# Enhancing Periodontial Bone Loss Diagnosis Through Advanced AI Techniques
## 2 Materials and Methods
### Dataset Collection and Preparation
#### 2.2.2 Exclusion Criteria  
* Poor Image Quality: Radiographs with severe distortions, artifacts, or insufficient contrast that may hinder accurate diagnosis.
* Incomplete Dental Records: Cases lacking sufficient clinical history or periodontal diagnosis details.
* Primary Dentition: Radiographs showing primary or mixed dentition were excluded to maintain focus on adult periodontal conditions.
* Post-Surgical Radiographs: Images taken after surgical interventions that alter bone structure significantly.
* Non-Panoramic Images: Radiographs such as periapical, bitewing, or CBCT scans that do not meet the panoramic format requirement.
* Pathological Conditions: Cases with extensive bone pathology unrelated to periodontal disease (e.g., tumors and cysts) were excluded to avoid confounding factors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset Source** & **Size** & **Trained AI Model** \\ \hline City University Ajman & 562 & MobileNetV2 and YOLOv8 \\ Cerda Mardini dataset (2022) [17] & 255 & YOLOv8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the dataset used in the training phase of the proposed AI models.  
For the annotation process, we utilized Roboflow, a leading platform for computer vision and image annotation tasks [24]. Roboflow provides an intuitive interface that simplifies the labeling of complex medical images, allowing for high precision in identifying features such as periodontal bone loss. The platform supports various data formats and offers robust tools for data augmentation, which were critical in enhancing the diversity and quality of our dataset.  
The annotation process involved carefully marking regions of interest in each image to indicate the severity of bone loss. Figures 6 and 7 illustrate the pre- and post-annotation versions of some images. All annotated images had been developed and validated by a board of certified oral and maxillofacial radiologists. These annotated images served as the foundation for training our deep learning models, ensuring that the models could accurately detect and classify different levels of bone loss.  
To ensure the quality of the data, 80% of the images were designated for training purposes, while 20% were reserved for validation. Additionally, for testing purposes, a separate set of 600 panoramic radiographs was utilized from the Roboflow library. Access to these images can be granted upon request. All images underwent resizing to dimensions of 350 \(\times\) 350 pixels with a 10% increase in brightness for binary classification and to 640 \(\times\) 640 pixels with 13% gray-scaling for multi-classification purposes. Only data augmentations were applied during preprocessing as rotation and scaling, with no additional steps taken. In summary, our total training dataset for both models comprises 817 annotated panoramic radiographic images, while a separate dataset of 600 panoramic radiographs ([[https://zenodo.org/records/4457648](https://zenodo.org/records/4457648)]([https://zenodo.org/records/4457648](https://zenodo.org/records/4457648)), accessed on 6 June 2025) was used for testing purposes.