Dental implant reconstruction and restoration based on digital twin

A method, computer system, and a computer program product for simulating dental implants is provided. The present invention may include generating one or more digital twins for a potential dental implant based on a user profile. The present invention may include simulating a performance of each of the one or more digital twins in one or more user modeled environments. The present invention may include ranking the one or more digital twins based on the performance of the potential dental implant in each of the one or more user modeled environments. The present invention may include monitoring a selected dental implant using a corresponding digital twin.

BACKGROUND

The present invention relates generally to the field of computing, and more particularly for simulating dental implants.

Dental implant surgery may be a procedure that replaces tooth roots with metal screwlike posts and/or replaces missing teeth with artificial teeth that may function as original teeth. Dental implant surgery may offer an alternative to dentures and/or bridgework that may not work for a patient. How dental implant surgery may be performed may depend on at least a type of implant and/or the condition of a patient's jawbone. Dental implant surgery may require more than one procedure and may require bone of the patient to heal around the implant, which may be a months long process.

Dental implant surgery may require significant due diligence on the part of a surgeon both pre and post-operation. Pre-operation the surgeon may need to assess a variety of factors, including at least implant materials, length and width of an implant, implant position, type of abutment, angle of abutment, tooth conditions, jaw conditions, and/or bone conditions, amongst other determinations. Furthermore, post-operation monitoring the implant as well as patient behavior may be critical to placing a successful implant.

SUMMARY

Embodiments of the present invention disclose a method, computer system, and a computer program product for simulating dental implants. The present invention may include generating one or more digital twins for a potential dental implant based on a user profile. The present invention may include simulating a performance of each of the one or more digital twins in one or more user modeled environments. The present invention may include ranking the one or more digital twins based on the performance of the potential dental implant in each of the one or more user modeled environments. The present invention may include monitoring a selected dental implant using a corresponding digital twin.

DETAILED DESCRIPTION

The following described exemplary embodiments provide a system, method and program product for dental implants. As such, the present embodiment has the capacity to improve the technical field of dental implants by simulating dental implant surgery using one or more digital twins. More specifically, the present invention may include generating one or more digital twins for a potential dental implant based on a user profile. The present invention may include simulating a performance of each of the one or more digital twins in one or more user modeled environments. The present invention may include ranking the one or more digital twins based on the performance of the potential dental implant in each of the one or more user modeled environments. The present invention may include monitoring a selected dental implant using a corresponding digital twin.

As described previously, dental implant surgery may be a procedure that replaces tooth roots with metal screwlike posts and/or replaces missing teeth with artificial teeth that may function as the original teeth. Dental implant surgery may offer an alternative to dentures and/or bridgework that may not work for a patient. How dental implant surgery may be performed may depend on at least a type of implant and/or the condition of a patient's jawbone. Dental implant surgery may require more than one procedure and may require bone of the patient to heal around the implant, which may be a several month long process.

Dental implant surgery may require significant due diligence on the part of a surgeon both pre and post-operation. Pre-operation the surgeon may need to determine at least, implant materials, length and width of the implant, implant position, type of abutment, angle of abutment, tooth conditions, jaw conditions, and bone conditions, amongst other determinations. Furthermore, post-operation monitoring the implant as well as patient behavior may be critical to a successful implant.

Therefore, it may be advantageous to, among other things, generate one or more digital twins for a potential dental implant based on a user profile, simulate a performance of each of the one or more digital twins in one or more user modeled environments, rank the one or more digital twins based on the performance of the potential dental implant in each of the one or more user modeled environments, and monitor a selected dental implant using a corresponding digital twin.

According to at least one embodiment, the present invention may improve the operational process of and/or the implant materials used for dental implant surgery by simulating the performance of one or more digital twins in one or more user modeled environments.

According to at least one embodiment, the present invention may improve preventative medicine by utilizing one or more digital twins to simulate the performance of a dental implant surgery.

According to at least one embodiment, the present invention may improve user personalized dental implants by generating 3D printing instructions for one or more components of the dental implant selected by the user.

According to at least one embodiment, the present invention may improve dental implant surgery by simulating at least biocompatibility performance, bone and/or soft tissue cultivation performance, implant side effects, and implant stress performance.

According to at least one embodiment, the present invention may improve user evaluation of unique dental implant options based on at least, simulation data, potential treatment plans, and projected cost.

According to at least one embodiment, the present invention may improve preventative medicine by monitoring the performance of a dental implant using a corresponding digital twin. The digital twin may also be used for one or more life cycle simulations in identifying one or more potential complications associated with a dental implant. Additionally, the digital twin may be used for providing one or more recommendations to a user and/or dental specialist based on the one or more life cycle simulations of the corresponding digital twin.

According to at least one embodiment, the present invention may reduce the side effects experienced by dental implant surgery patients. Side effects of dental implant surgery may include, but are not limited to including, aesthetic complications, peri-implant inflammation, damage to surrounding structures such as teeth or blood vessels, swelling of the gums or face, bruising of the skin or gums, pain at the implant site, prolonged bleeding, health of soft and hard tissue, nerve damage, and/or sinus problems, amongst other side effects.

According to the present embodiment, a user using a client computer102or a server computer112may use the dental implant program110a,110b(respectively) to simulate a plurality of dental implant surgeries utilizing one or more generated digital twins. The dental implant method is explained in more detail below with respect toFIG.2.

Referring now toFIG.2, an operational flowchart illustrating the exemplary dental implant process200used by the dental implant program110aand110b(hereinafter dental implant program110) according to at least one embodiment is depicted.

At202, the dental implant program110receives user data. The user data may be for a patient requiring dental implant surgery. User data may include, but is not limited to including, Electronic Dental Records (EDR), Electronic Medical Records (EMR), dental scans, tomographic imaging, radiograph imaging, dental profiling, X-rays, dental impressions, teeth color, pre-existing medical condition(s), bone conditions, bone quality, characteristics of the bone matrix, microarchitecture, bone turnover, accumulation of microdamage, degree of calcification, collagen content, teeth conditions, jaw conditions, IoT (Internet of Things) data, consumption data, amongst other user data relevant to dental implant surgery. All user data received by the dental implant program110shall not be construed as to violate or encourage the violation of any local, state, federal, or international law with respect to privacy protection. The dental implant program110may receive consent from the user and/or a dental specialist prior to receiving the user data.

As will be explained in more detail below, consumption data may be gathered for the user pre-operation and/or post-operation. Consumption data may include, but is not limited to including, food intake, saliva pH (power of hydrogen), quantity of acidic beverages consumed, quantity of acidic foods consumed, and/or blood alcohol levels, amongst other consumption data. Consumption data may be received based on at least, one or more of, user input, IoT device tracking, a tele-dentistry appointment, and/or a consultation with a periodontist, oral surgeon, dentist, or other dental specialist, amongst other methods. The consumption data received from IoT device tracking may be received in real time by the dental implant program110. The dental implant program110may further require periodic consent with respect to any additional user data received from the user through at least user input and/or IoT devices. The dental implant program110may only receive user data relevant to dental implant surgery. An example of user inputted consumption data may be a user's tracked diet in a mobile application. An example of receiving consumption data through IoT device tracking may include, but is not limited to including, receiving data from one or more, smart toothbrushes, IoT diet wearables, and/or smart teeth. A smart toothbrush may be embedded with technologies such as cameras, and/or pressure sensors, amongst other technologies. Smart teeth may involve the utilization of a microchip either implanted in a tooth or mounted to a tooth with the ability to track user consumption.

The dental implant program110may utilize the user data in generating a user profile (e.g., a profile which may be specific to a user, also referred to as a patient, where the data utilized in generating and/or updating the user profile is the patient's data relevant to dental implant surgery). The dental implant program110may store the user profile in a knowledge corpus (e.g., database114). The knowledge corpus (e.g., database114) may store a plurality of user profiles (e.g., plurality of profiles each specific to a unique user), a sub-set of which may be utilized by the dental implant program110. As will be explained in more detail below, the user data and user profile may be utilized by the dental implant program110in generating one or more digital twins for a potential dental implant placement based on one or more similar user profiles (e.g., one or more other user profiles with similar data relevant to dental implant surgery).

At204, the dental implant program110generates one or more digital twins for a potential dental implant. A digital twin may be a virtual representation of an object or system that spans the object or system's lifecycle, may be updated using real-time data, and may utilize at least, simulation, machine learning, and/or reasoning in aiding informed decision making. The dental implant program110may generate the one or more digital twins based on at least the user data and/or user profile and the sub-set of one or more similar user profiles stored in the knowledge corpus (e.g., database114). The dental implant program110may also generate one or more digital twins based on specifications received from a dental specialist.

The dental implant program110may generate the one or more digital twins for the potential dental implant based on at least, one or more of, a virtual representation of the user, an operational process, and/or implant properties. The virtual representation of the user may include at least the fourteen facial bones, soft tissue, and teeth of the user. The dental implant program110may generate the virtual representation based on at least the user data received. The virtual representation may also incorporate user data such as, but not limited to, jawbone conditions and bone mineral density. As will be explained in more detail below with respect to step210, the dental implant program110may update the user data incorporated into the virtual representation of the user. Each of the one or more digital twins generated by the dental implant program110may be unique with respect to at least, the operational process and/or the implant properties.

Dental implant surgery may include five main steps. First, the removal of one or more teeth. Second, insertion of a dental implant. Third, osseointegration of the dental implant. Fourth, abutment placement atop the dental implant. Fifth, the addition of a crown or an artificial tooth. The operational process with respect to each step may vary considerably. The operational process may vary with respect to at least, inserting the dental implant into the jawbone or under the gumline, implant loading types, healing periods, osseointegration time, sinus augmentation, bone level tapering, amongst other operational processes. The implant properties may also vary considerably. The implant properties may vary with respect to at least, materials of an artificial tooth and/or crown, abutment materials, implant materials, width of implant, length of implant, abutment angles, amongst other implant properties. Materials utilized in dental implant surgery may include, but are not limited to including, one or more of, Niobium, Zirconia, Titanium, porcelain, steel, amongst other materials currently utilized and/or hereinafter utilized.

The dental implant program110may utilize the one or more similar user profiles in determining different combinations of operational processes and/or implant properties that either resulted in a successful dental implant surgery and/or resulted in one or more complications in the dental implant surgery.

For example, the dental impact program110receives user data for User 1. Based on the user data from User 1 the dental impact program110may generate User Profile 1 and may store User Profile 1 in the knowledge corpus (e.g., database114). Based on User Profile 1 the dental implant program may identify four similar user profiles within the knowledge corpus. Of the four similar user profiles three reported a dental implant surgery and one reported an unsuccessful dental implant surgery. The dental impact program110may generate five unique combinations of operational processes and/or implant properties based on the three successful surgeries and the one unsuccessful surgery. The dental impact program110may utilize the five unique combinations of operational processes and/or implant properties to generate five digital twins, Digital Twin 1, Digital Twin 2, Digital Twin 3, Digital Twin 4, and Digital Twin 5. Additionally, the dental impact program110may receive specifications from the oral surgeon scheduled to perform the dental implant surgery for User 1. The specifications from the oral surgeon may include both the operational process and implant properties the oral surgeon had planned to utilize in the surgery based on the surgeon's examination and/or user data available to the surgeon. The dental impact program110may generate Digital Twin 6 based on the specifications received from the oral surgeon. Digital Twin 1 may be for a titanium implant inserted into the jawbone of User 1, a 12 week osseointegration period, a 15 degree titanium angular abutment, and porcelain crown. Digital Twin 2 may be for a titanium, niobium, and zirconia implant inserted into the jawbone of User 1, a 12 week osseointegration period, a 15 degree titanium angular abutment, and porcelain crown. Digital Twin 3 may be for a titanium implant inserted under the gumline of User 1, a 16 week osseointegration period, a 25 degree titanium angular abutment, and porcelain crown. Digital Twin 4 may be for a titanium, niobium, and zirconia implant inserted under the gumline of User 1, a 14 week osseointegration period, a 35 degree titanium angular abutment, and zirconium crown. Digital Twin 5 may be for a titanium implant inserted into the jawbone of User 1, a 12 week osseointegration period, a 15 degree titanium angular abutment, porcelain crown, and sinus augmentation. Digital Twin 6 may be for a titanium implant inserted into the jawbone of User 1, a 16 week osseointegration period, no angular abutment, and porcelain crown. As will be explained in more detail below, the dental implant program110may simulate the performance of each of the six digital twins in a user modeled environment. It should be noted that any specific material (e.g., titanium) or process (e.g., osseointegration) may be merely exemplary with the present invention not being limited to either presently or future developed materials and/or processes.

At206, the dental implant program110simulates the performance for each of the one or more digital twins. The dental implant program110may simulate the performance for each of the one or more digital twins in one or more user modeled environments. The dental implant program110may utilize one or more machine learning models in simulating the performance of the one or more digital twins in the one or more user modeled environments. The one or more machine learning models may include, but are not limited to including, Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, and/or a hybrid model. The hybrid model may be trained to combine the predictions of two or more machine learning models.

The dental implant program110may generate the one or more user modeled environments using, at least one or more of, the user profile, user data, and/or consumption data. The dental implant program110may adjust, at least one or more of, the user profile user data, and/or consumption data. The dental implant program110may generate a user modeled environment for each adjustment, wherein each adjustment may deviate from at least one or more of, the user profile, user data, and/or consumption data, such that each of the one or more user modeled environments may allow the dental implant program110to account for at least errors and/or lack of data received from the user. As will be explained in more detail below, the dental implant program110may data based on the quantity and/or quality of the data to account for at least errors and/or deviations. For example, User 1 may have provided 3 weeks of consumption data using a diet tracking mobile application and User 2 may have provided 3 months of consumption data from a smart toothbrush and IoT diet wearable. The dental implant program110may adjust the user modeled environment of User 1, 20 times to account for a likelihood User 1 deviates from the consumption data provided. On the other hand, the dental implant program110may adjust the user modeled environment of User 2, 3 times to account the likelihood User 2 deviates from the consumption data provided. In this example, the diet tracking mobile application used by User 1 may be considered lower quality data as compared to the higher quality data provided from User 2 from the smart toothbrush and IoT diet wearable. Additionally, the 3 weeks of consumption data provided by User 1 may be considered a lower quantity of data compared to the 3 months of consumption data provided by User 2. The number of user modeled environments may impact a confidence score of the simulated performance for each of the one or more digital twins. In the above example, if the dental impact program110generated three digital twins for User 1, the dental impact program110would simulate each of the three digital twins 20 times, for a total of 60 digital twin simulations. If the dental impact program110generated ten digital twins for User 2, the dental impact program110would simulate each of the ten digital twins 3 times, for a total of 30 digital twin simulations.

Simulating the performance for each of the one or more digital twins may include, but is not limited to including, implant stress performance, implant biocompatibility performance, bone and/or soft tissue cultivation performance, implant side effects, amongst other performance indicators.

The dental implant program110may simulate implant stress performance by monitoring at least, tensile stress, vertical stress, and/or angular stress, which may be simulated for each of the one or more digital twins in the one or more user modeled environments. For example, the dental implant program110may have received several weeks of consumption data from User 1 from a diet tracking mobile application. The dental implant program110may simulate the performance of each of the one or more digital twins based on the consumption data of User 1 and how each model performed with respect to implant stress. In this example, a digital twin which chipped, fractured, or loosened may be weighted negatively with respect to implant stress performance.

The dental implant program110may simulate implant biocompatibility performance based on at least, biological microbial interactions, toxicity generation, and/or corrosive monitoring, amongst other factors. The dental implant program110may simulate the implant biocompatibility performance over a lifecycle span of each of the one or more digital twins. For example, User 1 may require a lifecycle of 50 years for the dental implant and User 2 may require a lifecycle of 20 years for the dental implant. In both the User 1 and User 2 simulations the digital twins with a titanium implant corroded after 40 years and the digital twins with a zirconia implant showed no signs of corrosion but required replacement after 60 years. For User 1 the titanium implant may be weighted slightly negatively, and the zirconia implant may be weighted positively with respect to biocompatibility performance. For User 2 both the titanium implant and the zirconia implant may be weighted positively.

The dental implant program110may simulate bone and/or soft tissue cultivation performance for each of the one or more digital twins in the one or more user modeled environments by monitoring at least bone turnover markers (BTMs). BTMs may include, but are not limited to including, bone mineral density, soft bone tissue, bone width, and cortical-trabecular bone thickness. BTMs levels may vary significantly based on preanalytical factors and/or comorbid clinical conditions. Additionally, BTMs may respond rapidly to changes in bone physiology.

In an embodiment, the dental implant program110may also include a potential treatment plan for each of the one or more digital twins based on at least the simulated resulting side effects. As will be explained in more detail below, the potential treatment plan for each of the one or more digital twins may be included in the simulation data provided to the user.

At208, the dental implant program110ranks the one or more digital twins. The dental implant program110may rank the one or more digital twins based on the simulation performance, wherein each of the performance of each of the one or more digital twins may represent the performance of a potential dental implant in each of the one or more user modeled environments. The ranking of the one or more digital twins may include a recommended dental implant for the user. The recommended dental implant for the user may be a highest ranked digital twin.

The dental implant program110may include simulation data for each of the one or more digital twins based on the simulation performance of the digital twin in the one or more user modeled environments. The simulation data for each of the one or more digital twins may include at least, implant stress performance data, implant biocompatibility performance data, and soft tissue cultivation data, implant side effects data, when an implant failed, why an implant failed, complications with each dental implant, and/or the effect of consumption data on the performance simulation for each digital twin, amongst other data. The dental implant program110may also include a result probability and/or confidence score with respect to the simulation data. The result probability and/or confidence score may be represented using a percentage, fraction, term (e.g., high, medium, low), or other representation.

The dental implant program110may also provide a potential treatment plan for each of the one or more digital twins. The treatment plan may include, but is not limited to including, oral hygiene recommendations, dietary restrictions, a timeline for recommended follow up appointments with the dental specialist, and/or medications, amongst other potential treatments.

The dental implant program110may also include a projected cost for a dental implant surgery with respect to each of the one or more digital twins. The dental implant program110may determine the projected cost based on at least the implant materials and/or the user's insurance provider, only as provided by the user. A projected cost may be dynamic and/or modifiable based on a change in a user's insurance provider and/or plan, a change in cost of implant materials, and/or a change in physician. All insurance provider data received by the dental implant program110shall not be construed as to violate or encourage the violation of any local, state, federal, or international law with respect to privacy protection. The dental implant program110may receive consent from the user prior to determining the projected cost for each of the one or more digital twins.

For example, the dental implant program110may generate two digital twins for User 1 based on five similar patient profiles, Digital Twin 1 and Digital Twin 2. The dental implant program110may generate a third digital twin based on specifications received from User 1's dental specialist, Digital Twin 3. The dental implant program110may have received a limited amount of consumption data for User 1, requiring the dental implant program110to simulate the performance of each digital twin in 20 user modeled environments. The dental implant program110may have ranked Digital Twin 3 the highest, Digital Twin 2 second, and Digital Twin 1 the lowest. Digital Twin 3 may have had no major complications during any of the performance simulations, a 24 week osseointegration period, but corroded in under 25 years in all simulations. Accordingly, the dental implant program110may have determined a high result probability of no major complications for Digital Twin 3 with a high confidence score. Digital Twin 2 may have had two major complications including an implant failure, a 12 week osseointegration period, and corroded after 75 years in 18 of the 20 simulations. Accordingly, the dental implant program110may have determined a high result probability of major complications. Digital Twin 1 may have had six major complications during the 20 simulation performances, implant side effects in four simulation performances, a 24 week osseointegration period, and corroded after 15 years. Accordingly, the dental implant program110may have determined a high result probability of major complications. In this example, User 1 and the dental specialist may review the rankings of the three digital twins as well as the simulation data and notice the two user modeled environments that resulted in major complications for Digital Twin 2 are unrepresentative of User 1's consumption habits. User 1 may have stopped smoking, drinking alcohol, or the consumption data was unrepresentative of User 1's dietary habits and the potential treatment plan's dietary restrictions for Digital Twin 2 are easily achievable. Additionally, the potential treatment plan's dietary restrictions for Digital Twin 2, the osseointegration period and the projected cost are more favorable to User 1 as compared to Digital Twin 3. Accordingly, User 1 and the dental specialist may select Digital Twin 2. User 1 and the dental specialist may also provide updated consumption data to the dental implant program110to re-run the simulation performance of the three digital twins.

In an embodiment, the dental implant program110may request additional consumption data of the user based on at least, the simulation performance results, a threshold of user modeled environments being exceeded, one or more confidence scores below a threshold confidence score, amongst other reasons. The dental implant program110may request the additional consumption data by at least displaying a message and/or notification with the rankings of the one or more digital twins.

In an embodiment, the dental implant program110may generate 3D printing instructions for one or more components of the dental implant selected by the user. For example, the user may select a dental implant based on the ranking of the one or more digital twins, the corresponding dental implant for Dental Implant 2 may be Digital Twin 2. The crown virtually represented in Digital Twin 2 may be a ceramic with infused anti-microbial properties in a shade that matches the user's natural tooth color. Accordingly, the dental implant program110may generate 3D printing instructions for the ceramic crown with infused anti-microbial properties in the shade that matches the user's natural tooth color.

In an embodiment, the dental implant program110may enable a dental specialist to adjust at least the operational processes and/or implant properties of the one or more digital twins and simulate the performance of the one or more adjusted digital twins for the user. The dental implant program110may retrain the machine learning model based on the simulated performance of the one or more adjusted digital twins for the user. For example, if Digital Twin 1 is the highest ranked digital twin and then the dental specialist adjusts an implant property and the adjusted digital twin ranks higher than Digital Twin 1, the dental implant program may retrain the machine learning model.

At210, the dental implant program110monitors a selected dental implant. The dental implant program110may monitor the dental implant (e.g., selected dental implant) through the one or more steps of dental implant surgery and post-operation. The dental implant program110may monitor the dental implant (e.g., selected dental implant) using a corresponding digital twin, the corresponding digital twin being the digital twin of the dental implant selected by the user from the ranking of the one or more digital twins. The dental implant program110may monitor the dental implant (e.g., selected dental implant) using new data, such as, but not limited to, surgical procedure data, additional consumption data, updated user data, and/or additional user data, amongst other new data. The dental implant program110may also utilize the new data to update the user profile stored in the knowledge corpus (e.g., database114). As detailed above with respect to step204, the dental implant program110may utilize the user profile (e.g., patient profile) in generating digital twins for one or more similar users.

The dental implant program110may monitor the dental implant (e.g., selected dental implant) surgical procedure using one or more hand and/or tool motion tracking techniques, such as, but not limited to, Optical Tracking, Electromagnetic Tracking, amongst other motion tracking systems. Optical Tracking Systems may consist of a receiver until including two or more cameras and a set of special markers attached to the object. Electromagnetic Tracking Systems may consist of three components, a field generator, sensor unit, and central control unit. The dental implant program110may utilize the one or more hand and/or tool motion tracking techniques in monitoring at least, the removal of one or more teeth, insertion of the dental implant, abutment placement atop the dental implant, and the addition of an artificial tooth or crown. The dental implant program110may utilize the data received from the dental implant surgical procedure to update the corresponding digital twin. The dental implant program110may utilize surgical procedure data such as, but not limited to, insertion torque, probing torque, heat production, bone socket diameter, bone socket tapering angles, amongst other data, captured images of the dental implant (e.g., selected dental implant, in updating the corresponding digital twin.

The dental implant program110may utilize at least, additional consumption data, updated user data, and/or additional user data, such as data received from follow up appointments, to monitor the dental implant and/or update the corresponding digital twin throughout osseointegration and/or post-operation.

The dental implant program110may utilize the one or more machine learning models to update the corresponding digital twin using the new data. The dental implant program110may run a simultaneous simulation of the corresponding digital twin to monitor the dental implant (e.g., selected dental implant). The dental implant program110may retrain the one or more machine learning models based on a comparison of the dental implant and the corresponding digital twin. For example, the dental implant program110may receive the surgical procedure data, updated Electronic Dental Records, new radiograph imaging, amongst other new data, following the insertion of the dental implant. At this point the corresponding digital twin is a virtual representation of the dental implant (e.g., selected dental implant). The osseointegration period between the insertion of the dental implant and the abutment placement atop the dental implant may be 14 weeks with a scheduled follow up appointment mid-way through at 7 weeks. Between the insertion of the dental implant and the follow up appointment the dental implant program may receive additional consumption data from one or more user IoT devices. The dental implant program110may run the simultaneous simulation of the corresponding digital twin for 7 weeks using the additional consumption data. At the 7-week follow up appointment the dental implant program110may receive new data detailing the progress, state, and other details of the dental implant, which may be utilized in determining the actual performance of the dental implant implanted in the user during that 7-week period. The dental implant program110may compare the corresponding digital twin with the dental implant (e.g., selected dental implant) based on the new data received. The dental implant program110may store the differences in the knowledge corpus (e.g., database114) and utilize the differences in retraining the one or more machine learning models.

The differences may be stored in the knowledge corpus (e.g., database114) and utilized in retraining the one or more machine learning models to improve performance simulations of future digital twins. In the above example, based on the consumption data received from the user leading up to the 7-week follow up appointment the dental implant program110simulated the performance of the dental implant using the consumption data. The new data received at the 7-week follow up appointment shows the dental implant (e.g., selected dental implant) did not perform as expected based on the corresponding digital twin in the 7 intervening weeks. The dental implant program110may compare the corresponding digital twin and the dental implant (e.g., selected dental implant) and store the differences in the knowledge corpus (e.g., database114). User 2 may be a user with a similar user profile in day 1 of the osseointegration period. The dental implant program110may receive very similar consumption data from User 2 during User 2's osseointegration period. The one or more machine learning models, having been retrained using the differences stored in the knowledge corpus (e.g., database114), may simulate the performance of User 2's dental implant (e.g., the dental implant selected by User 2) using User 2's corresponding digital twin. At User 2's 7-week follow up appointment the new data may show that User 2's corresponding digital twin accurately reflects the dental implant (e.g., the dental implant selected by User 2). The dental implant program110may store the small differences between User 2's corresponding digital twin and User 2's dental implant (e.g., the dental implant selected by User 2) in the knowledge corpus (e.g., database114). The dental implant program110may utilize the simulations to identify relationships between consumption data and user profiles to provide personalized recommendations to the user.

The dental implant program110may also run one or more life cycle simulations of the corresponding digital twin each time new data is received using the one or more machine learning models. The one or more life cycle simulations may be utilized in identify potential complications in the dental implant (e.g., selected dental implant). The dental implant program110may utilize the one or more life cycle simulations in providing one or more recommendations to the user and/or dental specialist.

In an embodiment, the dental implant program110may generate a smart contract between a user and an insurance company. A smart contract may be a program stored on a blockchain the executes upon the fulfillment of predetermined conditions. For example, the dental implant program110may attach incentives to the treatment plan of the user in exchange for reduction of liability if the user's implant fails following a violation of the treatment plan. The smart contract may be stored on the blockchain and executed based on consumption data received by the dental implant program110from one or more agreed IoT devices.

At212, the dental implant program110provides one or more recommendations. The dental implant program110may provide the one or more recommendations to the user and/or to a dental specialist (e.g., a dentist, a dental hygienist, among other dental specialists). The dental implant program110may provide the recommendations based on any new data collected as a result of any life cycle simulations of the corresponding digital twin. The new data may reveal issues with the dental implant and/or the user's dental hygiene for which there may be a solution and/or a recommendation may be provided.

The recommendations may include, but are not limited to including, oral hygiene recommendations, such as an interdental brush, recommended dietary adjustments, such as food and/or drink substitutions based on consumption data, chewing recommendations, scheduling a follow up appointment, amongst other recommendations. In an embodiment the dental implant program110may schedule appointments automatically. The dental implant program110may utilize calendaring data of the dental specialist and request confirmation from the user.

The dental implant program110may request the user upload images of the dental implant (e.g., selected dental implant) prior to providing the recommendations. The dental implant program110may analyze the one or more uploaded images and compare with the corresponding digital twin. The dental implant program110may utilize the images of the dental implant in determining an accuracy of the life cycle simulations of the corresponding digital twin.

For example, the dental implant program110may run one or more life cycle simulations of the corresponding digital twin based on new data received. In one or more of the life cycle simulations the corresponding digital twin may fail prior to the user's next scheduled appointment. The dental implant program110may request the user upload images of the dental implant to determine if the one or more life cycle simulations are accurate based on the current state of the dental implant. The user may upload the images and the dental implant program110may compare those images to the dental implant and determine any differences between the corresponding digital twin and the dental implant. If the corresponding digital twin is an accurate representation of the dental implant the dental implant program110may determine the one or more life cycle simulations are accurate. Accordingly, the dental implant program110may recommend the user schedule a follow up appointment with the dental specialist prior to the simulated dental implant failure. Alternatively, the corresponding digital twin may be inaccurate. The user may have for example forgot to input consumption data and/or the data received from an IoT device may have been inaccurate. Here, the dental implant program110may utilize the uploaded images to update the corresponding digital twin to accurately reflect the dental implant. The dental implant program110may then run one or more life cycle simulations of the updated corresponding digital twin and determine that the dental implant does not fail in any life cycle simulations prior to the user's next scheduled appointment. The dental implant program110may not recommend scheduling a follow up appointment with the dental specialist prior to the next scheduled appointment. The dental implant program110may still provide other recommendations such as oral hygiene recommendations.

It may be appreciated thatFIG.2provides only an illustration of one embodiment and do not imply any limitations with regard to how different embodiments may be implemented. Many modifications to the depicted embodiment(s) may be made based on design and implementation requirements.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Referring now toFIG.5, a set of functional abstraction layers1100provided by cloud computing environment1000is shown. It should be understood in advance that the components, layers, and functions shown inFIG.5are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:

Workloads layer1144provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation1146; software development and lifecycle management1148; virtual classroom education delivery1150; data analytics processing1152; transaction processing1154; and dental implant1156. A dental implant program110a,110bprovides a way to simulate dental implant surgery utilizing one or more digital twins.

The present disclosure shall not be construed as to violate or encourage the violation of any local, state, federal, or international law with respect to privacy protection.