## 4 Proposed Methodology
### Ensemble Learning
The individual models in [17,18] provide a basic assessment of prediction effectiveness, but the Ensemble model (V6) demonstrates the advantages of combining many predictors. The results from the comparative table show that the Ensemble model regularly outperforms both separate models across all metrics. The Ensemble model obtains an astounding 90%, surpassing the 69% and 77% values of models [17,18]. This notable improvement indicates a significant

Figure 12: The samples of output image for YOLO8, RT-DETR, and Ensemble for the same images. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & **YOLO8** & **YOLO8** & **YOLO8** & **RT-DETR** & **RT-DETR** & **RT-DETR** & **Ensemble** & **Ensemble** & **Ensemble** \\  & **(V4)** & **(V5)** & **(V6)** & **(V4)** & **(V5)** & **(V6)** & **(V4)** & **(V5)** & **(V6)** \\ \hline Precision(B) & 50\% & 74\% & 68\% & 66\% & 81\% & 78\% & 85\% & 88\%



reduction in false positive occurrences, demonstrating the Ensemble model's dependability in making true positive predictions. The accuracy statistic highlights the Ensemble model's efficacy, with a noteworthy 92% correctness in predictions compared to the individual models' 77% and 83% accuracy, respectively. This significant difference demonstrates the benefits of Ensemble techniques, which combine the capabilities of multiple models to improve overall performance. The Ensemble model demonstrates improved precision, recall, and overall accuracy, making it a superior option to individual models [17; 18]. These findings call for a larger use of Ensemble learning approaches in predictive modeling, particularly in domains where high precision and reliability are crucial. The Ensemble model's excellent metrics not only represent improved performance but also indicate promising potential for future developments within machine learning frameworks. ## 6 Conclusions

The integration of various artificial intelligence models into dental healthcare systems increases the accuracy and robustness of the diagnosis of dental diseases. The proposed integrated model demonstrates significant improvements in diagnostic accuracy as well as efficiency. It also simplified initial interventions as well as improved patient outcomes out of Ensemble Non-Maximum Suppression (NMS). Experimental results reveal the high performance of the proposed model, with an average 30% improvement in accuracy and 18% reduction in execution time compared to other deep learning algorithms. In addition, this research has been practically implemented and validated by specialized dental professionals, ensuring its relevance and applicability to real-world scenarios. Unlike purely theoretical studies, this work has been integrated into practical settings, demonstrating its effectiveness and value in addressing actual challenges faced by experts in the field. These improvements not only enhance the reliability of the system within dental teams but also simplify the workflow for dental professionals, enabling them to handle and manage more complex tasks with greater efficiency and ease. Conceptualization, S.M.N., S.A.S. and I.T.A.H. ; Methodology, S.M.N., S.A.S. and I.T.A.H. ; Software, S.M.N. and R.S.S. ; Validation, S.M.N., R.S.S., S.A.S. and I.T.A.H. ; Formal analysis, S.M.N., S.A.S. and I.T.A.H. ; Investigation, R.S.S., S.A.S. and I.T.A.H. ; Resources, S.M.N., R.S.S., S.A.S. and I.T.A.H. ; Data curation, S.M.N., R.S.S. and S.A.S. ; Writing--original draft, S.M.N., R.S.S. and S.A.S. ; Writing--review & editing, S.M.N., S.A.S. and I.T.A.H. ; Visualization, S.M.N., R.S.S. and S.A.S. ; Supervision, S.M.N. ; Project administration, S.M.N. ; Funding acquisition, S.M.N. and I.T.A.H. ; All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this study are available on request from the corresponding author. The authors declare no conflicts of interest.