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

Abstract

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.

Applications of artificial intelligence in dentistry: A comprehensive review

“…Besides, different approaches have been followed for the diagnosis of the gingivitis disease, by employing shallow FFNNs 63,64 and CNNs. 79 An extreme learning machine (a simple way of training a model by using randomly assigned parameters) over a basic ANN architecture with manually extracted features was used to diagnose gingivitis. 63 The features extracted were based on contrast-limited adaptive histogram equalization (CLAHE) and the gray-level co-occurrence matrix (GLCM).…”

“…CNNs were also used for the early identification of the disease in intraoral images, reaching great results. 79 New approaches, such as mobile health (mHealth) alternatives, are currently under development for the self-examination and identification of different oral conditions (diseases or early disease signals) using a smartphone camera and the internet-of-thing (IoT) approaches. 75 A smart dental health IoT platform, which uses the Mask-RCNN network 177 for the detection and classification of seven different oral diseases, reaching a mean accuracy of 93.6%, was proposed.…”

“…Example of a general CNN architecture. 169 CNN, convolutional neural network in maxillary sinus lesions on panoramic radiographs, 72 oral squamous cell carcinoma segmentation in whole-slide imaging (WSI), 61 gingivitis segmentation in intraoral images, 79 the segmentation of periodontal bone loss and stage periodontitis classification using panoramic radiographs, 73 lesion segmentation using CT images, 80 or dental caries segmentation using near-infrared transillumination images. 53 The segmentation of other oral surfaces has also been widely explored in literature by using dental radiographs, [99][100][101][102] intraoral ultrasound imaging, 97 3D intraoral scans, 96 or CBCT Scans.…”

“…The methodology was built upon previous work 64 and showed improvements in the results, reaching 74% accuracy, 75% sensitivity, and 73% specificity. CNNs were also used for the early identification of the disease in intraoral images, reaching great results 79 …”

Gingivitis detection by Fractional Fourier Entropy and Particle Swarm Optimization

“…Alalharith, et al [5] investigated and developed a fast regional CNN for detection of gingivitis, in which the model was divided into two models, one for detection of regions of interest for model tooth localization, and the second model for detection of gingival inflammation to automatically detect periodontitis in orthodontic patients using intraoral images. Obuchowicz, et al [6] uses different texture features to transform digital oral radiography images of patients with dental caries.…”