Despite the rapid improvement in dental health over the last few decades, a significant portion of our population continue seek dental care every year. Estimates show that 13% of adults seek dental care for dental infection or fatigue within four years. The Social and individual burden of this disease can be reduced by its early detection. However, the symptoms of teeth infection in the early stages are not clear, hence, it would be relatively difficult to predict teeth infections based solely on human skills and experience. Big Data (BD) technologies have a great potential in transforming dental care, as they have revolutionized other industries. In addition to reducing cost, they could save millions of lives and improve patient outcomes. This paper proposes a novel integrated prediction model that extracts hidden knowledge from radiographic datasets containing a large volume of dental X-ray images and utilizes this knowledge to predict dental infections. Initially, preprocessing techniques using morphological skeleton and mean approach is applied to eliminate noise and enhance the images. Next, Multi Scale Segmented Region (MSR) approach, Watershed Approach (WA), Sobel edge Detection (SD), Histogram based Segmentation (HS), Trainable Segmentation (TS), Dual Clustering (DC), and Fuzzy C-Means clustering (FCM) are examined for image segmentation and feature extraction. Among these methods, MSR was selected for feature extraction since it outperformed other methods in terms of accuracy, specificity, precision, recall and F1-score. Then, a set of neural network classifiers are trained to identify patterns in the extracted optimized features and predict dental infections. For this purpose, we have examined Bacterial Optimized Recurrent Neural Networks (BORNN), Deep Learning Neural Networks (DANN), Genetic Optimized Neural Networks (GONN) and Adaptive Neural Networks Algorithm (ADNN). BORNN have shown maximum accuracy and Roc value (98.1% and 0.92 respectively), and minimum error values (MSE = 0.189, MAE = 0.143). The output of the proposed integrated prediction model is fed into a dental robot who proceeds with the treatment process with high accuracy and minimum delay. The proposed prediction model was implemented using a big data analytics tool called Apache SAMOA and experimental results showed its correctness and effectiveness.

尽管在过去的几十年中，牙齿健康状况得到了快速改善，但我们每年仍有很大一部分人口继续寻求牙科护理。据估计，有13％的成年人在四年内因牙齿感染或疲劳而寻求牙科护理。通过早期发现，可以减轻这种疾病的社会和个人负担。然而，早期阶段牙齿感染的症状尚不清楚，因此，仅根据人类技能和经验来预测牙齿感染将相对困难。大数据（BD）技术已经彻底改变了其他行业，因此在改变牙科保健方面具有巨大潜力。除了降低成本外，它们还可以挽救数百万的生命并改善患者的预后。本文提出了一种新颖的综合预测模型，该模型可以从包含大量牙齿X射线图像的射线照相数据集中提取隐藏的知识，并利用这些知识来预测牙齿感染。最初，应用了使用形态骨架和均值方法的预处理技术来消除噪声并增强图像。接下来，采用多尺度分段区域（MSR）方法，分水岭方法（WA），Sobel边缘检测（SD），基于直方图的分段（HS），可训练的分段（TS），双重聚类（DC）和模糊C均值聚类（ FCM）进行图像分割和特征提取。在这些方法中，选择MSR进行特征提取是因为它在准确性，特异性，精密度，召回率和F1评分方面均优于其他方法。然后，训练了一组神经网络分类器，以识别提取的优化特征中的模式并预测牙齿感染。为此，我们检查了细菌优化的递归神经网络（BORNN），深度学习神经网络（DANN），遗传优化的神经网络（GONN）和自适应神经网络算法（ADNN）。BORNN显示出最大精度和Roc值（分别为98.1％和0.92）和最小误差值（MSE = 0.189，MAE = 0.143）。所提出的集成预测模型的输出被输入到牙科机器人中，该机器人以高精度和最小延迟进行治疗过程。所提出的预测模型是使用称为Apache SAMOA的大数据分析工具实现的，实验结果表明了该模型的正确性和有效性。