Method of monitoring change of ock

Abstract

A method for monitoring jaw movement over time is disclosed. Primary and secondary relative jaw movement data sets are obtained using an intraoral scanner. The method also includes a computer-implemented method, wherein the computer-implemented method includes the steps of: receiving a primary relative jaw movement dataset and a secondary relative jaw movement dataset; obtaining a model class representing desired and / or regularized occlusion characteristics; obtaining primary model parameters by fitting the primary relative jaw motion dataset to a model class; obtaining secondary model parameters by fitting the secondary relative jaw motion dataset to the model class; monitoring information is determined based on comparing the primary model parameters to the secondary model parameters, and the monitoring information is displayed.

Description

Background Art

[0001] The masticatory system is an extremely complex musculoskeletal system consisting of two bony structures, the jaw and the skull, which are connected by two temporomandibular joints (TMJs). The masticatory system is used for daily tasks such as speaking and chewing, which is essential for extracting nutrients from food.

[0002] The mechanism by which the human jaw operates to chew food, speak, etc. is a complex operation involving many individual muscles and two interconnected but separate TMJs that connect the mandible (lower jaw) to the temporal bones on either side of the skull.

[0003] The jaw muscles move the jaw in a complex three-dimensional manner during jaw movement. There are three jaw closing muscles (masseter, temporalis, and medial pterygoid) and four jaw opening muscles (lateral pterygoid, digastric, mylohyoid, and geniohyoid). The basic functional unit of a muscle is the motor unit. The internal structure of the jaw muscles is complex, and many exhibit a complex pennate (feather-like) internal structure. In each jaw muscle, the central nervous system (CNS) appears to be able to activate separate areas of muscle fibers with specific orientations. This means that each jaw muscle is capable of generating a range of force vectors (magnitude and direction) required for a specific jaw movement. The CNS activates motor units in different muscles to generate any desired movement. Movements are classified as voluntary, reflex, and rhythmic. Many parts of the CNS are involved in the generation of jaw movements. The facial motor cortex is the final output pathway of the cerebral cortex to generate voluntary movements (e.g., mouth opening, closing, protrusion, and lateral jaw movement). Reflexes prove to be pathways that help refine movements and can be used by higher motor centers to produce more complex movements. Chewing or chewing is a rhythmic movement that is controlled by central pattern generators in the brainstem. The central pattern generators can be modified by sensory information about the food bolus and by voluntary commands from higher centers.

[0004] The movement of chewing is complex and involves jaw, facial and tongue movements driven by the jaw, facial and tongue muscles. Changes in occlusion appear to have a significant impact on the activity of the jaw muscles and the movement of the jaw joint.

[0005] The temporomandibular joint (TMJ) is a movable joint between the mandibular condyle and the glenoid cavity in the skull, with the articular disc interposed between the two. Movement occurs through a combination of rotation (between the condyle and the articular disc) and translation (between the condyle-articular disc complex and the glenoid cavity).

[0006] This high degree of mobility, especially translational movement, is reflected in the way the articular disc is attached to the condyle and the absence of hyaline cartilage. The condyle and fossa are covered by fibrocartilage, and the articular disc consists of a dense network of collagen fibers that are oriented in different directions related to functional loads. Condylar movements are complex, and both condyles are loaded during chewing. The TMJ is able to adapt to changes in load by remodeling.

[0007] Disorders of the masticatory system may result from even the smallest deviations in the performance of the different components mentioned above. Temporomandibular disorder (TMD) may be one of such disorders, which includes a variety of different disorders involving the TMJ, the muscles of mastication, and related structures, occurring alone or simultaneously. More than 100 different diseases affect the musculoskeletal system, many of which may also involve the TMJ. Some of these diseases are rare, but there are also a few that are relatively common, such as osteoarthrosis and osteoarthritis (OA) and rheumatoid arthritis (RA). Obviously, several diseases that affect the TMJ may lead to occlusal disorders. The term "occlusion" represents the combined effects of the static and dynamic relationship of the teeth and the jaw muscles and the temporomandibular joint (TMJ).

[0008] Osteoarthritis (OA) is a degenerative form of the TMJ, but is generally a benign disease with mild or no symptoms and a potentially poor prognosis. In OA, an inflammatory component is added to the joint degeneration. If diagnosed early, the acute inflammatory phase associated with pain and functional impairment can often be reversed with simple treatments.

[0009] TMD can cause discomfort to the patient, such as pain and decreased function of the masticatory system or other parts of the body. As with many other diseases, early recognition and diagnosis can lead to reversible conditions for the patient by starting early treatment.

[0010] It is difficult to make an early diagnosis of a condition related to jaw movement such as TMD at a specific point in time because there is no clear relationship between a condition and simple measurements that can be used to classify TMD as arthrogenic or myogenic. This is because patients with primary joint disease often have secondary muscle dysfunction, while patients with primary muscle disease may present with joint symptoms.

[0011] In all aspects of dental practice, a detailed assessment of the static and dynamic relationships of the teeth is very important for clinical evaluation. This information will help understand the specific tooth relationships related to function and parafunction and base treatment on this. The current method of clinical occlusal assessment involves measuring the tooth contacts in different relative positions of the jaws through physical paper. In addition, clinical signs such as tooth and restoration wear, muscle pain, and observation of sounds originating from the TMJ can be checked using a visual analog scale (VAS). This is a tedious and time-consuming process that requires a lot of manual work and subjective assessment skills from the clinician.

[0012] With the advent of digital solutions in dentistry, such occlusal assessments can be performed more quickly and accurately using 3D acquisition devices and computer software.

[0013] Therefore, there is a need for faster and more accurate assessment of occlusal changes. Summary of the invention

[0014] Thus, in one aspect, disclosed herein is a method for monitoring jaw movement over time, wherein the method comprises the following steps:

[0015] - obtaining a primary relative jaw motion dataset at a primary time point using an intraoral scanner, wherein the primary relative jaw motion dataset represents the relative motion between the upper jaw and the lower jaw,

[0016] - obtaining a secondary relative jaw motion dataset at a secondary time point using an intraoral scanner, wherein the secondary relative jaw motion dataset represents relative motion between the upper jaw and the lower jaw,

[0017] Wherein the method also includes a computer-implemented method, wherein the computer-implemented method comprises the following steps:

[0018] - receiving the primary relative jaw movement dataset and the secondary relative jaw movement dataset,

[0019] - obtain a model class representing desired and / or regularized occlusal characteristics,

[0020] - obtaining primary model parameters by fitting said primary relative jaw motion dataset to said model class,

[0021] - obtaining secondary model parameters by fitting said secondary relative jaw motion dataset to said model class,

[0022] - determining monitoring information based on comparing said primary model parameters with said secondary model parameters,

[0023] - displaying said monitoring information.

[0024] The disclosed computer-implemented method is able to quantify and track dynamic jaw motion in detail over time. This allows early identification of any emerging differences in jaw mobility, which may be a warning sign of a potential developing condition. It enables dentists to take preventive measures at an early stage when most conditions are reversible.

[0025] Thus, as will be disclosed and further discussed, the disclosed method is capable of early identification of occlusal disorders by quantifying and monitoring the mobility of specific jaw movements of a patient's chewing system over time. By obtaining 3D information of the patient's dentition (e.g., a 3D digital model of the maxilla and mandible), mandibular movement can be quantified. By obtaining multiple patient-specific bite configurations, describing the relative jaw movement between the maxilla and mandible, and fitting these bite configurations to a mathematical model that approximates the mechanics of the chewing system, a set of patient-specific parameters can be provided that characterize the patient's chewing system at the time of data acquisition. The mathematical model can be a relatively simple model with a limited number of parameters, or a more advanced model that is highly close to the natural anatomy of the human chewing system. By acquiring a set of bite configurations at a certain point in time and obtaining a first set of jaw model parameters, the patient-specific jaw movement can be compared with a second set of jaw model parameters obtained from a second set of bite configurations obtained at a second point in time. This enables a detailed evaluation of the patient-specific model parameters or the boundary movement of the mandible.

[0026] As used herein, the terms "primary" and "secondary" are used to distinguish features and items in time. Thus, a primary relative jaw motion dataset or a primary bite scan are all taken at the same approximate point in time, typically at the first patient visit. Thus, a secondary relative jaw motion dataset or a secondary bite scan is a set of bite scans taken at another point in time, such as a subsequent patient visit. However, a primary relative jaw motion dataset and a secondary relative jaw motion dataset may also be taken at the same patient visit, where treatment was performed between the acquisition of the relative jaw motion datasets. The treatment may be a surgical procedure to fit a crown or implant, which may affect jaw motion. Although not discussed in further detail, tertiary, quaternary, quinary, etc. bite scans may be obtained at different points in time.

[0027] The primary bite scan may include a first primary bite scan and a second primary bite scan. Since the patient needs to move between bite positions, the first primary bite scan and the second primary bite scan may not be performed at exactly the same time. However, for purposes such as treatment, assessment, and clinical observation, the first primary bite scan and the second primary bite scan may be considered to be performed at the same time point in order to obtain coherent motion data that can be used for the monitoring purposes described herein.

[0028] As described herein, a relative jaw motion dataset comprises data describing the relative motion between the upper jaw and the lower jaw of a patient. Herein, a relative jaw motion dataset may also be referred to as a jaw motion dataset or jaw motion data. The jaw motion dataset may be obtained using an intraoral scanner, for example, by continuously scanning the upper jaw and the lower jaw of a patient as they move relative to each other.

[0029] In one embodiment, the relative jaw motion dataset can be obtained by obtaining a bite scan. A bite scan is a single discrete scan of the patient's maxillary and mandibular static occlusion. Thus, multiple (e.g., first, second, third, fourth, etc.) bite scans can represent frames that can be provided in a sequence as a video stream, for example, if dynamic occlusion is obtained by recording the dynamic occlusion using an intraoral scanner.

[0030] Mathematical models that simplify the complexity of the anatomy can be used to map the recorded data. Even if these models cannot replicate all aspects of anatomically correct jaw movement, they may still be very useful for patient diagnosis and treatment planning because they provide a way to track and monitor patient-specific changes, such as static and / or dynamic occlusion.

[0031] By "mathematical modeling" or "modeling" we understand that we derive / estimate / fit a model that describes a data set according to some optimality criteria. Mathematical models can include model classes and fitted models. More specifically, a model class is a mathematical expression (aimed at modeling some data) with one or more free parameters to be determined / calculated / fitted. A fitted model is a model class with determined free parameters.

[0032] One of the simplest examples is a line, where the model class would be y=a*x+b, where x and y are the input and output data respectively, and a and b are free parameters. The fitted model could be y=2*x+3, where a=2 and b=3 are free parameters.

[0033] Model classes can have deterministic and / or stochastic components, meaning that some (or all) parts of a model or model class can be formulated according to a statistical distribution. The latter is the stochastic part. An example could be y=a*x+b+e, where e is an element of a normal distribution with mean zero and standard deviation 1.2. Then a*x+b would be the deterministic part and e the stochastic part. Note that this model could also be described as a normal distribution with mean a*x+b and standard deviation 1.2.

[0034] "Desired properties of dynamic occlusion" can be understood as the properties we want to know, such as the distribution of occlusal contact points or the functioning of the TMJ.

[0035] The term "regularizing property" is commonly used in statistical model fitting, where a bias is often added to the estimate in order to reduce noise. This is known as the bias-variance trade-off. This bias is often based on prior assumptions or knowledge, known as "priors" in the Bayesian statistical framework. A common example of a regularizing property is smoothing data (e.g. a mesh surface) to reduce noise.

[0036] In another aspect, a method for assessing jaw movement is disclosed, wherein the method comprises the steps of:

[0037] - obtaining a primary relative jaw motion dataset at a primary time point using an intraoral scanner, wherein the primary relative jaw motion dataset represents the relative motion between the upper jaw and the lower jaw,

[0038] Wherein the method also includes a computer-implemented method, wherein the computer-implemented method comprises the following steps:

[0039] - receiving said primary relative jaw motion dataset,

[0040] - obtain a model class representing desired and / or regularized occlusal characteristics,

[0041] - obtaining primary model parameters by fitting said primary relative jaw motion dataset to said model class,

[0042] - determining monitoring information based on comparing said primary model parameters with a reference data set,

[0043] - displaying said monitoring information.

[0044] This provides a solution where the patient's bite can be diagnostically assessed by observing the movement at a certain point in time.

[0045] For example, jaw movement can be described by hysteresis, that is, the relative movement between the upper and lower jaws is different when the jaws are opened compared to when the jaws are closed. The absence of hysteresis may be a sign of TMJ disease.

[0046] Thus, in the second aspect, in one embodiment, the reference data set may include a hysteresis criterion.The step of determining monitoring information based on comparing the primary model parameters to the hysteresis criterion may include determining whether the primary model parameters describe hysteresis.

[0047] In one embodiment, the reference data set may include a cone beam computed tomography (CBCT) scan of the maxilla and / or mandible. The accuracy of the primary model parameters may be verified by comparing the primary model parameters to a reference data set in the form of a CBCT scan. Differences between the primary model parameters and the CBCT scan may be determined, wherein the CBCT scan provides anatomically accurate data of the patient's skull relative to the mandible. On a CBCT scan, the orientation of the TMJ in one configuration may be directly extracted and compared to the primary model parameters.

[0048] In another embodiment, the reference data set may include a treatment plan. The treatment plan may be a simulated movement pattern of the jaw based on the orthodontic treatment. By comparing the primary model parameters with the reference data set in the form of a treatment plan, differences between the treatment plan and the jaw movement may be determined. The determined differences may indicate a need to modify the treatment plan. Based on the determined differences, the user may create a modified treatment plan based on the primary model parameters.

[0049] In yet another embodiment, the reference data set may include typical jaw reference parameters describing jaw motion. The typical jaw reference parameters may be obtained from multiple recorded jaw motions by statistical methods. An example of a statistical method may be a best fit line method. The statistical method may take into account one or more of age, gender, race, living conditions, genes to achieve a realistic representation of the patient.

[0050] The jaw motion described by the typical jaw reference parameters can be called the ideal motion trajectory of the jaw. The ideal motion trajectory can describe a smooth motion consisting of only rotation or only translation. By comparing the primary model parameters with the ideal motion trajectory of the jaw, rotation and translation can be detected in the monitoring information, indicating potential TMJ disorders.

[0051] Monitoring jaw movement over time can be useful for tracking the progress of orthodontic treatment. More specifically, it can be monitored whether tooth movement deviates from the desired movement specified by the planned orthodontic treatment.

[0052] According to one embodiment, a computer-implemented method for monitoring jaw movement over time is disclosed, wherein the method comprises the following steps:

[0053] - receiving a primary relative jaw motion dataset and a secondary relative jaw motion dataset,

[0054] - determining a change in jaw movement by comparing a secondary relative jaw movement dataset with the primary relative jaw movement dataset, wherein the secondary relative jaw movement dataset corresponds to one or more teeth, wherein the one or more teeth are repositioned compared to the position of the one or more teeth corresponding to the primary relative jaw movement dataset, and

[0055] - Displays the determined changes in jaw movement.

[0056] The repositioned one or more teeth may correspond to a stage of planned orthodontic treatment, for example the repositioned one or more teeth may correspond to an interim treatment stage of planned orthodontic treatment.

[0057] The primary relative jaw motion dataset may correspond to primary jaw motion scanned at a first patient visit. The secondary relative jaw motion dataset may correspond to secondary jaw motion scanned at a subsequent patient visit.

[0058] The one or more teeth may include at least one tooth that establishes occlusal contact during primary jaw movement and secondary jaw movement. Alternatively, the one or more teeth may include at least one tooth that establishes occlusal contact only during primary jaw movement or only during secondary jaw movement.

[0059] In an embodiment of the present disclosure, a non-transitory computer-readable medium is disclosed, which includes instructions that, when executed by a computer, may cause the computer to perform a method according to any of the presented embodiments.

[0060] In an embodiment of the present disclosure, a computer program product embodied in a non-transitory computer-readable medium is disclosed, the computer program product comprising instructions which, when executed by a computer, cause the computer to perform a method according to any of the presented embodiments.

[0061] In another aspect, a scanner system for intraoral scanning of a dental object is disclosed. The scanner may include a scanning probe for receiving an image of the dental object, a peripheral output device for visualizing a digital 3D representation of the dental object, and a computer processor coupled to the scanning probe and the peripheral output device. The computer processor may receive data from the scanning probe and may output calculated data to the peripheral output device. The scanner system may be used by applying the steps of the method discussed herein.

[0062] It is to be understood that the current disclosure focuses on using an intraoral scanner to obtain a relative jaw motion dataset in a method for monitoring jaw motion changes. In another embodiment, other devices besides an intraoral scanner may be used.

[0063] Such other devices may be, for example, a laboratory tabletop scanner, such as the E-series scanners from 3Shape A / S, or an X-ray scanner such as a CBCT scanner. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Various aspects of the present disclosure may be best understood from the following detailed description in conjunction with the accompanying drawings. The drawings are schematic and simplified for clarity, and they only show details to improve understanding of the claims, while other details are omitted. Throughout, the same reference numerals are used for identical or corresponding parts. The various features of each aspect may be combined with any or all features of the other aspects. These and other aspects, features and / or technical effects will be apparent from and elucidated with reference to the illustrations described below:

[0065] Figure 1 Schematic diagram showing an embodiment of a method for monitoring jaw movement over time as disclosed herein,

[0066] Figure 2a – 2c shows a first example of an embodiment of a method for monitoring jaw movement over time using a six-degree-of-freedom model as a model class,

[0067] Figure 3a – 3c shows a second example of an embodiment of a method for monitoring jaw movement over time using a digital representation of an articulator as a model class,

[0068] Figure 4 A third example of an embodiment of a method for monitoring jaw motion over time using a synthetic rigid body model as a model class is shown,

[0069] Figure 5 shows an embodiment of a boundary envelope diagram,

[0070] Figure 6 An embodiment of another aspect of assessing jaw movement disclosed herein is shown,

[0071] Figure 7 A scanner system for use with the methods disclosed herein is shown. DETAILED DESCRIPTION

[0072] Figure 1 A schematic diagram of a monitoring method 100 disclosed herein is shown, wherein various steps and additional or alternative embodiments will be further discussed.

[0073] In the scanning phase 101, a primary relative jaw motion data set 102 is obtained at a specific time T1, and a secondary relative jaw motion data set 103 is obtained at a specific time T2. The relative jaw motion data represents the relative motion between the patient's upper and lower jaws. There is no predetermined time interval to obtain the two jaw motions. In some cases, they are obtained at intervals of six months or longer to monitor slow changes in jaw motion over time. The jaw motion data set can be obtained over days or hours to observe changes in jaw motion that may occur due to dental treatment.

[0074] The relative jaw motion dataset can be obtained in different ways. In one embodiment, the patient directly records the lower jaw as they move relative to the upper jaw. This can be obtained, for example, by recording a bite configuration or a bite scan sequence. The corresponding 3D representation of the bite configuration can be used to align the 3D representation of the patient's upper jaw and the 3D representation of the patient's lower jaw.

[0075] In one embodiment, the method may further comprise obtaining at least a first digital 3D representation of at least a portion of an upper jaw and at least a portion of a lower jaw of the patient.

[0076] The obtained digital 3D representation data may be used in the step of obtaining model parameters by fitting the primary and / or secondary relative jaw motion data to the model class. This may be advantageous because the obtained digital 3D representation may be used as a reference frame during the fitting process of the jaw motion data. The 3D digital representation may improve the mapping of the jaw motion data to the model class because the correspondence between the jaw motion data and the digital 3D representation may be easily established through an alignment process. Furthermore, the signal-to-noise ratio may be reduced when using the 3D digital representation compared to comparing the relative jaw motion data without a reference frame. This is because the 3D digital representation is a very accurate representation because it consists of dense data.

[0077] In one embodiment, a CBCT scan of the maxilla and / or mandible may be used as a reference frame in the fitting process of the jaw motion data. The CBCT scan may improve the mapping of the jaw motion data to the model class because the correspondence between the jaw motion data and the CBCT scan may be easily established through an alignment process. In addition, the signal-to-noise ratio may be reduced when using the CBCT scan compared to comparing relative jaw motion data without a reference frame.

[0078] The digital 3D representation may be obtained prior to obtaining the jaw motion data by scanning and reconstructing a digital 3D representation of the patient's dentition using an intraoral scanner. The digital 3D representation may constitute a complete representation of the patient's maxilla and mandible or only a portion of the maxilla and mandible, such as a quadrant.

[0079] The digital 3D representation may also be obtained by importing a digital 3D representation of at least part of the patient's dentition into a priori generated software and / or by other means, such as an intraoral scanner for obtaining a relative jaw motion dataset. Other examples of generating at least part of the digital 3D representation of the patient's dentition may be scanning a physical impression or plaster model, CBCT (X-ray), or any other suitable means of generating a digital 3D representation.

[0080] In one embodiment, additional digital 3D representations may be obtained relative to jaw motion data obtained at different time points. This may be particularly advantageous if the patient has been / is undergoing orthodontic treatment resulting in tooth movement or other factors causing significant changes in the patient's dentition between different time points.

[0081] If the patient's dentition is not expected to change significantly, the computer-implemented method can use the same 3D digital representation for both sets of jaw motion data, because a correspondence between the primary and secondary jaw motion data and the same 3D digital representation can be established.

[0082] In one embodiment, the method of obtaining model parameters by fitting primary and / or secondary relative jaw motion data to a model class can be performed without obtaining a 3D digital representation of at least a portion of the patient's maxilla and mandible. This can be achieved by directly acquiring the jaw motion data as a series of consecutive small 3D data blocks, which can be the occlusal scan described herein, containing partial information of the maxillary dentition and partial information of the mandibular patient's dentition.

[0083] Thus, in one embodiment, an initial data block (e.g., the first primary occlusal scan) can be used as a primary reference frame for the model class, and the correspondence between the initial data block and subsequent data blocks (e.g., the second, third, fourth, etc. primary occlusal scans) can be established directly or indirectly through the registration of intermediate scan blocks. This process is also referred to as simultaneous localization and mapping. Similarly, this can also be applied to secondary occlusal scans to establish secondary reference frames.

[0084] Thus, by determining frame correspondences in the primary and secondary reference frames, a common coordinate system can be established, subsequently allowing the primary model parameters to be compared with the secondary model parameters to determine monitoring information. The correspondences can be landmarks, data points, or other features.

[0085] The reference frame established by the above embodiment mimics the steps of obtaining a digital 3D representation while simultaneously acquiring jaw motion data. It may be advantageous to apply additional data processing steps to the individual data blocks to assist and improve the registration process to take into account the relative motion between the jaws. Such data processing may be real-time identification of the maxilla and mandible in each scan block, so that sub-portions of the same scan block can be registered to different sub-portions of a previous scan block. This may have the advantage of omitting a separate step of obtaining a digital 3D representation.

[0086] Therefore, in one embodiment, the step of obtaining a primary and / or secondary relative jaw motion dataset may include using an intraoral scanner to obtain at least a first and a second primary bite scan and / or a secondary bite scan at a primary time point and / or a secondary time point, respectively, each primary bite scan and / or secondary bite scan including at least a portion of the upper jaw and the lower jaw at different jaw motion positions relative to each other.

[0087] Subsequently, in one embodiment, the step of obtaining the primary and / or secondary relative jaw motion dataset may further include performing a first primary alignment and / or secondary alignment of the digitally 3D represented upper jaw and lower jaw based on the first primary bite scan and / or the secondary bite scan, and performing a second primary and / or secondary alignment of the digitally 3D represented upper jaw and lower jaw based on the second primary bite scan and / or the secondary bite scan.

[0088] The alignment process may include an initial alignment followed by an optimization, such as by using an iterative closest point (ICP) algorithm. For more information, see, e.g., Paul J. Besl and Neil D. McKay, "Method for registration of 3-D shapes," Proc. SPIE 1611, Sensor Fusion IV: Control Paradigms and Data Structures, 586 (April 30, 1992).

[0089] The method is particularly advantageous because it allows alignment of a 3D digital representation of the patient's upper and lower jaws with a bite configuration data block, which may be a 3D data block containing only partial information of the patient's upper and lower jaw dentition.

[0090] More than two different bite configurations may be needed to determine or estimate the occlusal contact movement of the patient's jaws relative to each other, such as three, four, five, six, seven, eight, nine, ten, etc.

[0091] The bite configuration may be recorded using an intraoral 3D scanner, such as 3Shape's TRIOS scanner. The dentist may ask the patient to bite the upper and lower jaws together. While the patient briefly holds the bite to form a first configuration, the dentist will scan the patient's teeth with the intraoral scanner to acquire a first 3D representation. After releasing the first bite, the dentist may ask the patient to make a second bite. While the patient bites to form the second configuration, the dentist will scan the patient's teeth with the intraoral scanner to acquire a second 3D representation. More bite configurations may be needed or required to provide enough data to correctly fit the mathematical model and obtain patient-specific model parameters, and the dentist may ask the patient to bite the teeth a third, fourth, fifth, etc. time and scan each bite.

[0092] In one embodiment, it may be particularly advantageous to record jaw motion data that contains at least some information including the relative positions of the upper and lower jaws at their extreme or maximum range positions to perform full range jaw motion analysis. Such maximum range positions may be one or more of the following:

[0093] ·Central Relationship (CR)

[0094] Intercusp position (ICP)

[0095] Reverse contact position (RCP)

[0096] Recognition jaw position (RCP)

[0097] Posture jaw position (P)

[0098] Maximum jaw opening (O)

[0099] Open the hinge arc (H).

[0100] The patient may be instructed and / or assisted to guide the jaw to a certain maximum range of positions. Jaw movement data may be recorded during movement from one position to another and / or at different positions. Certain extreme positions may be difficult to record using an intraoral scanner, so subsequently applying a model class to fit the jaw movement data may be advantageous, as certain positions may be derived from the mathematical description of the fit.

[0101] After obtaining the relative jaw motion dataset, the method further comprises a computer-implemented method for performing a plurality of steps. The computer-implemented method may be executed on the computer processor 105 or a plurality of computer processors.

[0102] After scanning 101, the primary and secondary relative jaw motion data sets are sent to a computer and received 106, 107 in a software application running, for example, on a computer processor.

[0103] The acquisition of a plurality of bite configurations may comprise a continuous sequence of at least two 3D representations of the bite configurations of the patient's jaws in respective occlusions.

[0104] By taking such a continuous sequence of occlusal configurations, a large number of interdependent occlusal configurations can be obtained, which can provide a suitable data set to accurately fit the mathematical model.

[0105] It is possible to directly compare primary and secondary relative jaw motion datasets. However, the recording of relative jaw motion data (e.g., bite configuration) can be noisy, as is the case with most geometric physical measurements. The noise can partly originate from sensor noise, semi-transparent materials (e.g., saliva or teeth during scanning).

[0106] Therefore, the signal-to-noise ratio of the observed parts of the jaw movement may be of low quality. Relative jaw movement or patient-specific movement can be obtained by scanning the molar area on one side of the mouth. Here, the signal-to-noise ratio of rotation about an axis pointing away from the face is generally of low observability.

[0107] The acquired jaw motion data may not record all possible / possible relative jaw motions / positions of the jaws relative to each other, as this would require the patient to perform all possible movements while being scanned. Therefore, interpolation of the relative jaw motion data may be performed from multiple bite configurations. Performing such interpolation between the acquired bite configurations will typically provide a linear interpolation of the data, which is often inaccurate, as jaw motion is rarely linear. Therefore, providing a mathematical model, such as a model class of possible / possible motions given the observed data, will reduce the risk of errors in the interpolation.

[0108] Furthermore, the model parameters and model classes can be used to perform data extrapolation to predict occlusal configurations outside the jaw motion dataset.

[0109] The accuracy of the extrapolated data can be improved by selecting an appropriate model class, which may include limits or ranges assigned to various model class parameters. The ranges of some model class parameters may also be dependent on each other. Examples of such limits or ranges can be obtained from statistical values ​​known in the art, such as:

[0110] Approximate range of jaw movement in adults:

[0111] ·RCP to ICP 0.5-2.0 mm

[0112] ICP to O 40-70 mm

[0113] ·RCP to H15-20 mm

[0114] ·P to ICP 2-4 mm

[0115] ICP to P 5-10 mm.

[0116] For more complex model classes, known physical factors (such as maximum muscle or tendon tension) may provide different specified intervals for model class parameters.

[0117] A mathematical model consisting of possible / possible relative motions can be digitally fitted to the acquired occlusal configuration data. Thus, a class of models 108 representing desired and / or regularized occlusal characteristics is obtained.

[0118] By fitting each of the primary relative jaw motion and the secondary relative jaw motion to a model class, primary model parameters 109 and secondary model parameters 110 may be obtained.

[0119] A mathematical model (e.g., a model class) can describe the relative motion of the jaws. It can express Euclidean transformations, i.e., rotations and translations in 3D space. It is advisable to restrict the space of possible rotations and translations by forbidding certain configurations or making some configurations less likely than others. The latter applies if a probabilistic approach is adopted to describe relative jaw motion. These constraints in the space of Euclidean transformations are captured by the mathematical model chosen to fit the data. At any moment of jaw motion, the jaw can be described as rotating about an instantaneous (i.e., at a given point in time) axis of rotation, but generally a six-degree-of-freedom model will accurately describe the complexity of such motion.

[0120] The mathematical model can be fitted by a nonlinear iterative minimization scheme (e.g., a version of the damped Newton method). Other numerical schemes are also possible. The objective function for such a fit is an error metric between the measured data and the state of the data if the data were constrained to a given mathematical model. An example of such a metric is the 2-norm (or a robust version thereof) on a point-to-point basis between the captured scan points and their projections onto the mathematical model.

[0121] Therefore, a solution is disclosed for making a mathematical model that constrains the possible or possible Euclidean relative positions over time. This has the advantage that noise that is inconsistent with the model can be efficiently filtered out or at least greatly reduced. In addition, it is also convenient to interpolate between measured relative jaw positions (i.e. movements). This is also true when extrapolating to possible movements in space.

[0122] Therefore, different types of model classes disclosed in this article can be used. For example, a model class can describe six degrees of freedom, three degrees of rotation, and three degrees of translation. Figure 2a-2c The first example described is such an embodiment.

[0123] In another embodiment, such a model class may be a digital representation of an articulator, wherein the present invention relates to Figure 3b-3cThe second example described is an embodiment thereof.

[0124] In yet another embodiment, the model class may be a digital representation of a standardized human jaw. For example, this is similar to Figure 4 Described together.

[0125] As a first example, one embodiment of using a mathematical model to fit the occlusal configuration to the model is described with reference to FIG. Figure 2a , Figure 2b and Figure 2c For example, this embodiment can be used in this article about Figure 1 described in the method.

[0126] The input to our estimation is multiple occlusal configurations 201, 202, 203 and 204 as a sequence P of poses of the mandible 206 i , located in the coordinate system 210 (maxillary coordinate system) where the maxilla 207 is fixed. The posture here has 6 degrees of freedom, which are determined by the rotation R i and pan t i where i represents "time" or "sequence number".

[0127] P i =[R i t i ]

[0128] Model 205 can fit these poses, given by the global pose P g This global pose is considered to be the positioning of the TMJ relative to the maxilla and a model-specific coordinate system 211 can be defined. Then, for each P i , which can fit 3 DOF poses, including rotation around the x-axis Rotation around the y-axis and translation along the z-axis Then translate P g Expressed as

[0129] P g =[R g t g ]

[0130] R i and t i The modeling is as follows:

[0131]

[0132] This modeling is equivalent to first applying a “local” time-varying transformation and then applying a global transformation to the points on the jaw. Note that the combined pose at time i is given by P i =[R i ti ] is given by . This is considered to be 3 degrees of freedom for each pose (plus 6 degrees of freedom for the entire system), thus restricting almost half of the system's degrees of freedom. The model is fitted in a least squares manner to each P i .

[0133] The above model is explained as follows; it should be noted that the posture P at time i i is given in an “arbitrary” reference system, which is assumed here to be the coordinate system chosen for the maxilla. g is the transformation from the maxillary coordinate system to the model coordinate system. and i related) and translation is naturally represented in this system. is the rotation around the x-axis in the model coordinate system. Therefore, in estimating P g When we estimate the coordinate system of the model, we are actually also estimating the x-axis rotation that best describes all the data. and the z-axis and You can do something similar. This means is the axis of rotation that can be used for all time instances i, which is defined as the x-axis in the model coordinate system. Given this x-axis, is the angle This can be described as a quaternion;

[0134]

[0135] Similarly, for and angle

[0136]

[0137] as well as and the distance δ i :

[0138] [0,0,δ i ]

[0139] In short, P g is an estimate of where the 2 rotation axes and translation direction should be relative to the maxillary system, but with the constraint that they should be perpendicular.

[0140] Given that P g is constant at all time instances, the time-dependent vector can be expressed as:

[0141]

[0142] In a second example, an embodiment of a mathematical model is disclosed that represents a physical dental articulator that can be used, such as Figure 3a , Figure 3b and Figure 3c As shown. Dental articulators have screws, hinges and scales for adjusting the range of motion of the articulator and, accordingly, setting the specific jaw movement possibilities in the articulator and the inherent parameters of the model construction that restrict the free movement. For the popular Artex CR articulator 300 from Amann Girrbach AG, at least four specific parameters are worth paying attention to: the left Bennett angle (BL), the right Bennett angle (BR) and the left and right horizontal condylar paths (CL and CR), as shown Figure 3a These parameters BL, BR, CL, CR can be called static model parameters, because they are set only once for the entire occlusion and are independent of time. In other types of dental articulators, the available parameters and their adjustment possibilities may differ, but if a mathematical model can be built to represent the articulator, then the parameters can be fitted using the method described here.

[0143] In order to find the parameters that most closely match multiple bite configurations, a data fitting method can be used to minimize the difference between the motion provided by the articulator model and the motion provided by the acquired bite configuration. To do this in practice, a transformation can be found that positions the 3D digital representation of the patient's dentition at Figure 3b The static occlusal coordinate system 310 in the articulator coordinate system 320 .

[0144] This transformation can be defined as Tstaticocclusion, which is the transformation from Tpose to Tarticulator. A first guess of this transformation can be found in a number of ways, including using tooth segmentation to identify the teeth in the scan and matching their positions with a template model placed in the articulator. Alternatively, estimating the occlusal plane from a 3D digital dental model and its center of gravity provides another approach.

[0145] The transformation Tstaticocclusion is included in the data fitting optimization to ensure that the fitted articulator parameters are based on the best arrangement of the scan in the articulator. Since it is a ridged transformation, it contains six (6) parameters, three (3) for translation and three (3) for rotation. Like the four (4) articulator parameters, these are static parameters and fit the entire occlusion at once.

[0146] In addition to the static parameters described above, there are also dynamic parameters that describe the position of the mandible relative to the maxilla at a given point in time i. For the ACR articulator, there are four parameters: occlusal opening 330, lateral protrusion 331, protrusion 332, and setback 332. These parameters correspond to the position of the incisor pins adjusted during articulator movement through the articulator model, thereby generating a relationship between the pose of the 3D digital representation of the maxilla and the static pose of the 3D digital representation of the mandible. This directly corresponds to the data obtained in multiple occlusal configurations 301, 302, 303, 304, showing Figure 3c Medium Pose i The relative transformation between the mandibular and maxillary parts. Since we need to fit the articulator parameters from poses obtained from multiple occlusal configurations, we also need to consider the dynamic parameters and add them to the data fitting operation.

[0147] Once you have outlined your data fitting problem, you can sketch out possible implementations to solve it:

[0148] - Set the articulator model to the default position that describes the standard setup,

[0149] - Find an initial guess for Tstaticocclusion, for example by using tooth segmentation or occlusal plane estimation.

[0150] For each step in the optimization, the static occlusion parameters and the Tstaticocclusion parameters may be iterated.

[0151] For each image in the motion scan, you can:

[0152] - Iterate dynamic articulator parameters,

[0153] - Compute the error, i.e. the difference between the pose found in the i-th articulation configuration and the corresponding pose given by the articulator model function using the current dynamic parameters. The computation can be the difference between the eight corners of the scanned bounding box transformed using the two pose transforms.

[0154] The optimization is completed when the parameters have converged or the maximum number of steps have been taken.

[0155] exist Figure 4In the third example shown, a comprehensive rigid body model 400 encompassing the entire masticatory region combined with a detailed representation of the TMJ can be used to closely approximate the human anatomy. The model contains a large number of parameters and is based on the work of Sagl et al. (doi: 10.3389 / fphys.2019.01156). Here, skeletal structures such as the skull 401, hyoid bone 403, and jaw (mandible) 402 are modeled as rigid bodies. The inertial properties of the mandible are estimated from the mesh geometry, assuming a mandibular mass of 200g (Langenbach and Hannam, 1999). The hyoid bone 403 and skull 401 remain static in all simulations presented, so no inertial properties need to be defined. The muscles are represented as Hill-type point-to-point muscles 405 (Hill, 1953; Peck et al., 2000; Hannam et al., 2008). For clarity, only a few Hill-type point-to-point muscles are shown with reference symbols, but it will be appreciated that Figure 4 More are shown. Since these muscle models apply forces in a one-dimensional direction of the force vector defined by the origin and insertion point, larger muscles are divided into multiple strings to more accurately simulate the activation of muscle compartments. The muscles included may be the posterior 405-1, medial 405-2, and anterior 405-3 of the temporalis muscle; the superior head 405-6 and inferior head 405-7 of the lateral pterygoid muscle, the superficial masseter muscle 405-5 and the deep masseter muscle 405-4, and the medial pterygoid muscle 405-8. Other muscles that may also be included (although not shown) may be (but are not limited to) the anterior digastric muscle, the geniohyoid muscle, the anterior mylohyoid muscle, and the posterior mylohyoid muscle.

[0156] In addition to examples, such models can be enhanced with statistical probabilities, i.e. what is the probability density function of relative jaw movement. For motivation, many of the questions that patient specific movements should and can be addressed are related to the most likely occlusion, such as a filling or orthodontic treatment. This does not mean that patients cannot have relative jaw movement, which never actually happens, but in theory it can, and should be given lower priority when performing treatment, such as with regard to daily chewing movements.

[0157] The embodiments presented herein go beyond mathematical modeling of mechanical articulators. It is important to note that these articulators themselves are mechanical (not digital) models of the physiological system of the human jaw. Obviously, extensions include more flexible modeling of the TMJ, which has considerable flexibility built in, such as is associated with metal joints. For example, the TMJ has an element of hysteresis, i.e. the path the jaw moves forward is different from the path it moves backward. Other factors that can be included are temporal constraints, e.g. a Kalman filter form of the jaw posture. Furthermore, the jaw is not completely rigid, even though it is often modeled as such (e.g. scans or plaster models), and may deform slightly, for example if bitten hard. This can also be well included in the fitted model.

[0158] Opportunity identification of the mobility of the patient's jaw mobility can be quantified by performing acquisition of a first set of bite configurations at a first time point T1 and a second set of bite configurations at a second time point T2. For each set of bite configurations, a process of fitting the same mathematical model to both sets of bite configurations can be performed.

[0159] Using the fitted model 109 , eg, primary model parameters, obtained at T1 , and the fitted model 110 , eg, secondary model parameters, obtained at T2 , monitoring information 111 , eg, changes in jaw movement, may be determined by comparing the two fitted models.

[0160] The monitoring information 112 can then be displayed to the user to indicate the relevant changes in jaw movement. The monitoring information can be displayed in different ways, for example, it can simply display the model parameters and / or indicate changes therein. When these changes exceed a certain standard, these changes can be indicated or highlighted.

[0161] Alternatively or additionally, the monitoring information may be visualized by showing a color / heat map indicating where key changes have occurred or some difference in jaw motion, while playing a 3D digital motion video of the upper and lower jaws.

[0162] Using the model described in Example 1, several forms of data analysis can be performed. The statistics can give an indication of the patient's jaw "flexibility". For example, The min-max range can be directly converted to the patient's range of motion.

[0163] This can also be used in a "change detection setting". That is, if a patient's health is monitored over time, for example, two scans of jaw movement data taken six months apart. The changes acquired at the primary time T1 are then compared to the changes in the Statistics and obtained at secondary time T2 Statistics. This analysis can reveal any changes in jaw mobility between two time points. Identifying small perturbations in jaw mobility can be an early sign of potential problems with a patient’s masticatory system.

[0164] Such jaw dynamics can be compared based on boundary motion analysis. Mandibular motion is anatomically limited by the temporomandibular joint (TMJ), surrounding and associated ligaments, the neuromuscular system, and the teeth. The boundaries or limits of mandibular motion can be referred to as the "boundary motion" or "motion envelope." All possible mandibular motion occurs within its boundaries. One way to visualize and analyze the mandibular boundary motion is to use a Pareto (Posselt) diagram. By recording various but distinct positions of the mandible from different angles, a Posselt diagram can create a three-dimensional representation of the different positions of the mandible, including the maximum motion the mandible is capable of. As the jaw passes through the boundary path, the full range of jaw motion can be described in three planes by tracing the path of the lower incision point. The boundary path traces the maximum range of jaw motion, which is determined by the jaw muscles, ligaments, limitations of the TMJ, and the teeth. The teeth define the top of the boundary diagram, which is of particular interest in restorative dentistry because Figure 5 The relationship between ICP(IP) and CO(RCP) is shown graphically.

[0165] Boundary maps can be displayed in the sagittal, frontal, and horizontal planes. Sagittal views of mandibular boundary motion in dentate individuals are captured on the lower incisors and provide features of particular interest.

[0166] This type of boundary map can be constructed based on a mathematical model that serves as a representation of multiple bite configurations. If the bite configurations obtained cover at least some of the extreme mandibular movements, a boundary map can be obtained based on a sequence of bite configurations acquired at time T1, and another boundary map can be obtained based on a bite configuration acquired at time T2. Two boundary maps can be compared by displaying a contour map of the differences on a display. The contour map can be obtained by aligning the two maps and calculating the distance. The contour map can be displayed as a colored overlay on one of the boundary maps.

[0167] The use of a mathematical representation framework to describe the motion of the mandible relative to the skull enables a wide range of analysis options. One option is to plot the direct trajectory of the mandible along its motion from one position to another, for example between two boundary motions. This will allow for a detailed examination of the model parameters of the mandible along that specific path. If the trajectory of the mandible following a specific motion deviates from the expected path, or changes in a specific motion path over time can be observed, this may indicate a change in the patient's masticatory system.

[0168] Therefore, in one embodiment, the step of comparing the primary model parameters with the secondary model parameters comprises using boundary motions. Such boundary motions may be described, for example, by boundary motion diagrams, such as Posselt diagrams.

[0169] Figure 5One embodiment of a boundary motion map 500 is shown. The boundary motion describes a motion volume defined by the patient's mandibular motion boundaries. The motion volume is a representation of the natural relative jaw motions that a patient can perform.

[0170] Figure 5 The patient's Posselt motion envelope is shown. Other types of visualization can also be used. Certain boundary points are marked, such as centric relation CR, centric occlusion CO, maximum right position MRL, maximum left position MLL, maximum protrusion MP, side-to-side relation ER, and maximum mouth opening MMP. In addition, motion segments are indicated, such as the true hinge axis THA and translation-after-rotation RAT. The basic movements of the mandible are rotation, translation, and lateral movements, which can be recorded using the Posselt motion envelope. The mandible can be opened up to 20 mm while the condyle remains in its terminal hinge position, rotating only about this hinge axis THA. Further opening causes the condyle to leave its glenoid cavity and translate downward along the articular eminence. This movement is indicated by the RAT.

[0171] In another embodiment of the lateral movement, the condyle on the working side rotates about the sagittal axis with minimal lateral movement. As the condyle descends anteriorly and inferiorly along the medial wall of the glenoid, the condyle on the non-working side moves significantly medially (Bennett movement). This lateral movement can occur at different stages of the downward movement, such as before the downward movement (immediate lateral movement = ISS), at the beginning of the condylar downward movement (early lateral movement = ESS), or gradually throughout the downward movement (progressive / distributed lateral movement = PSS). The form of Bennett's movement has an impact on the occlusal surface and should be considered to prevent interference of the cusps during lateral movement. However, each person can only perform one of the forms because the movement is determined by the anatomy of the masticatory system.

[0172] Using boundary motion can be done in different ways and can also be combined.

[0173] In one embodiment, a model class is established to represent boundary motions. For example, a six-degree-of-freedom class model described in the first example can be established, whose limits are defined by boundary motions. Thus, when relative jaw motions are applied, the class model can also be used to extrapolate boundary motions that may not be obtained through the recorded relative jaw motions.

[0174] Alternatively or additionally, the primary relative jaw motion data set and the secondary relative jaw motion data set at least partially represent one or more boundary motion positions of the upper jaw and the lower jaw. Figure 5The boundary movement positions are obtained from the relative position of the jaws at one of the points described in , i.e. centric relation CR, centric occlusion CO, maximum right position MRL, maximum left position MLL, maximum protrusion MP, side-to-side relation ER and maximum mouth opening MMP. This can be obtained, for example, as an occlusal scan.

[0175] It will be appreciated that, in one embodiment, the primary model parameters and the secondary model parameters may describe / represent boundary motion and boundary motion location, and thus they may be compared to obtain monitoring information.

[0176] Even further monitoring comparisons can be performed using the boundary envelope 500. In one embodiment, where the primary model parameters represent the primary boundary envelope and the secondary model parameters represent the secondary boundary envelope, a heat map comparison can be performed to visually display the differences in motion exceeding a certain threshold where significant changes occur. Furthermore, in some embodiments, corresponding portions of the primary boundary envelope and the secondary boundary envelope are compared, which can be, for example, cross-sections thereof, boundary motion during occlusion, or boundary motion when opening and closing the jaw.

[0177] Another analytical method to perform mandibular motion change detection could be to analyze the differences in the rotation axis. This method would not be based on estimating the boundaries or extremes of mandibular motion. By using the model described in the first example and aligning the coordinate system of the maxilla in both sessions (based on a stable reference, such as a landmark, a single segmented tooth, or the like), the P values ​​of the two sessions could then be compared. g This is beneficial because any changes in the TMJ will be reflected in the comparison. Alternatively, use the P of the first session in the last g The motion between two scanning sessions can be directly compared, i.e.

[0178] By applying the mathematical representation of the articulator described in the second example, changes or differences in mandibular movement can be monitored by tracking and comparing the static model parameters obtained at the first time point and the second time point, which in the case of the example using the Artex CR articulator from AmannGirrbach AG can be the left and right Bennett angles BL and BR and the left and right angles CL and CR of the condylar path.

[0179] In general, analysis of mandibular motion within the normal operating range (e.g., the positions the jaws typically use in daily function) is of great interest for quantification and monitoring. One approach to obtaining a sequence of bite configurations might be to use an intraoral scanner to continuously record the relative positions of the patient's jaws while the patient chews gum or a suitable rubber / silicone sheet. This would allow acquisition of multiple bite configurations during the patient's natural chewing motions.

[0180] Another approach to analyzing changes in a patient's jaw movement patterns over time is to use a more complex mathematical model to approximate the human anatomy, as described in the third example. Here, the model parameters are based on a physical structure that represents the masticatory system, and changes or direct parameters extracted from such a model may contain information about the condition of specific muscles, ligaments, etc.

[0181] As a complement to the multiple bite configurations obtained using intraoral scanners, additional data may be included in the model optimization to improve the accuracy of the mathematical model used. Such additional data may be a record of an extraoral facial scan, which can provide additional information since the teeth in the facial scan can be aligned with the 3D digital representation obtained. X-ray imaging of the patient can provide information about the TMJ in one static position, which can be added to the model parameter optimization with high weight or confidentiality so that this data point can be strongly considered. Bite force measurements are another additional data source that can provide supporting information on the tooth contact points, which can also be added to the optimization process. Other types of data records that provide information on the relative position of the patient's maxilla and mandible can enhance the modeling.

[0182] Machine learning and neural networks can be used to identify specific changes in jaw movement patterns and provide a probability score for a specific clinical condition. This requires a large amount of clinical training data, where patients are monitored before, during, and after clinical diagnosis of a condition related to the masticatory system.

[0183] In another aspect, Figure 6 As shown, a method for assessing jaw motion is disclosed 600. In a scanning step 601, a primary relative jaw motion dataset 602 at a primary time point is obtained using an intraoral scanner. The primary jaw motion dataset represents the relative motion between the upper jaw and the lower jaw.

[0184] The primary relative jaw motion data set is sent 604 to a computer processor 603 for further processing in a computer implemented method.

[0185] A model class representing desired and / or normalized occlusal characteristics 605 may be provided in the processor, for example stored for use, or imported into the processor when needed. Primary model parameters 606 may be obtained by fitting a primary relative jaw motion dataset to the model class.

[0186] A reference data set defining hysteresis criteria 607 may be provided, and monitoring information 608 may be determined based on comparing the primary model parameters to the reference data set.

[0187] The monitoring information may then subsequently be displayed to the user 609, which may be done as previously described. As previously described, the monitoring information may be displayed in a number of different ways, for example it may simply display the model parameters and / or indicate changes therein. If these changes are above a certain standard, such as a hysteresis standard, these changes may be indicated or highlighted.

[0188] Alternatively or additionally, the monitoring information may be visualized by displaying a color / heat map indicating where key changes have occurred or where some differences in jaw motion exist, while playing a 3D digital motion video of the maxillary and mandibular bones.

[0189] Figure 7 An embodiment of a scanner system 700 for intraoral scanning of a dental object is shown. The scanner system 700 can be used to perform the methods for monitoring jaw movement over time and the methods for evaluating jaw movement disclosed herein.

[0190] The scanner system may include a scanning probe 701 for receiving an image of a dental object.

[0191] A laptop computer 706 may be provided for wireless communication with the scanning probe, the laptop computer 706 having a peripheral output device 702 in the shape of a monitor for visualizing a digital 3D representation 704 of the dental object. Alternatively, a desktop computer or other computing device (e.g., a tablet) may be provided for communication with the scanning probe. The communication may be wireless or wired as described.

[0192] The display may display the monitoring information disclosed herein instead of displaying the digital 3D representation 704 , for example as an overlay on the digital 3D representation 704 .

[0193] The computer processor 705 and other electronic hardware may be arranged in the scanning probe, and wireless communication may be achieved through the controller 706 in the scanning probe and the controller 707 in the laptop computer 706 .

[0194] The laptop computer can receive data from the scanning probe and output the calculated data to the monitor.

[0195] The computer processors provided in scanning probes and laptops are generally composed of different types of electronic hardware. The electronic hardware may include microprocessors, microcontrollers, digital signal processors (DSPs), field programmable gate arrays (FPGAs), programmable logic devices (PLDs), gated logic, discrete hardware circuits, and other suitable hardware configured to perform the various functions described in this disclosure. Computer programs should be broadly interpreted as instructions, instruction sets, codes, code segments, program codes, programs, subroutines, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, processes, functions, etc., whether they are referred to as software, firmware, middleware, microcode, hardware description languages, or others.

[0196] The scanner system disclosed herein may be an intraoral scanning device, such as the TRIOS series scanner from 3Shape A / S. The scanning device may employ a scanning principle suitable for intraoral scanning, such as triangulation-based scanning, confocal scanning, focal scanning, ultrasound scanning, stereoscopic vision, motion structure, or any other scanning principle. In one embodiment, the scanning device operates by projecting a pattern and translating a focal plane along the optical axis of the scanning device and capturing a plurality of 2D images at different focal plane positions, so that each series of 2D images corresponding to each focal plane captured forms a stack of 2D images. The acquired 2D images are also referred to herein as raw 2D images, wherein raw in this context means that the images have not been image processed. The focal plane position is preferably shifted along the optical axis of the scanning system, so that the 2D images captured at multiple focal plane positions along the optical axis form a 2D image stack (also referred to herein as a sub-scan) for a given view of the object (i.e., for a given arrangement of the scanning system relative to the object). After the scanning device is moved relative to the object or the object is imaged in a different view, a new 2D image stack for the view may be captured. The focal plane position may be changed by at least one focusing element (e.g., moving a focusing lens). The scanning device is typically moved and tilted during a scanning session so that at least some of the groups of sub-scans at least partially overlap in order to achieve stitching during scanning. The result of stitching is a digital 3D representation of a surface that is larger than what can be captured by a single sub-scan, i.e., larger than the field of view of the 3D scanning device. Stitching, also known as registration, works by identifying overlapping areas of the 3D surface in the 3D surfaces of previous sub-scans / recorded 3D surfaces and transforming the new sub-scans into a common coordinate system so that the overlapping areas match, ultimately producing a digital 3D model. An iterative closest point (ICP) algorithm can be used for this purpose. Another example of a scanning device is a triangulation scanner, in which a time-varying pattern is projected onto the dental object and a series of images of different pattern configurations are acquired by one or more cameras placed at a certain angle relative to the projector unit.

[0197] The scanning device includes one or more light projectors configured to generate an illumination pattern to be projected onto a three-dimensional dental object during a scanning session. The light projector preferably includes a light source, a mask having a spatial pattern, and one or more lenses, such as a collimating lens or a projection lens. The light source can be configured to generate light of a single wavelength or a combination of wavelengths (monochromatic or polychromatic). The wavelength combination can be generated by using a light source configured to generate light containing different wavelengths (e.g., white light). Alternatively, the light projector can include multiple light sources, such as LEDs, which individually generate light of different wavelengths (e.g., red, green, and blue), which can be combined to form light containing different wavelengths. Therefore, the light generated by the light source can be defined by different wavelength ranges defining a wavelength of a specific color or defining a color combination (e.g., white light). In one embodiment, the scanning device includes a light source configured to excite a fluorescent material of a tooth to obtain fluorescent data from a dental object. Such a light source can be configured to generate a narrow range of wavelengths. In another embodiment, the light from the light source is infrared (IR) light, which can penetrate tooth tissue. The light projector can be a DLP projector that uses a micromirror array to generate a time-varying pattern, or a diffractive optical element (DOF), or a backlit mask projector, in which a light source is placed behind a mask having a spatial pattern, whereby the light projected on the surface of the dental object is patterned. The backlit mask projector can include a collimating lens for collimating the light from the light source, the collimating lens being placed between the light source and the mask. The mask can have a checkerboard pattern so that the generated illumination pattern is a checkerboard pattern. Alternatively, the mask can have other patterns, such as lines or dots.

[0198] The scanning device preferably also includes an optical component for directing light from the light source to the surface of the dental object. The specific arrangement of the optical component depends on whether the scanning device is a focused scanning device, a scanning device using triangulation, or any other type of scanning device. The same applicant further describes a focused scanning device in EP2442720B1, which is incorporated herein in its entirety.

[0199] Light reflected from a dental object in response to illumination of the dental object is directed to an image sensor using optical components of a scanning device. The image sensor is configured to generate a plurality of images based on incident light received from the illuminated dental object. The image sensor may be a high-speed image sensor, such as an image sensor configured to capture images with an exposure time of less than 1 / 1000 second or a frame rate of more than 250 frames per second (fps). As an example, the image sensor may be a rolling shutter (CCD) or a global shutter sensor (CMOS). The image sensor may be a monochrome sensor including a color filter array, such as a Bayer filter and / or an additional filter, which may be configured to substantially remove one or more color components from the reflected light before converting the reflected light into an electrical signal and retain only other unremoved components. For example, such an additional filter may be used to remove a portion of the white light spectrum, such as a blue component, and retain only red and green components in a signal generated in response to exciting a fluorescent material of a tooth.

[0200] The dental scanning system preferably also includes a processor configured to generate scan data (intraoral scan data) by processing a two-dimensional (2D) image acquired by the scanning device. The processor may be part of the scanning device. As an example, the processor may include a field programmable gate array (FPGA) and / or an advanced RISC machine (ARM) processor located on the scanning device. The scan data includes information related to a three-dimensional dental object. The scan data may include any of the following: a 2D image, a 3D point cloud, depth data, texture data, intensity data, color data, and / or a combination thereof. As an example, the scan data may include one or more point clouds, wherein each point cloud includes a set of 3D points describing a three-dimensional dental object. As another example, the scan data may include images, each image including image data, for example, described by image coordinates and a timestamp (x, y, t), wherein the depth information may be inferred from the timestamp. The image sensor of the scanning device may acquire multiple original 2D images of a dental object in response to illuminating the object using one or more light projectors. Multiple original 2D images may also be referred to herein as a stack of 2D images. The 2D images may then be provided as input to the processor, which processes the 2D images to generate scan data. The processing of 2D images may include a step of determining which part of each 2D image is in focus in order to infer / generate depth information from the image. The depth information may be used to generate a 3D point cloud, which includes a set of 3D points in space, for example, described by Cartesian coordinates (x, y, z). The 3D point cloud may be generated by a processor or another processing unit. In addition, each 2D / 3D point may also include a timestamp indicating the recording time of the 2D / 3D point, i.e., which image in the 2D image stack the point originated from. The timestamp is associated with the z coordinate of the 3D point, i.e., the z coordinate may be inferred from the timestamp. Thus, the output of the processor is scan data, and the scan data may include image data and / or depth data, for example, described by image coordinates and timestamps (x, y, t) or alternatively described as (x, y, z). The scanning device may be configured to transmit other types of data in addition to the scan data. Examples of data include 3D information, texture information (e.g., infrared (IR) images), fluorescent images, reflective color images, X-ray images, and / or combinations thereof.

[0201] Although some embodiments have been described and shown in detail, the present disclosure is not limited to these details, but may also be embodied in other ways within the scope of the subject matter defined in the following claims. In particular, it should be understood that other embodiments may be utilized and structural and functional modifications may be made without departing from the scope of the present invention.

[0202] Benefits, other advantages, and solutions to problems have been described herein with respect to specific embodiments. However, the benefits, advantages, solutions to problems, and any components / units that may cause any benefit, advantage, or solution to occur or become more apparent should not be construed as key, essential, or essential features or components / elements of any or all claims or inventions. Therefore, the scope of the invention is limited only by the appended claims, in which references to singular components / units / elements are not intended to mean "one and only one", unless expressly stated, but rather "one or more". A claim may refer to any of the preceding claims, and "any" should be understood to mean "any one or more" of the preceding claims.

[0203] It is intended that structural features of the apparatus described above (whether in the detailed description or in the claims) may be combined with steps of the method when appropriately replaced by corresponding processes.

[0204] When used, unless otherwise expressly stated, the singular forms "one", "an" and "the" are also intended to include the plural form (i.e., have the meaning of "at least one"). It should also be understood that when the terms "include", "comprise", "include" and / or "comprise" are used in this specification, the presence of the features, integers, steps, operations, elements and / or parts is specified, but the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups thereof is not excluded. It should also be understood that when an element is "connected" or "coupled" to another element, the element can be directly connected or coupled to another element, but there can also be intermediate elements, unless otherwise expressly stated. In addition, "connected" or "coupled" used herein can include wireless connections or couplings. As used herein, the term "and / or" includes any and all combinations of one or more related listed items. The steps of any disclosed method are not limited to the exact order described herein, unless otherwise expressly stated.

[0205] It should be understood that references to "one embodiment" or "embodiment" or "aspect" or "may" include features throughout this specification mean that the specific features, structures, or characteristics described in connection with the embodiment are included in at least one embodiment of the present disclosure. In addition, the specific features, structures, or characteristics may be combined in one or more embodiments of the present disclosure as needed. The previous description is provided to enable those skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects.

[0206] The claims are not limited to the aspects shown herein, but rather should be consistent with the claim language, in which reference to a singular element does not mean "one and only one" unless expressly stated, but rather means "one or more." Unless expressly stated otherwise, the term "some" means one or more.

Claims

1. A method for monitoring jaw movement over time, wherein the method comprises the following steps: - obtaining a primary relative jaw motion dataset at a primary time point using an intraoral scanner, wherein the primary jaw motion dataset represents relative motion between the upper jaw and the lower jaw, - obtaining a secondary relative jaw motion dataset at a secondary time point using an intraoral scanner, wherein the secondary relative jaw motion dataset represents relative motion between the upper jaw and the lower jaw, The method further comprises a computer-implemented method, wherein the computer-implemented method comprises the following steps: - receiving the primary relative jaw motion dataset and the secondary relative jaw motion dataset, - obtain a model class representing desired and / or regularized occlusal properties, - obtaining primary model parameters by fitting said primary relative jaw motion dataset to said model class, - obtaining secondary model parameters by fitting said secondary relative jaw motion dataset to said model class, - determining monitoring information based on comparing said primary model parameters with said secondary model parameters, - displaying said monitoring information. The method of claim 1 , wherein the model class describes six degrees of freedom. The method of claim 1 , wherein the model class is a digital representation of an articulator.

4. The method of claim 1, wherein the model class is a digital representation of a standardized human jaw.

5. A method according to any one of the preceding claims, wherein the model class is deterministic.

6. The method of any one of claims 1-4, wherein the model classes are random.

7. The method according to any one of the preceding claims, wherein the monitoring information is displayed by displaying at least one of the primary model parameter and the secondary model parameter in a digital manner. The method of claim 7 , wherein the monitoring information includes changes between the primary model parameters and the secondary model parameters.

9. The method according to any of the preceding claims, wherein the monitoring information is visualized as a heat map.

10. The method of any preceding claim, wherein comparing the primary model parameters and the secondary model parameters comprises using boundary motion. The method of claim 10 , wherein the boundary motion is determined using a boundary motion map. The method of claim 11 , wherein the boundary motion map is a Posselt envelope map.

13. The method of any of claims 10-12, wherein the model class includes a representation of the boundary motion.

14. The method according to any one of claims 10-13, wherein the primary relative jaw motion dataset and the secondary relative jaw motion dataset at least partially represent one or more boundary motion positions of the maxillary and mandibular bones.

15. The method according to any one of claims 10-14, wherein the primary model parameters and the secondary model parameters represent boundary motion.

16. A method according to any one of the preceding claims, wherein the step of obtaining the primary and / or secondary relative jaw motion dataset comprises obtaining at least a first and a second primary and / or secondary occlusal scan using an intraoral scanner at primary and / or secondary time points, respectively, each primary and / or secondary occlusal scan comprising at least a portion of the maxilla and mandible at different jaw motion positions relative to each other.

17. The method of claim 16, wherein a primary reference frame is established by determining a correspondence between the at least first and second primary bite scans, and a secondary reference frame is established by determining a correspondence between the at least first and second secondary bite scans.

18. The method of claim 17, wherein a frame correspondence between the primary reference frame and the secondary reference frame is determined.

19. The method according to any one of the preceding claims, wherein the method further comprises: At least a first digital 3D representation of at least a portion of an upper jaw and at least a portion of a lower jaw of the patient is obtained.

20. The method according to claims 16 and 19, wherein obtaining the primary and / or secondary relative jaw motion set further comprises performing a first primary and / or secondary alignment of the maxilla and mandible of the at least first digital 3D representation based on the first primary and / or secondary occlusal scan, and performing a second primary and / or secondary alignment of the maxilla and mandible of the at least first digital 3D representation based on the second primary and / or secondary occlusal scan.

21. A method for assessing jaw movement, wherein the method comprises the following steps: - obtaining a primary relative jaw motion dataset at a primary time point using an intraoral scanner, wherein the primary relative jaw motion dataset represents relative motion between the maxillary and mandibular limbs, The method further comprises a computer-implemented method, wherein the computer-implemented method comprises the following steps: - receiving said primary relative jaw motion dataset, - obtain a model class representing desired and / or regularized occlusal properties, - obtaining primary model parameters by fitting said primary relative jaw motion dataset to said model class, - determining monitoring information based on comparing said primary model parameters with a reference data set, - displaying said monitoring information.

22. The method of claim 21, wherein the reference data set comprises a hysteresis standard.

23. The method of claim 22, wherein the step of determining monitoring information based on comparing the primary model parameters to the hysteresis criteria comprises: A determination is made as to whether the primary model parameters describe hysteresis.

24. The method of claim 21, wherein the reference dataset comprises a CBCT scan of the maxilla and / or mandible.

25. The method of claim 21, wherein the reference dataset comprises an ideal motion trajectory of the jaw, wherein the ideal motion trajectory describes a smooth motion consisting of only rotation or only translation.

26. The method of claim 21, wherein the reference dataset comprises a treatment plan.

27. A computer-implemented method for monitoring jaw movement over time, wherein the method comprises the steps of: - receiving, by a processor, a primary relative jaw motion dataset and a secondary relative jaw motion dataset; - determining a change in jaw motion by comparing a secondary relative jaw motion dataset with the primary relative jaw motion dataset, wherein the secondary relative jaw motion dataset corresponds to one or more teeth, wherein the one or more teeth are repositioned compared to a position of the one or more teeth corresponding to the primary relative jaw motion dataset, and - Displays the changes in jaw movement determined.

28. A scanning system for intraoral scanning of a dental object, comprising a scanning probe for receiving an image of the dental object, a peripheral output device for visualizing a digital 3D representation of the dental object, and a computer processor coupled to the scanning probe and the peripheral output device, wherein the computer processor receives data from the scanning probe and outputs calculated data to the peripheral output device, wherein the scanning system is used by applying the steps of the method according to any one of claims 1 to 26.

29. A non-transitory computer-readable medium comprising instructions that, when executed by a computer, cause the computer to perform the method according to any one of claims 1-26.

30. A computer program product embodied in a non-transitory computer readable medium, the computer program product comprising instructions which, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 26.

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