Bibliographic Details
| Title: |
Precision Medicine Assessment of the Radiographic Defect Angle of the Intrabony Defect in Periodontal Lesions by Deep Learning of Bitewing Radiographs |
| Authors: |
Patricia Angela R. Abu, Yi-Cheng Mao, Yuan-Jin Lin, Chien-Kai Chao, Yi-He Lin, Bo-Siang Wang, Chiung-An Chen, Shih-Lun Chen, Tsung-Yi Chen, Kuo-Chen Li |
| Source: |
Bioengineering, Vol 12, Iss 1, p 43 (2025) |
| Publisher Information: |
MDPI AG, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Technology
LCC:Biology (General) |
| Subject Terms: |
convolutional neural network, image detection, image enhancement, machine learning, radiographic defect angle, intrabony defect, Technology, Biology (General), QH301-705.5 |
| More Details: |
In dental diagnosis, evaluating the severity of periodontal disease by analyzing the radiographic defect angle of the intrabony defect is essential for effective treatment planning. However, dentists often rely on clinical examinations and manual analysis, which can be time-consuming and labor-intensive. Due to the high recurrence rate of periodontal disease after treatment, accurately evaluating the radiographic defect angle of the intrabony defect is vital for implementing targeted interventions, which can improve treatment outcomes and reduce recurrence. This study aims to streamline clinical practices and enhance patient care in managing periodontal disease by determining its severity based on the analysis of the radiographic defect angle of the intrabony defect. In this approach, radiographic defect angles of the intrabony defect greater than 37 degrees are classified as severe, while those less than 37 degrees are considered mild. This study employed a series of novel image enhancement techniques to significantly improve diagnostic accuracy. Before enhancement, the maximum accuracy was 78.85%, which increased to 95.12% following enhancement. YOLOv8 detects the affected tooth, and its mAP can reach 95.5%, with a precision reach of 94.32%. This approach assists dentists in swiftly assessing the extent of periodontal erosion, enabling timely and appropriate treatment. These techniques reduce diagnostic time and improve healthcare quality. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2306-5354 |
| Relation: |
https://www.mdpi.com/2306-5354/12/1/43; https://doaj.org/toc/2306-5354 |
| DOI: |
10.3390/bioengineering12010043 |
| Access URL: |
https://doaj.org/article/ce787f5c89e94b93b8cb4db3021c020d |
| Accession Number: |
edsdoj.787f5c89e94b93b8cb4db3021c020d |
| Database: |
Directory of Open Access Journals |
