OKC, and AB who underwent histopathological diagnosis. These patients were randomly divided into training (70%) and test (30%) cohorts. Radiomics features were extracted from the images, and the optimal features were incorporated into random forest model, support vector classifier (SVC) model, logistic regression model, and a soft VotingClassifier based on the above 3 algorithms. The performance of the models was evaluated using a receiver operating characteristic (ROC) curve and the area under the curve (AUC). The optimal model among these was then used to establish the final radiomics prediction model, whose performance was evaluated using the sensitivity, accuracy, precision, specificity, and F1 score in both the training cohort and the test cohort.

Results: The 6 optimal radiomics features were incorporated into a soft VotingClassifier. Its performance was the best overall. The AUC values of the One-vs-Rest (OvR) multi-classification strategy were AB-vs-Rest 0.963; OKC-vs-Rest 0.928; OC-vs-Rest 0.919 in the training cohort and AB-vs-Rest 0.814; OKC-vs-Rest 0.781; OC-vs-Rest 0.849 in the test cohort. The overall accuracy of the model in the training cohort was 0.757, and in the test cohort was 0.711.

Conclusions: The VotingClassifier model demonstrated the ability of the CBCT radiomics to distinguish the multiple types of diseases (OC, OKC, and AB) in the jaw and may have the potential to diagnose accurately under non-invasive conditions.

Objectives: This study aimed to evaluate the accuracy of airway volume measurements obtained from cone beam computed tomography (CBCT) images using various software programmes, with a focus on assessing the performance of NemoStudio compared to other tools. The estimated volumes were compared with the volume of the solid model's cavity filled with water (gold standard).

Methods: A single 3D-printed airway model was created based on CBCT data and scanned 10 times under identical conditions. Volume measurements were performed using semi-automatic segmentation in 4 software programmes (NemoStudio, NNT Viewer, ITK-SNAP, and 3D Slicer). The results were compared to the gold standard using repeated measures analysis of variance, Bland-Altman plots, and post hoc comparisons.

Results: Nemo Studio demonstrated a systematic bias and higher variability compared to the gold standard, resulting in lower accuracy than the other software programmes. ITK-SNAP and 3D Slicer showed the highest agreement with the gold standard, while NNT Viewer also exhibited acceptable performance. Statistical analyses revealed significant differences in the accuracy of volume measurements among the software tools (P < .001). Bland-Altman plots highlighted Nemo Studio's broader limits of agreement, emphasizing its deviation from the gold standard.

Conclusion: Variability in airway volume measurement accuracy underscores the need for careful software selection and methodological standardization. Further refinement of segmentation algorithms is essential for improved consistency and reliability in clinical applications.

Advances in knowledge: This study provides the first evaluation of NemoStudio's volumetric accuracy for CBCT-based airway measurements, offering novel insights into software reliability and the impact of algorithm selection in clinical and academic settings.

Objectives: This study aimed to establish accurate correspondence between landmarks on lateral (LA) and posteroanterior (PA) cephalograms and to demonstrate the locational relationship of these landmarks in 2-dimensional (2D) and 3-dimensional (3D) images.

Methods: Twenty patients with skeletal Class III jaw deformity, without significant facial asymmetry, were recruited for this study. LA and PA cephalograms, taken simultaneously just before the orthognathic surgery, were utilized. Twelve mandibular landmarks were identified on each cephalogram, and their 3D coordinates values, as well as the residual differences between LA and PA cephalograms were calculated. To clarify the geometric correspondence between the LA and PA landmarks, the epipolar line method was introduced, which aimed to reduce residuals. In addition, 3D coordinate values of landmarks were compared between cephalograms and CT images in 5 patients with minimal asymmetry, by measuring the distance between corresponding landmarks as an error metric.

Results: The use of epipolar lines significantly reduced the residuals for 3D coordinates across all landmarks compared to those without epipolar lines. In comparison of 3D coordinate values between cephalograms and CT images, the average error across all landmarks was approximately 0.64 mm, which was lower than reported in previous study.

Conclusions: Incorporating epipolar lines during landmark extraction from LA and PA cephalograms significantly improved the accuracy and reliability of landmark correspondence between the 2 cephalograms. The 3D coordinate values derived from cephalograms closely matched those obtained from CT images, demonstrating high accuracy although they were independently derived.

Objectives: Recently, SPECT/CT has been widely accepted as a valuable diagnostic tool in dentistry. The aim of this study was to investigate submandibular sialolithiasis with CT and SPECT/CT, especially CT values, standardized uptake values (SUVs), and salivary gland excretion in the parotid and submandibular glands.

Methods: A prospective study was performed in 13 patients with submandibular sialolithiasis who underwent CT and salivary gland SPECT/CT. The CT values and the SUVs of parotid and submandibular glands were obtained using a workstation and software. The salivary gland excretion in the parotid and submandibular glands was defined as ratio of pre- to post-stimulation on SUVs. A P-value lower than .05 was considered as statistically significant.

Results: In the submandibular glands with sialoliths, the average CT values were significantly correlated with the maximum SUVs at ratio of pre-stimulation (r = 0.558, P<.05). The maximum SUVs at ratio of pre- to post-stimulation in the submandibular glands with and without sialoliths were 1.5 ± 1.1 and 2.1 ± 0.7, respectively (P = .026).

Conclusion: The salivary gland SPECT/CT SUVs can be useful in clinical practice for the quantitative management of parotid and submandibular glands in patients with submandibular sialolithiasis.

Objectives: The purpose of this study was to propose an automatic landmark identification method using curvature to improve the reproducibility of landmark identification and compare its performance with that of a previously established method.

Methods: A total of 30 patients with facial deformities associated with mandibular prognathism were included. CT images were utilized to construct 3D surface models, followed by an analysis of their surface curvature distribution. A statistical shape model (SSM) was created as a deformable mean model to identify the 6 landmarks. These landmarks were automatically identified in each patient model by registering the SSM in the individual patient models. Two registration methods were employed: the proposed curvature-based and previously established methods. Both methods involved rigid and nonrigid registration processes; however, the proposed method included additional curvature-based registration using a curvature-driven, nonrigid iterative closest point (ICP) algorithm. The Euclidean distances between the manually and automatically identified landmarks were measured and compared between the 2 methods.

Results: The Euclidean distance was significantly lower in the gonion and right coronoid process when the proposed method was used compared to the previous method. No significant differences were observed in the condylion or left coronoid process.

Conclusions: These findings suggest that the curvature-based registration successfully automates landmark identification on 3D mandibular images, providing higher accuracy in convex regions and improved reproducibility in landmark identification.