A Deep Learning-Based Approach for the Detection of Early Signs of Gingivitis in Orthodontic Patients Using Faster Region-Based Convolutional Neural Networks
| Autor: | Muhanad Alhareky, Nida Aslam, Kasumi K. Barouch, Simona Dianiskova, Hajar M. Alharthi, Dima M. Alalharith, Yasmine M. Alsenbel, Suliman Y Shahin, Irfan Ullah Khan, Wejdan M. Alghamdi |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Tooth Movement Techniques
Computer science Health Toxicology and Mutagenesis periodontal disease lcsh:Medicine 02 engineering and technology Convolutional neural network Article 03 medical and health sciences Gingivitis 0302 clinical medicine Region of interest convolutional neural networks 0202 electrical engineering electronic engineering information engineering medicine Humans Periodontitis Recall business.industry Deep learning lcsh:R Public Health Environmental and Occupational Health deep learning Pattern recognition Usability 030206 dentistry medicine.disease Object detection 020201 artificial intelligence & image processing Artificial intelligence Neural Networks Computer medicine.symptom business Algorithms gingivitis |
| Zdroj: | International Journal of Environmental Research and Public Health Volume 17 Issue 22 International Journal of Environmental Research and Public Health, Vol 17, Iss 8447, p 8447 (2020) |
| ISSN: | 1660-4601 |
| DOI: | 10.3390/ijerph17228447 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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