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Mao YC, Huang YC, Chen TY, Li KC, Lin YJ, Liu YL, Yan HR, Yang YJ, Chen CA, Chen SL, Li CW, Chan ML, Chuo Y, Abu PAR. Deep Learning for Dental Diagnosis: A Novel Approach to Furcation Involvement Detection on Periapical Radiographs. Bioengineering (Basel) 2023; 10:802. [PMID: 37508829 PMCID: PMC10376376 DOI: 10.3390/bioengineering10070802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/15/2023] [Accepted: 06/21/2023] [Indexed: 07/30/2023] Open
Abstract
Furcation defects pose a significant challenge in the diagnosis and treatment planning of periodontal diseases. The accurate detection of furcation involvements (FI) on periapical radiographs (PAs) is crucial for the success of periodontal therapy. This research proposes a deep learning-based approach to furcation defect detection using convolutional neural networks (CNN) with an accuracy rate of 95%. This research has undergone a rigorous review by the Institutional Review Board (IRB) and has received accreditation under number 202002030B0C505. A dataset of 300 periapical radiographs of teeth with and without FI were collected and preprocessed to enhance the quality of the images. The efficient and innovative image masking technique used in this research better enhances the contrast between FI symptoms and other areas. Moreover, this technology highlights the region of interest (ROI) for the subsequent CNN models training with a combination of transfer learning and fine-tuning techniques. The proposed segmentation algorithm demonstrates exceptional performance with an overall accuracy up to 94.97%, surpassing other conventional methods. Moreover, in comparison with existing CNN technology for identifying dental problems, this research proposes an improved adaptive threshold preprocessing technique that produces clearer distinctions between teeth and interdental molars. The proposed model achieves impressive results in detecting FI with identification rates ranging from 92.96% to a remarkable 94.97%. These findings suggest that our deep learning approach holds significant potential for improving the accuracy and efficiency of dental diagnosis. Such AI-assisted dental diagnosis has the potential to improve periodontal diagnosis, treatment planning, and patient outcomes. This research demonstrates the feasibility and effectiveness of using deep learning algorithms for furcation defect detection on periapical radiographs and highlights the potential for AI-assisted dental diagnosis. With the improvement of dental abnormality detection, earlier intervention could be enabled and could ultimately lead to improved patient outcomes.
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Affiliation(s)
- Yi-Cheng Mao
- Department of General Dentistry, Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan
| | - Yen-Cheng Huang
- Department of General Dentistry, Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan
| | - Tsung-Yi Chen
- Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan
| | - Kuo-Chen Li
- Department of Information Management, Chung Yuan Christian University, Taoyuan City 320317, Taiwan
| | - Yuan-Jin Lin
- Department of Electrical Engineering and Computer Science, Chung Yuan Christian University, Chung Li City 32023, Taiwan
| | - Yu-Lin Liu
- Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan
| | - Hong-Rong Yan
- Department of Electrical Engineering and Computer Science, Chung Yuan Christian University, Chung Li City 32023, Taiwan
| | - Yu-Jie Yang
- Department of Electrical Engineering and Computer Science, Chung Yuan Christian University, Chung Li City 32023, Taiwan
| | - Chiung-An Chen
- Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan
| | - Shih-Lun Chen
- Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan
| | - Chun-Wei Li
- Department of General Dentistry, Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan
| | - Mei-Ling Chan
- Department of General Dentistry, Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan
- School of Physical Educational College, Jiaying University, Meizhou 514000, China
| | - Yueh Chuo
- Department of General Dentistry, Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan
| | - Patricia Angela R Abu
- Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City 1108, Philippines
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