Background/Objectives: Infraocclusion in pediatric patients may result in space loss, malocclusion and the need for complex orthodontic treatment if not detected early. Conventional diagnosis may be subject to human error and can be challenging, particularly in pediatric cases. The aim of this study is to design and evaluate a lightweight, two-stage deep learning framework with integrated explainable AI (XAI) techniques for automated infraocclusion detection in pediatric panoramic radiographs. Methods: Annotated panoramic radiographs of pediatric patients aged 7–11 years were used for training and validation. In the first stage, a MobileNet V2 Lite model was used to detect the region of interest (ROI) comprising premolars and molars. In the second stage, a custom CNN classifier was proposed to distinguish between infraocclusion and no infraocclusion. Model performance was evaluated in terms of diagnostic accuracy, computational complexity, and statistical significance. XAI techniques were also incorporated to visualize model attention and enhance interpretability. Results: The detection stage achieved high reliability with a precision, recall, F1-score, and AP50 values of 0.99, and an AP75 of 0.89, indicating accurate ROI localization. The classification stage reached an overall accuracy of 98.78%, with class-specific accuracies of 99.25% for infraocclusion and 98.31% for no infraocclusion cases. The framework also demonstrated computational efficiency, requiring only 1.88 M trainable parameters (7.19 MB), with short training times and low inference latency (0.8 ms for classification and 19 ms for detection). XAI visualizations consistently highlighted clinically relevant regions, such as occlusal margins and interproximal areas, confirming the model’s alignment with radiographic features recognized by clinicians. Conclusions: The proposed two-stage framework provides an accurate, computationally efficient, and interpretable solution for automated infraocclusion detection in pediatric patients. Its modular design and reduced complexity support practical integration into routine clinical workflows, including resource-constrained environments. These findings indicate that lightweight and XAI systems may enhance early infraocclusion detection while maintaining clinical transparency.

Aim: Malocclusion is a prevalent public health concern, impacting craniofacial growth and development, and significantly affecting the quality of life of children in terms of oral health. This study investigated the prevalence of malocclusion in children aged 3 to 5 years, aiming to understand its occlusal characteristics, deleterious oral habits, correlations, and their parents’ attitudes toward malocclusion. Materials and Method: This cross-sectional study included 275 children aged 3-5 and their parents. The occlusion characteristics of the children participating in the study, including the relationship of the primary molar and canine teeth, overjet, crossbite, deep bite and anterior open bite, were evaluated and recorded by a researcher. A 7-item questionnaire was administered to the parents to collect sociodemographic data and oral health habits of the children's parents. The chi-square test was used for statistical assessment of results. Results: The results revealed that 40.7% of the children had malocclusion during their primary dentition period. It was found that the most common malocclusion was deep bite (15.7%) and harmful oral habits, especially bruxism (16.3%), were commonly observed among children in the researched age group. Conclusion: The results of this study will play an important role in shaping future oral health planning, especially regarding malocclusions seen in the primary dentition. It is thought that the findings from the study will guide strategies to improve oral health and preventive measures.

Background: People consumed more sugary foods and neglected oral hygiene during the lockdowns due to the coronavirus disease (COVID-19) pandemic. The study aimed to compare the oral hygiene status and dietary habits of children with high caries risk and their parents before and after COVID-19. Methods: The study was conducted with the parents of 155 children aged 3–5 years. A questionnaire including both the children’s and parents’ dietary and oral hygiene habits was completed by the parents. Decay, missing, filling teeth (dmft/DMFT) status of the children was recorded before and after COVID-19. Results: A significant decrease was observed in the parents’ tooth brushing frequency (p = 0.028), while no difference was observed in the children’s brushing frequency before and after COVID-19 (p = 0.216). Both children and parents ate significantly more frequently (p = 0.047, p = 0.043). dmft/DMFT values increased significantly after COVID-19 (p = 0.005). Conclusions: The study showed that the COVID-19 pandemic had a limited effect on tooth brushing habits in children with high caries risk. Additionally, it resulted in increased consumption of sugary foods among both children and parents, exacerbating caries in children aged 3–5 years. Healthcare professionals and parents can work together to mitigate the negative effects of the COVID-19 pandemic on oral health, especially for high-caries-risk children.

The application of artificial intelligence (AI) technologies in pediatric dentistry has expanded rapidly and is assuming an increasingly important role in the assessment of oral health and diagnostic processes in children. In particular, image-based analyses and data-driven predictive models offer significant potential to enhance diagnostic accuracy and reduce observer-dependent variability. This review addresses AI applications in pediatric dentistry within a structured framework, summarizing the methodological foundations of machine learning and deep learning approaches while critically evaluating the current body of evidence. It further identifies key limitations in the existing literature and outlines future research priorities, aiming to provide a perspective for the safe and sustainable clinical integration of AI in pediatric dentistry.

Background: Assessment of root development and apical closure is critical in dental disciplines, including endodontics, trauma management, and age estimation. This study aims to leverage advances in deep learning Convolutional Neural Networks (CNNs) to automatically evaluate the apical region status of permanent first molars, highlighting a digital health application of AI in dentistry. Methods: In this retrospective study, 262 Cone Beam Computed Tomography (CBCT) scans were reviewed, and 147 anonymized dental images were cropped from pseudopanoramic radiographs, including standard measurements. Tooth regions were resized to 471 × 1075 pixels and split into training (80%) and test (20%) sets. CNN performance was assessed using accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC) curves with area under the curve (AUC), demonstrating AI-based image analysis in a dental context. Results: Precision, recall, and F1-scores were 0.79 for open roots and 0.81 for closed roots, with a macro average of 0.80 across all metrics. The overall accuracy and AUC were also 0.80. Conclusions: These results suggest that CNNs can be effectively used to assess apical patency from ROI images derived from pseudopanoramic radiographs.

Amaç: Bu çalışmada amaç 3-5 yaş arası çocukların, anne sütü alma süresi, emzik ve biberon kullanım süresi, doğum şekli, çocuğun diş fırçalama alışkanlığı ve ailenin sosyoekonomik durumu ile erken çocukluk çağı çürüğü (EÇÇ) arasındaki ilişkinin araştırılmasıdır. Gereç ve Yöntem: Kliniğimize başvuran 3-5 yaş aralığında süt dentisyon dönemindeki çocukların doğum şekli, anne sütü alma süresi ve dönemi, emzik ve biberon kullanımı, diş hekimi ziyareti ve çocuğun diş fırçalama sıklığı ebeveynlere yapılan anketler ile kaydedilmiş; ağız muayenesi ile de dmft değerleri belirlenmiştir. EÇÇ görülme riskine etki eden bağımsız değişkenlerin incelenmesinde Binary Lojistik Regresyon analizi kullanılmıştır. Bulgular: Modeller çok değişkenli olarak incelendiğinde, 18- 36 ay biberon kullananlarda EÇÇ görülme riski daha yüksektir (p=0.027). Diş fırçalama sayısı arttıkça EÇÇ görülme riski azalmaktadır (p0.05). Sezaryen ve vajinal doğum yapanlar arasında EÇÇ değerleri arasında anlamlı farklılık izlenmemiştir (p>0.05). Sonuç: Uzun süreli anne sütü ve emzik kullanımının erken çocukluk çağı çürüğü riskini artırmadığı söylenebilir; ancak biberonun uzun süreli kullanımına dikkat edilmelidir. Bununla birlikte annelerin emzirme dönemlerinde çocukların düzenli diş fırçalama ve ağız hijyeni konularına özen göstermeleri EÇÇ oluşma riskini azaltacaktır.