Background: The relationship between malocclusion and the oral health related quality of life (OHRQoL) of children needs to be explored further as existing literature presents conflicting evidence. This study aims to determine the association between malocclusion and OHRQoL of 11–14-year-old children. Methods: This cross-sectional study was conducted among 250 caregiver/child dyads seeking orthodontic consultation at a tertiary care hospital. The OHRQoL was assessed using child perception questionnaire for 11–14-year-old children (CPQ_11–14) and the severity of malocclusion was assessed using the Dental Aesthetic Index (DAI). CPQ_11–14 scores ranged from 0 to 64, with lower scores representing better quality of life. Analysis of variance (ANOVA) was used to assess differences between domain and total CPQ_11–14 scores. Results: The mean CPQ_11–14 score was 19.89 ± 9.8. Mean scores for the oral symptoms, functional limitations, emotional well-being, and social well-being domains were 5.26 ± 3.22, 3.67 ± 3.58, 3.98 ± 3.89 and 2.08 ± 2.98, respectively. Normal or slight malocclusion was seen in 37.6%, definite malocclusion was seen in 22.4%, severe malocclusion in 15.2% and handicapping malocclusion in 24.8% of the subjects. In comparisons by pairs, it was found that children with handicapping malocclusion had significantly (p < 0.05) higher scores for the social well-being domain as compared with children having normal/minor malocclusion, indicating a poorer quality of life. Conclusion: Handicapping malocclusion had a significant negative impact on the social well-being domain of OHRQoL among 11–14-year-old children in this population.

The study aims to investigate signs and symptoms of temporomandibular disorders (TMD) among physicians in a tertiary health‐care center. It has estimated the level of symptomatology, determined the association with demographic data, and identified the related occupational risk factors. A cross‐sectional survey was used, and physicians of genders, all age groups, and nationalities from King Abdulaziz Medical City in Riyadh, Saudi Arabia, were recruited. Subjects who had rheumatic arthritis, osteoarthritis, trigeminal neuralgia, or temporomandibular joint (TMJ) trauma were excluded. The data were collected through a self‐administered questionnaire that measured TMD severity and oral parafunctional behaviors. Fonseca's anamnestic index (FAI) and an oral validated behavior checklist were used to assess the signs and symptoms of TMD. A total of 282 physicians participated in the study, and the prevalence of TMD signs among physicians was 37% (106); among them, 88 (83%) were within the light dysfunction category. Female physicians reported significantly higher FAI than males for side‐to‐side mandibular movement (12% vs. 5%, P = 0.04), reporting ear pain (18% vs. 10%, P = 0.04), and noticing clicking when chewing or opening the mouth (35% vs. 20%, P = 0.006). Younger practitioners (28–31 years old) who reported clicking while chewing or opening the mouth tended to have reported higher TMD dysfunction (35%) than those aged 40 and above (13%; P = 0.007). Self‐reported signs of TMD were 37% among our population. Information collected from FAI is useful in early diagnosis and prevention of TMD.

Background/Objective: Periodontal disease is a prevalent inflammatory condition affecting the supporting structures of teeth, with radiographic bone loss (RBL) being a critical diagnostic marker. The accurate and consistent evaluation of RBL is essential for the staging and grading of periodontitis, as outlined by the 2017 AAP/EFP Classification. Advanced tools such as deep learning (DL) technology, including Denti.AI, an FDA-cleared software utilizing convolutional neural networks (CNNs), offer the potential for enhancing diagnostic accuracy. This study evaluated the diagnostic accuracy of Denti.AI for detecting RBL in intraoral radiographs. Methods: A dataset of 39 intraoral radiographs (22 periapical and 17 bitewing), covering 316 tooth surfaces (123 periapical and 193 bitewing), was selected from a de-identified pool of 500 radiographs provided by Denti.AI. RBL was assessed using the 2017 AAP/EFP Classification. A consensus panel of three board-certified dental specialists served as the reference standard. Performance metrics, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and mean absolute error (MAE), were calculated. Results: For periapical radiographs, Denti.AI achieved a sensitivity of 76%, specificity of 86%, PPV of 83%, NPV of 80%, and accuracy of 81%, with an MAE of 0.046%. For bitewing radiographs, sensitivity was 65%, specificity was 90%, PPV was 88%, NPV was 70%, and accuracy was 76%, with an MAE of 0.499 mm. Conclusions: Denti.AI demonstrated clinically acceptable performance in detecting RBL and shows potential as an adjunctive diagnostic tool, supporting clinical decision-making. While performance was robust for periapical radiographs, further optimization may enhance its accuracy for bitewing radiographs.

A new era in 3-dimensional analysis has begun with the use of cone-beam computed tomography (CBCT) in orthodontics, which promises to provide a more thorough understanding of the craniofacial skeletal architecture. This study aimed to investigate the correlation between the transverse basal arches discrepancy and dental compensation by utilizing CBCT width analysis. An observational study was conducted to retrospectively review 88 CBCT scans of patients presented to dental clinics from 2014 to 2020 obtained from the Planmeca Romexis x-ray system at three centers. Dental compensation data across normal and narrow maxillae were analyzed and a Pearson correlation was used to find the relationship between molar inclination and width difference. Significant maxillary molar compensation differences were observed between the normal maxilla and narrow maxilla group, where the amount of dental compensation (164.73 ± 10.15) was higher in the narrow maxilla group. A significant negative correlation (r = −0.37) was observed between width difference and maxillary molar inclination. Maxillary molars were tipped buccally to compensate for the reduced maxillary arch width. These findings are important to determine the amount of needed maxillary expansion taking into account the buccal inclination while treating cases.

Background/Objective: Deep learning (DL) technology has shown potential in enhancing diagnostic accuracy in dentomaxillofacial radiology, particularly for detecting carious lesions, apical lesions, and periodontal bone loss. However, its effect on general dentists’ ability to detect radiographic bone loss (RBL) in clinical practice remains unclear. This study investigates the impact of the Denti.AI DL technology on general dentists’ ability to identify bone loss in intraoral radiographs, addressing this gap in the literature. Methods: Ten dentists from the university’s dental clinics independently assessed 26 intraoral radiographs (periapical and bitewing) for bone loss using a Likert scale probability index with and without DL assistance. The participants viewed images on identical monitors with controlled lighting. This study generated 3940 data points for analysis. The statistical analyses included receiver operating characteristic (ROC) curves, area under the curve (AUC), and ANOVA tests. Results: Most dentists showed minor improvement in detecting bone loss on periapical radiographs when using DL. For bitewing radiographs, only a few dentists showed minor improvement. Overall, the difference in diagnostic accuracy between evaluations with and without DL was minimal (0.008). The differences in AUC for periapical and bitewing radiographs were 0.031 and −0.009, respectively, and were not statistically significant. Conclusions: This study found no statistically significant improvement in experienced dentists’ diagnostic accuracy for detecting bone loss in intraoral radiographs when using Denti.AI deep learning technology.