## 4 Discussion
There are also chances of social desirability bias, since the parents were asked to complete the questionnaire related to their child's oral health. [PERSON], [PERSON], et al. [25] also reported on an AI model for predicting dental caries based on chosen polymorphisms. This model showed excellent performance with a sensitivity of 90%, specificity of 96% and overall accuracy of 93%. [PERSON], [PERSON], et al. [26] studied the possibility of predicting caries risk based on environmental and genetic factors using an AI-based ML model. This model recorded an AUC of 0.73 and was able to accurately categorize individuals with high and very high caries risk. However, the applicability of the studies' findings was affected by the authors' use of the cariostatic score to assess the cariogenicity of dental plaque, where the prediction performance can be affected by microbiome markers. Another limitation was that the sample was from one center. [PERSON], [PERSON], et al. [27] reported on ML-based AI models: XG Boost, random forest, Light GBM algorithms and final model for predicting early childhood caries. These models displayed a favorable performance in predicting dental caries and can be of great use in identifying high-risk groups and implementing preventive treatments. Patients' age assessment is exceptionally useful for dentists in the planning and evaluation of treatment results. It is also useful in anthropology and forensic dentistry for determining the metric age of human remains [46; 47]. The most conventional ways of dental age determination are through [PERSON]'s, [PERSON]'s, [PERSON]'s, [PERSON]', [PERSON]'s, [PERSON]'s, or [PERSON]'s methods [47; 48; 49; 50; 51; 52; 53]. However, there were discrepancies between the chronological age, the age estimated through the charts and the tables developed using these methods. This could be due to the acceleration or growth spurts in the population [54; 55; 56]. AI models have also been widely designed for determining the chronological age of children. [PERSON], [PERSON], et al. [24] reported on AI-based neural models for determining the chronological age using digital pan tomographic images. The model demonstrated an excellent accuracy of 99.7% for chronological age assessment and is considered an innovative tool. However, it was developed using 2D images alone, which could be a possible limitation. [PERSON], [PERSON], et al. [36] also reported on the AI-based model designed for age estimation. The model demonstrated an excellent accuracy of 93.8% for predicting the actual age and can be applied in forensic sciences. [PERSON], [PERSON], et al. [37] reported on ML algorithms designed for predicting dental age in children. This model demonstrated acceptable performance, and these ML methods were significantly more accurate than the two reference methods. [PERSON], [PERSON], et al. [38] reported on a random forest (RF), support vector machine (SVM), and linear regression (LR) based on the Cameriere method to predict the dental age of children [57]. This model demonstrated better accuracy than the traditional Cameriere formula. However, it requires further assessment using samples from different regions, as this sample was only from one region. [PERSON], [PERSON], et al. [20] reported on an AI model that has been designed for predicting the OHS and TN of children. This model demonstrated a sensitivity of 93% and could be of great assistance in school oral health programs. [PERSON], [PERSON], et al. [29] reported on an AI model for determining the impact of oral health on adolescents' quality of life. This model has been found effective and can be useful in clustering children into different characteristic groups. [PERSON], [PERSON], et al. [39] reported on an AI model designed for automated detection and numbering of deciduous teeth on panoramic radiographs. It can be a promising tool for the automated charting of panoramic dental radiographs and can serve as a time-saving measure while dealing with pediatric patients.