# Use of the Deep Learning Approach to Measure Alveolar Bone Level

arXiv: https://arxiv.org/abs/2109.12115 · HTML: https://mlsanity.com/paper/2109.12115/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 36 references that are in the map.
- First posted on arXiv 24 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)

## Built on by (most cited first, 3 of 3)

- [Exploring the Role of Convolutional Neural Networks (CNN) in Dental Radiography Segmentation: A Comprehensive Systematic Literature Review](https://mlsanity.com/paper/2401.09190/) (2024)
- [AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns](https://mlsanity.com/paper/2506.20522/) (2025)
- [DentAgent: Evidence-Centric Multi-Agent Coordination for Multimodal Dental Reasoning](https://mlsanity.com/paper/2608.18878/) (2026)

## Abstract (arXiv)

Abstract: Aim: The goal was to use a Deep Convolutional Neural Network to measure the radiographic alveolar bone level to aid periodontal diagnosis. Material and methods: A Deep Learning (DL) model was developed by integrating three segmentation networks (bone area, tooth, cementoenamel junction) and image analysis to measure the radiographic bone level and assign radiographic bone loss (RBL) stages. The percentage of RBL was calculated to determine the stage of RBL for each tooth. A provisional periodontal diagnosis was assigned using the 2018 periodontitis classification. RBL percentage, staging, and presumptive diagnosis were compared to the measurements and diagnoses made by the independent examiners. Results: The average Dice Similarity Coefficient (DSC) for segmentation was over 0.91. There was no significant difference in RBL percentage measurements determined by DL and examiners (p=0.65). The Area Under the Receiver Operating Characteristics Curve of RBL stage assignment for stage I, II and III was 0.89, 0.90 and 0.90, respectively. The accuracy of the case diagnosis was 0.85. Conclusion: The proposed DL model provides reliable RBL measurements and image-based periodontal diagnosis using periapical radiographic images. However, this model has to be further optimized and validated by a larger number of images to facilitate its application.