Oral health detection system

Abstract

The invention relates to the technical field of oral cavity detection, and particularly discloses an oral cavity health detection system which comprises a tooth brushing behavior detection and analysis module, an oral cavity plaque detection and analysis module, a breath state detection and analysis module, an oral cavity health state detection and evaluation module and a detection report generation terminal. The tooth brushing behavior detection and analysis module can accurately quantify the tooth brushing behavior, supervise and promote the development of good habits and reduce the risk of oral diseases, the oral plaque detection and analysis module can accurately evaluate the plaque state, early warn oral problems in advance and guide targeted cleaning and treatment, and the breath state detection and analysis module can accurately reflect the breath state. The oral health state detection and evaluation module synthesizes various indexes to generate a report, and the detection report generation terminal pushes the report in time, so that a patient can know the oral health state conveniently, the scientificity, accuracy and effectiveness of oral health management are comprehensively improved, the patient is assisted in preventing and improving oral diseases, and the oral health management efficiency is improved. The life quality is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oral cavity detection, and in particular to an oral cavity health detection system. Background Art

[0002] In today's society, people pay more and more attention to oral health, but traditional oral health assessment methods are often rough and subjective. Usually, oral examinations mainly rely on dentists' naked eye observation and simple tool measurements, which may be difficult to accurately detect some early and subtle oral problems. For example, irregular brushing behavior may not be recognized by patients for a long time. Its potential harm, and only occasional bad breath or slight dental discomfort can not fully and accurately understand the true condition inside the mouth. In addition, important indicators such as the growth and distribution of plaque and changes in breath composition lack effective quantitative monitoring methods, making it difficult to achieve ideal results in the prevention and early intervention of oral diseases, and there is a lot of room for improvement in oral health management.

[0003] Some existing oral detection technologies have obvious limitations. Although some intelligent oral devices can monitor basic parameters such as brushing frequency, the evaluation of brushing quality is not comprehensive and in-depth enough. It is impossible to comprehensively consider factors such as the stability of brushing time, the balance of cleaning in each area, and the rationality of brushing force. It is difficult to give targeted and comprehensive oral hygiene guidance suggestions. In terms of plaque detection, traditional methods are mostly manual observation, which is not only inefficient, but also easily affected by subjective factors in accuracy and objectivity. It is impossible to accurately analyze the type of plaque (such as early plaque and mature plaque) and the importance of distribution location, and it is difficult to warn of potential oral disease risks in advance. For breath detection, common detection methods can often only simply determine whether there is an odor, and cannot accurately analyze the breath composition and its intrinsic relationship with oral health. It is impossible to deeply explore oral hygiene risks and clues to systemic diseases from the breath level, and cannot meet the needs of modern oral health management for precision and scientificity. Summary of the invention

[0004] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an oral health detection system to solve the above-mentioned technical defects.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an oral health detection system, including a toothbrushing behavior detection and analysis module, an oral plaque detection and analysis module, a breath state detection and analysis module, an oral health state detection and evaluation module, and a detection report generation terminal:

[0006] The toothbrushing behavior detection and analysis module detects and analyzes the toothbrushing behavior parameters of each patient in each examination cycle, and obtains the toothbrushing behavior health detection coefficient of each patient in each examination cycle;

[0007] The oral plaque detection and analysis module detects and analyzes the oral plaque status of each patient in each examination cycle, and obtains the oral plaque status detection coefficient of each patient in each examination cycle;

[0008] The breath state detection and analysis module detects and analyzes the various oral breath states of each patient in each examination cycle, and obtains the oral breath state detection coefficient of each patient in each examination cycle;

[0009] The oral health status detection and evaluation module performs comprehensive evaluation and analysis based on the toothbrushing behavior health detection coefficient, plaque coverage area status index, and oral breath status detection coefficient of each patient in each examination cycle, and obtains the oral health status report of each patient in each examination cycle;

[0010] The detection report generating terminal stores and sends the oral health status report generated by the oral health status detection and evaluation module in each examination cycle of each patient to the mobile Internet terminal of each patient.

[0011] Furthermore, based on the brushing behavior parameters of each patient in each examination cycle, the specific detection and analysis method is as follows:

[0012] The toothbrushing parameters of each patient in each examination period are statistically obtained by using a smart toothbrush, and the toothbrushing parameters of each patient in each examination period include the toothbrushing frequency, single toothbrushing duration, number of interruptions, cleaning time of each tooth area and single toothbrushing pressure of each patient in each examination period;

[0013] According to their age and oral disease risk factors, each patient was divided into a high-risk group and a low-risk group. The standard brushing frequencies of the high-risk group and the low-risk group were denoted as fh and fl, respectively. For the brushing frequency score of each patient in the high-risk group in each examination period, the brushing frequency of each patient in each examination period was compared with the standard brushing frequency fh of the high-risk group, and the minimum value between the calculation result and 1 was taken; for the brushing frequency score of each patient in the low-risk group in each examination period, the brushing frequency of each patient in each examination period was compared with the standard brushing frequency fl of the low-risk group, and the minimum value between the calculation result and 1 was taken. The brushing frequency score of each patient in each examination period was counted and denoted as YLi; wherein, i=1,2,…,n, i represents the number of each examination period, and n represents the total number of each examination period number.

[0014] Furthermore, after summing up the single toothbrushing time of each patient in each inspection cycle, the summation result is compared with the toothbrushing frequency of each patient in each inspection cycle to obtain the average toothbrushing time of each patient in each inspection cycle, and then the standard deviation of the toothbrushing time of each patient in each inspection cycle is obtained according to the standard deviation calculation formula, which is recorded as σYT; the toothbrushing interruption ratio of each patient in each inspection cycle is obtained according to the comparison of the number of interruptions and the toothbrushing frequency of each patient in each inspection cycle, which is recorded as Yri;

[0015] Based on the above-mentioned standard deviation of the brushing time and the brushing interruption ratio of each patient in each examination cycle, a comprehensive calculation and analysis is performed to obtain the stability coefficient KT of the brushing time of each patient in each examination cycle.

[0016] Furthermore, each patient is divided into a healthy group and a group with periodontal problems according to their oral health status in each inspection cycle, and the recommended brushing time for the healthy group is recorded as tj, and the recommended brushing time for the group with periodontal problems is recorded as tw. The brushing time of each patient in each inspection cycle and the recommended brushing time are compared and analyzed to obtain the brushing time coefficient TS of each patient in each inspection cycle.

[0017] Finally, by multiplying the brushing time stability coefficient KT of each patient in each examination cycle and the brushing time coefficient TS of each patient in each examination cycle, the brushing time score of each patient in each examination cycle is obtained, which is recorded as YSi.

[0018] Furthermore, the cleaning time of each area of ​​the teeth for each patient in each examination cycle is summed up and divided by the single brushing time of each patient in each examination cycle to obtain the cleaning time ratio of each area of ​​the teeth for each patient in each examination cycle. Then, the standard deviation of the cleaning time ratio of each area of ​​the teeth for each patient in each examination cycle is obtained according to the standard deviation calculation formula, which is recorded as σQTj. At the same time, a cleaning difficulty weight coefficient is assigned according to the cleaning difficulty of each area of ​​the teeth, which is recorded as Wj; wherein j=1,2,...,m, j represents the number of each area of ​​the teeth, and m represents the total number of numbers of each area of ​​the teeth;

[0019] According to the formula Calculate the weighted standard deviation σQ of the proportion of tooth cleaning time for each patient in each examination cycle, and calculate and analyze the value of the weighted standard deviation of the proportion of tooth cleaning time for each patient in each examination cycle to obtain the cleaning time balance coefficient QE for each patient in each examination cycle;

[0020] By formula Calculate the ideal ratio of cleaning time for each patient in each inspection cycle, then take the absolute value of the difference between the cleaning time ratio of each area of ​​the teeth in each inspection cycle and the ideal cleaning time ratio, and get the difference between the cleaning time ratio of each area of ​​the teeth in each inspection cycle and the ideal cleaning time ratio, recorded as Δp, according to the formula Calculate the cleaning time deviation coefficient QP for each patient in each examination cycle;

[0021] Finally, by multiplying the cleaning time balance coefficient QE of each patient in each examination cycle and the cleaning time deviation coefficient QP of each patient in each examination cycle, the cleaning time score of each patient in each examination cycle is obtained, which is recorded as YTi.

[0022] Furthermore, the average toothbrushing pressure of each patient in each inspection cycle is calculated by summing up the single toothbrushing pressure of each patient in each inspection cycle and comparing it with the toothbrushing frequency of each patient in each inspection cycle. At the same time, the maximum pressure is extracted from the single toothbrushing pressure of each patient in each inspection cycle, and a safe pressure range is set for each cleaning area in each inspection cycle of each patient. If the average pressure of all cleaning areas in each inspection cycle of each patient is within the safe range, the average toothbrushing intensity score of each patient in each inspection cycle is YPi=1. Otherwise, the average toothbrushing intensity score of each patient in each inspection cycle is YPi=1. Where Fmaxj represents the maximum value of the safety pressure range of each clean area, and F l imj represents the maximum safety pressure limit of each clean area;

[0023] The toothbrushing frequency score, toothbrushing duration score, cleaning time score and average toothbrushing intensity score of each patient in each examination period were comprehensively calculated according to the formula The toothbrushing behavior health detection coefficient YX of each patient corresponding to each oral hygiene indicator in each examination cycle is calculated, where a1, a2, a3 and a4 represent the corresponding weight factors of the toothbrushing frequency score, toothbrushing time score, cleaning time score and average toothbrushing intensity, respectively, and a1+a2+a3+a4=1.

[0024] Furthermore, based on the status of each oral plaque of each patient in each examination cycle, the specific detection and analysis method is as follows:

[0025] The oral plaque status of each patient in each examination cycle is collected by using a high-definition oral camera. The oral plaque status of each patient in each examination cycle includes the plaque coverage area and the plaque distribution position, which are recorded as A and LA respectively. The plaque of each patient in each examination cycle is divided into early plaque and mature plaque by using a deep learning image analysis algorithm combined with multimodal image features, and the early plaque area and mature plaque area of ​​each patient in each examination cycle are obtained, which are recorded as AE and AM respectively.

[0026] The allowable total plaque coverage ratio of each patient in the good oral plaque state in each examination cycle is set as RA, the allowable early plaque ratio is ZA, and the allowable mature plaque ratio is CA;

[0027] The above parameters are combined to calculate the total plaque coverage state index RS, early plaque coverage state index ZS and mature plaque coverage state index CS of each patient in each examination cycle;

[0028] Finally, the plaque coverage area status index JS of each patient in each examination cycle was calculated using the formula JS=RS×ZS×CS.

[0029] Furthermore, by using a deep learning image analysis algorithm and combining multimodal image features, the oral cavity of each patient in each examination cycle is divided into various examination areas. According to the image analysis results, the probability of the plaque distribution position appearing in each examination area is obtained, which is recorded as JWk, and an importance weight is set for each plaque distribution area, which is recorded as αk; where k = 1, 2, ..., g, k represents the number of each examination area, and g represents the total number of the numbers of each examination area;

[0030] According to the formula Calculate the plaque distribution position status index JL for each patient in each examination cycle; finally, use the formula The oral plaque status detection coefficient JZ of each oral plaque status in each examination cycle of each patient is calculated, b1 and b2 respectively represent the set weight factors corresponding to the plaque coverage area status index and the plaque distribution position status index of each patient in each examination cycle, and b1+b2=1.

[0031] Furthermore, based on the oral and breath conditions of each patient in each examination cycle, the specific detection and analysis method is as follows:

[0032] The breath parameters of each patient at each time point in each examination cycle are collected and obtained by using a breath sensor. The breath parameters of each patient at each time point in each examination cycle include volatile sulfide concentration, ammonia concentration and organic acid concentration, which are recorded as NSi, NNi and NJ i respectively;

[0033] The volatile sulfide concentration threshold, ammonia concentration threshold and organic acid concentration threshold in the normal breath of each patient in each examination cycle are set and recorded as NSmax, NNmax and NJmax respectively.

[0034] Furthermore, the above parameters are combined to calculate the breath volatile sulfide state index PS, breath ammonia state index PN and breath organic acid state index PJ of each patient in each examination cycle;

[0035] Finally, the formula The oral breath state detection coefficient KZ of each patient in each examination cycle is calculated, c1, c2 and c3 respectively represent the corresponding weight factors of the breath volatile sulfide state index, breath ammonia state index and breath organic acid state index of each patient in each examination cycle, and c1+c2+c3=1.

[0036] Beneficial effects of the present invention:

[0037] 1. The present invention uses a smart toothbrush to accurately count parameters such as brushing frequency, duration, number of interruptions, cleaning time of each area, and brushing pressure, which can comprehensively and meticulously reflect the details of the patient's brushing behavior, provide a rich data basis for subsequent analysis, and classify the population according to age and oral disease risk factors to determine the standard brushing frequency, so as to calculate the brushing frequency score, which can specifically measure the brushing frequency compliance of different risk groups, so that the evaluation is more in line with the actual needs of individuals. The analysis of brushing time comprehensively considers the standard deviation, interruption ratio, and comparison with the recommended time to obtain the brushing time stability coefficient and coefficient score, which helps to urge patients to develop a stable and sufficient brushing habit, so as to better clean the oral cavity and prevent oral problems caused by insufficient or unstable brushing time. In terms of domain cleaning time, the cleaning time score is obtained by calculating the weighted standard deviation, coefficient of deviation, etc., which can guide patients to pay attention to the balanced cleaning of various areas of the teeth, avoid excessive cleaning of some areas and insufficient cleaning of other areas, and effectively reduce the risk of local plaque accumulation and oral diseases. Finally, the brushing behavior health detection coefficient is calculated by combining the brushing frequency score, duration score, cleaning time score and average brushing force score. The health level of the patient's brushing behavior is intuitively presented in a quantitative manner, which is convenient for medical staff and patients themselves to clearly understand the oral hygiene status, and provide a strong basis for personalized oral hygiene guidance and improvement measures, thereby significantly improving the scientificity, accuracy and effectiveness of oral health management, helping to prevent the occurrence of various oral diseases such as caries and periodontitis, and improving the overall oral health level.

[0038] 2. The present invention uses a high-definition oral camera to collect plaque coverage and distribution position, and uses a deep learning algorithm to distinguish early plaque from mature plaque, which can accurately and meticulously quantify the state of oral plaque, set the allowable ratio of different plaque types and calculate the coverage state index accordingly, which helps to clarify the degree of deviation between the patient's plaque condition and the health standard, and timely discover potential oral problem risks, such as caries, gingivitis and other diseases caused by excessive plaque. The calculation of the plaque distribution position state index takes into account the differences in the impact of plaque in different areas on oral health, which can make the evaluation more comprehensive and scientific, and focus on those areas with greater plaque hazards. It provides key information for subsequent targeted oral cleaning and treatment, and at the same time analyzes the patient's oral health and hygiene in combination with the brushing behavior detection and analysis module. The quality of brushing behavior directly affects the formation and accumulation of plaque. Good brushing behavior helps to control plaque, and the monitoring results of plaque status can in turn guide the improvement of brushing habits. The two complement each other and together constitute an important part of oral health management. They comprehensively protect oral hygiene from different angles, improve the overall oral health level, provide strong support for the prevention and early intervention of oral diseases, and help patients establish more scientific and effective oral care habits, and reduce the incidence and severity of oral diseases.

[0039] 3. The present invention uses a breath sensor to accurately collect breath parameters such as volatile sulfides, ammonia and organic acids, can accurately quantify the breath state, set the normal concentration threshold of each component and calculate the corresponding state index, and then obtain the oral breath state detection coefficient, which clearly reflects the difference between breath quality and health standards. It not only helps to detect breath odor problems in a timely manner and enhance social confidence, but also reveals potential hygiene hazards and disease risks in the oral cavity from the breath level, such as periodontal disease, digestive system problems, etc. that may cause abnormal breath; in addition, it is highly related to the brushing behavior detection and analysis module and the oral plaque detection and analysis module. Improper brushing and plaque accumulation may breed bacteria, and then produce bad breath components. The breath detection results can in turn encourage patients to pay attention to the development of oral hygiene habits and the control of plaque, forming a virtuous circle and jointly safeguarding oral health. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present invention will be further described below in conjunction with the accompanying drawings.

[0041] Figure 1 The present invention is a block diagram of an oral health monitoring system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work also fall within the scope of protection of the present invention.

[0043] As shown in the present invention and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular, but also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list, and the method or device may also include other steps or elements.

[0044] Although the present invention makes various references to certain modules in the system according to an embodiment of the present invention, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0045] The present invention uses a flow chart to illustrate the operations performed by the system according to an embodiment of the present invention. It should be understood that the preceding or following operations are not necessarily performed precisely in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0046] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0047] Embodiment 1:

[0048] See also Figure 1 As shown, an oral health detection system includes: a brushing behavior detection and analysis module, an oral plaque detection and analysis module, a breath state detection and analysis module, an oral health state detection and evaluation module, and a detection report generation terminal. The brushing behavior detection and analysis module detects and analyzes each brushing behavior parameter corresponding to each patient in each inspection cycle, and obtains the brushing behavior health detection coefficient of each patient corresponding to each brushing behavior parameter in each inspection cycle. The specific detection and analysis method is as follows:

[0049] The toothbrushing parameters of each patient in each examination period are statistically obtained by using a smart toothbrush, and the toothbrushing parameters of each patient in each examination period include the toothbrushing frequency, single toothbrushing duration, number of interruptions, cleaning time of each tooth area and single toothbrushing pressure of each patient in each examination period;

[0050] According to their age and oral disease risk factors, each patient was divided into a high-risk group and a low-risk group. The standard brushing frequencies of the high-risk group and the low-risk group were denoted as fh and fl, respectively. For the brushing frequency score of each patient in the high-risk group in each examination period, the brushing frequency of each patient in each examination period was compared with the standard brushing frequency fh of the high-risk group, and the minimum value between the calculation result and 1 was taken; for the brushing frequency score of each patient in the low-risk group in each examination period, the brushing frequency of each patient in each examination period was compared with the standard brushing frequency fl of the low-risk group, and the minimum value between the calculation result and 1 was taken. The brushing frequency score of each patient in each examination period was counted and denoted as YLi; wherein, i=1,2,…,n, i represents the number of each examination period, and n represents the total number of each examination period number.

[0051] After summing up the single brushing time of each patient in each inspection cycle, the summation result is compared with the brushing frequency of each patient in each inspection cycle to obtain the average brushing time of each patient in each inspection cycle, and then the standard deviation of the brushing time of each patient in each inspection cycle is obtained according to the standard deviation calculation formula, which is recorded as σYT; according to the number of interruptions and brushing frequency of each patient in each inspection cycle, the brushing interruption ratio of each patient in each inspection cycle is obtained, which is recorded as Yri;

[0052] According to the above-mentioned standard deviation of brushing time and brushing interruption ratio of each patient in each inspection cycle, a comprehensive calculation and analysis is performed to obtain the stability coefficient KT of brushing time of each patient in each inspection cycle. The specific calculation and analysis method is as follows: if the standard deviation of brushing time σYT of each patient in each inspection cycle is ≤0.15 minutes, and the brushing interruption ratio Yri of each patient in each inspection cycle is ≤0.2, then the stability coefficient KT of brushing time of each patient in each inspection cycle is 1; if the standard deviation of brushing time σYT of each patient in each inspection cycle is greater than 0.15 minutes, or the brushing interruption ratio Yri of each patient in each inspection cycle is greater than 0.2, then the stability coefficient KT of brushing time of each patient in each inspection cycle is

[0053] According to the oral health status of each patient in each inspection cycle, each patient is divided into a healthy group and a group with periodontal problems. The recommended brushing time for the healthy group is recorded as tj, and the recommended brushing time for the group with periodontal problems is recorded as tw. The brushing time of each patient in each inspection cycle and the recommended brushing time are compared and analyzed to obtain the brushing time coefficient TS of each patient in each inspection cycle. The specific comparative analysis method is as follows:

[0054] If the brushing time of each patient in each examination cycle is ≥ the recommended brushing time, then the brushing time coefficient TS of each patient in each examination cycle is 1. Otherwise, the value of the brushing time TS of each patient in each examination cycle is the ratio of the brushing time of each patient in each examination cycle to the recommended brushing time.

[0055] Finally, the brushing time score of each patient in each inspection cycle is obtained by multiplying the brushing time stability coefficient KT of each patient in each inspection cycle and the brushing time coefficient TS of each patient in each inspection cycle, which is recorded as YSi;

[0056] After summing up the cleaning time of each area of ​​the teeth for each patient in each examination cycle, divide it by the single brushing time of each patient in each examination cycle to get the proportion of cleaning time of each area of ​​the teeth for each patient in each examination cycle, and then use the standard deviation calculation formula to get the standard deviation of the proportion of cleaning time of each area of ​​the teeth for each patient in each examination cycle, denoted as σQTj, and at the same time, assign a cleaning difficulty weight coefficient according to the cleaning difficulty of each area of ​​the teeth, denoted as Wj; where j = 1, 2, ..., m, j represents the number of each area of ​​the teeth, and m represents the total number of numbers of each area of ​​the teeth. According to the formula The weighted standard deviation σQ of the proportion of tooth cleaning time for each patient in each examination cycle is calculated, and the cleaning time balance coefficient QE of each patient in each examination cycle is obtained according to the numerical value of the weighted standard deviation of the proportion of tooth cleaning time for each patient in each examination cycle. The specific calculation and analysis method is as follows:

[0057] If the weighted standard deviation of the proportion of tooth cleaning time for each patient in each examination cycle is σQ≤0.18, then the cleaning time balance coefficient QE of each patient in each examination cycle is 1. Otherwise, the cleaning time balance coefficient of each patient in each examination cycle is By formula Calculate the ideal ratio of cleaning time for each patient in each inspection cycle, then take the absolute value of the difference between the cleaning time ratio of each area of ​​the teeth in each inspection cycle and the ideal cleaning time ratio, and get the difference between the cleaning time ratio of each area of ​​the teeth in each inspection cycle and the ideal cleaning time ratio, recorded as Δp, according to the formula Calculate the cleaning time deviation coefficient QP for each patient in each examination cycle;

[0058] Finally, the cleaning time score of each patient in each examination cycle is obtained by multiplying the cleaning time balance coefficient QE of each patient in each examination cycle and the cleaning time deviation coefficient QP of each patient in each examination cycle, which is recorded as YTi;

[0059] The average toothbrushing pressure of each patient in each inspection cycle is calculated by summing up the single toothbrushing pressure of each patient in each inspection cycle and comparing it with the toothbrushing frequency of each patient in each inspection cycle. At the same time, the maximum pressure is extracted from the single toothbrushing pressure of each patient in each inspection cycle. A safe pressure range is set for each cleaning area in each inspection cycle of each patient. If the average pressure of all cleaning areas in each inspection cycle of each patient is within the safe range, the average toothbrushing intensity score of each patient in each inspection cycle is YPi=1. Otherwise, the average toothbrushing intensity score of each patient in each inspection cycle is Where Fmaxj represents the maximum value of the safety pressure range of each clean area, and F l imj represents the maximum safety pressure limit of each clean area;

[0060] The toothbrushing frequency score, toothbrushing duration score, cleaning time score and average toothbrushing intensity score of each patient in each examination period were comprehensively calculated according to the formula The toothbrushing behavior health detection coefficient YX of each patient corresponding to each oral hygiene indicator in each examination cycle is calculated, where a1, a2, a3 and a4 represent the corresponding weight factors of the toothbrushing frequency score, toothbrushing time score, cleaning time score and average toothbrushing intensity, respectively, and a1+a2+a3+a4=1.

[0061] In a specific embodiment, the present invention uses a smart toothbrush to accurately count parameters such as brushing frequency, duration, number of interruptions, cleaning time of each area, and brushing pressure, which can comprehensively and meticulously reflect the details of the patient's brushing behavior, provide a rich data basis for subsequent analysis, and classify the population according to age and oral disease risk factors to determine the standard brushing frequency, thereby calculating the brushing frequency score, which can specifically measure the brushing frequency compliance of different risk groups, so that the assessment is more in line with the actual needs of individuals. The analysis of brushing duration comprehensively considers the standard deviation, interruption ratio, and comparison with the recommended duration to obtain the brushing duration stability coefficient and coefficient score, which helps to urge patients to develop a stable and sufficient brushing habit, thereby better cleaning the oral cavity and preventing oral problems caused by insufficient or unstable brushing time. In terms of cleaning time for each area of ​​the teeth, the cleaning time score is obtained by calculating the weighted standard deviation, coefficient of deviation, etc., which can guide patients to pay attention to the balanced cleaning of each area of ​​the teeth, avoid excessive cleaning of some areas and insufficient cleaning of other areas, and effectively reduce the risk of local plaque accumulation and oral diseases. Finally, the brushing behavior health detection coefficient is calculated by combining the brushing frequency score, duration score, cleaning time score and average brushing force score. The health level of the patient's brushing behavior is intuitively presented in a quantitative manner, which is convenient for medical staff and patients themselves to clearly understand the oral hygiene status, and provide a strong basis for personalized oral hygiene guidance and improvement measures, thereby significantly improving the scientificity, accuracy and effectiveness of oral health management, helping to prevent the occurrence of various oral diseases such as caries and periodontitis, and improving the overall oral health level.

[0062] The oral plaque detection and analysis module detects and analyzes the oral plaque status of each patient in each examination cycle, and obtains the oral plaque status detection coefficient of each patient in each examination cycle. The specific detection and analysis method is as follows:

[0063] The oral plaque status of each patient in each examination cycle is collected by using a high-definition oral camera. The oral plaque status of each patient in each examination cycle includes the plaque coverage area and the plaque distribution position, which are recorded as A and LA respectively. The plaque of each patient in each examination cycle is divided into early plaque and mature plaque by using a deep learning image analysis algorithm combined with multimodal image features, and the early plaque area and mature plaque area of ​​each patient in each examination cycle are obtained, which are recorded as AE and AM respectively.

[0064] The allowable total plaque coverage ratio of each patient in the good oral plaque state in each examination cycle is set as RA, the allowable early plaque ratio is ZA, and the allowable mature plaque ratio is CA;

[0065] If the total plaque coverage area A≤RA for each patient in each examination cycle, the total plaque coverage state index RS=1 for each patient in each examination cycle; otherwise, RS=1-(A-RA) / RA;

[0066] If the early plaque coverage area AE of each patient in each examination cycle is ≤ ZA, then the early plaque coverage state index ZS of each patient in each examination cycle is 1, otherwise, ZS = 1-(A-ZA) / ZA;

[0067] If the mature plaque coverage area AM of each patient in each examination cycle is less than or equal to CA, then the mature plaque coverage state index CS of each patient in each examination cycle is 1; otherwise, CS is 1-(A-CA) / CA.

[0068] Finally, the plaque coverage area status index JS of each patient in each examination cycle was calculated by the formula JS = RS × ZS × CS;

[0069] Through deep learning image analysis algorithm, combined with multimodal image features, the oral cavity of each patient in each examination cycle is divided into various examination areas. According to the image analysis results, the probability of the plaque distribution position appearing in each examination area is obtained, denoted as JWk, and an importance weight is set for each plaque distribution area, denoted as αk; where k = 1, 2, ..., g, k represents the number of each examination area, and g represents the total number of numbers of each examination area;

[0070] According to the formula Calculate the plaque distribution position status index JL for each patient in each examination cycle; finally, use the formula The oral plaque status detection coefficient JZ of each oral plaque status in each examination cycle of each patient is calculated, b1 and b2 respectively represent the set weight factors corresponding to the plaque coverage area status index and the plaque distribution position status index of each patient in each examination cycle, and b1+b2=1.

[0071] In a specific embodiment, the present invention uses a high-definition oral camera to collect plaque coverage and distribution position, and uses a deep learning algorithm to distinguish early plaque from mature plaque, which can accurately and meticulously quantify the state of oral plaque, set the allowable ratio of different plaque types and calculate the coverage state index accordingly, which helps to clarify the degree of deviation between the patient's plaque condition and the health standard, and timely discover potential oral problem risks, such as caries, gingivitis and other diseases caused by excessive plaque. The calculation of the plaque distribution position state index takes into account the differences in the impact of plaque in different areas on oral health, which can make the evaluation more comprehensive and scientific, and focus on those plaques that are more harmful. The monitoring module can monitor the oral health of patients over a large area, providing key information for subsequent targeted oral cleaning and treatment. At the same time, it can analyze the oral health of patients in combination with the toothbrushing behavior detection and analysis module. The quality of toothbrushing behavior directly affects the formation and accumulation of plaque. Good toothbrushing behavior helps to control plaque, and the monitoring results of plaque status can in turn guide the improvement of toothbrushing habits. The two complement each other and together constitute an important part of oral health management. They comprehensively protect oral hygiene from different angles, improve the overall oral health level, provide strong support for the prevention and early intervention of oral diseases, help patients establish more scientific and effective oral care habits, and reduce the incidence and severity of oral diseases.

[0072] The breath state detection and analysis module detects and analyzes the various oral breath states of each patient in each examination cycle, and obtains the oral breath state detection coefficient of each patient in each examination cycle. The specific detection and analysis method is as follows:

[0073] The breath parameters of each patient at each time point in each examination cycle are collected and obtained by using a breath sensor. The breath parameters of each patient at each time point in each examination cycle include volatile sulfide concentration, ammonia concentration and organic acid concentration, which are recorded as NSi, NNi and NJ i respectively;

[0074] The volatile sulfide concentration threshold, ammonia concentration threshold and organic acid concentration threshold in the normal breath of each patient corresponding to each examination cycle are set, which are recorded as NSmax, NNmax and NJmax respectively. If the volatile sulfide concentration NSi of each patient in each examination cycle is ≤NSmax, then the volatile sulfide state index PS of each patient in each examination cycle is 1. Otherwise, PS is 1-(NSi-NSmax) / NSmax. Similarly, the breath ammonia state index and breath organic acid state index of each patient in each examination cycle are calculated, which are recorded as PN and PJ respectively.

[0075] Finally, the formula The oral breath state detection coefficient KZ of each patient in each examination cycle is calculated, c1, c2 and c3 respectively represent the corresponding weight factors of the breath volatile sulfide state index, breath ammonia state index and breath organic acid state index of each patient in each examination cycle, and c1+c2+c3=1.

[0076] In a specific embodiment, the present invention uses a breath sensor to accurately collect breath parameters such as volatile sulfides, ammonia and organic acids, can accurately quantify the breath state, set the normal concentration threshold of each component and calculate the corresponding state index, and then obtain the oral breath state detection coefficient, which clearly reflects the difference between breath quality and health standards. It not only helps to detect breath odor problems in a timely manner and improve social confidence, but also reveals potential health hazards and disease risks in the oral cavity from the breath level, such as periodontal disease, digestive system problems, etc. that may cause abnormal breath; in addition, it is also combined with the brushing behavior detection and analysis module and the oral plaque detection and analysis module. The modules are highly correlated. Improper brushing and plaque accumulation may breed bacteria, which in turn produce bad breath components. The breath detection results can in turn encourage patients to pay attention to the development of oral hygiene habits and the control of plaque, forming a virtuous circle and jointly safeguarding oral health. For example, if the breath detection coefficient is not good, combined with the analysis of brushing behavior and plaque status, it can accurately locate the root cause of the problem and formulate a comprehensive oral care and treatment plan, including strengthening brushing technique guidance, strengthening plaque removal measures, and screening for systemic diseases, etc., to comprehensively improve oral health levels, effectively prevent and improve oral diseases, and improve patients' quality of life.

[0077] The oral health status detection and evaluation module conducts a comprehensive evaluation and analysis based on the toothbrushing behavior health detection coefficient, plaque coverage area status index and oral breath status detection coefficient of each patient in each inspection cycle, and obtains the oral health status report of each patient in each inspection cycle. The specific evaluation and analysis method is as follows:

[0078] If YX≥0.8, it means that the toothbrushing behavior is excellent, which can effectively clean the oral cavity and reduce the risk of oral diseases. The generated oral health status report is: "The toothbrushing behavior is excellent during this period, and all aspects meet the oral health standards. The oral hygiene is good. If it is maintained, it can effectively prevent oral diseases."

[0079] If 0.6≤YX<0.8, it means that the toothbrushing behavior is good, but there are some details that can be improved, such as the cleaning time of some areas can be optimized, and the overall oral hygiene is less affected by the toothbrushing behavior. The generated oral health status report is: "The toothbrushing behavior in this inspection is good, with slight room for improvement. Maintaining the existing habits and optimizing them appropriately will help stabilize oral health";

[0080] If 0.4≤YX<0.6, it means that the toothbrushing behavior is moderate, but there are some problems, which may lead to incomplete cleaning of some areas. It is necessary to pay attention to and adjust the toothbrushing habits to maintain oral health. The generated oral health status report is: "The toothbrushing behavior in this period is at a moderate level, which is insufficient. The habit needs to be adjusted as recommended to prevent the risk of oral diseases from increasing";

[0081] If 0.2≤YX<0.4, it means that the toothbrushing behavior is poor, the oral cleaning effect is not good, and oral diseases are likely to occur. Problems such as toothbrushing method and frequency need to be corrected immediately. The generated oral health status report is: "The current toothbrushing behavior is poor, which poses a great threat to oral health. It is urgent to improve habits and strengthen oral cleaning to improve the situation";

[0082] If YX < 0.2, it means that the toothbrushing behavior is extremely poor, which poses a serious threat to oral health and urgently needs professional oral hygiene guidance and habit reshaping. The generated oral health status report is: "The toothbrushing behavior is extremely poor, and the oral hygiene risk is extremely high. Be sure to consult a doctor as soon as possible to make comprehensive improvements to avoid worsening of oral problems";

[0083] If JZ ≥ 2, it means that the plaque control is good, and the generated oral health status report is: "The oral hygiene is good. Continuing to maintain the current oral cleaning habits will help prevent diseases such as caries and periodontitis and maintain stable oral health";

[0084] If 1.5≤JZ<2, it means that the plaque condition is acceptable, and the generated oral health status report is: "There is a small amount of plaque accumulation, and you need to pay attention to the quality of daily brushing and increase the frequency of using cleaning tools such as dental floss to further optimize oral hygiene and reduce the risk of oral diseases";

[0085] If 1≤JZ<1.5, it means that the plaque problem is more obvious, and the generated oral health status report is: "There is a certain amount of plaque on the tooth surface, and oral hygiene measures should be strengthened. You can consider using mouthwash to assist in cleaning, and go to the oral institution as soon as possible to check whether there are early caries or periodontal problems."

[0086] If JZ is less than 1, it means that the plaque is seriously exceeded, and the generated oral health status report is: "Oral hygiene is worrying, which is very likely to cause a variety of oral diseases. Professional teeth cleaning and other cleaning treatments are required immediately. At the same time, correct bad brushing habits and strictly implement the correct oral cleaning process";

[0087] If KZ≥2, it means the breath is fresh and has no odor, and the generated oral health status report is: "The oral environment is good, reflecting that the oral hygiene is in place and the health is ideal. Continue to maintain good oral hygiene habits and a reasonable diet structure"; if 1.5≤KZ<2, it means that the breath is basically normal, and the generated oral health status report is: "There is a very slight odor, and you need to pay attention to oral hygiene details, such as cleaning the tongue coating, brushing your teeth regularly, using dental floss, and avoiding eating irritating foods to maintain good breath";

[0088] If 1≤KZ<1.5, it means that there is a problem with the bad breath, and the generated oral health status report is: "The bad breath is obvious, there is poor oral hygiene leading to bacterial growth, or there is a potential oral disease. It is necessary to strengthen oral cleaning, check for caries, periodontitis, etc., and adjust diet and daily routine";

[0089] If KZ is less than 1, it means that the bad breath is severe, and the generated oral health status report is: "There may be multiple oral diseases or systemic diseases as factors. You need to have a comprehensive oral and physical examination as soon as possible, and take comprehensive treatment measures to improve your breath and oral health."

[0090] The detection report generating terminal stores and sends the oral health status report generated by the oral health status detection and evaluation module in each examination cycle of each patient to the mobile Internet terminal of each patient.

[0091] The above formulas are all dimensionless and numerical calculations. The formula is a formula that is obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The size of the coefficient is to quantify each parameter to obtain a specific value. Regarding the size of the coefficient, it is acceptable as long as it does not affect the proportional relationship between the parameter and the quantified value.

[0092] In addition, those skilled in the art will appreciate that various aspects of the present invention may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present invention may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present invention may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0093] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention belongs. It should also be understood that terms such as those defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology and should not be interpreted in an idealized or extremely formal sense, unless explicitly defined as such herein.

[0094] The above is an explanation of the present invention and should not be considered as a limitation thereof. Although several exemplary embodiments of the present invention have been described, it will be readily appreciated by those skilled in the art that many modifications may be made to the exemplary embodiments without departing from the novel teachings and advantages of the present invention. Therefore, all such modifications are intended to be included within the scope of the present invention as defined in the claims. It should be understood that the above is an explanation of the present invention and should not be considered as being limited to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The present invention is defined by the claims and their equivalents.

Claims

1. An oral health detection system, characterized in that: It includes brushing behavior detection and analysis module, oral plaque detection and analysis module, breath status detection and analysis module, oral health status detection and evaluation module and detection report generation terminal: The toothbrushing behavior detection and analysis module detects and analyzes the toothbrushing behavior parameters of each patient in each examination cycle, and obtains the toothbrushing behavior health detection coefficient of each patient in each examination cycle; The oral plaque detection and analysis module detects and analyzes the oral plaque status of each patient in each examination cycle, and obtains the oral plaque status detection coefficient of each patient in each examination cycle; The breath state detection and analysis module detects and analyzes the various oral breath states of each patient in each examination cycle, and obtains the oral breath state detection coefficient of each patient in each examination cycle; The oral health status detection and evaluation module performs comprehensive evaluation and analysis based on the toothbrushing behavior health detection coefficient, plaque coverage area status index, and oral breath status detection coefficient of each patient in each examination cycle, and obtains the oral health status report of each patient in each examination cycle; The detection report generating terminal stores and sends the oral health status report generated by the oral health status detection and evaluation module in each examination cycle of each patient to the mobile Internet terminal of each patient.

2. An oral health detection system according to claim 1, characterized in that: Based on the detection and analysis of each patient's brushing behavior parameters in each examination cycle, the specific detection and analysis methods are as follows: The toothbrushing parameters of each patient in each examination period are statistically obtained by using a smart toothbrush, and the toothbrushing parameters of each patient in each examination period include the toothbrushing frequency, single toothbrushing duration, number of interruptions, cleaning time of each tooth area and single toothbrushing pressure of each patient in each examination period; According to the age and oral disease risk factors of each patient, each patient is divided into a high-risk group and a low-risk group. The standard brushing frequencies of the high-risk group and the low-risk group are recorded as fh and fl, respectively. For the brushing frequency score of each patient in the high-risk group in each examination cycle, the brushing frequency of each patient in each examination cycle is compared with the standard brushing frequency fh of the high-risk group, and the minimum value between the calculation result and 1 is taken; For the toothbrushing frequency scores of each patient in the low-risk population in each examination cycle, the toothbrushing frequency of each patient in each examination cycle is compared with the standard toothbrushing frequency fl of the low-risk population, and the minimum value between the calculation result and 1 is taken to count the toothbrushing frequency scores of each patient in each examination cycle, which are recorded as YLi; wherein, i=1,2,...,n, i represents the number of each examination cycle, and n represents the total number of numbers of each examination cycle.

3. An oral health detection system according to claim 2, characterized in that: After summing up the single toothbrushing time of each patient in each examination cycle, the summation result is compared with the toothbrushing frequency of each patient in each examination cycle to obtain the average toothbrushing time of each patient in each examination cycle. Then, the standard deviation of the toothbrushing time of each patient in each examination cycle is obtained according to the standard deviation calculation formula, which is recorded as σYT; According to the ratio of the interruption times and toothbrushing frequency of each patient in each examination cycle, the toothbrushing interruption ratio of each patient in each examination cycle is obtained, which is recorded as Yri; Based on the above-mentioned standard deviation of the brushing time and the brushing interruption ratio of each patient in each examination cycle, a comprehensive calculation and analysis is performed to obtain the stability coefficient KT of the brushing time of each patient in each examination cycle.

4. An oral health detection system according to claim 3, characterized in that: According to the oral health status of each patient in each inspection cycle, each patient is divided into a healthy group and a group with periodontal problems. The recommended brushing time for the healthy group is recorded as tj, and the recommended brushing time for the group with periodontal problems is recorded as tw. The brushing time of each patient in each inspection cycle and the recommended brushing time are compared and analyzed to obtain the brushing time coefficient TS of each patient in each inspection cycle. Finally, by multiplying the brushing time stability coefficient KT of each patient in each examination cycle and the brushing time coefficient TS of each patient in each examination cycle, the brushing time score of each patient in each examination cycle is obtained, which is recorded as YSi.

5. An oral health detection system according to claim 4, characterized in that: The cleaning time of each area of ​​the teeth for each patient in each inspection cycle is summed up and divided by the single brushing time of each patient in each inspection cycle to obtain the cleaning time ratio of each area of ​​the teeth for each patient in each inspection cycle. Then, the standard deviation of the cleaning time ratio of each area of ​​the teeth for each patient in each inspection cycle is obtained according to the standard deviation calculation formula, which is recorded as σQTj. At the same time, a cleaning difficulty weight coefficient is assigned according to the cleaning difficulty of each area of ​​the teeth, which is recorded as Wj; wherein j = 1, 2, ..., m, j represents the number of each area of ​​the teeth, and m represents the total number of numbers of each area of ​​the teeth; According to the formula Calculate the weighted standard deviation σQ of the proportion of tooth cleaning time for each patient in each examination cycle, and calculate and analyze the value of the weighted standard deviation of the proportion of tooth cleaning time for each patient in each examination cycle to obtain the cleaning time balance coefficient QE for each patient in each examination cycle; By formula Calculate the ideal ratio of cleaning time for each patient in each inspection cycle, then take the absolute value of the difference between the cleaning time ratio of each area of ​​the teeth in each inspection cycle and the ideal cleaning time ratio, and get the difference between the cleaning time ratio of each area of ​​the teeth in each inspection cycle and the ideal cleaning time ratio, recorded as Δp, according to the formula Calculate the cleaning time deviation coefficient QP for each patient in each examination cycle; Finally, by multiplying the cleaning time balance coefficient QE of each patient in each examination cycle and the cleaning time deviation coefficient QP of each patient in each examination cycle, the cleaning time score of each patient in each examination cycle is obtained, which is recorded as YTi.

6. An oral health detection system according to claim 5, characterized in that: The average toothbrushing pressure of each patient in each inspection cycle is calculated by summing up the single toothbrushing pressure of each patient in each inspection cycle and comparing it with the toothbrushing frequency of each patient in each inspection cycle. At the same time, the maximum pressure is extracted from the single toothbrushing pressure of each patient in each inspection cycle. A safe pressure range is set for each cleaning area in each inspection cycle of each patient. If the average pressure of all cleaning areas in each inspection cycle of each patient is within the safe range, the average toothbrushing intensity score of each patient in each inspection cycle is YPi=1. Otherwise, the average toothbrushing intensity score of each patient in each inspection cycle is Where Fmaxj represents the maximum value of the safe pressure range of each clean area, and Flimj represents the maximum safe pressure limit of each clean area; The toothbrushing frequency score, toothbrushing duration score, cleaning time score and average toothbrushing intensity score of each patient in each examination period were comprehensively calculated according to the formula The toothbrushing behavior health detection coefficient YX of each patient corresponding to each oral hygiene indicator in each examination cycle is calculated, where a1, a2, a3 and a4 represent the corresponding weight factors of the toothbrushing frequency score, toothbrushing time score, cleaning time score and average toothbrushing intensity, respectively, and a1+a2+a3+a4=1.

7. An oral health detection system according to claim 1, characterized in that: Based on the detection and analysis of each patient's oral plaque status in each examination cycle, the specific detection and analysis methods are as follows: The oral plaque status of each patient in each examination cycle is collected by using a high-definition oral camera. The oral plaque status of each patient in each examination cycle includes the plaque coverage area and the plaque distribution position, which are recorded as A and LA respectively. The plaque of each patient in each examination cycle is divided into early plaque and mature plaque by using a deep learning image analysis algorithm combined with multimodal image features, and the early plaque area and mature plaque area of ​​each patient in each examination cycle are obtained, which are recorded as AE and AM respectively. The allowable total plaque coverage ratio of each patient in the good oral plaque state in each examination cycle is set as RA, the allowable early plaque ratio is ZA, and the allowable mature plaque ratio is CA; The above parameters are combined to calculate the total plaque coverage state index RS, early plaque coverage state index ZS and mature plaque coverage state index CS of each patient in each examination cycle; Finally, the plaque coverage area status index JS of each patient in each examination cycle was calculated using the formula JS=RS×ZS×CS.

8. An oral health detection system according to claim 7, characterized in that: Through deep learning image analysis algorithm, combined with multimodal image features, the oral cavity of each patient in each examination cycle is divided into various examination areas. According to the image analysis results, the probability of the plaque distribution position appearing in each examination area is obtained, denoted as JWk, and an importance weight is set for each plaque distribution area, denoted as αk; where k = 1, 2, ..., g, k represents the number of each examination area, and g represents the total number of numbers of each examination area; According to the formula Calculate the plaque distribution position status index JL for each patient in each examination cycle; finally, use the formula The oral plaque status detection coefficient JZ of each oral plaque status in each examination cycle of each patient is calculated, b1 and b2 respectively represent the set weight factors corresponding to the plaque coverage area status index and the plaque distribution position status index of each patient in each examination cycle, and b1+b2=1.

9. The oral health detection system according to claim 1, characterized in that: Based on the detection and analysis of each patient's oral breath status in each examination cycle, the specific detection and analysis methods are as follows: The breath parameters of each patient at each time point in each examination cycle are collected and obtained by using a breath sensor. The breath parameters of each patient at each time point in each examination cycle include volatile sulfide concentration, ammonia concentration and organic acid concentration, which are recorded as NSi, NNi and NJi respectively; The volatile sulfide concentration threshold, ammonia concentration threshold and organic acid concentration threshold in the normal breath of each patient in each examination cycle are set and recorded as NSmax, NNmax and NJmax respectively.

10. An oral health detection system according to claim 9, characterized in that: The above parameters are combined to calculate the breath volatile sulfide state index PS, breath ammonia state index PN and breath organic acid state index PJ of each patient in each examination cycle; Finally, the formula The oral breath state detection coefficient KZ of each patient in each examination cycle is calculated, c1, c2 and c3 respectively represent the corresponding weight factors of the breath volatile sulfide state index, breath ammonia state index and breath organic acid state index of each patient in each examination cycle, and c1+c2+c3=1.

Images