NDLI: Fully automated deep learning approach to dental development assessment in panoramic radiographs

Content Provider	Springer Nature : BioMed Central
Author	Ong, Seung-Hwan Kim, Hyuntae Song, Ji-Soo Shin, Teo Jeon Hyun, Hong-Keun Jang, Ki-Taeg Kim, Young-Jae
Abstract	Background Dental development assessment is an important factor in dental age estimation and dental maturity evaluation. This study aimed to develop and evaluate the performance of an automated dental development staging system based on Demirjian’s method using deep learning. Methods The study included 5133 anonymous panoramic radiographs obtained from the Department of Pediatric Dentistry database at Seoul National University Dental Hospital between 2020 and 2021. The proposed methodology involves a three-step procedure for dental staging: detection, segmentation, and classification. The panoramic data were randomly divided into training and validating sets (8:2), and YOLOv5, U-Net, and EfficientNet were trained and employed for each stage. The models’ performance, along with the Grad-CAM analysis of EfficientNet, was evaluated. Results The mean average precision (mAP) was 0.995 for detection, and the segmentation achieved an accuracy of 0.978. The classification performance showed F1 scores of 69.23, 80.67, 84.97, and 90.81 for the Incisor, Canine, Premolar, and Molar models, respectively. In the Grad-CAM analysis, the classification model focused on the apical portion of the developing tooth, a crucial feature for staging according to Demirjian’s method. Conclusions These results indicate that the proposed deep learning approach for automated dental staging can serve as a supportive tool for dentists, facilitating rapid and objective dental age estimation and dental maturity evaluation.
Related Links	https://bmcoralhealth.biomedcentral.com/counter/pdf/10.1186/s12903-024-04160-6.pdf
Ending Page	12
Page Count	12
Starting Page	1
File Format	HTM / HTML
ISSN	14726831
DOI	10.1186/s12903-024-04160-6
Journal	BMC Oral Health
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-04-06
Access Restriction	Open
Subject Keyword	Dentistry Oral and Maxillofacial Surgery Dental development Artificial intelligence Deep learning Demirjian method Panoramic radiographs
Content Type	Text
Resource Type	Article
Subject	Dentistry
Journal Impact Factor	2.6/2023
5-Year Journal Impact Factor	3.2/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Evaluation of tooth development stages with deep learning-based artificial intelligence algorithm

Detection of dental caries under fixed dental prostheses by analyzing digital panoramic radiographs with artificial intelligence algorithms based on deep learning methods

Segmentation of periapical lesions with automatic deep learning on panoramic radiographs: an artificial intelligence study

Combining public datasets for automated tooth assessment in panoramic radiographs

Automatic dental age calculation from panoramic radiographs using deep learning: a two-stage approach with object detection and image classification

An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population

Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: a preliminary study

A fully automated method of human identification based on dental panoramic radiographs using a convolutional neural network.

Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study

Fully automated deep learning approach to dental development assessment in panoramic radiographs

Similar Documents

Evaluation of tooth development stages with deep learning-based artificial intelligence algorithm

Detection of dental caries under fixed dental prostheses by analyzing digital panoramic radiographs with artificial intelligence algorithms based on deep learning methods

Segmentation of periapical lesions with automatic deep learning on panoramic radiographs: an artificial intelligence study

Combining public datasets for automated tooth assessment in panoramic radiographs

Automatic dental age calculation from panoramic radiographs using deep learning: a two-stage approach with object detection and image classification

An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population

Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: a preliminary study

A fully automated method of human identification based on dental panoramic radiographs using a convolutional neural network.

Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study

Fully automated deep learning approach to dental development assessment in panoramic radiographs