Patent Application
20220257341
This disclosure relates to dental treatment planning using virtual articulation.
The field of orthodontics relates to repositioning a patient's teeth for improved function and aesthetic appearance. Orthodontic devices and treatment methods generally involve the application of forces to move teeth into a proper bite configuration, or occlusion. As one example, orthodontic treatment may involve the use of slotted appliances, known as brackets, which are fixed to the patient's anterior, cuspid, and bicuspid teeth. An archwire may be placed in the slot of each bracket and serves as a track to guide movement of the teeth to desired orientations. The ends of the archwire are received in appliances known as buccal tubes that are secured to the patient's molar teeth. Such dental appliances remain in the mouth of the patient and are periodically adjusted by an orthodontist until proper alignment is achieved.
Orthodontic treatment may also involve the use of alignment trays, such as clear or transparent, polymer-based tooth positioning trays, often referred to as clear tray aligners (CTAs). For example, orthodontic treatment with CTAs may include forming a tray having shells that engage one or more teeth. Each shell may have a shape that is deformed upon being installed over the patient's teeth. The deformed position of a respective shell of the CTA may apply a force to a respective tooth toward a desired position of the tooth that is an intermediate position between an initial position of the respective tooth and a final position resulting from the orthodontic treatment. However, orthodontic treatment may require some tooth movements that are difficult for a CTA to achieve, such as, for example, tooth root movements and rotations of cuspids and bicuspids. In these instances, the forces and moments that a CTA is capable of applying directly to the surfaces of a tooth may be insufficient to achieve the desired tooth movement.
Digital dentistry is a growing trend with an increasing number of dentists using digital impressioning systems. These systems use an intra-oral scanning camera, or scanning of a traditional physical impression, and an associated processing system to generate a digital three-dimensional (3D) model of patients' teeth (e.g., a patient's maxillary and mandibular arches). The digital 3D models can then be used to make prosthodontic restorations and for orthodontic treatment planning.
The goal of the orthodontic treatment planning process is to determine where the post-treatment positions of a person's teeth (setup state) should be, given the pre-treatment positions of the teeth in a malocclusion state. This process is typically performed manually using interactive software and is a very time-consuming process. Intermediate staging of teeth from a malocclusion state to a final state may include determining accurate individual teeth motions in a way that teeth are not colliding with each other, the teeth move toward their final state, and the teeth follow an optimal (preferably short) trajectories. Since each tooth has 6 degrees-of-freedom and an average arch has about 14 teeth, finding the optimal teeth trajectory from initial to final stage has a large and complex search space.
Accurate articulation is one factor in making such orthodontic treatment plans. Current data acquisition for mechanical articulation is time consuming and requires expensive analog devices. In particular, current example techniques involve a manual process using a face bow and lab articulator to capture mandibular articulation data for complex rehabilitations.
In general, this disclosure describes a computer-implemented method and system for evaluating, modifying, and determining setups for orthodontic treatment using metrics computed during virtual articulation. Virtual articulation may refer to the measurement and/or visualization of how three-dimensional scans of a patient's teeth (e.g., the mandibular arch and the maxillary arch) interact with each other at one or more stages of a treatment plan for the purpose of determining a final orthodontic treatment and other dental health planning. The virtual articulation techniques of this disclosure may further be combined with techniques for determining dynamic collision metrics, determining a comfort measurement, determining treatment efficacy, and/or determining dental conditions, such as bruxism. The techniques of this disclosure further include user interface techniques that provide an orthodontist/dentist/technician with information regarding various treatment plans based on metrics gathered during virtual articulation.
In some examples, this disclosure describes a fully automated approach to create intermediate states (i.e., arrangements of teeth) that enable a valid trajectory from a maloccluded state (e.g., an initial state) to a setup state (e.g., a final state). The techniques and systems of this disclosure may be used in conjunction with orthodontic treatment planning for clear tray aligners as well as other treatment modalities (e.g., the Incognito™ Appliance System made by 3M, brackets and wires, etc.) or even multi-modality treatment. Elements of this disclosure may also be used for the visualization or prediction of tooth movement in digital dentistry. The techniques of this disclosure may be implemented in a fully automated treatment planning system or in an interactive software system used by a technician or orthodontist who is designing the treatment plan.
The techniques and systems of this disclosure may enable several improvements over the current treatment planning approaches, including improved scalable clear tray aligner throughput and sales, the ability to generate multiple options to present to a doctor and patient, ability for a doctor to review final setup and intermediate setups together (from a two-phased to one-phased approach), and/or the ability to incorporate increased complexity introduced by biomechanics-based rules and/or complicated, multi-appliance treatments.
In one example, this disclosure describes a method comprising receiving, by a computing device, data indicative of a scan of a virtual maxillary arch representing a maxillary arch of a patient and a virtual mandibular arch representing a mandibular arch of the patient, determining one or more treatment plans for the patient based on an initial state of the virtual maxillary arch and the virtual mandibular arch, modifying, by the computing device, the virtual maxillary arch and the virtual mandibular arch to generate a target state for each of the one or more treatment plans, virtually articulating, by the computing device, the modified virtual maxillary arch and the modified virtual mandibular arch to determine contact points at the target state for each of the one or more treatment plans, computing, by the computing device, a dynamic collision metric based on the contact points at the target state for each of the one or more treatment plans, and outputting, by the computing device, data indicative of the dynamic collision metric for each of the one or more treatment plans.
In another example, this disclosure describes a system comprising a memory configured to store data indicative of a scan of a virtual maxillary arch representing a maxillary arch of a patient and a virtual mandibular arch representing a mandibular arch of the patient, and a processor in communication with the memory, the processor configured to determine one or more treatment plans for the patient based on an initial state of the virtual maxillary arch and the virtual mandibular arch, modify the virtual maxillary arch and the virtual mandibular arch to generate a target state for each of the one or more treatment plans, virtually articulate the modified virtual maxillary arch and the modified virtual mandibular arch to determine contact points at the target state for each of the one or more treatment plans, compute a dynamic collision metric based on the contact points at the target state for each of the one or more treatment plans, and output data indicative of the dynamic collision metric for each of the one or more treatment plans.
In another example, this disclosure describes a non-transitory computer-readable storage medium storing instructions that, when executed, cause at least one processor to receive data indicative of a scan of a virtual maxillary arch representing a maxillary arch of a patient and a virtual mandibular arch representing a mandibular arch of the patient, determine one or more treatment plans for the patient based on an initial state of the virtual maxillary arch and the virtual mandibular arch, modify the virtual maxillary arch and the virtual mandibular arch to generate a target state for each of the one or more treatment plans, virtually articulate the modified virtual maxillary arch and the modified virtual mandibular arch to determine contact points at the target state for each of the one or more treatment plans, compute a dynamic collision metric based on the contact points at the target state for each of the one or more treatment plans, and output data indicative of the dynamic collision metric for each of the one or more treatment plans.
The details of one or more aspects of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims.
Patient scan data 12 may comprise 3D models of the mandibular arch and maxillary arch of a patient. The use of digital 3D models in the dental market is becoming more prevalent. In one example, patient scan data 12 can be acquired directly in vivo using an intra-oral scanner, Cone Beam Computed Tomography (CBCT) scanning (i.e., 3D X-ray), or Magnetic Resonance Imaging (MRI). In other examples, patient scan data 12 can be acquired indirectly by scanning an impression of the teeth or a casting made from an impression of the teeth. Some examples of indirect data acquisition methods include, but are not limited to, industrial Computed Tomography (CT) scanning (i.e., 3D X-ray), laser scanning, and patterned light scanning. Patient scan data 12 can be used for varied clinical tasks including treatment planning, crown and implant preparation, prosthodontic restorations, orthodontic setup design, orthodontic appliance design, and in diagnostic aides, for example to assess or visually illustrate tooth wear. As will be explained in more detail below, system 10 may use patient scan data 12 to perform virtual articulation at one or more stages of a dental treatment plan, calculate dynamic collision metrics based on the virtual articulation, and output the data indicative of the dynamic collision metrics in a way that allows a user to determine the efficacy of dental treatment plans, select particular dental treatment plans, and/or modify a dental treatment process.
An example of a digital 3D model of a patient's mandibular arch (e.g., patient scan data 12) from a scan is shown in
Intra-oral structures include dentition, and more typically human dentition, such as individual teeth, quadrants, full arches, pairs of arches which may be separate or in occlusion of various types, soft tissue (e.g., gingival and mucosal surfaces of the mouth, or perioral structures such as the lips, nose, cheeks, and chin), and the like, as well as bones and any other supporting or surrounding structures. Intra-oral structures can possibly include both natural structures within a mouth and artificial structures such as dental objects (e.g., prosthesis, implant, appliance, restoration, restorative component, or abutment).
Returning to
System 10 may further include an input device 18 for receiving user commands or other information. In some examples, input device 18 is part of computer 14, and in other examples, input device 18 may be separate from computer 14. Input device 18 can be implemented with any device for entering information or commands, for example a keyboard, microphone, cursor-control device, or touch screen. The components of system 10 may also be combined, e.g., a tablet computer can incorporate the processor, display and touch screen input devices into a single unit.
Intermediate staging of teeth from a malocclusion state to a final state includes determining accurate individual teeth motions in such a way that teeth have an acceptably low amount of collision with each other, the teeth move toward their final state, and the teeth follow optimal (preferably short) trajectories. Since each tooth has 6 degrees-of-freedom and an average arch has about 14 teeth, finding the optimal teeth trajectory from initial to final stage has a large and complex search space. An orthodontist may define a treatment plan that defines a target final state of the patient's teeth. The treatment plan may also define one or more desired intermediate states of the teeth as well as the treatment modalities used to achieve the target final state.
System 10 may be configured to receive one or more treatment plans 26. In some examples, a user (e.g., orthodontist) may input treatment plans to computer 14 using input device 18. Computer system 14 may store treatment plans 26 in memory 22. In some examples, treatment plans 26 may include an initial state of the virtual maxillary arch and the virtual mandibular arch as well as a target state (e.g., the final position after treatment) for the patient's teeth. Using the techniques of this disclosure described below, system 10 may perform virtual articulation to determine the efficacy of the target state for treatment plans 26. System 10 may also be configured to determine one or more intermediate states to include in treatment plans 26. In other examples, system 10 or a user may not determine intermediate states until the efficacy and desirability of the target final state is determined.
In other examples, treatment plans 26 may include one or more intermediate states as well as a target final state. Using the techniques of this disclosure described below, system 10 may perform virtual articulation at each of these states to determine the efficacy of the target state for treatment plans 26.
Processor 20 may be configured to use patient scan data 12 and treatment plans 26 to perform virtual articulation and calculate metrics in accordance with the techniques of this disclosure. In the example of
In various examples, processor 20 may include, be, or be part of programmable processing circuitry, fixed function circuitry, one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuitry, as well as any combination of such components.
In the example of
Scan modifier 28 may be configured to receive patient scan data 12 and treatment plans 26. As discussed above, in some examples, treatment plans 26 may define a desired final state of the patient's teeth. In other examples, treatment plans 26 may define one or more intermediate states of the patient's teeth as well as a desired final state of the teeth.
Scan modifier 28 may be configured to extract the state information for each of treatment plans 26 and modify the virtual maxillary arch and the virtual mandibular arch of patient scan data 12 at the target state for each of the one or more treatment plans 26. If treatment plans 26 include intermediate states, scan modifier 28 may be further configured to modify the virtual maxillary arch and the virtual mandibular arch of patient scan data 12 at each of the intermediate states for each of the one or more treatment plans 26. Scan modifier 28 may modify the virtual maxillary arch and the virtual mandibular arch to match the teeth position at each of the states of treatment plans 26.
Virtual articulator 30 may receive the modified virtual maxillary arch and the modified virtual mandibular arch for each of treatment plans 26 and perform virtual articulation on the modified scans. In general, virtual articulation may involve virtually moving the modified scans through various mandibular motions to simulate how a patient's teeth interact in different states during the treatment process. In one example, virtual articulator 30 may articulate the modified virtual maxillary arch and the modified virtual mandibular arch to determine contact points of the patient's teeth at the target state for each of the one or more treatment plans 26. In other examples, virtual articulator 30 may articulate the modified virtual maxillary arch and the modified virtual mandibular arch to determine contact points of the patient's teeth at one or more intermediate states and at the target state for each of the one or more treatment plans 26.
Virtual articulator 30 may be configured to move the modified virtual maxillary arch and the modified virtual mandibular arch through various mandibular poses to simulate a normal range of motion of a patient's teeth. Example mandibular poses may include motions, including one or more of a protrusive excursion, a retrusive excursion, a left lateral excursion, or a right lateral excursion.
Using the virtual maxillary arch as a fixed reference coordinate system, virtual articulator 30 may be configured to transform the relative relationships between the virtual maxillary arch and the virtual mandibular arch into a shared coordinate system to attain transforms describing the various mandibular poses for each individual type of articulation relative to a closed pose, in particular closed to open, closed to protrusive, closed to lateral left, and closed to lateral right. Various forms of interpolation of the mandible position of the virtual mandibular arch and orientation between the closed and respective bite pose, reflecting the mandible motion to attain that specific pose, are then possible. The overall mandibular motion in the virtual articulation model can then be expressed as composite transforms of individual articulation transforms at various stages of interpolation.
The movement of the mandible from the closed pose to any of the other poses can be described, for each pose, as the combination of a rotation matrix (the composite of three rotations around the coordinate axes x, y, z) and a translation vector of the origin of coordinates. This combination (rotation plus translation vector) is usually called a “3D transformation matrix” or more narrowly a “rigid body transform.”
In the particular case of human mandible movement, the possible movements are mechanically conditioned to the condyle and fossa, acting as a “ball joint.” This particular condition of “ball joint” movements permits describing any of those mandible movements (coming from the different poses) as a unique pure rotation (without translation) instead of the combination of a rotation plus a translation (as any generic movement requires).
By moving the modified mandibular arch through various poses relative to the modified maxillary arch, virtual articulator 30 may determine contact points of the teeth at various states of treatment plans 26 (e.g., final target states and/or one or more intermediate states).
In one example, when determining contact points, virtual articulator 30 may additionally be further configured to predict wear facets at these contact points over time resulting from various mandibular motions, such as protrusive/retrusive and left/right lateral excursions.
In other examples, virtual articulator 30 may be configured to determine whether proper canine guidance is achieved. Virtual articulator 30 may be configured to make such a determination as a result of first contact occurring between upper and lower canines as the mandible is shifted laterally (i.e., lateral excursion), thus discluding the posterior teeth (i.e., opening the gape and removing contact between opposing teeth).
In other examples, virtual articulator 30 may be configured to determine whether proper anterior guidance is achieved. Virtual articulator 30 may be configured to make such a determination as a result of first contact occurring between upper and lower incisors as the mandible is protruded, thus discluding the posterior teeth (i.e., opening the gape and removing contact between opposing teeth).
Virtual articulation adds sophistication to the treatment planning frameworks. Some example dental treatment planning systems use static collision measurements given a current mandible and maxilla state of teeth to make determinations of the efficacy of the dental plan. In accordance with the techniques of this disclosure, dynamic collision module 32 may receive the contact points and other measurements produced by virtual articulator 30 and compute a dynamic collision metric. In general, dynamic collision module 32 may map the contact points from virtual articulator 32 and then additionally determine if the contact points are acceptable per clinical requirements. Dynamic collision module 32 may compute a dynamic collision metric based on what level of dynamic occlusion is acceptable.
In some examples, dynamic collision module 32 may compute a dynamic collision metric based on one or more of the following inputs received from virtual articulator 30. In general, dynamic collision module 32 may compute a dynamic collision metric based on contact measures from the various mandibular poses, including maximum intercuspation, lateral left excursion, lateral right excursion, protrusive, and retrusive.
In some examples, dynamic collision module 32 may determine a dynamic collision score based on the following factors. In maximum intercuspation (fully closed bite), a best score may be derived from a maximal number of contacts between opposing teeth, preferably all teeth and preferably a plurality of contacts per tooth. In lateral left excursion, ideally there is only one point of contact between upper and lower left canines; and one contact between upper and lower right canines for lateral right excursion. This single point of contact should exist through nearly the full range of motion from a displacement of slightly greater than the rest position at maximum intercuspation. For a protrusive excursion, there can be more than one point of contact between upper and lower incisors, preferably equal in number between left and right quadrants, and these contacts should be isolated to only the incisors through nearly the full range of motion from a displacement slightly greater than the rest position at maximum intercuspation. Additional contacts will reduce the quality of the dynamic occlusion and thus adversely affect the score.
In another example, dynamic collision module 32 may compute a dynamic collision metric based on a count of the number of collisions. A count of the number of collisions may include a count of collisions (unique colliding pairs of teeth) in a particular state (e.g., target final state and/or one or more intermediate states.
In another example, dynamic collision module 32 may compute a dynamic collision metric based on penetration depth. In one example, penetration depth may be the sum of penetration depths of all collisions in a particular state.
In another example, dynamic collision module 32 may compute a dynamic collision metric based on a collision contact points count. In one example, the collision contact points count is the total count of contact points in all collisions in a particular state. Collision contact points count may serve as an estimate of penetration since it will tend to increase as collisions get deeper.
In another example, dynamic collision module 32 may compute a dynamic collision metric based on a weighted sum of several of the metrics described above, e.g., α×Collision count+β×Penetration depth, where a and 13 are configurable weight values.
The geometric information discussed above can be used to provide physical information to inform treatment planning and facilitate effective communication with clinicians and patients. Scores may also be combined with other information, including landmarks, tooth movement between states, and tooth position to provide holistic oral health and comfort information. Such a system would go beyond being an orthodontic tool and rather serve as a unified treatment platform for dentists, orthodontists, and others.
In one example, a user may use the dynamic collision metric to determine a particular one of treatment plans 26 to use. In another example, a user may manually modify one or more of the intermediate and/or final states of treatment plans 26 based on the dynamic collision metric. In another example, virtual articulation system 24 may automatically determine a particular one of treatment plans 26 to use based on the dynamic collision metric. In another example, virtual articulation system 24 may automatically modify one or more of the intermediate and/or final states of treatment plans 26 based on the dynamic collision metric. For example, virtual articulation system 24 may choose the treatment plan that minimizes the dynamic collision metric as the final setup. In other examples, virtual articulation system 24 may be configured to output the selected treatment plan as a recommended/suggested treatment plan that the user can review and accept.
In addition to calculating a dynamic collision metric, virtual articulation system 24 may further determine a discomfort score based on the dynamic collision metric. Discomfort score module 34 may receive the dynamic collision metric from dynamic collision metric 32 and determine a discomfort score. The discomfort score may indicate the general level of discomfort a patient may experience given the quality and type of dynamic occlusion determined by the virtual articulation.
In one example, a user may use the discomfort score to determine a particular one of treatments plans 26 to use. In another example, a user may manually modify one or more of the intermediate and/or final states of treatment plans 26 based on the discomfort score. In another example, virtual articulation system 24 may automatically determine a particular one of treatments plans 26 to use based on the discomfort score. In another example, virtual articulation system 24 may automatically modify one or more of the intermediate and/or final states of treatment plans 26 based on the discomfort score. For example, virtual articulation system 24 may choose the treatment plan that minimizes the discomfort score as the final setup. Using a discomfort score to make treatment planning decisions may improve patient compliance. In general, patients are less likely to comply with treatment plans that create more discomfort compared to plans that create less discomfort.
For intermediate states, as part of the intermediate staging algorithm which explores different intermediate stages to interpolate between initial (malocclusion) and final (setup) states, virtual articulation system 24 may be configured to evaluate each stage for dynamic occlusion using the virtual articulator and contact points can be mapped which in turn can be converted to a discomfort score for the patient. Since multiple treatment trajectories can be produced between malocclusion and setup, virtual articulation system 24 may determine the trajectory of least discomfort.
In other examples, a user or virtual articulation system 24 may be configured to make treatment planning decisions based on a combination of dynamic collision metrics and discomfort scores.
Virtual articulation system 24 may optionally include a bruxism score module 38. Bruxism score module 38 may receive the output of dynamic collision module 32 (e.g., the dynamic collision metrics) and determine the likelihood of bruxing at any of the stages of treatment plans 26. Bruxing is a condition where a patient may grind, clench, and/or gnash their teeth. The dynamic collision metrics may indicate premature contacts between posterior teeth, i.e., a lack of canine and/or anterior guidance. Such lack of canine and/or anterior guidance may lead to discomfort, but also may tend to lead to a bruxing habit that results in significant tooth wear and/or facet creation. Tooth wear and facet creation may lead to a host of other pathologies, such as dental caries, chips, cracks, gingival recession, infection, and tooth loss.
Bruxism module 38 may receive the dynamic collision metric described above, along with dynamic measures of relevant metrics (e.g., class relationship of canines, mesial step of molars) to determine. Bruxism module 38 may determine a value (e.g., score) that is computed from such dynamic measures, where the score indicates an increased risk factor for tooth grinding during intermediate stages and/or final stage of treatment plans 26. An orthodontist may use this bruxing score to select and/or modify treatment plans 26. Additionally, data capturing intermediate bite configurations and the appearance or resolution of bruxism could be used to identify non-optimal bite configurations for the patient in question. For example, if an aesthetically preferable configuration is correlated to bruxism, a modified treatment can be proposed by the orthodontist, or if an intermediate bite configuration is correlated to bruxism, an alternative treatment path can be selected.
Based on the dynamic collision metrics and/or discomfort scores, virtual articulation system 24 may also determine one or more digital setups 36 that specify the form of various treatment modalities (e.g., clear tray aligners). In one example, virtual articulation system 24 may be configured to determine an exterior form of an aligner based on at least one of dynamic collision metric or the discomfort score. Typically, the exterior form of the aligners is manufactured to match to the teeth below the form. Because teeth are moving during treatment, they may move through a state where the actual teeth would generate discomfort. However, the external part of the aligner does not have to follow the shape of the teeth. Virtual articulation system 24 may output digital setups 36 that include plans and/or instructions for modifying the external aligner, given the virtual articulation, such that the upper and lower bites of the external aligner match up even when the teeth within the aligner would not line up. This would allow virtual articulation system 24 to find the “fastest” path to the final setup even if some of the intermediate positions would generate some discomfort because of misalignment.
In addition to the above techniques, virtual articulation system 24 may also include one or more user interface features where various aspects of the virtual articulation, dynamic collision metrics, and/or discomfort scores are displayed to a user on display device 16. Virtual articulation system may be configured to output and display data indicative of the dynamic collision metric and/or discomfort score for each of the one or more treatment plans. This data could be visual in nature, such as color-coding of the contact points or areas to indicate severity of discomfort. For example, contacts that are closer to the hinge axis of the condyles (e.g., in the temporomandibular joint (TMJ), i.e., more distal or more posterior) are likely to result in greater discomfort than those that are further from the hinge axis, both for reasons of neurology and for basic reasons of increased mechanical leverage and thus force or pressure given the same input force from the masseter muscles. Virtual articulation system 24 may use different colors that indicate the severity of the collision and/or potential discomfort (e.g., red for high discomfort, yellow for medium discomfort, green for low discomfort).
The user interface features described below enable user experience options for the orthodontist to visually evaluate a given state either by looking at the contact maps or looking at a simulation of the virtual articulation. This information can also be shared with a restorative or prosthodontic dentist for combined treatment plans.
In other examples, virtual articulation system 24 may be further configured to display comparison of contact maps, virtual articulation, dynamic collision metrics, and/or comfort scores to: (1) past scans acquired from the same patient, and/or (2) average values generated from population data. For contact maps and virtual articulation, virtual articulation system 24 may be configured to display this comparison as a side-by-side view or an overlay. For comfort scores, virtual articulation system 24 may display a single patient's current and historic scores as part of a distribution of scores across population data.
Regardless of whether the treatment plans include only desired final setup or final setups along with suggested intermediate states, virtual articulation system 24 may be configured to analyze multiple treatment plans (66). The orthodontist may enter the multiple treatment plans 66 into virtual articulation system 24 (see
Virtual articulation system 24 may be further configured to determine treatment plan(s), where the treatment plans include at least a target (e.g., setup) state (112). In some examples, the treatment plans do not yet have a target state. Instead, one or more target states may be tested using virtual articulation system 24. In some examples, virtual articulation system 24 may be configured to receive the treatment plans from a user (e.g., an orthodontist). In other examples, virtual articulation system 24 may be configured to determine one or more treatment plans for the patient based on an initial state of the virtual maxillary arch and the virtual mandibular arch and a target state for the virtual maxillary arch and the virtual mandibular arch.
Virtual articulation system 24 may be further configured to modify the patient scans at the target state (114). For example, virtual articulation system 24 may be configured to modify the virtual maxillary arch and the virtual mandibular arch at the target state for each of the one or more treatment plans. In some examples, virtual articulation system 24 may be configured to modify the virtual maxillary arch and the virtual mandibular arch to generate a target state for each of the one or more treatment plans.
Virtual articulation system 24 may be further configured to virtually articulate the modified scans and determine contact points of the patient's teeth (116). For example, virtual articulation system 24 may be configured to virtually articulate the modified virtual maxillary arch and the modified virtual mandibular arch to determine contact points at the target state for each of the one or more treatment plans. In one example, virtual articulation system 24 may virtually articulate the modified virtual maxillary arch and the modified virtual mandibular arch to determine contact points at one or more mandibular poses, wherein the one or more mandibular poses include a maximum intercuspation, a lateral left excursion, a lateral right excursion, a protrusive excursion, or a retrusive excursion.
In one example of the disclosure, through the virtual articulation, virtual articulation system 24 may be configured to predict wear facets at the contact points resulting from one or more mandibular motions. In another example, virtual articulation system 24 may be configured to determine whether proper canine guidance is achieved based on the virtual articulation. In another example, virtual articulation system 24 may be configured to determine whether proper anterior guidance is achieved based on the virtual articulation.
In another example, virtual articulation system 24 may be configured to virtually articulate the modified virtual maxillary arch and the modified virtual mandibular arch to determine penetration depth between contacting teeth. Virtual articulation system 24 may further predict an amount of facet wear based on the determined penetration depth.
Virtual articulation system 24 may be further configured to compute a dynamic collision metric based on the contact points at the target state for each of the one or more treatment plans (118). In one example, virtual articulation system 24 may compute the dynamic collision metric based on the contact points at one or more of the maximum intercuspation, the lateral left excursion, the lateral right excursion, the protrusive excursion, or the retrusive excursion.
Optionally, virtual articulation system 24 may further determine a discomfort score based on the dynamic collision metric at the target setup state for each of the one or more treatment plans (120). The discomfort score indicates a level of predicted discomfort in the patient.
Virtual articulation system 24 may further output data indicative of the discomfort score (if computed) for each of the one or more treatment plans, and output data indicative of the dynamic collision metric for each of the one or more treatment plans (122). In some examples, virtual articulation system 24 may display contact maps based on the contact points. In some examples, virtual articulation system 24 may display a simulation of the virtual articulation. In some examples, virtual articulation system 24 may display past scans of the patient alongside the virtual mandibular arch and the virtual maxillary arch. In some examples, virtual articulation system 24 may display at least one of the dynamic collision metric or the discomfort score alongside average values for a population of patients.
Optionally, through an automatic process or through user input, virtual articulation system 24 may modify one of the plurality of treatment plans based on at least one of the dynamic collision metric or the discomfort score (124). If so, virtual articulation system 24 may repeat process 114-122 based on the modified treatment plans.
Virtual articulation system 24 may further select a treatment plan, whether through user input or an automatic process (126). In one example, virtual articulation system 24 may determine a final setup state based on at least one of the dynamic collision metric or the discomfort score. In another example, virtual articulation system 24 may select one of the plurality of treatment plans based on at least one of the dynamic collision metric or the discomfort score.
In other examples, virtual articulation system 24 may determine a static collision metric of the virtual mandibular arch and the virtual maxillary arch and select one of the plurality of treatment plans based on the dynamic collision metric and the static collision metric.
The techniques of
Various examples have been described. These and other examples are within the scope of the following claims.
1. A method comprising:
receiving, by a computing device, data indicative of a scan of a virtual maxillary arch representing a maxillary arch of a patient and a virtual mandibular arch representing a mandibular arch of the patient;
determining one or more treatment plans for the patient based on an initial state of the virtual maxillary arch and the virtual mandibular arch;
modifying, by the computing device, the virtual maxillary arch and the virtual mandibular arch to generate a target state for each of the one or more treatment plans;
virtually articulating, by the computing device, the modified virtual maxillary arch and the modified virtual mandibular arch to determine contact points at the target state for each of the one or more treatment plans;
computing, by the computing device, a dynamic collision metric based on the contact points at the target state for each of the one or more treatment plans; and
outputting, by the computing device, data indicative of the dynamic collision metric for each of the one or more treatment plans.
2. The method of embodiment 1, further comprising:
determining, by the computing device, a discomfort score based on the dynamic collision metric at the target setup state for each of the one or more treatment plans, wherein the discomfort score indicates a level of predicted discomfort in the patient; and
outputting, by the computing device, data indicative of the discomfort score for each of the one or more treatment plans.
3. The method of embodiment 1 or embodiment 2, wherein the maxillary arch and the mandibular arch are in a maloccluded state.
4. The method of embodiment 1 or embodiment 2, further comprising:
predicting, by the computing device, wear facets at the contact points resulting from one or more mandibular motions.
5. The method of embodiment 4, wherein the one or more mandibular motions include one or more of a protrusive excursion, a retrusive excursion, a left lateral excursion, or a right lateral excursion.
6. The method of embodiment 1 or embodiment 2, further comprising:
determining, by the computing device, whether proper canine guidance is achieved based on the virtual articulation.
7. The method of embodiment 1 or embodiment 2, further comprising:
determining, by the computing device, whether proper anterior guidance is achieved based on the virtual articulation.
8. The method of embodiment 1 or embodiment 2, wherein virtually articulating, by the computing device, the modified virtual maxillary arch and the modified virtual mandibular arch comprises:
virtually articulating, by the computing device, the modified virtual maxillary arch and the modified virtual mandibular arch to determine contact points at one or more mandibular poses, wherein the one or more mandibular poses include a maximum intercuspation, a lateral left excursion, a lateral right excursion, a protrusive excursion, or a retrusive excursion.
9. The method of embodiment 8, wherein computing, by the computing device, the dynamic collision metric based on the contact points comprises:
computing, by the computing device, the dynamic collision metric based on the contact points at one or more of the maximum intercuspation, the lateral left excursion, the lateral right excursion, the protrusive excursion, or the retrusive excursion.
10. The method of embodiment 1 or embodiment 2, wherein virtually articulating further comprises:
virtually articulating, by the computing device, the modified virtual maxillary arch and the modified virtual mandibular arch to determine penetration depth between contacting teeth, the method further comprising:
predicting an amount of facet wear based on the determined penetration depth.
11. The method of embodiment 1 or embodiment 2, wherein the one or more treatment plans comprise a plurality of treatment plans, the method further comprising:
selecting one of the plurality of treatment plans based on at least one of the dynamic collision metric or the discomfort score.
12. The method of embodiment 11, the method further comprising:
determining a static collision metric of the virtual mandibular arch and the virtual maxillary arch; and
selecting one of the plurality of treatment plans based on the dynamic collision metric and the static collision metric.
13. The method of embodiment 11, further comprising:
modifying one of the plurality of treatment plans based on at least one of the dynamic collision metric or the discomfort score.
14. The method of embodiment 1 or embodiment 2, further comprising:
determining a final setup state based on at least one of the dynamic collision metric or the discomfort score.
15. The method of embodiment 1 or embodiment 2, further comprising:
determining an aligner form based on at least one of dynamic collision metric or the discomfort score.
16. The method of any combination of embodiments 1-15, further comprising:
displaying a contact map based on the contact points.
17. The method of any combination of embodiments 1-16, further comprising:
displaying a simulation of the virtual articulation.
18. The method of any combination of embodiments 1-17, further comprising:
displaying past scans of the patient alongside the virtual mandibular arch and the virtual maxillary arch.
19. The method of any combination of embodiments 1-18, further comprising:
displaying at least one of the dynamic collision metric or the discomfort score alongside average values for a population of patients.
20. The method of any combination of embodiments 1-19, wherein the one or more treatment plans further include at least one intermediate state.
21. The method of embodiment 20, the method further comprising:
modifying, by the computing device, the virtual maxillary arch and the virtual mandibular arch at each of the at least one intermediate state and the target state for each of the one or more treatment plans;
virtually articulating, by the computing device, the modified virtual maxillary arch and the modified virtual mandibular arch to determine contact points at each of the at least one intermediate state and the target state for each of the one or more treatment plans;
computing, by the computing device, a dynamic collision metric based on the contact points at each of the at least one intermediate state and the target state for each of the one or more treatment plans;
outputting, by the computing device, data indicative of the dynamic collision metric for each of the at least one intermediate state and the target state for each of the one or more treatment plans.
22. The method of embodiment 21, further comprising:
modifying, by the computing device, one or more intermediate stages based on the dynamic collision metric.
23. The method of embodiment 21, further comprising:
determining, by the computing device, one or more intermediate states based on the dynamic collision metric.
24. An apparatus comprising:
a memory configured to store data indicative of a scan of a virtual maxillary arch representing a maxillary arch of a patient and a virtual mandibular arch representing a mandibular arch of the patient; and
a processor in communication with the memory, the processor configured to:
determine a discomfort score based on the dynamic collision metric at the target setup state for each of the one or more treatment plans, wherein the discomfort score indicates a level of predicted discomfort in the patient; and
output data indicative of the discomfort score for each of the one or more treatment plans.
26. The apparatus of embodiment 24 or embodiment 25, wherein the maxillary arch and the mandibular arch are in a maloccluded state.
27. The apparatus of embodiment 24 or embodiment 25, wherein the processor is further configured to:
predict wear facets at the contact points resulting from one or more mandibular motions.
28. The apparatus of embodiment 27, wherein the one or more mandibular motions include one or more of a protrusive excursion, a retrusive excursion, a left lateral excursion, or a right lateral excursion.
29. The apparatus of embodiment 24 or embodiment 25, wherein the processor is further configured to:
determine whether proper canine guidance is achieved based on the virtual articulation.
30. The apparatus of embodiment 24 or embodiment 25, wherein the processor is further configured to:
determine whether proper anterior guidance is achieved based on the virtual articulation.
31. The apparatus of embodiment 24 or embodiment 25, wherein to virtually articulate the modified virtual maxillary arch and the modified virtual mandibular arch, the processor is further configured to:
virtually articulate the modified virtual maxillary arch and the modified virtual mandibular arch to determine contact points at one or more mandibular poses, wherein the one or more mandibular poses include a maximum intercuspation, a lateral left excursion, a lateral right excursion, a protrusive excursion, or a retrusive excursion.
32. The apparatus of embodiment 31, wherein to compute the dynamic collision metric based on the contact points, the processor is further configured to:
compute the dynamic collision metric based on the contact points at one or more of the maximum intercuspation, the lateral left excursion, the lateral right excursion, the protrusive excursion, or the retrusive excursion.
33. The apparatus of embodiment 24 or embodiment 25, wherein the processor is further configured to virtually articulate the modified virtual maxillary arch and the modified virtual mandibular arch to determine penetration depth between contacting teeth; and predict an amount of facet wear based on the determined penetration depth.
34. The apparatus of embodiment 24 or embodiment 25, wherein the one or more treatment plans comprise a plurality of treatment plans, and wherein the processor is further configured to:
select one of the plurality of treatment plans based on at least one of the dynamic collision metric or the discomfort score.
35. The apparatus of embodiment 34, wherein the processor is further configured to:
determine a static collision metric of the virtual mandibular arch and the virtual maxillary arch; and
select one of the plurality of treatment plans based on the dynamic collision metric and the static collision metric.
36. The apparatus of embodiment 34, wherein the processor is further configured to:
modify one of the plurality of treatment plans based on at least one of the dynamic collision metric or the discomfort score.
37. The apparatus of embodiment 24 or embodiment 25, wherein the processor is further configured to:
determine a final setup state based on at least one of the dynamic collision metric or the discomfort score.
38. The apparatus of embodiment 24 or embodiment 25, wherein the processor is further configured to:
determine an aligner form based on at least one of dynamic collision metric or the discomfort score.
39. The apparatus of any combination of embodiments 24-38, wherein the processor is further configured to:
display a contact map based on the contact points.
40. The apparatus of any combination of embodiments 24-39, wherein the processor is further configured to:
display a simulation of the virtual articulation.
41. The apparatus of any combination of embodiments 24-40, wherein the processor is further configured to:
display past scans of the patient alongside the virtual mandibular arch and the virtual maxillary arch.
42. The apparatus of any combination of embodiments 24-41, wherein the processor is further configured to:
display at least one of the dynamic collision metric or the discomfort score alongside average values for a population of patients.
43. The apparatus of any combination of embodiments 24-42, wherein the one or more treatment plans further include at least one intermediate state.
44. The apparatus of embodiment 43, wherein the processor is further configured to:
modify the virtual maxillary arch and the virtual mandibular arch at each of the at least one intermediate state and the target state for each of the one or more treatment plans;
virtually articulate the modified virtual maxillary arch and the modified virtual mandibular arch to determine contact points at each of the at least one intermediate state and the target state for each of the one or more treatment plans;
compute a dynamic collision metric based on the contact points at each of the at least one intermediate state and the target state for each of the one or more treatment plans;
output data indicative of the dynamic collision metric for each of the at least one intermediate state and the target state for each of the one or more treatment plans.
45. The apparatus of embodiment 44, wherein the processor is further configured to:
modify one or more intermediate stages based on the dynamic collision metric.
46. The apparatus of embodiment 45, wherein the processor is further configured to:
determine one or more intermediate states based on the dynamic collision metric.
47. A non-transitory computer-readable storage medium storing instructions that, when executed, causes a processor to:
receive data indicative of a scan of a virtual maxillary arch representing a maxillary arch of a patient and a virtual mandibular arch representing a mandibular arch of the patient;
determine one or more treatment plans for the patient based on an initial state of the virtual maxillary arch and the virtual mandibular arch;
modify the virtual maxillary arch and the virtual mandibular arch to generate a target state for each of the one or more treatment plans;
virtually articulate the modified virtual maxillary arch and the modified virtual mandibular arch to determine contact points at the target state for each of the one or more treatment plans;
compute a dynamic collision metric based on the contact points at the target state for each of the one or more treatment plans; and
output data indicative of the dynamic collision metric for each of the one or more treatment plans.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/IB2020/056799 | 7/20/2020 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 62875866 | Jul 2019 | US |
