Applicability and performance of convolutional neural networks for the identification of periodontal bone loss in periapical radiographs: a scoping review.
Authors
Affiliations (4)
Affiliations (4)
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Jalan Prof. Dr. Mayjen Moestopo No. 47, Surabaya, 60132, East Java, Indonesia. [email protected].
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Jalan Prof. Dr. Mayjen Moestopo No. 47, Surabaya, 60132, East Java, Indonesia.
- Bachelor of Dental Medicine Study Program, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia.
- Division of Dental Informatics and Radiology, Tohoku University Graduate School of Dentistry, Sendai, Japan.
Abstract
The study aimed to review the applicability and performance of various Convolutional Neural Network (CNN) models for the identification of periodontal bone loss (PBL) in digital periapical radiographs achieved through classification, detection, and segmentation approaches. We searched the PubMed, IEEE Xplore, and SCOPUS databases for articles published up to June 2024. After the selection process, a total of 11 studies were included in this review. The reviewed studies demonstrated that CNNs have a significant potential application for automatic identification of PBL on periapical radiographs through classification and segmentation approaches. CNN architectures can be utilized to classify the presence or absence of PBL, the severity or degree of PBL, and PBL area segmentation. CNN showed a promising performance for PBL identification on periapical radiographs. Future research should focus on dataset preparation, proper selection of CNN architecture, and robust performance evaluation to improve the model. Utilizing an optimized CNN architecture is expected to assist dentists by providing accurate and efficient identification of PBL.