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A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos
Wen Li1, Yuan Liang2, Xuan Zhang3
1Department of Endodontics, Nanjing Stomatological Hospital, Medical School of Nanjing University, No.30 Zhongyang Road, Xuanwu District, Nanjing, Jiangsu, People's Republic of China.
Scientific Reports
|August 20, 2021
Summary
A new deep learning model can detect gingivitis, dental calculus, and soft deposits from oral photos. This cost-effective AI solution offers a promising approach for early dental disease screening in underserved populations.
Area of Science:
- Artificial Intelligence in Dentistry
- Medical Imaging Analysis
- Public Health Informatics
Background:
- Gingivitis detection typically relies on routine dental visits, which are not always accessible or affordable.
- Limited medical resources and cost barriers hinder early dental disease diagnosis in certain populations.
Purpose of the Study:
- To develop and evaluate a novel Multi-Task Learning convolutional neural network (CNN) model for screening gingivitis and its common irritants from oral photographs.
- To provide a cost-effective and accessible solution for early detection of dental issues, promoting public oral health.
Main Methods:
- A Multi-Task Learning CNN model was developed to analyze oral photographs for the presence of gingivitis, dental calculus, and soft deposits.
- The model's performance was evaluated on a dataset of 625 patients, assessing both classification accuracy and localization capabilities.
- Comparative analysis was conducted against general-purpose CNNs to demonstrate the effectiveness of the proposed model.
Main Results:
- The model achieved high classification Area Under the Curve (AUC) scores: 87.11% for gingivitis, 80.11% for dental calculus, and 78.57% for soft deposits.
- The CNN model demonstrated moderate accuracy in localizing these dental findings within oral photos, aiding in result interpretation.
- The proposed Multi-Task Learning CNN significantly outperformed general-purpose CNNs in both classification and localization tasks.
Conclusions:
- Deep learning, particularly the developed Multi-Task Learning CNN, shows significant potential for widespread dental disease screening.
- This AI-driven approach offers a cost-effective and accessible method for early detection of common dental problems.
- The study highlights the effectiveness of Multi-Task Learning in enhancing dental disease detection accuracy and localization.
