# AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns

arXiv: https://arxiv.org/abs/2506.20522 · HTML: https://mlsanity.com/paper/2506.20522/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 62 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Learning non-maximum suppression](https://mlsanity.com/paper/1705.02950/) (2017)
- [Real-Time Flying Object Detection with YOLOv8](https://mlsanity.com/paper/2305.09972/) (2023)
- [YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss](https://mlsanity.com/paper/2204.06806/) (2022)
- [Global AI Ethics: A Review of the Social Impacts and Ethical Implications of Artificial Intelligence](https://mlsanity.com/paper/1907.07892/) (2019)
- [The Transformative Potential of Artificial Intelligence](https://mlsanity.com/paper/1912.00747/) (2019)
- [Localization of Critical Findings in Chest X-Ray without Local Annotations Using Multi-Instance Learning](https://mlsanity.com/paper/2001.08817/) (2020)
- [Use of the Deep Learning Approach to Measure Alveolar Bone Level](https://mlsanity.com/paper/2109.12115/) (2021)
- [The Global Impact of AI-Artificial Intelligence: Recent Advances and Future Directions, A Review](https://mlsanity.com/paper/2401.12223/) (2023)

## Abstract (arXiv)

Periodontitis, a chronic inflammatory disease causing alveolar bone loss, significantly affects oral health and quality of life. Accurate assessment of bone loss severity and pattern is critical for diagnosis and treatment planning. In this study, we propose a novel AI-based deep learning framework to automatically detect and quantify alveolar bone loss and its patterns using intraoral periapical (IOPA) radiographs. Our method combines YOLOv8 for tooth detection with Keypoint R-CNN models to identify anatomical landmarks, enabling precise calculation of bone loss severity. Additionally, YOLOv8x-seg models segment bone levels and tooth masks to determine bone loss patterns (horizontal vs. angular) via geometric analysis. Evaluated on a large, expertly annotated dataset of 1000 radiographs, our approach achieved high accuracy in detecting bone loss severity (intra-class correlation coefficient up to 0.80) and bone loss pattern classification (accuracy 87%). This automated system offers a rapid, objective, and reproducible tool for periodontal assessment, reducing reliance on subjective manual evaluation. By integrating AI into dental radiographic analysis, our framework has the potential to improve early diagnosis and personalized treatment planning for periodontitis, ultimately enhancing patient care and clinical outcomes.