Oral health dispenser based on oral health detection and image recognition

Abstract

The invention aims to provide an oral health dispenser based on oral health detection and image recognition, which comprises an oral health detector used for monitoring the oral health state of a patient, detecting the oral pH value, the bacterial level, the dental plaque accumulation, the gingival health condition and the oral temperature, the detector consists of at least one sensing element selected from a pH sensor, an electrochemical sensor, a temperature sensor, an optical sensor and a conductivity sensor; the oral cavity photographing instrument is used for photographing high-definition images of the interior of the oral cavity and teeth of the patient by adopting an endoscope, a 3D scanner or other camera equipment, and transmitting image data to the data analysis and fusion system in a wireless communication mode; the problem that an existing product cannot be individually blended according to the oral cavity condition of each patient, so that the treatment effect is limited is solved. How to automatically blend appropriate therapeutic molecules according to the health condition of a patient and accurately release the therapeutic molecules into the oral cavity of the patient is still the technical problem to be solved urgently.

Description

Technical Field

[0001] This invention is designed for the field of oral care, specifically including an oral health dispenser based on oral health detection and image recognition. Background Technology

[0002] Oral health is a vital component of overall health, affecting not only basic oral functions such as chewing food and speech, but also directly impacting overall well-being. Numerous medical studies have demonstrated a close correlation between oral health problems and many systemic diseases, including cardiovascular disease, diabetes, and respiratory illnesses. Therefore, early prevention and effective treatment of oral diseases are crucial for improving quality of life, reducing medical costs, and promoting overall social health.

[0003] Despite increasing attention being paid to oral health, existing oral care technologies and products still have certain shortcomings. The limitations of current technologies are mainly reflected in the following aspects:

[0004] I. Limitations of Traditional Oral Care Products and Methods

[0005] Traditional oral care products, such as manual toothbrushes, electric toothbrushes, and toothpaste, primarily rely on manual operation by the patient to clean their mouth. However, these products typically employ a standardized treatment approach, failing to be customized to the individual needs of each patient. Specifically, most existing toothpaste products focus on functions such as anti-caries, cavity prevention, and whitening, without adequately considering other oral health issues such as periodontal disease, gingivitis, and xerostomia. This results in oral care products that are ineffective and lack specificity.

[0006] Furthermore, current oral care methods rely on manual operation, and the effectiveness and cleanliness of brushing are closely related to factors such as the patient's brushing technique, frequency, and duration. Although electric toothbrushes can provide relatively effective cleaning, they still do not address the issue of individualized needs. Patients' oral conditions vary greatly, and their oral health problems (such as cavities, tartar, and gingivitis) also differ, meaning that a single toothpaste and brushing method may not meet their specific treatment needs.

[0007] II. Shortcomings of existing oral health testing technologies

[0008] Oral health monitoring and assessment is a crucial part of oral care. Oral health monitoring encompasses multiple aspects, including dental health, gum condition, bacterial levels, and oral pH. While existing oral health testing technologies have made some progress, certain technical bottlenecks and limitations in application remain.

[0009] Common methods for oral health testing include:

[0010] Oral pH testing: This method measures the acidity or alkalinity of the oral cavity to assess the health of the oral environment. Changes in pH are closely related to tooth erosion and the formation of cavities. Although pH-based oral health monitoring devices are available on the market, these devices generally lack real-time capabilities and often only monitor a single indicator, making it difficult to comprehensively reflect the overall state of oral health.

[0011] Bacterial level testing: This involves collecting and analyzing bacterial samples from the oral cavity to determine the presence of pathogenic microorganisms. However, current technologies generally suffer from problems such as bulky equipment, complex operation, and usually require professional intervention to complete the test, making it difficult for patients to perform self-testing in their daily lives.

[0012] Dental plaque accumulation monitoring: Dental plaque accumulation is a major cause of oral diseases such as tooth decay and gingivitis. Although some dental plaque detection tools exist, such as staining agents and test strips, these tools are often disposable, making continuous monitoring impossible and difficult to assess the specific types and quantities of bacteria.

[0013] Oral temperature and other physiological parameter monitoring: Some oral health monitoring devices attempt to combine monitoring methods such as temperature sensors and electrochemical sensors, but the accuracy and stability of these devices are still problematic, and they usually only provide limited physiological data.

[0014] Existing oral health monitoring technologies often rely on single detection methods and lack a multi-dimensional, comprehensive assessment system. Furthermore, most devices are bulky, cumbersome to operate, and inconvenient, making it difficult for patients to effectively monitor their oral health in daily life. Therefore, how to achieve comprehensive oral health detection and real-time assessment remains a pressing technical challenge in the field of oral care.

[0015] III. Applications and Challenges of Oral Image Recognition Technology

[0016] With the development of computer vision technology, the application of oral image recognition technology in oral health management has gradually attracted attention. Imaging patients' oral conditions using high-definition image acquisition technologies (such as endoscopes and 3D scanners) and combining this with image recognition algorithms to assess dental health has become a promising technological solution.

[0017] However, although oral image recognition technology has improved the early diagnosis of oral problems to some extent, it still faces the following challenges:

[0018] Image quality and acquisition issues: Current oral image acquisition tools, such as endoscopes and 3D scanners, are expensive and complex to operate, making them inconvenient for patients to use daily. Furthermore, due to the confined space within the oral cavity, the captured images are often affected by factors such as lighting, angle, and equipment stability, resulting in low image quality and impacting subsequent analysis results.

[0019] Algorithm Accuracy and Universality: While technologies such as deep learning and convolutional neural networks (CNNs) have made some progress in oral image analysis, they still suffer from insufficient accuracy and poor universality. Oral diseases are diverse, and patients' oral conditions vary greatly. Existing algorithms do not always perform ideally on oral images from different patients, especially in the early stages of disease, where image recognition technology may fail to accurately capture subtle health issues.

[0020] Data fusion and comprehensive analysis issues: Existing oral image recognition systems typically focus on the analysis of individual images, lacking fusion analysis with data from other sensors (such as pH value, bacterial levels, etc.). Single image analysis cannot fully reflect the complex state of oral health, resulting in limitations in treatment planning.

[0021] Therefore, how to integrate image recognition technology with other sensor data to form a comprehensive and accurate oral health assessment system remains an important direction for current technological research.

[0022] IV. Personalized and precise treatment plans

[0023] Most current oral care products and treatments rely on single drug ingredients or methods, lacking precise formulation tailored to individual oral health conditions. For example, most toothpaste products on the market are not customized based on the user's actual health condition, and different brands of toothpaste have overlapping functions, lacking sufficient differentiation. Moreover, existing oral care products often focus on a single aspect of treatment, such as anti-caries or anti-inflammation, lacking comprehensive treatment solutions.

[0024] Oral health needs vary greatly among patients. Some may require stronger antibacterial capabilities, while others require desensitizing, restorative, or anti-inflammatory functions. Existing products cannot be personalized to each patient's oral condition, leading to limitations in treatment effectiveness. How to automatically adjust the appropriate therapeutic molecules based on the patient's health condition and precisely deliver them into the patient's mouth remains a pressing technical challenge. Summary of the Invention

[0025] The purpose of this invention is to provide an oral health dispenser based on oral health detection and image recognition. This invention aims to address the limitations of existing products in providing personalized treatment based on each patient's oral condition, thus hindering treatment effectiveness. How to automatically dispense appropriate therapeutic molecules based on the patient's health status and precisely deliver them into the patient's oral cavity remains a pressing technical challenge.

[0026] To achieve the above objectives, the present invention provides the following technical solution: an oral health dispenser based on oral health detection and image recognition, comprising:

[0027] An oral health detector is used to monitor a patient's oral health status, including oral pH, bacterial levels, plaque accumulation, gingival health, and oral temperature. The detector consists of at least one sensing element selected from pH sensors, electrochemical sensors, temperature sensors, optical sensors, and conductivity sensors.

[0028] Oral imaging instruments use endoscopes, 3D scanners or other camera devices to capture high-definition images of the inside of a patient's mouth and teeth, and transmit the image data to a data analysis and fusion system via wireless communication;

[0029] The data analysis and fusion system receives data from oral health detectors and oral imaging instruments, and generates personalized oral health reports through image recognition and sensor data fusion analysis. The system includes the following steps:

[0030] Deep learning algorithms are used to analyze tooth images to detect oral problems such as tooth damage, cavities, tartar, and gingivitis.

[0031] Calculate oral health index using sensor data

[0032] HCI=w1·pH+w2·BacterialLevel+w3·PlaqueLevel+w4·Gum Health+w5·Temperature

[0033] Among them, w1, w2, w3, and w4 are the corresponding weighting coefficients, which are dynamically adjusted according to the specific condition of the patient.

[0034] Personalized treatment plans are generated based on health indices and image recognition results.

[0035] The dispenser is used to mix therapeutic molecular capsules with toothpaste and automatically dispenses and releases the capsules based on data analysis, thereby providing personalized oral care.

[0036] Furthermore, the oral health detector includes a pH sensor, a temperature sensor, and an electrochemical sensor to monitor changes in oral pH, temperature, and bacterial levels, and transmits the sensor data to a data analysis and fusion system via wireless communication.

[0037] Furthermore, the oral imaging instrument includes an endoscope and a 3D scanner, which are used to capture real-time images of the patient's oral cavity and three-dimensional data of the teeth. The images are then transmitted to a data analysis and fusion system via Wi-Fi or Bluetooth. The system can process the images, extract the characteristic information of the teeth, and classify them.

[0038] Furthermore, the data analysis and fusion system uses a convolutional neural network (CNN) algorithm for image processing to identify conditions such as dental caries, tartar, and gingivitis. The loss function of the convolutional neural network is:

[0039]

[0040] Where yi is the actual label, y^i is the model prediction, λ is the regularization parameter, and Wj is the weight of the convolutional network.

[0041] Furthermore, the dispenser contains multiple therapeutic molecule balloons, each containing antibacterial, anti-inflammatory, repairing, and desensitizing molecules to improve the patient's oral health. The release rate of the therapeutic molecule balloons is calculated using the following formula:

[0042] D release =f(HCI,C Plaque C Gum T Bacteria )

[0043] Where Drelease represents the amount of therapeutic molecules released, HCI represents the oral health index, and C... Plaque C Gum ,T Bacteri The corresponding assessment values ​​for dental plaque, gingival health, and bacterial levels are provided by the function f(), which is dynamically adjusted by the system based on the patient's oral health status.

[0044] Furthermore, the dispenser includes an automated mixing system to ensure thorough mixing of the therapeutic molecular capsule with the toothpaste and to dispense the therapeutic dose as needed. The mixing system controls the dosage in the following ways:

[0045]

[0046] Wherein, Max Dose is the maximum dose, HCImax is the maximum value of the oral health index, and HCI is the oral health index calculated in real time.

[0047] Furthermore, the data analysis and fusion system includes a cloud synchronization function, which can synchronize analysis results to the user's mobile terminal application, providing real-time oral health reports and care recommendations. The cloud synchronization system uses encryption protocols to ensure the security of user data.

[0048] Furthermore, the dispenser synchronizes treatment results to a cloud server via wireless communication methods such as Bluetooth or Wi-Fi. Users can query oral health data and historical care records through mobile devices. The cloud server provides personalized long-term care recommendations to users through machine learning models based on user data.

[0049] Furthermore, the data analysis and fusion system provides real-time feedback based on changes in the patient's oral health status, helping users adjust their brushing techniques or oral care methods, and optimizing future treatment plans through analysis of the patient's historical data.

[0050] Furthermore, the formulation of the therapeutic molecular balloon is based on changes in the user's oral health condition, and the appropriate proportion of therapeutic molecules is determined through data analysis to achieve the best antibacterial, anti-inflammatory, or restorative effects.

[0051] The oral health dispenser based on oral health detection and image recognition provided by this invention has the following significant beneficial effects:

[0052] Personalized oral care: This system monitors patients' oral health status in real time (such as pH level, bacterial levels, and gum health), and analyzes high-definition dental images to generate a personalized oral health report for each user. It also provides customized oral care plans based on specific health conditions. This personalized care can more effectively improve patients' oral health and reduce the limitations of traditional "uniform care" methods.

[0053] Improving the prevention and treatment of oral problems: Through the analysis of oral images using deep learning algorithms, common oral problems such as tooth decay, tartar, and gingivitis can be identified at an early stage. Based on sensor data, the Oral Health Index (HCI) is calculated to generate an accurate health assessment. Combined with a therapeutic molecular balloon delivery system, targeted antibacterial, anti-inflammatory, and restorative treatments can be provided, effectively improving treatment outcomes and preventing the occurrence or aggravation of oral diseases.

[0054] Intelligent dispensing and automation: This dispenser can automatically dispense therapeutic molecular capsules and toothpaste based on the patient's real-time health data, and adjust the treatment dosage according to changes in health indices, ensuring that the treatment plan used by the patient always matches their oral health condition, avoiding over- or under-dosing, and improving the precision and effectiveness of care.

[0055] Cloud-based data synchronization and remote management: Through cloud synchronization, patients' oral health data can be transmitted to the cloud in real time and synchronized to the user's mobile application. Patients can view oral health reports and care recommendations at any time, and the cloud server provides personalized long-term care plans based on user data. Furthermore, data encryption protocols ensure user privacy and security.

[0056] Optimize oral care practices: Based on patients' historical data and real-time feedback, the system can help patients adjust their brushing techniques, oral care methods, or treatment plans, thereby improving their oral hygiene. Patients can monitor their oral health in real time through smart devices, avoiding the aggravation of oral problems due to habitual incorrect care methods.

[0057] Convenient, easy to use, and efficient: By integrating multiple functional modules such as detectors, imaging instruments, data analysis systems, and dispensers, the system of this invention is easy to operate and has comprehensive functions, providing users with an efficient and convenient oral care experience, reducing the cumbersome steps and time consumption in traditional oral examinations and care.

[0058] Promoting the intelligent development of oral health management: This invention integrates advanced technologies such as smart hardware, data analysis, and deep learning algorithms to achieve the automation and intelligence of oral health management, promotes technological innovation in the oral care industry, and helps improve the overall health level of society. In particular, it has an important health promotion effect on groups that are susceptible to oral diseases, such as the elderly and children.

[0059] In summary, this invention is not only technically innovative, but also significantly improves the efficiency and accuracy of oral health management in practice, providing users with higher quality and safer oral care services. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of the overall structure of the present invention. Detailed Implementation

[0061] The present invention will be described in detail below with reference to specific embodiments.

[0062] The entire system of this invention is divided into a hardware layer and a software layer. The hardware layer includes multiple sensors, imaging instruments, treatment dispensing modules, and other components, while the software layer is mainly responsible for data processing, analysis, fusion, and decision support. Hardware layer: Oral health detector module (pH sensor, temperature sensor, bacterial detection sensor, dental plaque detection sensor), oral imaging instrument module (endoscope, 3D scanner), personalized treatment dispensing module (treatment molecule dispensing, dosage control, and release); Software layer: Data analysis and fusion system (image processing and deep learning, sensor data fusion).

[0063] Firstly, at the hardware level, pH sensors, temperature sensors, bacterial detection sensors, and dental plaque detection sensors are deployed in the sensor array of the oral health monitoring instrument. These sensors can operate simultaneously and transmit the collected health data to the system's central processing unit (CPU) for processing via data cables or wireless communication modules (such as Bluetooth or Wi-Fi).

[0064] These sensor data are independent of each other, but need to be integrated through sensor data fusion algorithms to ensure the accuracy of oral health assessment. The data from each sensor is time-sensitive, and real-time performance is crucial; therefore, the connection method between these sensors and the system ensures high-frequency data uploads.

[0065] Sensors for pH, temperature, and bacterial concentration can be connected to a microcontroller (MCU) via an analog-to-digital converter (ADC) to convert analog signals into digital signals, which are then transmitted to the central processing system.

[0066] An endoscope uses a high-definition camera to acquire images of the inside of the oral cavity. These images are transmitted in real time to a central processing unit or mobile device via USB or wireless transmission. The main task of the endoscope is to provide high-definition images for further analysis by the backend image processing module.

[0067] A 3D scanner scans the oral cavity structure and generates a three-dimensional model, typically using laser or structured light technology. The scan data is processed in real time via Wi-Fi or Bluetooth connected to the system's central control unit.

[0068] Secondly, at the software layer, image data input is obtained from endoscopes and 3D scanners and transmitted to the central processing unit via dedicated interfaces (USB, Wi-Fi, etc.).

[0069] Deep learning algorithms such as Convolutional Neural Networks (CNNs) are used to analyze images and identify oral health problems, such as cavities, tartar, and gingivitis. Image data is fed into a deep learning module for feature extraction and classification, and the results (health status, problem type, etc.) are output.

[0070] Image processing and sensor data (such as pH, temperature, bacterial concentration, etc.) need to be fused. Using data fusion algorithms (such as weighted averaging or Bayesian methods), the final health assessment result (Oral Health Index, HCI) is derived by integrating various sensor and image data.

[0071] The fusion of image data and sensor data usually occurs in the software processing layer, located within the central processing unit. Image data processing and sensor data fusion processing are performed in parallel, and the final result is output according to a specific logical order (processing the image first, then fusing it with the sensor data).

[0072] Finally, regarding data fusion, at the hardware layer, sensors such as pH, temperature, and bacterial concentration collect data in real time. The system uses sensor fusion algorithms to perform weighted averaging or multimodal fusion of these data to form a comprehensive health assessment index (Oral Health Index, HCI). This index integrates sensor data and image analysis results.

[0073] The specific data fusion algorithm involves transmitting sensor data to the data fusion module via a central processing unit (MCU or processor), using a fusion algorithm (e.g., Kalman filter, weighted average method) to weight the sensor data, and then correlating it with the image processing results to obtain the final comprehensive evaluation result.

[0074] For the personalized treatment dispenser module, the therapeutic molecule ratio is based on the oral health assessment (HCI) results. The personalized treatment module automatically selects appropriate molecules (such as antibacterial agents, anti-inflammatory agents, or repair molecules) according to the preset treatment plan. The treatment plan will be recommended by intelligent algorithms, dynamically adjusting the drug formulation based on the individual's oral health status.

[0075] The therapeutic molecules are precisely dispensed within the oral treatment device via a micropump and pneumatic system. Based on the assessment results, the system uses a central processing unit (MCU) to precisely control the dosage of the medication and releases it through devices such as micropumps and pneumatic valves.

[0076] Dosage calculation automatically determines the required therapeutic dose based on the HCI value. The system uses an algorithm to map the HCI value to a control value for the dose, thereby precisely dispensing the therapeutic molecules.

[0077] The dosage delivery mechanism utilizes a micropump and solenoid valve system to achieve dosage control precision down to the microgram level. Therapeutic molecules are released via an oral sprayer or delivery tube, directly reaching the desired site within the oral cavity.

[0078] The oral health detector module continuously collects data, while the endoscope and 3D scanner continuously capture images. The data is uploaded in real-time to the central processing unit for processing. Image data and sensor data are comprehensively evaluated through a data fusion system to generate the Oral Health Index (HCI). Based on the HCI value, the personalized treatment module calculates the required treatment dose, dispenses the necessary molecules, and precisely releases the drug via a micropump.

[0079] The HCI assessment algorithm is continuously adjusted based on user feedback (such as symptom improvement and gum recovery) to optimize the treatment plan.

[0080] All data transmissions are conducted in real time via Wi-Fi or Bluetooth, allowing users to view data, receive health advice, and track treatment progress anytime via a mobile app.

[0081] Hardware module connectivity: The oral health detector (sensor array) and oral imaging instruments (endoscope, 3D scanner) operate in parallel. Sensor and image data are transmitted to the central processing unit via wireless communication (such as Bluetooth, Wi-Fi). Sensor and image data are collected independently and then fused.

[0082] Data Analysis and Fusion: The data fusion system performs in-depth processing on sensor data and image data to ultimately derive the Oral Health Index (HCI).

[0083] Personalized treatment module: Treatment plans are automatically adjusted based on HCI values, and treatment molecules are released via a micro-pump system. The treatment process is adjusted based on feedback to ensure optimal results.

[0084] This modular, hierarchical architecture ensures the system's flexibility, scalability, and real-time responsiveness.

[0085] One embodiment involves sensor selection. The pH sensor employs a solid electrode method, determining the pH value by measuring the potential difference between the electrode and oral fluid. This sensor typically consists of platinum or gold electrodes, offering good stability and a long service life.

[0086] Specific implementation:

[0087] Sensitivity: 0.1 pH unit.

[0088] Measurement range: pH 3.0~9.0.

[0089] Measurement accuracy: ±0.1 pH.

[0090] Signal processing: The measured signal is converted into a digital signal using a potentiometer and an analog-to-digital converter (ADC).

[0091] formula:

[0092] Vref is the reference voltage, Vsensor is the sensor's output voltage, and S is the sensor's sensitivity.

[0093] Temperature sensors employ either thermistors (RTDs) or thermocouples. Potential oral infections or inflammations are detected by measuring changes in oral temperature.

[0094] Specific implementation:

[0095] Accuracy requirement: ±0.1℃.

[0096] Measurement range: 25℃~42℃.

[0097] Response time: <1 second.

[0098] formula

[0099] Vsensor is the output voltage of the sensor, V0 is the zero-point voltage, and K is the sensitivity constant of the thermistor.

[0100] Bacterial detection uses electrochemical sensors to estimate bacterial concentration by detecting chemicals produced by bacteria in the oral cavity, such as lactic acid or volatile sulfur compounds.

[0101] Specific implementation:

[0102] Principle: Volatile gases are detected through an electrochemical reaction and converted into electrical signals.

[0103] Sensitivity: 0.01μM.

[0104] Response time: <2 seconds.

[0105] Formula: I = k·[C]

[0106] I represents the current signal, k represents the sensor constant, and [C] represents the bacterial concentration.

[0107] Dental plaque detection utilizes ultraviolet fluorescence technology, employing sensors to detect the presence of plaque on the tooth surface. Plaque deposition is typically accompanied by fluorescence changes, and the extent of plaque can be estimated based on the fluorescence intensity.

[0108] Specific implementation:

[0109] Excitation wavelength: 405nm.

[0110] Detection wavelength: 470nm.

[0111] Sensitivity: It can detect slight plaque deposits.

[0112] An endoscope uses a high-definition camera to acquire images of the inside of the oral cavity; LED-illuminated endoscopes are commonly used. Endoscopic devices are equipped with high-definition lenses, providing 1920x1080 pixel images, suitable for acquiring detailed images of teeth, gums, and oral soft tissues.

[0113] Specific implementation:

[0114] Lens: 120-degree wide-angle.

[0115] Light source: Adjustable LED light source.

[0116] Image sensor: CMOS sensor, supporting dynamic exposure adjustment.

[0117] 3D scanners use laser scanning or structured light technology to scan the inside of the oral cavity and generate a three-dimensional model. They are typically used to accurately obtain the shape of teeth and gums.

[0118] Specific implementation:

[0119] Resolution: 0.1mm.

[0120] Scanning time: within 10 seconds.

[0121] Data format: The output is in STL or OBJ format and can be used for subsequent analysis.

[0122] The image processing section primarily uses convolutional neural networks (CNNs) to analyze oral images. Through a trained CNN model, the system can automatically detect problems such as tooth decay, tartar, and gingivitis.

[0123] CNN model architecture:

[0124] Input layer: Input image (size 224x224x3).

[0125] Convolutional layers: Features are extracted through multiple convolutional layers.

[0126] Pooling layer: Use max pooling to reduce feature dimensionality.

[0127] Fully connected layer: Extracts oral health features.

[0128] Output layer: Outputs classification results (healthy / caries / tartar, etc.).

[0129] Training data:

[0130] Use publicly available oral disease image datasets, such as the Oral Disease Image Dataset, to train a model either through transfer learning or from scratch.

[0131] Loss function:

[0132] Using the cross-entropy loss function Optimize the classification task; where yiy is the actual label and y^i is the predicted probability output by the model.

[0133] Sensor data and image analysis results are integrated into a unified health assessment index, the Oral Health Index (HCI). Sensor data (such as pH, temperature, and bacterial concentration) are combined with image analysis results, with weighting coefficients adjusted based on the accuracy of each data type.

[0134] Simultaneously, a weighted average method is used to fuse sensor data and image analysis results.

[0135] HCI=w1·pH+w2·Temp+w3·Bacteria+w4·Image

[0136] ImagepH, Temp, Bacteria, and Image represent the respective metrics, while w1, w2, w3, and w4 are the weighting coefficients for the data types.

[0137] The dispenser automatically mixes the required therapeutic molecules based on the oral health index (HCI). These therapeutic molecules can be antibacterial, anti-inflammatory, or repairing molecules.

[0138] Therapeutic molecular types:

[0139] Antibacterial agents: chlorhexidine, hydrogen peroxide, etc.

[0140] Anti-inflammatory drugs: chlorothiazide, aspirin, etc.

[0141] Repair: Calcium and phosphorus compounds, vitamin D, etc.

[0142] Based on the measured HCI value each time, the dispenser calculates the required treatment dose and precisely releases the corresponding dose of therapeutic molecules through a micro-pump and solenoid valve.

[0143] Dosage control algorithm:

[0144] Dosage formula:

[0145]

[0146] Where Max_Dose is the maximum therapeutic dose, HCI is the current health assessment value, and HCImax is the ideal health index.

[0147] The therapeutic molecules are released via a micropump and pneumatic system, ensuring precise control of the dosage for each treatment. The system automatically adjusts itself through feedback mechanisms, such as pump operating time and flow rate.

[0148] The entire system connects to a cloud server via a wireless network (Wi-Fi or Bluetooth), and all data is synchronized to the cloud database in real time. Users can view oral health reports and historical records through a mobile application. The cloud also provides data analysis services to further optimize treatment plans.

[0149] The cloud management system is responsible for storing user data, analyzing treatment effects, and dynamically adjusting treatment plans based on changes in the patient's health.

[0150] The mobile application provides users with functions such as viewing oral health reports, managing treatment plans, and receiving care suggestions. Users can view oral health data in real time and ensure timely feedback.

[0151] This system achieves precise, real-time oral health management through multiple sensors, advanced image analysis technology, and a personalized treatment planning mechanism. Every technical detail is closely linked to ensure efficient and accurate personalized oral health care.

[0152] The circuits and controls involved in this invention are all existing technologies and will not be described in detail here.

[0153] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An oral health dispenser based on oral health detection and image recognition, characterized in that, include: An oral health detector is used to monitor the oral health status of a patient, including oral pH value, bacterial level, dental plaque accumulation, gingival health status and oral temperature. The detector consists of at least one sensing element selected from pH sensor, electrochemical sensor, temperature sensor, optical sensor and conductivity sensor. Oral imaging instruments use endoscopes, 3D scanners or other camera devices to capture high-definition images of the inside of a patient's mouth and teeth, and transmit the image data to a data analysis and fusion system via wireless communication; A data analysis and fusion system is used to receive data from oral health detectors and oral imaging instruments, and generate personalized oral health reports through image recognition and sensor data fusion analysis. The system includes the following steps: Deep learning algorithms are used to analyze tooth images to detect oral problems such as tooth damage, cavities, tartar, and gingivitis. Calculate oral health index using sensor data HCI=w1·pH+w2·Bacterial Level+w3·Plaque Level+w4·Gum Health+w5·Temperature Among them, w1, w2, w3, and w4 are the corresponding weighting coefficients, which are dynamically adjusted according to the specific condition of the patient. Personalized treatment plans are generated based on health indices and image recognition results. The dispenser is used to mix therapeutic molecular capsules with toothpaste and automatically dispenses and releases the capsules based on data analysis, thereby providing personalized oral care.

2. The oral health dispenser based on oral health detection and image recognition according to claim 1, characterized in that, The oral health detector includes a pH sensor, a temperature sensor, and an electrochemical sensor, used to monitor changes in oral pH, temperature, and bacterial levels, and transmits the sensor data to a data analysis and fusion system via wireless communication.

3. The oral health dispenser based on oral health detection and image recognition according to claim 1, characterized in that, The oral imaging instrument includes an endoscope and a 3D scanner, used to capture real-time images of the patient's oral cavity and three-dimensional data of teeth, and transmit the images to a data analysis and fusion system via Wi-Fi or Bluetooth. The system can process the images, extract the feature information of the teeth, and classify them.

4. The oral health dispenser based on oral health detection and image recognition according to claim 1, characterized in that, The data analysis and fusion system uses a convolutional neural network (CNN) algorithm for image processing to identify conditions such as dental caries, tartar, and gingivitis. The loss function of the convolutional neural network is: Where yi is the actual label, y^i is the model prediction, λ is the regularization parameter, and Wj is the weight of the convolutional network.

5. An oral health dispenser based on oral health detection and image recognition according to claim 1, characterized in that, The dispenser contains multiple therapeutic molecule balloons, each containing antibacterial, anti-inflammatory, repairing, and desensitizing therapeutic molecules to improve the patient's oral health. The release amount of each therapeutic molecule balloon is calculated using the following formula: D release =f(HCI,C Plaque ,C Gum ,T Bacteria ) Where Drelease represents the amount of therapeutic molecules released, HCI represents the oral health index, and C... Plaque C Gum ,T Bacteri The corresponding assessment values ​​for dental plaque, gingival health, and bacterial levels are provided by the function f(), which is dynamically adjusted by the system based on the patient's oral health status.

6. An oral health dispenser based on oral health detection and image recognition according to claim 1, characterized in that, The dispenser includes an automated mixing system to ensure thorough mixing of the therapeutic molecular capsules with the toothpaste and to dispense the therapeutic dose as needed. The mixing system controls the dosage in the following manner: Wherein, Max Dose is the maximum dose, HCImax is the maximum value of the oral health index, and HCI is the oral health index calculated in real time.

7. An oral health dispenser based on oral health detection and image recognition according to claim 2, characterized in that, The data analysis and fusion system includes a cloud synchronization function, which can synchronize the analysis results to the user's mobile terminal application, providing real-time oral health reports and care suggestions. The cloud synchronization system uses an encryption protocol to ensure the security of user data.

8. An oral health dispenser based on oral health detection and image recognition according to claim 1, characterized in that, The dispenser synchronizes treatment results to a cloud server via wireless communication methods such as Bluetooth or Wi-Fi. Users can query oral health data and historical care records through mobile devices. The cloud server provides personalized long-term care recommendations to users through machine learning models based on user data.

9. An oral health dispenser based on oral health detection and image recognition according to claim 8, characterized in that, The data analysis and fusion system provides real-time feedback based on changes in the patient's oral health status, helping users adjust their brushing techniques or oral care methods, and optimize future treatment plans through analysis of the patient's historical data.

10. An oral health dispenser based on oral health detection and image recognition according to claim 9, characterized in that, The formulation of the therapeutic molecular balloon is based on changes in the user's oral health condition. Data analysis is used to determine the appropriate ratio of therapeutic molecules to achieve the best antibacterial, anti-inflammatory, or restorative effects.

Images