Introduction
- Importance of early detection in periodontal disease.
- AI as a tool for improving diagnostic accuracy.
Data Collection and Study Design
- Retrospective evaluation of radiology data.
- Inclusion and exclusion criteria for image quality.
- Use of standardized panoramic imaging device.
Methodology
- Segmentation of bone loss areas on panoramic images.
- Standardized radiography parameters.
- Data anonymization before analysis.
Evaluation of AI Model
- Testing a PyTorch model on a 10% test dataset.
- Comparison of AI model accuracy with radiologist assessment.
- Confusion matrix used for performance assessment.
Results
- Metrics for true positives, true negatives, false positives, and false negatives.
- Discussion on regional vs. general detection success.
Discussion
- Implications for periodontal classification and diagnosis.
- Benefits of AI in improving periodontal disease detection.
Conclusion
- Summary of AI's effectiveness in detecting alveolar bone loss.
- Recommendations for future research and clinical use of AI.
Author Contributions
- Contributions of each author and proofreading acknowledgment.
Funding and Ethical Considerations
- No external funding received.
- Compliance with ethical standards and informed consent from participants.
