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Article

A Deep Learning-Based Approach for the Detection of Early Signs of Gingivitis in Orthodontic Patients Using Faster Region-Based Convolutional Neural Networks

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Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
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Division of Orthodontics, Department of Preventive Dental Science, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
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Department of Orthodontics, The Slovak Medical University, 833 03 Bratislava, Slovakia
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Division of Pediatric Dentistry, Department of Preventive Dental Science, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
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Division of Periodontology, Department of Preventive Dental Science, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
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Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(22), 8447; https://doi.org/10.3390/ijerph17228447
Received: 23 September 2020 / Revised: 2 November 2020 / Accepted: 3 November 2020 / Published: 15 November 2020
Computer-based technologies play a central role in the dentistry field, as they present many methods for diagnosing and detecting various diseases, such as periodontitis. The current study aimed to develop and evaluate the state-of-the-art object detection and recognition techniques and deep learning algorithms for the automatic detection of periodontal disease in orthodontic patients using intraoral images. In this study, a total of 134 intraoral images were divided into a training dataset (n = 107 [80%]) and a test dataset (n = 27 [20%]). Two Faster Region-based Convolutional Neural Network (R-CNN) models using ResNet-50 Convolutional Neural Network (CNN) were developed. The first model detects the teeth to locate the region of interest (ROI), while the second model detects gingival inflammation. The detection accuracy, precision, recall, and mean average precision (mAP) were calculated to verify the significance of the proposed model. The teeth detection model achieved an accuracy, precision, recall, and mAP of 100 %, 100%, 51.85%, and 100%, respectively. The inflammation detection model achieved an accuracy, precision, recall, and mAP of 77.12%, 88.02%, 41.75%, and 68.19%, respectively. This study proved the viability of deep learning models for the detection and diagnosis of gingivitis in intraoral images. Hence, this highlights its potential usability in the field of dentistry and aiding in reducing the severity of periodontal disease globally through preemptive non-invasive diagnosis. View Full-Text
Keywords: gingivitis; periodontal disease; deep learning; convolutional neural networks gingivitis; periodontal disease; deep learning; convolutional neural networks
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MDPI and ACS Style

Alalharith, D.M.; Alharthi, H.M.; Alghamdi, W.M.; Alsenbel, Y.M.; Aslam, N.; Khan, I.U.; Shahin, S.Y.; Dianišková, S.; Alhareky, M.S.; Barouch, K.K. A Deep Learning-Based Approach for the Detection of Early Signs of Gingivitis in Orthodontic Patients Using Faster Region-Based Convolutional Neural Networks. Int. J. Environ. Res. Public Health 2020, 17, 8447. https://doi.org/10.3390/ijerph17228447

AMA Style

Alalharith DM, Alharthi HM, Alghamdi WM, Alsenbel YM, Aslam N, Khan IU, Shahin SY, Dianišková S, Alhareky MS, Barouch KK. A Deep Learning-Based Approach for the Detection of Early Signs of Gingivitis in Orthodontic Patients Using Faster Region-Based Convolutional Neural Networks. International Journal of Environmental Research and Public Health. 2020; 17(22):8447. https://doi.org/10.3390/ijerph17228447

Chicago/Turabian Style

Alalharith, Dima M., Hajar M. Alharthi, Wejdan M. Alghamdi, Yasmine M. Alsenbel, Nida Aslam, Irfan U. Khan, Suliman Y. Shahin, Simona Dianišková, Muhanad S. Alhareky, and Kasumi K. Barouch. 2020. "A Deep Learning-Based Approach for the Detection of Early Signs of Gingivitis in Orthodontic Patients Using Faster Region-Based Convolutional Neural Networks" International Journal of Environmental Research and Public Health 17, no. 22: 8447. https://doi.org/10.3390/ijerph17228447

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