Background: Identifying the diagnostic value of bitewing radiographs (BW) is highly dependent on the operator’s knowledge and experience. The aim of this study is to assess the effectiveness of machine learning (ML) to classify the BW according to their diagnostic quality. Methods: 864 BW radiographs from records of 100 patients presented at King Abdulaziz University Dental Hospital, Jeddah, Saudi Arabia were assessed. The radiographic errors in representing proximal contact areas (n = 1951) were categorized into diagnostic and non-diagnostic. Labeling and training of the BW were done using Roboflow. Data were divided into validation, training, and testing sets to train the pre-trained model Efficientdet-d0 using TensorFlow. The model’s performance was assessed by calculating recall, precision, F1 score, and log loss value. Results: The model excelled at detecting “overlap within enamel” and “overlap within restoration (clear margins) with F1 score of 0.89 and 0.76, respectively. The overall system errors made by the built model showed a log loss value of 0.15 indicating high accuracy of the model. Conclusions: The model is a “proof of concept” for the effectiveness of ML in diagnosing the quality of the BW radiographs based on the contact areas. More dataset specification and optimization are needed to overcome the class imbalance.

Background and Clinical Significance: Chronic nonbacterial osteomyelitis (CNO) of the jaw is a rare autoinflammatory bone disorder that primarily affects children and adolescents. Diagnosing CNO of the mandible can be challenging due to its rarity, and the clinical and radiographic findings overlap with those of other bone disorders. Case Presentation: This case series retrospectively presents four female pediatric patients (9–12 years old) diagnosed with mandibular CNO. The patients were treated at King Abdulaziz University Dental Hospital, Jeddah, Saudi Arabia, between 2018 and 2024. Clinical features and radiographic and histopathological findings were evaluated. All cases had mandibular swelling and pain. Radiographic features consistently revealed mixed sclerotic and radiolucent lesions with bone expansion and periosteal reactions. Histopathological findings revealed viable bone interspersed with varying degrees of fibrous tissue. No evidence of bacterial colonies or inflammation was observed. This case series highlights the radiographic and histopathological features of CNO in the mandible of pediatric patients. The mixed radiographic features and variability of histopathological findings combined with the refractory nature of the lesions contribute to diagnostic complexity. Diagnostic challenges include differentiating CNO from other inflammatory and fibro-osseous conditions. The presence of recurrent episodes of pain, the formation of subperiosteal bone, periostitis, lysis of the cortical layer, expansion of the mandibular canal, and sterile bone biopsies with nonspecific inflammatory changes were related mainly to CNO. Conclusions: These findings underscore the need for increased awareness and a multidisciplinary approach for accurate diagnosis and management of CNO. Conservative management, particularly in dental cases, avoids prolonged unnecessary use of antibiotics, and the prescription of nonsteroidal anti-inflammatory drugs should be followed.

Background: Apical periodontitis (AP) is a common inflammatory condition of the periapical tissues, most often associated with persistent endodontic infection. Conventional two-dimensional radiography may underestimate AP because of anatomical superimposition and limited sensitivity. Cone-beam computed tomography (CBCT) allows three-dimensional visualization of periapical structures and has been increasingly used in epidemiological research. Objective: This study aimed to evaluate the prevalence and distribution of apical periodontitis, with particular emphasis on apical periodontitis associated with root canal-treated teeth (AP-RCT), in a Saudi subpopulation using CBCT imaging. Methods: This retrospective cross-sectional study analyzed CBCT scans of Saudi patients obtained for routine diagnostic purposes between 2017 and 2021. Apical periodontitis was identified using standardized radiographic criteria requiring the presence of periapical radiolucency in more than one imaging plane. Demographic and clinical variables were recorded. Descriptive statistics were used to estimate prevalence. Associations between demographic factors and AP-RCT counts were evaluated using multivariable negative binomial regression. Regional tooth distribution was analyzed using generalized estimating equation models accounting for within-participant clustering. Results: A total of 320 CBCT scans were analyzed. Apical periodontitis was detected in 231 participants (72.2%) and in 667 teeth (8.3% of examined teeth). Of the affected teeth, 457 (68.5%) were associated with root canal treatment. The mean number of AP-RCT per participant was 1.36 ± 1.81 (median: 1; IQR: 0–2). Multivariable analysis identified age as the only significant predictor of AP-RCT. Compared with individuals aged 21–30 years, higher AP-RCT rates were observed in the 31–40-year and 41–50-year age groups, while participants ≤20 years showed lower rates. Tooth-level analysis demonstrated higher AP-RCT prevalence in maxillary premolars, maxillary molars, and mandibular molars, whereas mandibular anterior teeth showed the lowest prevalence. Conclusions: Apical periodontitis, particularly AP-RCT, was frequently observed in this Saudi subpopulation when assessed using CBCT. Age and tooth location were the primary determinants of disease distribution. These findings provide population-level epidemiological data on the prevalence and anatomical distribution of apical periodontitis in root canal-treated teeth. Clinical Significance: CBCT-based epidemiological assessment enables detailed evaluation of the distribution of apical periodontitis in dentate populations and may assist in characterizing disease patterns in anatomically complex regions, without implying comparative diagnostic accuracy or treatment outcome assessment.

Background: Dental caries detection using intraoral radiographs is essential for early diagnosis but may be affected by observer variability. Artificial intelligence (AI)-based systems have emerged as potential tools to improve diagnostic consistency. This study evaluated the diagnostic accuracy of a deep learning-based AI system (Better Diagnostics Caries Assist (BDCA) Version 1.0) for detecting dental caries on bitewing (BW) and intraoral periapical (IOPA) radiographs, and examined its performance across demographic, technical, and lesion-based subgroups. Methods: A retrospective validation study was conducted using anonymized digital BW and IOPA radiographs with expert-defined ground truth at the tooth-surface level. The AI software independently analyzed each image to identify carious lesions. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated with 95% confidence intervals. Generalized estimating equations were applied to adjust for the clustering of multiple surfaces per image. Subgroup analyses were performed by age, sex, digital sensor type, and lesion category. Results: The AI system demonstrated high diagnostic accuracy for both modalities. For BW radiographs, sensitivity was 0.892 and specificity was 0.995, while for IOPA radiographs sensitivity was 0.882 and specificity was 0.991. NPVs exceeded 0.99 for both modalities. Across age groups, BW sensitivity ranged from 0.881 to 0.901 and IOPA sensitivity from 0.854 to 0.906, with consistently high specificity (>0.989). Sex-based differences were minimal. Sensor-wise analysis showed sensitivity ranging from 0.833 to 0.933 for BW and 0.828 to 0.933 for IOPA, while specificity remained above 0.984 for all sensors. Detection performance was comparable for primary (sensitivity 0.883) and secondary caries (0.879), although PPV was slightly lower for secondary lesions. The lower AUC indicated reduced accuracy in lesion identification in the absence of BDCA v 1.0, the difference between BDCA v1.0 and Ground truth was 0.042 at 95% CI 0.030 to 0.055, and the difference was also statistically significant with (p ≤ 0.001). Conclusions: The evaluated AI system demonstrated excellent and consistent performance for detecting dental caries on both BW and IOPA radiographs across demographic groups, sensor technologies, and lesion types, supporting its potential role as a reliable decision support tool in dental radiographic interpretation.

Background/Objectives: Successful endodontic treatment relies on a precise understanding of root canal morphology and effective disinfection of the entire canal system. Anatomical variations, such as additional canals, canal merging, and intercanal communications, may affect clinical outcomes. This retrospective CBCT study evaluated the root and canal morphology of mandibular first molars in a Saudi population, with specific focus on the prevalence and configuration patterns of middle mesial canals (MMCs) and isthmuses in the mesial root. Methods: A total of 552 CBCT scans taken between 2017 and 2021 were screened. After applying strict eligibility criteria, 167 scans from Saudi patients containing fully erupted mandibular first molars with completely formed apices were included, yielding 322 teeth. Teeth with prior root canal treatment or distorted scans were excluded. Root canal configurations were classified according to Weine’s system. The presence of MMCs, isthmuses, intercanal distance, and bilateral symmetry was recorded. Descriptive and comparative analyses were performed using Fisher’s exact test, chi-square test, Kruskal–Wallis test, and McNemar’s test. Results: Most mandibular first molars (75.5%) had two roots, whereas 24.5% exhibited two distinct distal roots. Mesial roots predominantly demonstrated Weine type II (50.9%) and type III (48.1%) configurations, while distal roots were mainly type I (69.3%). MMCs were rare (0.98%). Isthmuses were highly prevalent (82%) and occurred significantly more often in type III mesial roots (72.3%) compared with type II (1.2%) (p < 0.0001). Bilateral symmetry was substantial in both mesial and distal roots (86.8% and 88.4%, respectively). Conclusions: Middle mesial canals were uncommon, whereas isthmuses were frequently observed in the mesial roots of mandibular first molars among Saudi individuals. The high prevalence of isthmuses underscores the need to anticipate intercanal communications during endodontic treatment. CBCT evaluation enhances detection of such complex anatomical features and supports improved diagnostic accuracy and treatment planning.