Next Article in Journal
Towards Safety and Regulation Criteria for Clinical Applications of Decellularized Organ-Derived Matrices
Previous Article in Journal
Liposomal Nanocarriers to Enhance Skin Delivery of Chemotherapeutics in Cancer Therapy
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Bioengineering
Volume 12
Issue 2
10.3390/bioengineering12020134
bioengineering-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Deep Learning-Assisted Diagnostic System: Apices and Odontogenic Sinus Floor Level Analysis in Dental Panoramic Radiographs

by
Pei-Yi Wu
1,
Yuan-Jin Lin
2,
Yu-Jen Chang
3,
Sung-Tsun Wei
3,
Chiung-An Chen
4,*,
Kuo-Chen Li
5,*,
Wei-Chen Tu
6 and
Patricia Angela R. Abu
7
1
Department of Periodontics, Division of Dentistry, Taoyuan Chang Gung Memorial Hospital, Taoyuan City 333034, Taiwan
2
Department of Program on Semiconductor Manufacturing Technology, Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan City 701401, Taiwan
3
Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan
4
Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan
5
Department of Information Management, Chung Yuan Christian University, Taoyuan City 32023, Taiwan
6
Department of Electrical Engineering, National Cheng Kung University, Tainan City, 701401, Taiwan
7
Ateneo Laboratory for Intelligent Visual Environments, Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City 1108, Philippines
*
Authors to whom correspondence should be addressed.
Bioengineering 2025, 12(2), 134; https://doi.org/10.3390/bioengineering12020134
Submission received: 28 December 2024 / Revised: 21 January 2025 / Accepted: 28 January 2025 / Published: 30 January 2025
(This article belongs to the Special Issue Artificial Intelligence Revolution in Biomedical Image and Signal Processing: Innovations and Applications)
Versions Notes

Abstract

Odontogenic sinusitis is a type of sinusitis caused by apical lesions of teeth near the maxillary sinus floor. Its clinical symptoms are highly like other types of sinusitis, often leading to misdiagnosis as general sinusitis by dentists in the early stages. This misdiagnosis delays treatment and may be accompanied by toothache. Therefore, using artificial intelligence to assist dentists in accurately diagnosing odontogenic sinusitis is crucial. This study introduces an innovative odontogenic sinusitis image processing technique, which is fused with common contrast limited adaptive histogram equalization, Min-Max normalization, and the RGB mapping method. Moreover, this study combined various deep learning models to enhance diagnostic accuracy. The YOLO 11n model was used to detect odontogenic sinusitis single tooth position in dental panoramic radiographs and achieved an accuracy of 98.2%. The YOLOv8n-cls model diagnosed odontogenic sinusitis with a final classification accuracy of 96.1%, achieving a 16.9% improvement over non-enhanced methods and outperforming recent studies by at least 4%. Additionally, in clinical applications, the classification accuracy for non-odontogenic sinusitis was 95.8%, while for odontogenic sinusitis it was 97.6%. The detection method developed in this study effectively reduces the radiation dose patients receive during CT imaging and serves as an auxiliary system, providing dentists with reliable support for the precise diagnosis of odontogenic sinusitis.
Keywords: image enhancement; intelligent healthcare; medical image processing; object detection; odontogenic sinusitis; YOLOv8n-cls; YOLO 11n image enhancement; intelligent healthcare; medical image processing; object detection; odontogenic sinusitis; YOLOv8n-cls; YOLO 11n

Share and Cite

MDPI and ACS Style

Wu, P.-Y.; Lin, Y.-J.; Chang, Y.-J.; Wei, S.-T.; Chen, C.-A.; Li, K.-C.; Tu, W.-C.; Abu, P.A.R. Deep Learning-Assisted Diagnostic System: Apices and Odontogenic Sinus Floor Level Analysis in Dental Panoramic Radiographs. Bioengineering 2025, 12, 134. https://doi.org/10.3390/bioengineering12020134

AMA Style

Wu P-Y, Lin Y-J, Chang Y-J, Wei S-T, Chen C-A, Li K-C, Tu W-C, Abu PAR. Deep Learning-Assisted Diagnostic System: Apices and Odontogenic Sinus Floor Level Analysis in Dental Panoramic Radiographs. Bioengineering. 2025; 12(2):134. https://doi.org/10.3390/bioengineering12020134

Chicago/Turabian Style

Wu, Pei-Yi, Yuan-Jin Lin, Yu-Jen Chang, Sung-Tsun Wei, Chiung-An Chen, Kuo-Chen Li, Wei-Chen Tu, and Patricia Angela R. Abu. 2025. "Deep Learning-Assisted Diagnostic System: Apices and Odontogenic Sinus Floor Level Analysis in Dental Panoramic Radiographs" Bioengineering 12, no. 2: 134. https://doi.org/10.3390/bioengineering12020134

APA Style

Wu, P.-Y., Lin, Y.-J., Chang, Y.-J., Wei, S.-T., Chen, C.-A., Li, K.-C., Tu, W.-C., & Abu, P. A. R. (2025). Deep Learning-Assisted Diagnostic System: Apices and Odontogenic Sinus Floor Level Analysis in Dental Panoramic Radiographs. Bioengineering, 12(2), 134. https://doi.org/10.3390/bioengineering12020134

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop