# Enhancing Periodontial Bone Loss Diagnosis Through Advanced AI Techniques
## 5 Conclusions  
In this comprehensive study, an AI-powered two-stage diagnostic system was designed to enable early detection and accurate diagnosis of periodontitis from panoramic radiographs, specifically targeting primary healthcare settings with non-experts as well as specialized dental clinics. The proposed system utilized two advanced AI models, MobileNetV2 and YOLOv8, in a two-stage screening process, where the primary model identifies whether a radiograph shows signs of PBL, and, if detected, the second model pinpoints its location and assesses its severity. This two-stage detection and classification system aims to reduce false positives and optimize model performance, ultimately providing an efficient, reliable, and clinically practical diagnostic tool to support the early detection and effective management of periodontitis. We successfully collected and annotated a custom private dataset from various dental clinics, consisting of 817 panoramic radiographs for training and an additional 600 radiographs for testing. The primary model, MobileNetV2, demonstrated a high accuracy of 88% in detecting PBL, while the secondary model, YOLOv8, achieved 74% precision, effectively localizing PBL instances and classifying their severity. To validate our models' performance, we consulted a board-certified oral and maxillofacial radiologist, whose endorsement, along with our models' validation, ensures the accuracy and reliability of the detection and classification results and reinforces the practical relevance of our diagnostic system in clinical settings. Consequently, our research advances AI's role in enhancing dental imaging diagnostics by providing a precise, efficient tool for evaluating PBL, with the potential to transform dental diagnostics and improve patient outcomes. Notably, our proposed system addresses the observed research gap in the literature by introducing, to the best of our knowledge, the first diagnostic tool capable of classifying three levels of PBL severity, thereby aiding dentists in making more accurate diagnoses. Additionally, our developed models outperformed similar existing models in the literature, setting a new benchmark for AI-based dental diagnostics. For future study, other advanced AI models will be investigated, aiming to further enhance the reliability and efficiency of the diagnostic system. Furthermore, utilizing a larger and more balanced dataset is recommended to optimize performance and improve generalizability. Additionally, we believe that integrating our proposed system into a comprehensive software solution would greatly benefit dentists by providing an efficient, easy-to-use, and cost-effective tool for diagnosing and managing periodontitis, thus further enhancing clinical practice and patient outcomes. Conceptualization, N.N.F.R., A.Z.A. and S.A.; methodology, N.N.F.R., G.S. and S.A.; software, G.S. and S.A.; validation, N.N.F.R., G.S., A.Z.A. and S.A.; formal analysis, N.N.F.R., G.S. and S.A.; investigation, N.N.F.R., G.S. and S.A.; data curation, N.N.F.R., G.S., A.Z.A. and S.A.; writing--original draft preparation, G.S. and S.A.; writing--review and editing, N.N.F.R., G.S., A.Z.A. and S.A.; supervision, N.N.F.R. and S.A.; project administration, S.A. All authors have read and agreed to the published version of the manuscript. This research was partially funded by City University Ajman, United Arab Emirates. The authors contributed the remaining funding. This study was conducted following approval from the Institutional Review Board of City University Ajman, United Arab Emirates, on 19 September 2024, under protocol code 092024-02. All procedures adhered to ethical standards in accordance with the Declaration of Helsinki. Informed Consent Statement: Informed consent was obtained from all patients before the acquisition of their dental images.