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Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

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An automated framework for quantitative alveolar bone loss using deep learning-based landmark detection.

Erkang Tian¹, Linyu Huang¹, Bingkun Fu²

¹State Key Laboratory of Oral Diseases, West China School of Stomatology, National Clinical Research Center for Oral Diseases, Med-X Center for Manufacturing, Sichuan University, Chengdu 610064, China.

Journal of Dentistry

|March 11, 2026

Summary

This study introduces an AI framework for measuring radiographic alveolar bone loss (ABL) on panoramic radiographs. The AI tool offers a fast and objective alternative for ABL measurement, aiding clinical practice.

Keywords:

Alveolar bone loss Deep learning Diagnostic imaging Panoramic radiography Periodontitis

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Automated Joint Space Detection Improves Bone Segmentation Accuracy

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Automated Joint Space Detection Improves Bone Segmentation Accuracy

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Published on: November 28, 2025

Area of Science:

Dentistry
Medical Imaging
Artificial Intelligence

Background:

Radiographic alveolar bone loss (ABL) assessment is crucial for periodontal disease diagnosis.
Manual measurement of ABL on panoramic radiographs is time-consuming and subjective.

Purpose of the Study:

To develop and validate an automated deep-learning framework for full-mouth ABL quantification using panoramic radiographs.
To integrate deep learning for landmark detection with curve fitting for accurate ABL measurement.

Main Methods:

760 panoramic radiographs (PANs) were annotated by dentists.
Three deep learning networks (TransPose, HRNet, YOLOv8) were evaluated for landmark localization.
Polynomial curve fitting was used to derive ABL percentages, with agreement assessed by intraclass correlation coefficients (ICC).

Main Results:

TransPose showed the highest landmark localization accuracy (lowest MRE, highest SDR).
YOLOv8 demonstrated the strongest agreement with manual ABL measurements (ICC 0.633 maxilla, 0.771 mandible).
Automated analysis was significantly faster (12.3 ms) than manual annotation (14.2 min/image).

Conclusions:

An AI-based approach enables efficient and objective ABL quantification from PANs.
YOLOv8 shows clinical utility for ABL measurement, offering a rapid alternative for standardized screening.