Oral implant optimization method and system based on big data

Abstract

The invention discloses an oral implantation optimization method and system based on big data, and relates to the technical field of oral implantation, and the method comprises the following steps: collecting individual data of a patient and historical implantation case data, the individual data of the patient comprises basic information of the patient, oral examination data and iconography data, the method comprises the following steps: acquiring individual data of a patient, performing digital impression and facial three-dimensional scanning data, preprocessing the acquired individual data of the patient, extracting oral implant associated features including bone features and body health features of the patient, and analyzing bone conditions and body health conditions of the patient. By collecting and analyzing the oral cavity data and the historical planting case data of the patient, the bone condition, the body health condition and the surgical risk level of the patient are evaluated, and the position, angle, length and diameter of the implant are designed more accurately, so that the surgical accuracy is improved, the surgical risk is reduced, and the surgical success rate is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oral implantation, and in particular to an oral implantation optimization method and system based on big data. Background Art

[0002] In the field of stomatology, the application of digital technology is becoming more and more extensive. Digital oral implant treatment technology replaces traditional treatment methods by using digital equipment for oral implant treatment, which significantly improves the effect and efficiency of implant treatment. These digital technologies include digital radiographic scanning, digital impressions, digital facial 3D scanning, digital virtual frames and digital precision mechanical production. Oral implant treatment is an important method of implant restoration for missing teeth. The treatment process includes diagnosis and design, surgical procedures, restorative procedures, technical procedures and maintenance procedures. In traditional treatment methods, doctors mainly rely on experience and two-dimensional imaging data for diagnosis and treatment planning, which may lead to insufficient treatment accuracy and efficiency. At the same time, oral implant treatment also faces many challenges such as individual differences among patients, surgical risks, and postoperative recovery.

[0003] Oral implant treatment involves a wide variety of data. Due to the individual differences of patients, the mechanical properties and biocompatibility of implants, the risk of complications in patients is further increased. Therefore, how to formulate personalized implant plans based on the individual differences and needs of patients, conduct preoperative planning and simulation, and reduce the risk of complications is a problem we need to solve. To this end, an oral implant optimization method and system based on big data is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide an oral implant optimization method and system based on big data to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] In a first aspect, a method for optimizing oral implants based on big data comprises the following steps:

[0007] Step 1: Collect individual patient data and historical implant case data, where individual patient data includes basic patient information, such as age, gender, medical history, drug allergy history, etc., oral examination data, such as tooth loss, gum condition, periodontal disease condition, etc., imaging data, such as X-rays, CBCT scans, etc., as well as digital impressions and facial 3D scan data;

[0008] Step 2: pre-process the acquired individual patient data, extract features related to oral implants, including the patient's bone characteristics and physical health characteristics, and analyze the patient's bone condition and physical health status;

[0009] Step 3: Based on historical implant case data and combined with oral implant-related features, an implant plan evaluation model is constructed to obtain the bone condition index and physical health condition index, simulate surgical scenarios, predict the risk of complications that may occur to patients, such as infection and implant failure, and output simulated surgical results, including the success rate of the surgery, the estimated surgical time, possible complications, etc.;

[0010] Step 4: Based on the acquired bone condition index and physical health condition index, the implant risk assessment coefficient is obtained by matching with the historical implant case data. Combined with the simulated operation results, the surgical risk is comprehensively assessed, including the risk of surgical failure, the risk of postoperative infection, the risk of implant loosening or falling off, the risk of nerve damage, etc., and an assessment report is output. At the same time, based on the historical implant case data, the probability of each risk is quantified, the current surgical risk level is determined, and the corresponding risk assessment threshold is set. The risk level includes low risk level, medium risk level, high risk level and extremely high risk level;

[0011] Step 5. Based on the evaluation report, adjust the surgical plan and set corresponding preventive measures. During the patient's operation, monitor the surgical process in real time to promptly identify and deal with potential problems. After the operation, collect the patient's postoperative recovery data, including the healing of the implant, the healing of the soft tissue, the patient's subjective feelings, such as the degree of pain, the recovery of chewing function, etc., to develop a personalized oral maintenance plan.

[0012] A further improvement of the technical solution of the present invention is that in step 1, the process of collecting the patient's individual data and historical implant case data is as follows:

[0013] Step 101, using the patient registration form and electronic health record file to collect the patient's name, age, gender, contact information, past medical history and drug allergy history, and using the oral mirror to examine the patient's oral condition to obtain oral examination data, including tooth loss, periodontal disease and oral mucosal condition, and at the same time record the patient's tooth occlusion and dentition arrangement, which helps the doctor evaluate the patient's oral health status and formulate an implant plan;

[0014] Step 102, using an oral and maxillofacial cone beam CT imaging device to obtain the patient's craniomaxillofacial coronal, axial and sagittal imaging data, and using CBCT images to measure bone density, bone height, bone width, etc., to evaluate the patient's bone condition and determine the implantation position and angle of the implant;

[0015] Step 103, using a digital intraoral scanner to scan the patient's oral cavity to obtain the morphological information of the soft and hard tissues in the oral cavity, and using a facial 3D scanner to obtain the patient's facial contour information, including the shape, size and contour of the face, to perform surgery planning, design restorations and evaluate aesthetic effects, and use digital impressions and CBCT images to reconstruct a digital model of the patient's oral cavity;

[0016] Step 104, based on the publicly available oral implant case database, historical implant cases are extracted, including basic case information, surgical process records, postoperative recovery data, and complications and failure cases, and are classified and coded.

[0017] A further improvement of the technical solution of the present invention is that in step 2, during the extraction of oral implant related features:

[0018] Step 201, data cleaning and data integration of the collected individual patient data, including resampling, segmentation and smoothing of the patient's CBCT image data to obtain accurate bone tissue structure information, standardization and normalization of the digital impression and facial 3D scanning data for subsequent feature extraction, and arranging the patient's basic information into a structured format;

[0019] Step 202, extracting features from the pre-processed individual patient data, respectively extracting features for bone density, bone mass, jaw anatomical structure, occlusal relationship and bone type, to obtain the patient's bone features, and then extracting features for the patient's medical history, drug allergy history and health status of the patient, to obtain physical health features, wherein the patient's health status includes whether the patient suffers from diabetes, cardiovascular disease, etc., and living habits, such as smoking, drinking, etc.;

[0020] Step 203, based on the extracted bone features, the patient's bone condition is evaluated, and combined with the patient's past medical history, drug allergy history and health status, the patient's overall health status is evaluated to predict the risks during the operation.

[0021] A further improvement of the technical solution of the present invention is that in step 3, the process of obtaining the bone condition index and the physical health condition index is as follows:

[0022] Step 301, fusing the extracted bone characteristics and physical health characteristics with historical implant case data to form a unified data set, and constructing an implant program evaluation model;

[0023] Step 302, dividing the integrated data set into a training set, a validation set and a test set, using the training set data to train the planting scheme evaluation model, and using the validation set and the test set data to verify the trained planting scheme evaluation model, and evaluating the performance of the planting scheme evaluation model;

[0024] Step 303, based on the trained implant scheme evaluation model and combined with the bone characteristics and physical health characteristics, a bone condition index and a physical health condition index are calculated;

[0025] Step 304, using VR and AR technology in combination with the current patient's oral digital model and the implant plan evaluation model, construct a three-dimensional surgical scene, including the patient's oral structure, the position and angle of the implant, etc., and simulate the surgical process in the three-dimensional surgical scene, including the implantation position, angle, depth, etc. of the implant. At the same time, combined with the patient's bone condition index and physical health index, predict the risk of complications that may occur to the patient during the operation, including the probability of various risks such as implant loosening, infection, nerve damage, bleeding, etc., and generate simulated surgical results, which cover the patient's basic information, implant plan overview, bone and physical health analysis, description of key steps of the simulated operation, and predicted complication risks and probabilities.

[0026] A further improvement of the technical solution of the present invention is that the calculation formula of the bone condition index is:

[0027]

[0028] Among them, BCI is the bone condition index, BMD is bone density, which reflects the mineral content of bones, BS is the bone structure score, which comprehensively evaluates the morphology and structure of bones, BV is the bone volume, which indicates the total volume of bones, BS0 is the baseline value of the bone structure score, and σ is the standard deviation of the bone structure score, which reflects the discrete degree of the score;

[0029] The calculation formula of the physical health status index is:

[0030]

[0031] Among them, PHI is the physical health index, HR is the heart rate, which reflects the health of the heart, BMI is the body mass index, which comprehensively evaluates weight and height, AGE is age, which reflects the patient's physiological age, and SB is the health score, which comprehensively evaluates the patient's overall health. ref is the reference value of the health score, and τ is the sensitivity coefficient of the health score, reflecting the sensitivity of the score.

[0032] A further improvement of the technical solution of the present invention is that the calculation formula of the bone condition index is:

[0033]

[0034] Among them, BCI is the bone condition index, BMD is bone density, which reflects the mineral content of bones, BS is the bone structure score, which comprehensively evaluates the morphology and structure of bones, BV is the bone volume, which indicates the total volume of bones, BS0 is the baseline value of the bone structure score, and σ is the standard deviation of the bone structure score, which reflects the discrete degree of the score;

[0035] The calculation formula of the physical health status index is:

[0036]

[0037] Among them, PHI is the physical health index, HR is the heart rate, which reflects the health of the heart, BMI is the body mass index, which comprehensively evaluates weight and height, AGE is age, which reflects the patient's physiological age, and SB is the health score, which comprehensively evaluates the patient's overall health. ref is the reference value of the health score, and τ is the sensitivity coefficient of the health score, reflecting the sensitivity of the score.

[0038] A further improvement of the technical solution of the present invention is that the calculation formula of the planting risk assessment coefficient is:

[0039]

[0040] Among them, RAC is the implant risk assessment coefficient, BCI is the bone condition index, and BCI base is the baseline value of bone condition index, PHI is the physical health index, PHI base BCI is the baseline value of the physical health index. max is the maximum value of bone mass index, PHI max is the maximum value of the health status index, age is the current patient's age, ref is the reference value of age, which is used to evaluate the impact of age on surgical risk, and ∈ is the sensitivity coefficient of age.

[0041] A further improvement of the technical solution of the present invention is that: the multiple risk levels correspond to multiple risk assessment thresholds, wherein the risk assessment thresholds include an upper threshold and a lower threshold;

[0042] The multiple risk levels and the multiple risk assessment thresholds satisfy the following relationship:

[0043] Low risk RAC <S1;

[0044] Medium risk level S1≤RAC <S2;

[0045] High risk level S2≤RAC <S3;

[0046] Very high risk level S3≤RAC;

[0047] Among them, RAC is the planting risk assessment coefficient, S1 is the upper threshold corresponding to the low risk level and the lower threshold corresponding to the medium risk level, S2 is the upper threshold corresponding to the medium risk level and the lower threshold corresponding to the high risk level, and S3 is the upper threshold corresponding to the high risk level and the lower threshold corresponding to the extremely high risk level.

[0048] A further improvement of the technical solution of the present invention is that in step 5, the process of adjusting the surgical plan and setting corresponding preventive measures to formulate a personalized oral care plan is as follows:

[0049] Step 501, according to the assessment report, determine the surgical risk level of the patient, and adjust the position, number, length and diameter of the implant according to the corresponding risk level, and formulate targeted preventive measures, such as preparing necessary emergency equipment and drugs;

[0050] Step 502, using medical devices such as oral endoscope, X-ray and ECG monitor to monitor the patient's vital signs and surgical status in real time, and take immediate intervention measures when an emergency occurs, such as adjusting the depth of anesthesia, controlling bleeding, and dealing with accidental injuries;

[0051] Step 503, after the patient's surgery, the patient's postoperative pain level, swelling and signs of infection are recorded, and the changes in the tissue around the implant are evaluated. At the same time, the patient's postoperative recovery data is regularly collected, including real-time monitoring of physiological parameters and blood sample data, such as heart rate, blood pressure, blood oxygen level, etc., to develop a personalized oral maintenance plan, including pain management, physical therapy, nutritional support, etc.

[0052] In a second aspect, an oral implant optimization system based on big data is used to implement an oral implant optimization method based on big data: comprising a data management module, wherein the data management module is communicatively connected to a data acquisition module, a data analysis module, a surgery simulation and planning module, an implantation plan and risk assessment module, and an intraoperative real-time monitoring and early warning module, wherein electrical signals are connected between the modules;

[0053] The data acquisition module is used to collect historical implant case data, basic patient information, oral examination data, imaging data, digital impressions and facial 3D scanning data. These data provide a basis for subsequent analysis and decision-making, ensuring the accuracy and safety of oral implant surgery;

[0054] The data analysis module is used to pre-process the data collected by the data collection module, including cleaning, integration, classification and coding, to ensure the accuracy and consistency of the data, and to extract features related to oral implants, analyze the patient's bone condition and physical health, and the suitability of the implant;

[0055] The surgical simulation and planning module is used to simulate the implant surgery process and pre-plan the surgical plan, including the position, angle, depth, etc. of the implant, so as to reduce the uncertainty and risk during the surgery, provide surgical navigation and implantation guidance, and help doctors implant the implant more accurately during the surgery, thereby improving the safety and success rate of the surgery, and predicting the risk of complications that may occur to patients during the surgery;

[0056] The implantation plan and risk assessment module is used to build an implantation plan assessment model in combination with historical implantation case data, conduct a comprehensive assessment of surgical risks, determine the surgical risk level of the patient, adjust the surgical plan and set corresponding preventive measures;

[0057] The intraoperative real-time monitoring and early warning module is used to collect the patient's vital signs and surgical data in real time during the patient's operation, and to send out early warning signals when abnormalities are found, reminding the surgical team to take corresponding measures to ensure the safety and smooth progress of the operation. At the same time, the patient's recovery data is collected after the operation to formulate a personalized oral maintenance plan.

[0058] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0059] 1. The present invention provides an oral implant optimization method and system based on big data. By collecting and analyzing the patient's oral data and historical implant case data, the patient's bone condition, physical health status and surgical risk level are evaluated, and the position, angle, length and diameter of the implant are designed more accurately, thereby improving the accuracy of the operation. In addition, the application of big data can also predict and solve potential surgical problems, reduce surgical risks, and further improve the success rate of the operation.

[0060] 2. The present invention provides an oral implant optimization method and system based on big data. By collecting and analyzing the relationship between the patient's bone condition index, physical health condition index and postoperative complications, an implant program evaluation model is established. Before the patient's operation, the implant risk level can be accurately judged and a detailed response strategy can be formulated in advance. This not only meets the patient's needs, but also improves the pertinence and effectiveness of the operation. At the same time, it also helps to reduce complications during surgery and postoperative recovery time.

[0061] 3. The present invention provides an oral implant optimization method and system based on big data. By simulating surgical scenes and monitoring the surgical process in real time, the surgical conditions and patient status can be understood in real time, so as to adjust the surgical strategy in time. In addition, big data can also help doctors predict the operation time and recovery time, and provide patients with more accurate expectations, which not only reduces the uncertainty and risk in the operation, but also improves the overall efficiency of the operation.

[0062] 4. The present invention provides an oral implant optimization method and system based on big data. By collecting and analyzing historical implant case data, basic patient information, oral examination data, and imaging data, a personalized implant plan is generated. At the same time, through three-dimensional simulation and planning, the system can accurately determine the position, angle, and depth of the implant, significantly improving the accuracy of the operation. In addition, the system can monitor key indicators during the operation in real time. Once an abnormality is found, an early warning will be issued immediately, thereby effectively ensuring the safety of the operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0064] Figure 1 is a flow chart of the method of the present invention;

[0065] Figure 2 A flowchart for obtaining the bone condition index and the physical health condition index of the present invention;

[0066] Figure 3 A flowchart for obtaining the implant risk assessment coefficient and the surgical risk level of the present invention;

[0067] Figure 4 It is a module structure diagram of the present invention. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] Embodiment 1, as Figures 1 to 3 As shown, the present invention provides an oral implant optimization method based on big data, comprising the following steps:

[0070] Step 1: Collect individual patient data and historical implant case data, where individual patient data includes basic patient information, such as age, gender, past medical history, history of drug allergies, etc., oral examination data, such as tooth loss, gum condition, periodontal disease condition, etc., imaging data, such as X-rays, CBCT scans, etc., as well as digital impressions and facial three-dimensional scanning data; the collection process of individual patient data and historical implant case data is as follows: use the patient registration form and electronic health record archive to collect the patient's name, age, gender, contact information, past medical history and drug allergy history basic information, and use the oral mirror to examine the patient's oral condition to obtain oral examination data, including tooth loss, periodontal disease condition and oral mucosal condition, and record the patient's tooth occlusion relationship and dentition arrangement, which is helpful for doctors to evaluate the patient's oral health status and formulate implant plans, and use oral and maxillofacial cone beam CT imaging equipment to obtain the patient's craniofacial coronal, axial and Sagittal imaging data. Through these image information, doctors can clearly see the patient's jaw structure, tooth arrangement, occlusion relationship and surrounding important anatomical structures, such as the positional relationship of the mandibular nerve canal, mental foramen, maxillary incisor foramen and maxillary sinus floor. CBCT images are used to measure bone density, bone height, bone width, etc., to evaluate the patient's bone condition and determine the implantation position and angle of the implant. A digital intraoral scanner is used to scan the patient's oral cavity to obtain the morphological information of the soft and hard tissues in the mouth. A facial 3D scanner is used to obtain the patient's facial contour information, including the shape, size and contour of the face, to plan the operation, design the restoration and evaluate the aesthetic effect. Digital impressions and CBCT images are used to reconstruct the patient's oral digital model. According to the publicly available oral implant case database, historical implant cases are extracted, including basic case information, surgical process records, postoperative recovery data, complications and failure cases, and they are classified and coded.

[0071] Step 2: pre-process the acquired individual patient data, extract features related to oral implants, including the patient's bone characteristics and physical health characteristics, and analyze the patient's bone condition and physical health status, where bone characteristics include bone density, bone mass, jaw anatomical structure, occlusal relationship, etc., and physical health characteristics include whether the patient has diabetes, cardiovascular disease, etc., lifestyle habits, such as smoking, drinking, and other factors, as well as parameters such as the brand, model, and surface treatment process of the implant; in the process of extracting features related to oral implants: data cleaning and data integration of the collected individual patient data, including resampling, segmentation, and smoothing of the patient's CBCT image data to obtain accurate bone tissue structure information, standardization and normalization of digital impressions and facial 3D scanning data for subsequent feature extraction, and at the same time, organize the patient's basic information into a structured format, remove unnecessary spaces and special symbols, so that each piece of information is It can clearly correspond to specific fields, which is convenient for data query and call. It extracts features from the pre-processed individual data of patients, and extracts features for bone density, bone mass, jaw anatomical structure, occlusal relationship and bone type to obtain the patient's bone features. It then extracts features for the patient's medical history, drug allergy history and health status to obtain physical health features. The patient's health status includes whether he suffers from diabetes, cardiovascular disease, etc., and living habits, such as smoking and drinking. According to the extracted bone features, the patient's bone condition is evaluated to determine whether the patient's bone is suitable for oral implant surgery and whether additional measures are needed, such as bone augmentation surgery to improve bone quality. Combined with the patient's medical history, drug allergy history and health status, the patient's overall health status is evaluated to determine whether the patient can tolerate oral implant surgery and predict the risks during the operation, such as bleeding, infection, anesthesia reaction, etc.

[0072] Step 3, based on the historical implant case data and combined with the oral implant related features, construct an implant plan evaluation model, obtain the bone condition index and the physical health index, simulate the surgical scene, predict the risk of complications of the patient, such as infection, implant failure, etc., and output the simulated surgical results, including the success rate of the surgery, the estimated operation time, possible complications, etc.; the process of obtaining the bone condition index and the physical health index is: integrating the extracted bone characteristics and physical health characteristics with the historical implant case data to form a unified data set, and constructing an implant plan evaluation model, dividing the integrated data set into a training set, a validation set, and a test set, using the training set data to train the implant plan evaluation model, and using the validation set and test set data to verify the trained implant plan evaluation model, evaluating the performance of the implant plan evaluation model, and based on the trained implant plan evaluation model and Combined with bone characteristics and physical health characteristics, the bone condition index and physical health index are calculated. These indexes can reflect the patient's bone quality and overall health status, and provide a basis for formulating implant plans. VR and AR technologies are combined with the current patient's oral digital model and implant plan evaluation model to construct a three-dimensional surgical scene, including the patient's oral structure, the position and angle of the implant, etc., and the surgical process is simulated in the three-dimensional surgical scene, including the implantation position, angle, depth, etc. At the same time, combined with the patient's bone condition index and physical health index, the risk of complications that may occur during the operation is predicted, including the probability of various risks such as implant loosening, infection, nerve damage, and bleeding, and the simulated surgical results are generated, covering the patient's basic information, implant plan overview, bone and physical health analysis, description of key steps in simulated surgery, and predicted complication risks and probabilities;

[0073] Step 4: Based on the acquired bone condition index and physical health index, the implant risk assessment coefficient is matched with the historical implant case data to obtain the implant risk assessment coefficient. Combined with the simulated operation results, the surgical risk is comprehensively assessed, including the risk of surgical failure, the risk of postoperative infection, the risk of implant loosening or falling off, the risk of nerve damage, etc., and an assessment report is output. At the same time, based on the historical implant case data, the probability of each risk is quantified, the current surgical risk level is determined, and the corresponding risk assessment threshold is set. The risk level includes low risk level, medium risk level, high risk level and extremely high risk level; the implant risk assessment coefficient and the surgical risk level are obtained by associating and integrating the acquired bone condition index and physical health index with the patient's individual data, and matching them with the historical implant case data. Analyze the similarity between the patient's oral condition and historical cases. Based on historical implant case data, assign different weights to the bone condition index and physical health index. Perform weighted calculation on the two indexes to obtain the implant risk assessment coefficient. Combine the simulated surgery results with the calculated implant risk assessment coefficient to assess the surgical risk and generate an assessment report, which includes bone condition, physical health, surgical risk assessment coefficient, and predicted complication risk. According to the assessment results and matching historical implant case data, determine the surgical risk level, which is low risk, medium risk, high risk, and extremely high risk. Match the determined different risk levels with the implant risk assessment coefficient, set corresponding risk assessment thresholds for different risk levels, and formulate corresponding risk management plans.

[0074] Step 5. Based on the evaluation report, adjust the surgical plan and set corresponding preventive measures, such as adding bone transplantation steps, changing the implant placement path, etc., to effectively reduce surgical risks and ensure the safety of patients and treatment effects. During the patient's operation, monitor the surgical process in real time to promptly discover and deal with potential problems. After the patient's operation, collect the patient's postoperative recovery data, including the healing of the implant, the healing of the soft tissue, and the patient's subjective feelings, such as the degree of pain, the recovery of chewing function, etc., and formulate a personalized oral maintenance plan, which includes regular oral examinations, cleaning and necessary repairs. The specific process of real-time collection of intraoperative data is as follows: During the operation, use high-precision sensors and digital equipment to collect real-time information such as the position, angle, depth, etc. of the implant, as well as the patient's physiological parameters such as heart rate, blood pressure, etc., and transmit these data to the database in real time for intraoperative monitoring and postoperative analysis, so as to adjust the surgical plan in time and improve The accuracy and safety of the operation; adjusting the surgical plan and setting corresponding preventive measures, and the process of formulating a personalized oral maintenance plan is as follows: according to the evaluation report, determine the patient's surgical risk level, and adjust the position, number, length and diameter of the implants according to the corresponding risk level, and formulate targeted preventive measures, such as preparing necessary first aid equipment and drugs, using oral endoscopes, X-rays, ECG monitors and other medical devices to monitor the patient's vital signs and surgical status in real time, and take immediate intervention measures in case of emergency, such as adjusting the depth of anesthesia, controlling bleeding, dealing with accidental injuries, etc. After the patient's operation, record the patient's postoperative pain level, swelling and signs of infection, and evaluate changes in the tissues around the implants. At the same time, regularly collect the patient's postoperative recovery data, including real-time monitoring of physiological parameters and blood sample data, such as heart rate, blood pressure, blood oxygen level, etc., and formulate a personalized oral maintenance plan, including pain management, physical therapy, nutritional support, etc.

[0075] Embodiment 2, as Figures 1 to 3 As shown, based on Example 1, the present invention provides a technical solution: Preferably, the calculation formula of the bone condition index is:

[0076]

[0077] Among them, BCI is the bone condition index, BMD is bone density, which reflects the mineral content of bones, BS is the bone structure score, which comprehensively evaluates the morphology and structure of bones, BV is the bone volume, which indicates the total volume of bones, BS0 is the baseline value of the bone structure score, and σ is the standard deviation of the bone structure score, which reflects the discrete degree of the score;

[0078] The calculation formula of physical health status index is:

[0079]

[0080] Among them, PHI is the physical health index, HR is the heart rate, which reflects the health of the heart, BMI is the body mass index, which comprehensively evaluates weight and height, AGE is age, which reflects the patient's physiological age, and SB is the health score, which comprehensively evaluates the patient's overall health. ref is the reference value of the health score, τ is the sensitivity coefficient of the health score, reflecting the sensitivity of the score;

[0081] The calculation formula of planting risk assessment coefficient is:

[0082]

[0083] Among them, RAC is the implant risk assessment coefficient, BCI is the bone condition index, and BCI base is the baseline value of bone condition index, PHI is the physical health index, PHI base BCI is the benchmark value of the physical health index. max is the maximum value of bone mass index, PHI max is the maximum value of the health status index, age is the current patient's age, ref is the reference value of age, which is used to evaluate the impact of age on surgical risk, and ∈ is the sensitivity coefficient of age;

[0084] Multiple risk levels and multiple risk assessment thresholds satisfy the following relationship:

[0085] Low risk RAC <S1;

[0086] Medium risk level S1≤RAC <S2;

[0087] High risk level S2≤RAC <S3;

[0088] Very high risk level S3 ≤ RAC;

[0089] Among them, RAC is the planting risk assessment coefficient, S1 is the upper threshold corresponding to the low risk level and the lower threshold corresponding to the medium risk level, S2 is the upper threshold corresponding to the medium risk level and the lower threshold corresponding to the high risk level, and S3 is the upper threshold corresponding to the high risk level and the lower threshold corresponding to the extremely high risk level.

[0090] Embodiment 3, as Figure 4 As shown, on the basis of Example 1, the present invention further provides an oral implant optimization system based on big data, including a data management module, wherein the data management module is communicatively connected to a data acquisition module, a data analysis module, a surgery simulation and planning module, an implantation plan and risk assessment module, and an intraoperative real-time monitoring and early warning module, wherein electrical signals are connected between the modules;

[0091] The data collection module is used to collect historical implant case data, basic patient information, oral examination data, imaging data, digital impressions, and facial 3D scanning data. These data provide a basis for subsequent analysis and decision-making, ensuring the accuracy and safety of oral implant surgery.

[0092] The data analysis module is used to pre-process the data collected by the data acquisition module, including cleaning, integration, classification and coding, to ensure the accuracy and consistency of the data, and to extract features related to oral implants, analyze the patient's bone condition and physical health, and the suitability of implants;

[0093] The surgical simulation and planning module is used to simulate the implant surgery process and pre-plan the surgical plan, including the position, angle, depth, etc. of the implant, to reduce the uncertainty and risk during the surgery. It also provides surgical navigation and implantation guidance to help doctors implant implants more accurately during surgery, improve the safety and success rate of the surgery, and predict the risk of complications that may occur to patients during the surgery.

[0094] The implant plan and risk assessment module is used to build an implant plan assessment model based on historical implant case data, conduct a comprehensive assessment of surgical risks, determine the patient's surgical risk level, adjust the surgical plan, and set corresponding preventive measures;

[0095] The intraoperative real-time monitoring and early warning module is used to collect the patient's vital signs and surgical data in real time during the patient's operation, and send out early warning signals when abnormalities are found, reminding the surgical team to take corresponding measures to ensure the safety and smooth progress of the operation. At the same time, it collects the patient's recovery data after surgery and formulates a personalized oral maintenance plan.

[0096] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An oral implant optimization method based on big data, characterized in that: The following steps are involved: Step 1: Collect individual patient data and historical implant case data, where individual patient data includes basic patient information, oral examination data, imaging data, as well as digital impressions and facial 3D scanning data; Step 2: pre-process the acquired individual patient data, extract features related to oral implants, including the patient's bone characteristics and physical health characteristics, and analyze the patient's bone condition and physical health status; Step 3: Based on historical implant case data and combined with oral implant-related features, an implant plan evaluation model is constructed to obtain the bone condition index and physical health condition index, simulate the surgical scenario, predict the risk of complications for patients, and output the simulated surgical results; Step 4: Based on the acquired bone condition index and physical health condition index, the implant risk assessment coefficient is matched with the historical implant case data to obtain the implant risk assessment coefficient. Combined with the simulated surgery results, the surgical risk is comprehensively assessed and an assessment report is output. At the same time, based on the historical implant case data, the current surgical risk level is determined and the corresponding risk assessment threshold is set. The risk levels include low risk level, medium risk level, high risk level and extremely high risk level. Step 5: Based on the evaluation report, adjust the surgical plan and set corresponding preventive measures. During the operation, monitor the surgical process in real time. After the operation, collect the patient's postoperative recovery data and develop a personalized oral maintenance plan.

2. The oral implant optimization method based on big data according to claim 1, characterized in that: In step 1, the process of collecting patient individual data and historical implant case data is as follows: Step 101, using the patient registration form and electronic health record file to collect the patient's name, age, gender, contact information, past medical history and drug allergy history, and using the oral mirror to examine the patient's oral condition to obtain oral examination data, including tooth loss, periodontal disease and oral mucosal condition, and record the patient's tooth occlusion and dentition arrangement; Step 102, using an oral and maxillofacial cone beam CT imaging device to obtain the patient's craniomaxillofacial coronal, axial and sagittal imaging data, and using CBCT images to measure bone density, bone height, bone width, etc., to evaluate the patient's bone condition and determine the implantation position and angle of the implant; Step 103, using a digital intraoral scanner to scan the patient's oral cavity to obtain the morphological information of the soft and hard tissues in the oral cavity, and using a facial 3D scanner to obtain the patient's facial contour information, including the shape, size and contour of the face, to perform surgery planning, design restorations and evaluate aesthetic effects, and use digital impressions and CBCT images to reconstruct a digital model of the patient's oral cavity; Step 104, based on the publicly available oral implant case database, historical implant cases are extracted, including basic case information, surgical process records, postoperative recovery data, and complications and failure cases, and are classified and coded.

3. The oral implant optimization method based on big data according to claim 1, characterized in that: In step 2, during the extraction of oral implant related features: Step 201, data cleaning and data integration of the collected individual patient data, including resampling, segmentation and smoothing of the patient's CBCT image data, standardization and normalization of the digital impression and facial 3D scanning data, and arranging the patient's basic information into a structured format; Step 202, extracting features from the pre-processed individual patient data, respectively extracting features for bone density, bone mass, jaw anatomical structure, occlusal relationship and bone type to obtain the patient's bone features, and then extracting features for past medical history, drug allergy history and health status of the patient to obtain physical health features; Step 203, based on the extracted bone features, the patient's bone condition is evaluated, and combined with the patient's past medical history, drug allergy history and health status, the patient's overall health status is evaluated to predict the risks during the operation.

4. The oral implant optimization method based on big data according to claim 1, characterized in that: In step 3, the process of obtaining the bone condition index and the physical health condition index is as follows: Step 301, fusing the extracted bone characteristics and physical health characteristics with historical implant case data to form a unified data set, and constructing an implant program evaluation model; Step 302, dividing the integrated data set into a training set, a validation set and a test set, using the training set data to train the planting scheme evaluation model, and using the validation set and the test set data to verify the trained planting scheme evaluation model, and evaluating the performance of the planting scheme evaluation model; Step 303, based on the trained implant scheme evaluation model and combined with the bone characteristics and physical health characteristics, a bone condition index and a physical health condition index are calculated; Step 304, using VR and AR technology in combination with the current patient's oral digital model and the implant plan evaluation model, constructing a three-dimensional surgical scene, and simulating the surgical process in the three-dimensional surgical scene, while combining the patient's bone condition index and physical health index to predict the risk of complications during the operation and generate simulated surgical results.

5. The oral implant optimization method based on big data according to claim 1, characterized in that: The calculation formula of the bone condition index is: Among them, BCI is bone condition index, BMD is bone mineral density, BS is bone structure score, BV is bone volume, BS0 is the baseline value of bone structure score, and σ is the standard deviation of bone structure score; The calculation formula of the physical health status index is: Among them, PHI is the physical health index, HR is the heart rate, is the body mass index, AGE is age, SB is the health score, and SB ref is the reference value of the health score, and τ is the sensitivity coefficient of the health score.

6. The oral implant optimization method based on big data according to claim 1, characterized in that: In step 4, the implant risk assessment coefficient and the surgical risk level are obtained as follows: Step 401, associating and integrating the acquired bone condition index and physical health condition index with the patient's individual data, and matching them with historical implant case data, and analyzing the similarity between the patient's oral condition and historical cases; Step 402, based on historical implant case data, assign different weights to the bone condition index and the physical health condition index, perform weighted calculation on the two indexes, and obtain an implant risk assessment coefficient; Step 403, combining the simulated surgery results and the calculated implant risk assessment coefficient, assessing the surgery risk and generating an assessment report; Step 404, based on the evaluation results and matching the historical implant case data, determine the surgical risk level, which is low risk level, medium risk level, high risk level and extremely high risk level, and match the determined different risk levels with the implant risk assessment coefficient, set corresponding risk assessment thresholds for different risk levels, and formulate corresponding risk management plans.

7. The oral implant optimization method based on big data according to claim 1, characterized in that: The calculation formula of the planting risk assessment coefficient is: Among them, RAC is the implant risk assessment coefficient, BCI is the bone condition index, and BCI base is the baseline value of bone condition index, PHI is the physical health index, PHI base BCI is the baseline value of the physical health index. max is the maximum value of bone mass index, PHI max is the maximum value of the health status index, age is the current patient's age, ref is the reference value of age, ∈ is the sensitivity coefficient of age.

8. The oral implant optimization method based on big data according to claim 1, characterized in that: The plurality of risk levels correspond to the plurality of risk assessment thresholds, wherein the risk assessment thresholds include an upper threshold and a lower threshold; The multiple risk levels and the multiple risk assessment thresholds satisfy the following relationship: Low risk RAC <S1; Medium risk level S1≤RAC <S2; High risk level S2≤RAC <S3; Very high risk level S3≤RAC; Among them, RAC is the planting risk assessment coefficient, S1 is the upper threshold corresponding to the low risk level and the lower threshold corresponding to the medium risk level, S2 is the upper threshold corresponding to the medium risk level and the lower threshold corresponding to the high risk level, and S3 is the upper threshold corresponding to the high risk level and the lower threshold corresponding to the extremely high risk level.

9. The oral implant optimization method based on big data according to claim 1, characterized in that: In step 5, the process of adjusting the surgical plan and setting corresponding preventive measures to formulate a personalized oral care plan is as follows: Step 501, according to the assessment report, determine the surgical risk level of the patient, and adjust the position, number, length and diameter of the implant according to the corresponding risk level, and formulate targeted preventive measures, such as preparing necessary emergency equipment and drugs; Step 502, using an oral endoscope, X-ray and ECG monitor to monitor the patient's vital signs and surgical status in real time, and taking immediate intervention measures when an emergency occurs; Step 503, after the patient's surgery, the patient's postoperative pain level, swelling and infection signs are recorded, and the changes in the tissue around the implant are evaluated. At the same time, the patient's postoperative recovery data is regularly collected to develop a personalized oral maintenance plan.

10. An oral implant optimization system based on big data, used to implement an oral implant optimization method based on big data according to any one of claims 1 to 9, comprising a data management module, characterized in that: The data management module is communicatively connected with a data acquisition module, a data analysis module, a surgery simulation and planning module, a implantation plan and risk assessment module, and an intraoperative real-time monitoring and early warning module, wherein electrical signals are connected between the modules; The data acquisition module is used to collect historical implant case data, basic patient information, oral examination data, imaging data, and digital impression and facial three-dimensional scanning data; The data analysis module is used to pre-process the data collected by the data collection module, extract features related to oral implants, and analyze the patient's bone condition and physical health, as well as the suitability of the implant; The surgical simulation and planning module is used to simulate the implant surgery process, pre-plan the surgical plan, and predict the risk of complications that may occur to the patient during the surgery; The implantation plan and risk assessment module is used to build an implantation plan assessment model in combination with historical implantation case data, conduct a comprehensive assessment of surgical risks, determine the surgical risk level of the patient, adjust the surgical plan and set corresponding preventive measures; The intraoperative real-time monitoring and early warning module is used to collect the patient's vital signs and surgical data in real time during the patient's operation, and to send out early warning signals when abnormalities are found to remind the surgical team to take corresponding measures. At the same time, it collects the patient's recovery data after surgery and formulates a personalized oral maintenance plan.

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