Does Articular Disc Position Change Following Mandibular Setback Surgery?

BACKGROUND: Maintaining condyle position following bilateral sagittal split ramus osteotomy (BSSO) is crucial to minimizing postoperative relapse. However, the impact of BSSO on the articular disc position remains inconclusive. PURPOSE: This study aimed to investigate the changes in articular disc position following setback BSSO surgery. STUDY DESIGN, SETTING, AND SAMPLING: In this prospective cohort study, subjects with mandibular prognathism requiring setback BSSO were enrolled between August 2021 and June 2022 at the Oral and Maxillofacial Surgery Clinic, Faculty of Dentistry, Chiang Mai University, Thailand. Patients with surgical complications, loss of follow-up, or significant artifacts in their MR images were excluded. PREDICTOR/EXPOSURE/INDEPENDENT VARIABLES: The predictor variable was time. The articular disc position was assessed at 3 time points, preoperatively (T0), 3 months postsurgery (T1), and 6 months postsurgery (T2). MAIN OUTCOME: The primary and secondary outcome variables were the changes in articular disc position between T0-T2 and T0-T1, respectively. Articular disc position was coded as normal, anterior disc displacement with reduction (ADDwR), anterior disc displacement without reduction (ADDwoR), and anterior disc displacement without reduction and degenerative joint disease (ADDwoR + DJD). COVARIATES: Covariate variables collected included age (years), sex (male or female), asymmetry (present or absent), surgical procedure (single jaw (BSSO) or bimaxillary surgery), and setback distance (millimeters). ANALYSES: Friedman's test with 80% power and a significance level of 0.05 was employed. Pairwise comparisons were performed using the Dunn-Bonferroni posthoc test to identify statistically significant differences. RESULTS: The study included 16 subjects, 6 females and 10 males, with a total of 32 TMJs. Subjects had a mean age of 23.75 (4.57) years. The proportion of TMJs with normal disc position postoperatively increased from 3 (9.4%) to 19 (59.4%). Statistically significant differences were found in the changes in disc position over time (P < .001). CONCLUSIONS: Following setback BSSO, the articular discs underwent changes, with a majority of ADDwR cases transitioning to a normal position. Cases with ADDwoR also demonstrated disc reduction capability after surgery. The combined orthodontic treatment and setback BSSO appear to have an effect on articular disc position in skeleton class III patients.

Enhancing Caries Detection in Bitewing Radiographs Using YOLOv7.

The study aimed to evaluate the impact of image size, area of detection (IoU) thresholds and confidence thresholds on the performance of the YOLO models in the detection of dental caries in bitewing radiographs. A total of 2575 bitewing radiographs were annotated with seven classes according to the ICCMS™ radiographic scoring system. YOLOv3 and YOLOv7 models were employed with different configurations, and their performances were evaluated based on precision, recall, F1-score and mean average precision (mAP). Results showed that YOLOv7 with 640 × 640 pixel images exhibited significantly superior performance compared to YOLOv3 in terms of precision (0.557 vs. 0.268), F1-score (0.555 vs. 0.375) and mAP (0.562 vs. 0.458), while the recall was significantly lower (0.552 vs. 0.697). The following experiment found that the overall mAPs did not significantly differ between 640 × 640 pixel and 1280 × 1280 pixel images, for YOLOv7 with an IoU of 50% and a confidence threshold of 0.001 (p = 0.866). The last experiment revealed that the precision significantly increased from 0.570 to 0.593 for YOLOv7 with an IoU of 75% and a confidence threshold of 0.5, but the mean-recall significantly decreased and led to lower mAPs in both IoUs. In conclusion, YOLOv7 outperformed YOLOv3 in caries detection and increasing the image size did not enhance the model's performance. Elevating the IoU from 50% to 75% and confidence threshold from 0.001 to 0.5 led to a reduction of the model's performance, while simultaneously improving precision and reducing recall (minimizing false positives and negatives) for carious lesion detection in bitewing radiographs.

A web application for sex and stature estimation from radiographic proximal femur for a Thai population.

In both forensic and archaeological domains, the discovery of incomplete human remains is a frequent occurrence. Nevertheless, the estimation of biological profiles from such remains presents a challenge due to the absence of crucial skeletal elements, such as the skull and pelvis. This study aimed to assess the utility of the proximal femur in the forensic identification process by creating a web application for osteometric analysis of the proximal femur. The aim was to determine the sex and stature of an individual from radiographs of the left anteroposterior femur. To accomplish this, an automated method was developed for acquiring linear measurements from radiographic images of the proximal femur using Python tools. The application of Hough techniques and Canny edge detection was utilized to generate linear femoral dimensions from radiographs. A total of 354 left femora were radiographed and measured by the algorithm. The sex classification model employed in this study was the Naïve Bayes algorithm (accuracy = 91.2 %). Results indicated that Gaussian process regression (GPR) was the most effective method for estimating stature (mean error = 4.68 cm, SD = 3.93 cm). The proposed web application holds the potential to serve as a valuable asset in the realm of forensic investigations in Thailand, particularly in the estimation of biological profiles from fragmentary skeletal remains.

Feasibility of deep learning for dental caries classification in bitewing radiographs based on the ICCMS™ radiographic scoring system.

OBJECTIVE: To evaluate the potential of deep learning models for categorization of dental caries in bitewing radiographs based on the International Caries Classification and Management System (ICCMS™) radiographic scoring system (RSS). STUDY DESIGN: In total, 2758 annotated bitewing radiographs were randomly divided into 3 experiments to assess the ResNet-18, -50, -101, and -152. Experiment A tested 4-class ICCMS™-RSS training and validation using Carestream (CS) radiographs; experiment B tested training and validation using CS and VistaScan radiographs; experiment C tested 7-class ICCMS™-RSS training and validation using CS and VistaScan radiographs. The performance matrices and the areas under the receiver operating characteristic curves were analyzed to assess all procedures. RESULTS: In experiment A, ResNet-50 and ResNet-152 were equally accurate (71.11%) and approximately 78% sensitive. The latter presented the highest specificity (56.90%). In experiment B, ResNet-50 presented the highest sensitivity (79.51%) but ResNet-152 had the highest specificity (60.71%). In experiment C, all models markedly underperformed in distinguishing the 7-class ICCMS™-RSS with specificities of 16.46% to 22.41%. They had fewer classification errors in the 4-class classification (28.89%-35.56%) than in the 7-class classification (42.34%-53.06%). The areas under the receiver operating characteristic curves of all models were unanimously comparable. CONCLUSIONS: The ResNet models were able to classify dental caries according to the ICCMS™-RSS with average performances. The models underperformed in complicated classification tasks.

Assessment of YOLOv3 for caries detection in bitewing radiographs based on the ICCMS™ radiographic scoring system.

OBJECTIVES: To assess the feasibility of the YOLOv3 model under the intersection over union (IoU) thresholds of 0.5 (IoU50) and 0.75 (IoU75) for caries detection in bitewing radiographs based on the International Caries Classification and Management System (ICCMS™). MATERIALS AND METHODS: We trained the YOLOv3 model by feeding 994 annotated radiographs with the IoU50 and IoU75 thresholds. The testing procedure (n = 175) was subsequently conducted to evaluate the model's prediction metrics on caries classification based on the ICCMS™ radiographic scoring system. RESULTS: Regarding the 4-class classification representing caries severity, YOLOv3 could accurately detect and classify enamel caries and initial dentin caries (class RA) (IoU50 vs IoU75: precision, 0.75 vs 0.71; recall, 0.67 vs 0.64). Concerning the 7-class classification signifying specific caries depth (class 0, healthy tooth; classes RA1-3, initial caries affecting outer half, inner half of enamel, and the outer 1/3 of dentin; class RB4, caries extending to the middle 1/3 of dentin; classes RC5-6, extensively cavitated caries affecting the inner 1/3 of dentin and involving the pulp chamber), YOLOv3 could accurately detect and classify caries with pulpal exposure (class RC6) (IoU50 vs IoU75: precision, 0.77 vs 0.73; recall, 0.61 vs 0.57) but it failed to predict the outer half of enamel caries (class RA1) (IoU50 vs IoU75: precision, 0.35 vs 0.32; recall, 0.23 vs 0.21). CONCLUSIONS: YOLOv3 yielded acceptable performances in both IoU50 and IoU75. Although the performance metrics decreased in the 7-class detection, the two thresholds revealed comparable results. However, the model could not consistently detect initial-stage caries affecting the outermost surface of the enamel. CLINICAL RELEVANCE: YOLOv3 could be implemented to detect and classify dental caries according to the ICCMS™ classification with acceptable performances to assist dentists in making treatment decisions.

Expanding genotypic and phenotypic spectrums of LTBP3 variants in dental anomalies and short stature syndrome.

Mutations in LTBP3 are associated with Dental Anomalies and Short Stature syndrome (DASS; MIM 601216), which is characterized by hypoplastic type amelogenesis imperfecta, hypodontia, underdeveloped maxilla, short stature, brachyolmia, aneurysm and dissection of the thoracic aorta. Here we report a novel (p.Arg545ProfsTer22) and a recurrent (c.3107-2A > G) LTBP3 variants, in a Turkish family affected with DASS. The proband, who carried compound heterozygous variant c.3107-2A > G, p.Arg545ProfsTer22, was most severely affected with DASS. The proband's father, who carried the heterozygous variant c.3107-2A > G had short stature and prognathic mandible. The mother and brother of the proband carried the heterozygous variant p.Arg545ProfsTer22, but only the mother showed any DASS characteristics. The c.3107-2A > G and the p.Arg545ProfsTer22 variants are expected to result in abnormal LTPB3 protein, failure of TGFß-LAP-LTBP3 complex formation, and subsequent disruption of TGFß secretion and activation. This is the first report of heterozygous carriers of LTBP3 variants showing phenotypes. The new findings of DASS found in this family include taurodontism, single-rooted molars, abnormal dentin, calcified dental pulp blood vessels, prognathic mandible, failure of mandibular tooth eruption, interatrial septal aneurysm, secundum atrial septal defect, tricuspid valve prolapse, and a recurrent glenohumeral joint dislocation.

A comparison among gamma distribution, intravoxel incoherent motion, and mono-exponential models with turbo spin-echo diffusion-weighted MR imaging in the differential diagnosis of orofacial lesions.

OBJECTIVES: To compare the gamma distribution (GD), intravoxel incoherent motion (IVIM), and monoexponential (ME) models in terms of their goodness-of-fit, correlations among the parameters, and the effectiveness in the differential diagnosis of various orofacial lesions. METHODS: A total of 85 patients underwent turbo spin-echo diffusion-weighted imaging with six b-values. The goodness-of-fit of three models was assessed using Akaike Information Criterion. We analysed the correlations and compared the effectiveness in the differential diagnosis among the parameters of GD model (κ, shape parameter; θ, scale parameter; fractions of diffusion: ƒ1, cellular component; ƒ2, extracellular diffusion; ƒ3, perfusion component), IVIM model (D, true diffusion coefficient; D*, pseudodiffusion coefficient; f, perfusion fraction), and ME model (apparent diffusion coefficient, ADC). RESULTS: The GD and IVIM models showed a better goodness-of-fit than the ME model (p < 0.05). ƒ1 had strong negative correlations with D and ADC (ρ = -0.901 and -0.937, respectively), while ƒ3 had a moderate positive correlation with f (ρ = 0.661). Malignant entity presented significantly higher ƒ1 and lower D and ADC than benign entity (p < 0.0001). Malignant lymphoma had significantly higher ƒ1 in comparison to squamous cell carcinoma (p = 0.0007) and granulation (p = 0.0075). The trend in ƒ1 was opposite to the trend in D. Malignant lymphoma had significant lower ƒ3 than squamous cell carcinoma (p = 0.005) or granulation (p = 0.0075). CONCLUSIONS: The strong correlations were found between the GD- and IVIM-derived parameters. Furthermore, the GD model's parameters were useful for characterising the pathological structure in orofacial lesions.

The application of a gamma distribution model to diffusion-weighted images of the orofacial region.

OBJECTIVES: This study evaluated the correlation among the diffusion-derived parameters obtained by monoexponential (ME), intravoxel incoherent motion (IVIM) and γ distribution (GD) models and compared these parameters among representative orofacial tumours. METHODS: Ninety-two patients who underwent 1.5 T MRI including diffusion-weighted imaging were included. The shape parameter (κ), scale parameter (θ), ratio of the intracellular diffusion (ƒ1), extracellular diffusion (ƒ2) and perfusion (ƒ3) were obtained by the GD model; the true diffusion coefficient (D) and perfusion fraction (f) were obtained by the IVIM model; and the apparent diffusion coefficient (ADC) was obtained by the ME model. RESULTS: ƒ1 had a strongly negative correlation with the ADC (ρ = -0.993) and D (ρ = -0.926). A strong positive correlation between f and ƒ3 (ρ = 0.709) was found. Malignant lymphoma (ML) had the highest ƒ1, followed by squamous cell carcinoma (SCC), malignant salivary gland tumours, pleomorphic adenoma (Pleo) and angioma. Both the IVIM and GD models suggested the highest perfusion in angioma and the lowest perfusion in ML. The GD model demonstrated a high extracellular component in Pleo and revealed that the T4a+T4b SCC group had a lower ƒ2 than the T2+T3 SCC group, and poor to moderately differentiated SCC had a higher ƒ1 than highly differentiated SCC. CONCLUSIONS: Given the correlation among the diffusion-derived parameters, the GD model might be a good alternative to the IVIM model. Furthermore, the GD model's parameters were useful for characterizing the pathological structure.

Image Quality and ADC Assessment in Turbo Spin-Echo and Echo-Planar Diffusion-Weighted MR Imaging of Tumors of the Head and Neck.

RATIONALE AND OBJECTIVES: We aimed to compare the distortion ratio (DR), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) between turbo spin-echo (TSE)-diffusion-weighted imaging (DWI) and echo-planar imaging (EPI)-DWI of the orofacial region and prove the usefulness of TSE-DWI for the differential diagnosis of orofacial lesions. MATERIALS AND METHODS: The DR, SNR, and CNR of both sequences were compared in 42 cases. Then, the apparent diffusion coefficient (ADC) of various orofacial lesions obtained by TSE-DWI was investigated in 143 lesions. RESULTS: In the first study, 38 of 42 cases were analyzed. TSE-DWI showed a significantly lower DR (p < 0.05) and higher SNR and CNR than EPI-DWI (p < 0.05), indicating the superiority of TSE-DWI. In the second study, 114 cases (79.3%) were successfully analyzed. When lesions were divided into cysts, benign tumors, squamous cell carcinoma, malignant lymphoma, and other malignant tumors (OT), significant differences were observed in all pairs of lesions (p < 0.05) except squamous cell carcinoma and OT (p = 0.877). The area under the curve for distinguishing benign from malignant tumors was 0.80 with a cutoff ADC of 1.29 × 10-3 mm²/s. CONCLUSION: TSE-DWI produced better quality images than EPI-DWI. TSE-DWI yields the high possibility of obtaining ADC in the orofacial region, and this value was considered useful for the differential diagnosis of orofacial lesions.