A Deep Learning-Based Approach for the Detection of Early Signs of Gingivitis in Orthodontic Patients Using Faster Region-Based Convolutional Neural Networks.
A Deep Learning-Based Approach for the Detection of Early Signs of Gingivitis in Orthodontic Patients Using Faster Region-Based Convolutional Neural Networks.
复制标题
使用更快的基于区域的卷积神经网络检测正畸患者牙龈炎早期体征的基于深度学习的方法。
DOI：
10.3390/ijerph17228447
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发表时间：
2020-11-15
影响因子：
--
通讯作者：
Barouch KK
中科院分区：
文献类型：
--
作者：
Alalharith DM;Alharthi HM;Alghamdi WM;Alsenbel YM;Aslam N;Khan IU;Shahin SY;Dianišková S;Alhareky MS;Barouch KK
基于计算机的技术在牙科领域发挥着核心作用，因为它们提供了许多诊断和检测各种疾病的方法，如牙周炎。目前的研究旨在开发和评估最先进的物体检测和识别技术以及深度学习算法，用于使用口腔内图像自动检测正畸患者的牙周病。本研究将134张口腔内图像分为训练数据集（n = 107[80%]）和测试数据集（n = 27[20%]）。采用ResNet-50卷积神经网络（CNN），建立了两个更快的基于区域的卷积神经网络（R-CNN）模型。第一种模型检测牙齿定位感兴趣区域（ROI），第二种模型检测牙龈炎症。计算检测准确率、精密度、召回率和平均平均精密度（mAP）来验证所提模型的显著性。牙齿检测模型的准确率、精密度、召回率和mAP分别为100%、100%、51.85%和100%。炎症检测模型的准确率、精密度、召回率和mAP分别为77.12%、88.02%、41.75%和68.19%。本研究证明了深度学习模型在口腔内图像中检测和诊断牙龈炎的可行性。因此，这突出了它在牙科领域的潜在可用性，并通过先发制人的非侵入性诊断帮助降低全球牙周病的严重程度。
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.
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影响因子：
1.9
作者：
Lee JH;Kim DH;Jeong SN;Choi SH
通讯作者：
Choi SH
DOI：
10.1016/j.compmedimag.2016.07.004
发表时间：
2017-04-01
影响因子：
5.7
作者：
Sun, Wenqing;Tseng, Tzu-Liang (Bill);Qian, Wei
通讯作者：
Qian, Wei
影响因子：
3.4
作者：
Canakci, Varol;Canakci, Cenk Fatih
通讯作者：
Canakci, Cenk Fatih
影响因子：
4.3
作者：
OSBORN, JB;STOLTENBERG, JL;PIHLSTROM, BL
通讯作者：
PIHLSTROM, BL
影响因子：
2.9
作者：
Preshaw PM
通讯作者：
Preshaw PM