Automated periodontitis bone loss diagnosis in panoramic radiographs using a bespoke two-stage detector

Kong, Zhengmin, Ouyang, Hui, Cao, Yiyuan, Huang, Tao, Ahn, Euijoon, Zhang, Maoqi, and Liu, Hunan (2023) Automated periodontitis bone loss diagnosis in panoramic radiographs using a bespoke two-stage detector. Computers in Biology and Medicine, 152. 106374.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

[img] PDF (Accepted Publisher Version) - Accepted Version
Restricted to Repository staff only

View at Publisher Website:


Periodontitis is a serious oral disease that can lead to severe conditions such as bone loss and teeth falling out if left untreated. Diagnosis of radiographic bone loss (RBL) is critical for the staging and treatment of periodontitis. Unfortunately, the RBL diagnosis by examining the panoramic radiographs is time-consuming. The demand for automated image analysis is urgent. However, existing deep learning methods have limited performances in diagnosis accuracy and have certain difficulties in implementation. Hence, we propose a novel two-stage periodontitis detection convolutional neural network (PDCNN), where we optimize the detector with an anchor-free encoding that allows fast and accurate prediction. We also introduce a proposalconnection module in our detector that excludes less relevant regions of interests (ROIs), making the network focus on more relevant ROIs to improve detection accuracy. Furthermore, we introduced a large-scale, high-resolution panoramic radiograph dataset that captures various complex cases with professional periodontitis annotations. Experiments on our panoramicimage dataset show that the proposed approach achieved an RBL classification accuracy of 0.762. This result shows that our approach outperforms state-of-the-art detectors such as Faster R-CNN and YOLO-v4. We can conclude that the proposed method successfully improves the RBL detection performance. The dataset and our code have been released on GitHub. (

Item ID: 76945
Item Type: Article (Research - C1)
ISSN: 0010-4825
Keywords: Tooth detection; Panoramic radiograph detection; Convolutional neural network; Two-stage detection
Copyright Information: © 2022 Elsevier Ltd. All rights reserved.
Funders: Australian Research Council (ARC)
Projects and Grants: ARC Discovery Projects Funding Scheme Project DP220101634
Date Deposited: 13 Dec 2022 01:27
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 60%
46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460304 Computer vision @ 40%
SEO Codes: 20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions @ 60%
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 40%
Downloads: Total: 4
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page