Intelligent dental crown based on AI and multi-process integration and preparation method thereof

Abstract

The invention discloses an intelligent dental crown based on AI and multi-process integration and a preparation method thereof, and mainly relates to dental restoration. The sensor module is a microchip integrated in a dental crown and comprises a pH sensor with the measurement precision of + / -0.02, a temperature sensor with the precision of + / -0.1 DEG C, a pressure sensor with the measuring range of 0-500N and a pressure sensor with the measuring range of + / -1N and a bacterial concentration sensor with the measuring range of + / -1N; the biomaterial layer is composed of a self-repairing nano composite material, the mechanical property recovery rate within 24 hours after scratch damage is larger than or equal to 90%, the bacteriostasis rate on oral pathogenic bacteria is larger than or equal to 95%, the data processor is internally provided with an AI algorithm and used for analyzing sensor data and transmitting the sensor data to terminal equipment through Bluetooth, and the biomaterial layer comprises pH response type antibacterial ceramic. When the pH value of the oral cavity is less than 5.5, alkaline substances are automatically released to neutralize the acid environment. The dental crown has the advantages that the problems that a traditional dental crown is single in function and low in manufacturing efficiency are solved, and real-time monitoring and accurate repairing of the oral cavity environment are achieved.

Description

Technical Field

[0001] This invention relates to the field of dental restoration technology, and in particular to an intelligent dental crown based on AI and multi-process integration and its preparation method. Background Technology

[0002] A smart crown is a dental restoration product that combines traditional dental techniques with modern smart technology. It not only possesses the functions of a traditional crown, such as protecting and repairing damaged teeth, but also incorporates smart technologies such as sensors and wireless communication, enabling real-time monitoring of oral health.

[0003] Based on the above, the following shortcomings have been found in traditional intelligent dental crowns:

[0004] 1. Material limitations: Metal crowns have high strength but poor aesthetics, and the metal color is visible in the anterior teeth area; the metal base of porcelain crowns causes black lines at the gums, and the allergy rate is >15%; all-ceramic crowns have low strength (flexural strength ≤400MPa) and are expensive.

[0005] 2. Process defects: Relying on manual mold making, wax pattern making, casting and final polishing takes about 5-7 days. Differences in the experience of technicians lead to finished product errors >100μm and edge gap exceeding the standard rate of 30%.

[0006] 3. Functional loss: Unable to monitor oral pH and bacterial concentration, increasing the risk of secondary tooth decay by 40%. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of existing technologies, such as the single function and low manufacturing efficiency of traditional dental crowns, and to achieve real-time monitoring and precise restoration of the oral environment. The invention proposes an intelligent dental crown based on AI and multi-process integration, and its preparation method.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent dental crown based on AI and multi-process integration, comprising:

[0009] Sensor module: A microchip integrated inside the crown, containing a pH sensor with a measurement accuracy of ±0.02, a temperature sensor with an accuracy of ±0.1℃, a pressure sensor with a range of 0-500N and an accuracy of ±1N, and a bacterial concentration sensor.

[0010] Biomaterial layer: Composed of self-healing nanocomposite materials, mechanical properties recover ≥90% within 24 hours after scratch damage, and antibacterial rate against oral pathogens ≥95%;

[0011] Data processor: Built-in AI algorithms are used to analyze sensor data and transmit it to the terminal device via Bluetooth.

[0012] Preferably, the biomaterial layer comprises pH-responsive antibacterial ceramics that automatically release alkaline substances when the oral pH value is <5.5, neutralizing the acidic environment.

[0013] Preferably, the sensor module chip is encapsulated with a nano-coating to resist corrosion from the oral cavity environment.

[0014] Preferably, a method for preparing an intelligent dental crown based on AI and multi-process integration, using an intelligent dental crown based on AI and multi-process integration as described in any one of claims 1-3, includes the following steps: S1, acquiring three-dimensional data of the patient's teeth through an intraoral scanner;

[0015] S2. Personalized dental crown models are generated using the 3D-DCGAN algorithm: The generator takes the patient's tooth data as input and generates an initial model through a transposed convolutional layer; the discriminator compares the model with a real tooth database and iteratively optimizes it until the edge gap is ≤30μm.

[0016] S3. Based on the design model, a five-axis linkage engraving and milling machine is used for multi-process integrated processing: the processing status is monitored in real time through force sensors and vibration sensors; the spindle speed and feed rate are dynamically adjusted, and the processing accuracy is ±0.01mm.

[0017] Preferably, the training data for the 3D-DCGAN algorithm in step S2 includes ≥1000 cases of three-dimensional scan data of natural teeth, covering different occlusal mechanical distribution characteristics.

[0018] Preferably, the engraving and milling machine described in step S3 is equipped with diamond-coated tools and an automatic tool magazine, and can complete the cutting, engraving, and polishing processes in one clamping, with a total time of ≤2 hours.

[0019] Preferably, during the machining process, when the cutting force exceeds the threshold of 150N, the system automatically reduces the feed rate by 20%; when the vibration amplitude is detected to be greater than 5μm, the tool path is optimized to avoid stress concentration areas.

[0020] Preferably, a sensor chip is embedded in the milled and shaped crown substrate and covered with a biocompatible encapsulation material with an encapsulation layer thickness of ≤0.1mm.

[0021] Preferably, the wear of the final crown is <0.1mm after 100,000 simulated chewing tests, and the fatigue life is >500,000 load cycles.

[0022] Preferably, the system uses sensors to monitor oral pH, temperature, and chewing pressure in real time; when the pH remains below 5.5 for more than 10 minutes or the bacterial concentration exceeds the limit, the terminal device triggers an alarm; the AI ​​algorithm predicts the risk of tooth decay based on historical data with an accuracy rate of ≥90%.

[0023] Preferably, the sensor module:

[0024] A microchip integrated into the occlusal surface of a tooth crown (size 1.5×1.5×0.8mm) 3 ),Include:

[0025] pH sensor: nanoelectrode material, accuracy ±0.02 (pH 3.0-8.0), response time <3 seconds;

[0026] Temperature sensor: platinum resistance film, accuracy ±0.1℃ (35-42℃);

[0027] Pressure sensor: MEMS piezoelectric thin film, measuring range 0-500N, accuracy ±1N;

[0028] Bacterial sensor: Functionalized gold nanorods, SERS detection limit 10 3 CFU / mL;

[0029] Encapsulation protection: Nano-silica coating (50nm thick), resistant to saliva corrosion for >2 years.

[0030] Preferred, biomaterial layer:

[0031] Matrix material: Zirconia / lithium disilicate composite ceramic (tensile strength ≥ 600 MPa);

[0032] Functional materials:

[0033] Self-healing layer: Microencapsulated epoxy resin, recovers 90% of its strength within 24 hours after scratching;

[0034] Antibacterial layer: Silver-loaded mesoporous silica, which releases Ag at pH < 5.5. + Antibacterial rate ≥95%.

[0035] Preferably, the data processing unit:

[0036] Built-in ARM Cortex-M4 processor, running an oral health prediction model (92% accuracy in caries prediction).

[0037] In summary, the beneficial effects of the present invention are as follows:

[0038] In this invention, the dental crown integrates pH, temperature, pressure, and bacterial sensors, as well as a self-healing antibacterial layer. Personalized design (marginal gap ≤30μm) is achieved through a 3D-DCGAN algorithm, and processing is completed in a single setup using a five-axis milling machine (cycle ≤2 hours). This solves the problems of traditional dental crowns being functionally limited and inefficiently manufactured, enabling real-time monitoring and precise restoration of the oral environment. Attached Figure Description

[0039] Appendix Figure 1 This is a flowchart of the intelligent dental crown fabrication process of the present invention;

[0040] Appendix Figure 2 This is the 3D-DCGAN algorithm architecture of the present invention;

[0041] Appendix Figure 3 This invention relates to the adaptive engraving and milling control system;

[0042] Appendix Figure 4 This invention relates to the intelligent crown layered structure.

[0043] The labels in the attached figure are: 101, 3D-DCGAN algorithm; 1011, generator; 1012, discriminator; 102, sensor module. Detailed Implementation

[0044] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0045] Example 1: Refer to Figure 1 As shown, the present invention provides a technical solution: an intelligent dental crown based on AI and multi-process integration and its preparation method, including a sensor module 102: a microchip integrated inside the dental crown, including a pH sensor with a measurement accuracy of ±0.02, a temperature sensor with an accuracy of ±0.1℃, a pressure sensor with a range and an accuracy of 0-500N and ±1N respectively, and a bacterial concentration sensor.

[0046] Biomaterial layer: Composed of self-healing nanocomposite materials, mechanical properties recover ≥90% within 24 hours after scratch damage, and antibacterial rate against oral pathogens ≥95%;

[0047] Data processor: Built-in AI algorithms are used to analyze sensor data and transmit it to the terminal device via Bluetooth.

[0048] Let me explain its specific settings and functions in detail below.

[0049] Reference Figure 2 , Figure 3 and Figure 4 As shown, in this embodiment: the biomaterial layer comprises pH-responsive antibacterial ceramic, which automatically releases alkaline substances to neutralize the acidic environment when the oral pH value is <5.5. The chip of the sensor module (102) is encapsulated with a nano-coating to resist corrosion from the oral environment.

[0050] A smart dental crown based on AI and multi-process integration includes the following steps:

[0051] S1. Obtain three-dimensional data of the patient's teeth using an intraoral scanner;

[0052] S2. A personalized crown model is generated using the 3D-DCGAN algorithm (101): The generator (1011) takes the patient's tooth data as input and generates an initial model through a transposed convolutional layer; the discriminator (1012) compares the model with the real tooth database and iteratively optimizes it until the edge gap is ≤30μm.

[0053] S3. Based on the design model, a five-axis linkage milling machine is used for multi-process integrated machining: the machining status is monitored in real time by force sensors and vibration sensors; the spindle speed and feed rate are dynamically adjusted, and the machining accuracy is ±0.01mm. The training data of the 3D-DCGAN(101) algorithm mentioned in step S2 includes ≥1000 cases of three-dimensional scanning data of natural teeth, covering different occlusal mechanical distribution characteristics. The milling machine mentioned in step S3 is equipped with diamond-coated tools and an automatic tool magazine, and the cutting, milling, and polishing processes are completed in one clamping, with a total time of ≤2 hours. During the machining process, when the cutting force exceeds the threshold of 150N, the system automatically reduces the feed rate by 20%; when the vibration amplitude is detected to be >5μm, the tool path is optimized to avoid the stress concentration area. A sensor chip is embedded in the tooth crown matrix after milling and is covered with a biocompatible encapsulation material with an encapsulation layer thickness of ≤0.1mm. After 100,000 simulated chewing tests, the wear of the final tooth crown is <0.1mm, and the fatigue life is >500,000 load cycles. The system monitors oral pH, temperature, and chewing pressure in real time using sensors; when the pH remains below 5.5 for more than 10 minutes or the bacterial concentration exceeds the limit, the terminal device triggers an alert; the AI ​​algorithm predicts the risk of tooth decay based on historical data with an accuracy rate of ≥90%.

[0054] Example 2:

[0055] Mechanical testing:

[0056] Test Project parameter result Simulated chewing 100,000 cycles (200N load) Loss amount: 0.08mm Fatigue strength Alternating load 150N Lifespan > 520,000 cycles Marginal Adaptability Laser confocal scanning Average gap 28µm

[0057] Biocompatibility

[0058] Test type method result Cytotoxicity MTT assay (HGF cells) Value-added rate: 92.3% Allergenicity Guinea Pig Maximization Experiment Skin reaction score: 0.4 Genotoxicity Ames experiment Mutation rate < 0.1%

[0059] Patient data: Male, 47 years old, with missing molars; after 6 months of wearing:

[0060] Chewing efficiency increased from 58% to 89%;

[0061] Three pH tests revealed abnormalities, prompting timely intervention to prevent tooth decay; the gingival bleeding index decreased from 3.2 to 1.0.

Claims

1. A smart dental crown based on AI and multi-process integration, characterized in that, include: Sensor module (102): A microchip integrated inside the crown, including a pH sensor with a measurement accuracy of ±0.02, a temperature sensor with an accuracy of ±0.1℃, a pressure sensor with a range of 0-500N and an accuracy of ±1N, and a bacterial concentration sensor; Biomaterial layer: Composed of self-healing nanocomposite materials, mechanical properties recover ≥90% within 24 hours after scratch damage, and antibacterial rate against oral pathogens ≥95%; Data processor: Built-in AI algorithms are used to analyze sensor data and transmit it to the terminal device via Bluetooth.

2. The intelligent dental crown based on AI and multi-process integration according to claim 1, characterized in that, The biomaterial layer contains pH-responsive antibacterial ceramics that automatically release alkaline substances when the oral pH value is <5.5, neutralizing the acidic environment.

3. The intelligent dental crown based on AI and multi-process integration according to claim 1, characterized in that, The chip of the sensor module (102) is encapsulated with a nano-coating to resist corrosion from the oral environment.

4. A method for fabricating an intelligent dental crown based on AI and multi-process integration, employing an intelligent dental crown based on AI and multi-process integration as described in any one of claims 1-3, characterized in that, Includes the following steps: S1. Obtain three-dimensional data of the patient's teeth using an intraoral scanner; S2. A personalized crown model is generated using the 3D-DCGAN algorithm (101): The generator (1011) takes the patient's tooth data as input and generates an initial model through a transposed convolutional layer; the discriminator (1012) compares the model with the real tooth database and iteratively optimizes it until the edge gap is ≤30μm. S3. Based on the design model, a five-axis linkage engraving and milling machine is used for multi-process integrated processing: the processing status is monitored in real time through force sensors and vibration sensors; the spindle speed and feed rate are dynamically adjusted, and the processing accuracy is ±0.01mm.

5. The intelligent crown fabrication method based on AI and multi-process integration according to claim 4, characterized in that, The training data for the 3D-DCGAN algorithm (101) in step S2 includes ≥1000 three-dimensional scans of natural teeth, covering different occlusal mechanical distribution characteristics.

6. The intelligent crown fabrication method based on AI and multi-process integration according to claim 4, characterized in that, The engraving and milling machine described in step S3 is equipped with diamond-coated tools and an automatic tool magazine. It can complete the cutting, engraving, milling, and polishing processes in one clamping, with a total time of ≤2 hours.

7. The intelligent crown fabrication method based on AI and multi-process integration according to claim 4, characterized in that, During machining, when the cutting force exceeds the threshold of 150N, the system automatically reduces the feed rate by 20%; when the vibration amplitude is detected to be greater than 5μm, the tool path is optimized to avoid stress concentration areas.

8. The intelligent crown fabrication method based on AI and multi-process integration according to claim 4, characterized in that, A sensor chip is embedded in the milled and shaped tooth crown substrate and covered with a biocompatible encapsulation material with a thickness of ≤0.1mm.

9. The intelligent crown fabrication method based on AI and multi-process integration according to claim 4, characterized in that, The final crown showed wear of <0.1mm after 100,000 simulated chewing tests and a fatigue life of >500,000 load cycles.

10. The intelligent crown fabrication method based on AI and multi-process integration according to claim 4, characterized in that, The system monitors oral pH, temperature, and chewing pressure in real time using sensors; when the pH remains below 5.5 for more than 10 minutes or the bacterial concentration exceeds the limit, the terminal device triggers an alert; the AI ​​algorithm predicts the risk of tooth decay based on historical data with an accuracy rate of ≥90%.

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