## 4 Discussion  
Oral health is an integral component of the overall health and well-being of an individual. The main focus of pediatric dentistry is on the prevention and treatment of various oral diseases in the early childhood stage with the intent of establishing an optimal oral health condition in young children. The most common oral diseases affecting children include dental caries, pulpal and periapical lesions, gingival disease and other conditions, including dental trauma and abnormal oral habits. Untreated oral diseases, especially dental caries, may complicate the required treatment, result in pain, decreased masticatory function or asymmetrical mastication [39]. This could eventually result in compromised facial development, ultimately contributing to malocclusion and oro-facial deformities [40; 41]. Evidence also suggests that severe caries experienced in children is associated with a more severe caries experience in permanent dentition during adulthood [42; 43]. Severe caries experiences among children may also lead to malnutrition due to compromised masticatory function, affecting dietary preferences, and resulting in developmental delays [44]. Considering these facts, it is important for a pediatric dentist to be capable of assessing caries risk and applying various strategies designed for its prevention and intervention, which can contribute considerably to the health of their patients. With the advancements in the field of technology, new AI-based applications have been widely utilized for the detection, diagnosis and prediction of the prognosis of oral diseases. These applications have demonstrated excellent performance, with accuracies similar to trained and experienced dental professionals [14; 15]. In pediatric dentistry, these AI-based models have been applied for the detection of dental plaque, which is considered a precursor for most oral diseases, in particular, dental caries and gingival diseases [4]. The traditional method for the detection of dental plaque using an explorer (with or without a disclosing agent) is inconvenient to be used on children [7; 8]. [PERSON], et al. [19] studied the application of CNNs-based AI model for the detection of dental plaque in primary teeth. This model demonstrated a higher accuracy in detecting dental plaque, in comparison with experienced pediatric dentists. However, this model had a few limitations related to the limited number of intraoral photographs used for training, which were all obtained using a single camera. Obtaining photographs through different equipment may result in differences in aspects, such as color and resolution, which might affect the accuracy of the trained model. AI has also been applied for the detection and classification of dental caries. Dental caries is considered one of the most prevalent chronic childhood diseases [45]. [PERSON], [PERSON], et al. [21] reported on an ANNs-based model for the classification of early childhood caries (ECC). The model demonstrated similar performance to the reference models and could be of great value in screening for ECC. However, the study population was a limiting factor, as it was only representative of high-risk children from low-income families from  
Figure 2: QUADAS-2 assessment of the individual risk of bias domains and applicability concerns. one state in the United States. [PERSON], [PERSON], et al. [34] also reported on a CNNs-based model for detecting dental caries. The model demonstrated a sensitivity of 96.0% and specificity of 97.0% for caries with cavities, 95.8 and 99.0% for pit and fissure caries and 88.1 and 97.1% for proximal caries. [PERSON], [PERSON], et al. [22] also reported on an ML-based model for predicting dental caries. This model demonstrated excellent performance, similar to that of trained dentists, and showed a great potential for screening dental caries in children. However, the small sample size and including a small number of children with active caries were limitations of this study.